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QUALITY SPECIFICATIONS FOR ROADWAY BRIDGES, STANDARDIZATION AT A EUROPEAN LEVEL

Scientific Report on Short Term Scientific Mission

Researcher Mariano Angelo Zanini marianoangelo.zanini@dicea.unipd.it Home Institution University of Padova – Department of Civil,

Environmental and Architectural Engineering http://www.dicea.unipd.it

Host Institution Universitat Politécnica de Catalunya – Civil and Environmental Engineering Department

http://www.structech.upc.edu

Start Date October 3, 2016 End Date December 1, 2016 Reference Code STSM-TU1406-031016-079244

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CONTENTS 1.   Aims and Objectives .......................................................................................................................... 3  

1.1.   Background ................................................................................................................................. 3  1.2.   Obligations towards current needs of COST TU1406 ................................................................ 3  1.3.   Scientific developments .............................................................................................................. 3  

2.   Work carried out ................................................................................................................................ 4  2.1.   Obligations towards current needs of COST TU1406 ................................................................ 4  

2.1.1.   Literature review on main tools & technical indicators ......................................................... 4  2.1.2.   Literature review on main non-technical indicators .............................................................. 4  2.1.3.   Analysis of potential correlations between technical and sustainability indicators ............... 4  2.1.4.   Literature review on main deterioration models for technical indicators .............................. 4  2.1.5.   Formulation of a generic framework for maintenance costs forecasting .............................. 4  

2.2.   Scientific developments .............................................................................................................. 4  3.   Main results ....................................................................................................................................... 5  

3.1.   Literature review on main tools & technical indicators ................................................................ 5  3.1.1.   Tools ..................................................................................................................................... 5  3.1.2.   Technical indicators .............................................................................................................. 7  

3.2.   Literature review on non-technical indicators ........................................................................... 10  3.2.1.   Environmental indicators .................................................................................................... 11  3.2.2.   Social indicators ................................................................................................................. 12  3.2.3.   Economic indicators ........................................................................................................... 14  

3.3.   Analysis of potential correlations between technical and sustainability indicators ................... 15  3.4.   Literature review on main deterioration models for technical indicators ................................... 17  3.5.   Formulation of a generic framework for maintenance costs forecasting .................................. 19  

4.   Scientific developments ................................................................................................................... 22  5.   Future collaboration ......................................................................................................................... 22  6.   Foreseen publications/articles ......................................................................................................... 22  7.   Additional comments ....................................................................................................................... 22  8.   References ...................................................................................................................................... 23  9.   Annexes .......................................................................................................................................... 29  

9.1.   Confirmation by the host institution on the sucessful execution of the stsm ............................. 29  

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1. AIMS AND OBJECTIVES A brief description of the background and main aims of the STSM, split in obligations towards current needs of COST TU1406 Action and scientific developments, are reported in this section.

1.1. BACKGROUND

According to the aims of the WG1 focused on the definition of a set of possible performance indicators for the quality control of bridges, WG2 has the main objective to identify the respective performance goals and corresponding thresholds, thought as the boundaries within if a performance indicator is identified, the bridge owner can adopt an adequate countermeasures. Performance indicators, and also consequently goals, can be classified in three main areas: technical, environmental and economic/social. While in the first area, many studies were performed in literature aimed to identify the most reliable ones and to propose standardized frameworks for the assessment of deteriorated bridge structures, the other two categories are less explored, mainly due to the need by the researches of a multidisciplinary approach to this issue. In fact, environmental performance indicators/goals (PIs&PGs) require the knowledge of the concept of sustainability and the analysis of consequences of the decision-making process on the environment, whereas economic and social ones have to face with economical implications in terms of cost/benefit analyses, market pricing and utility theory.

1.2. OBLIGATIONS TOWARDS CURRENT NEEDS OF COST TU1406

The STSM proposal was focused to deepen the knowledge on environmental, economic and social PIs&PGs, first of all with the aim to summarize the scientific literature state of the art in this field: this specific objective is oriented to fulfill the obligations towards current needs of COST TU1406 Action. This activity was proposed for increasing knowledge in the quantification of PIs&PGs belonging to the branches of the spider diagram not dealing with classical technical aspects, usually oriented in the estimation of damage through condition rating techniques and/or research technical indicators, e.g. reliability, robustness, redundancy. The second objective of my work plan was to investigate and define potential correlations between technical PIs&PGs and environmental/economic/social PIs&PGs, in terms of consequence functions: this is, in fact, a key issue for the harmonization of the existing data and assessment methods actually available in Europe. The key step in this subtask is that of trying to identify variables and preliminary functional shapes of relationships coupling a technical performance indicator with an environmental/economic/social one. Hence, the following objective is trying to monetize both technical/environmental/social PIs with the aim to quantify only through an economic metric all those aspects, and thus facilitate the decision-making when multiple restoration alternatives can be implemented, given a certain damage condition.

1.3. SCIENTIFIC DEVELOPMENTS

Apart from the work described in the previous point 1.2, during the STSM period, several meetings were done with Prof. Casas and some scientific developments were carried out. In particular, the discussion was focused on how to overcome the separation between natural deterioration phenomena induced by progressive ageing and environmental conditions and effects of hazardous and/or sudden events on bridges. Some ideas direct to solve WG3 questions, taking into account at the same time both deterioration sources. Among others, in relation to the quality control plan framework, a method for the definition of the optimal time between inspections taking into account both natural aging and natural hazards was developed. Some insights were carried out also in the definition of the optimal maintenance scheduling of restoration works with the aim of minimizing overall costs and at the same time ensuring an adequate safety level and the maximum profitability of retrofit investments.

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2. WORK CARRIED OUT A brief description of the work steps carried out during the STSM is herein summarized.

2.1. OBLIGATIONS TOWARDS CURRENT NEEDS OF COST TU1406

2.1.1. LITERATURE REVIEW ON MAIN TOOLS & TECHNICAL INDICATORS

In the initial part of the period, a literature review between international peer-reviewed journals and international conferences was performed with the aim to summarize the most diffused tools and technical performance indicators usually adopted by infrastructural assets owners.

2.1.2. LITERATURE REVIEW ON MAIN NON-TECHNICAL INDICATORS

The work was subsequently focused in the survey of the non-technical indicators and goals mostly diffused in bridge management practice. Specific focus was given to environmental, social and economic indicators, comprehensively characterizing a suitable sustainability metrics in BMSs.

2.1.3. ANALYSIS OF POTENTIAL CORRELATIONS BETWEEN TECHNICAL AND SUSTAINABILITY INDICATORS

On the basis of the results of the previous researches, consequence functions able to link damage assessment outcomes from technical indicators with sustainability measures related to potential restoration solutions were investigated.

2.1.4. LITERATURE REVIEW ON MAIN DETERIORATION MODELS FOR TECHNICAL INDICATORS

In a BMS framework, one of the key components is the deterioration model. For an optimal maintenance scheduling, there is in fact a need of reliable deterioration forecasts. For this reason, a review of most commonly adopted models actually in used by infrastructure asset managers was carried out.

2.1.5. FORMULATION OF A GENERIC FRAMEWORK FOR MAINTENANCE COSTS FORECASTING

In a BMS framework and in any quality control plan, one of the key components is the deterioration model. For an optimal maintenance scheduling, there is in fact a need of reliable deterioration forecasts. Some methodological considerations were developed on how to design a comprehensive framework for the forecasting over time of maintenance costs for bridges belonging to a roadway network.

2.2. SCIENTIFIC DEVELOPMENTS

Besides the subtasks described above, some additional work developments emerged during the STSM, mainly on the issue of how to take into account natural hazards in the different goals of a quality control plan (WG3). In particular, a method for the definition of the optimal time schedule for inspections taking into account natural aging and natural hazards was developed, whereas some preliminary ideas on how to define optimal maintenance intervention scheduling of restoration works are currently under development.

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3. MAIN RESULTS

3.1. LITERATURE REVIEW ON MAIN TOOLS & TECHNICAL INDICATORS

3.1.1. TOOLS

First of all, it is necessary to classify tools for the assessment of technical indicators for bridge management. Four different macrogroups can be identified:

- visual surveys; - non-destructive techniques (NDT); - probing; - structural health monitoring (SHM) techniques.

Visual surveys are the most diffused method for assessing structural damage in BMSs. Traditional bridge management systems are in fact based on a condition grading system combining the information obtained by visual and detailed inspections from the components of the individual bridges into overall bridge condition grades. Usually inspections are carried out by trained technicians that make handwritten records during field inspection of infrastructure. Some efforts were carried out with the aim to improve the effectiveness of such tool: among others, Kenmotsu et al. 2006 illustrates a system for field inspection of infrastructure in snowy cold regions using speech recognition, which omits the need for handwriting on paper and reduces the risk that inspectors will meet with accidents. The technology is based on Converting the recorded audio data directly into text data also avoids transcription errors and reduces the workload. One other development in the field of visual inspection surveys is the use of robots for the detection of deterioration: cameras are installed on robot vehicles and transmit video images through wired or wireless communication for inspectors to read them on owner computers. Dae-Joong et al. (2008) proposed a system able to provide the visual image data to the existing BMS for the detection of cracks and the subsequent creation of a CAD file. Other robotic solutions were proposed for damage detection by Kim et al. (2008), Lee et al. (2008) also with the combined use of wall-climbing and flying robots (Koo et al. 2008). Such kind of techniques allows to make in a more safe way damage detection (Park et al. 2008). Oh et al. (2008) presented a robot system composed of the moving mechanism mounted on the specially designed car for bridge inspection and the novel image processing system. These kind of techniques, expecially with the use of flying robot, is powerful when dealing with large bridges and viaducts hardly accessible: a review of visual inspection strategies can be found in Hallermann and Morgenthal (2008). In US, the Federal Highway Administration in the framework of its Long Term Bridge Performance (LTBP) Program, developed a robotic assisted bridge inspection tool called RABBIT (Gucunsky et al. 2013) that implements four NDE technologies: electrical resistivity (ER), impact echo (IE), ultrasonic surface waves (USW) and ground-penetrating radar (GPR).

Figure 1: Innovative methods for the improvement of quality of visual inspection surveys. Regarding NDT methods, these are usually less adopted, mainly due to cost and time consumption and difficulties in data interpretation. In a few countries, bridge condition assessment procedures in fact include nondestructive testing techniques, but regular application and integration is still rare. When adopted, NDTs are oriented in the identification of defects and thus in the assessment of the most

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suitable condition state. A review of ND techniques and strategies for the optimization of their implementation in BMS can be found in the results of the EC funded project “Sustainable Bridges”, with particular emphasis to automatizated solutions (Niederleithinger et al. 2006), whereas a general discussion with possible implementation of NDTs in the framework of principal, general and special inspections can be found in Jensen et al. 2006. Among others, Goangseup et al. (2008) illustrated the use of infrared thermography, useful for the detection voids located few centimeters below the surface and debond between concrete surface and FRP sheet which may be used to strengthen the concrete. Novel technologies. The main issue when using these tools is how to translate NDT results in a proper condition rating for a reliable comparison of the outcomes with those of other bridges for which only visual inspection data are available. In this regard, Huston et al. (2008) presented the results of a study that examined the condition of a reinforced concrete bridge deck using multiple sensors. Some of the sensor systems were automated, while others were manually operated. The testing compared five different methods: 1. Visual inspection and photographic recording of position; 2. Half-cell electrochemical potential; 3. Impulse type multipoint scanning ground penetrating radar, i.e. the HERMES/PERES II system; 4. Chain drag; and 5. Impact echo, i.e. Portable Seismic Pavement Analyzer (PSPA). The data were registered, overlaid and compared. The potential for developing automated multisensor systems that fuse data for efficient and effective bridge deck measurements was also discussed. Another interesting work was presented by Gukunsky et al. (2012), evidencing how NDTs can be used for a proper segmentation of a bridge structure in subparts characterized by similar condition states, with the final aim of helping inspectors to make a more objective rating. In this study, half-cell potential, electrical resistivity, ultrasonic surface waves, ground penetrating radar and impact echo methods were used.

Figure 2: Examples of non-destructive techniques for a detailed condition assessment of aging bridges. Probing is indeed more focused on the definition of main materials properties, through the removal of samples of materials from the existing structures and the subsequent testing in laboratory (e.g. when dealing with the characterization of steel or concrete stress-strain curves) or with the execution of in-situ tests, like for masonry bridges, the use of single-double flat jack systems. Finally, SHM techniques represent the most advanced tools for the assessment of structural response of existing bridges. However, their costs make them in most of the cases less convenient for small to medium structures, with respect to large ones or bridges with a significant importance. In addition, SHM requires an adequate design of the monitoring system, avoiding the risk of deriving data with no significance with respect to the initial aims of the SHM. Several researchers dealt with the definition of standardized criteria for the development of SHMs: among others Lee and Park (2008) presented some insights about specification and standard for SHM according to the type, scale, condition and place of the bridge in terms of the measurement item and sensor location of the bridge measuring system. SHM installation means that large amounts of data will be collected and provided to the bridge owner for utilization. There is a risk that the amount of data becomes more important than the quality and interpretation of the data. Developing new metrics that use SHM information in a probabilistic approach when assessing the structural performance is, consequently, a great challenge. In this regard, Orcesi and Frangopol (2012) proposed some performance functions based on monitoring information that consider uncertainties and correlation in recorded measures. Results of SHM can also interpreted in terms of condition rating (like for NDTs outcomes), fixing suitable threshold on monitored parameters. An alternative technique based on continuous monitoring of a structure, without the use of specific sensors can be represented by digital image correlation (DIC). DIC is a non-contact optical measurement technique that can capture deformation in two and three dimensions through digital photography. The installation of traditional contact-based sensors can require in fact equipment for access to key elements and wiring for power supply and data acquisition. Digital image correlation (DIC) can be used as an alternative to traditional bridge response instruments such as strain gauges. The ability to capture a bridge’s behavior with DIC and calibrate a structural model with the collected data is illustrated in Bell et al. (2012).

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3.1.2. TECHNICAL INDICATORS

A literature review of the most adopted technical indicators was preliminary performed with the aim to give a brief overview and subsequently more to non-technical ones. In the framework of a modern BMS, one of the key steps is the damage assessment at component/system level. Damage assessment is strictly expressed with the use of suitable technical indicators, which can be considered as a metric for defining a technical judgment of the health state of a component or a bridge structure. When a restoration intervention is executed for extending bridge service-life, its technical benefits can also be quantified with the assessment of the updated technical indicator value. In general terms, technical indicators can be subdivided in two main categories: operational technical indicators and scientific ones. Operational technical indicators are those commonly used in practice by engineers and technicians of highway companies and institutions for the assessment of the deterioration state of huge stocks of bridges, whereas scientific technical indicators are more refined metrics for quantifying the technical performance of a bridge structure over time quantitatively and often probabilistically taking into account material properties, loading and stochastic deterioration models. One of the first literature studies in which a clear definition of the concepts of performance indicators and goals for enhanced bridge management was presented is that of Patidar et al. (1991). The authors stated that performance measures (both operational and scientific) should have the following properties: 1. Appropriateness. The performance measure should be an adequate reflection of at least one agency goal or objective. 2. Measurability. It should be possible to objectively measure the performance measure. 3. Dimensionality. The performance measure should be able to capture the required level of each dimension associated with the decision-making problem, and it should be comparable across different time periods or geographic regions. 4. Realistic and operational. It should be possible to obtain reliable data relating to the performance measure with available resources without excessive effort, cost, or time. 5. Comprehensible and defensible. The performance measure should be clear, simple, and concise in its definition as well as in its method of computation, so that it can be effectively communicated within a circle of bridge decision makers, stakeholders, and general public. 6. Predictable. It should be possible to reliably determine future levels of the performance measure using existing forecasting tools. 7. Unambiguous. The performance measure should be clearly stated and such that its levels are directly related to the consequences of alternative bridge actions. 8. Comprehensive. The range of levels of the performance measure should cover the full range of possible consequences. Regarding operational indicators, the most commonly adopted tool is the use of visual surveys performed by trained technicians using as performance indicator a qualitative condition rating (or named also state, value) ranging between a numerical scale (usually from 0-5 or 0-9). This is the first technique that was implemented in US starting from the ‘70s, and of which there is the availability of the highest quantity of historical data records (Charmpis et al. 2016). In addition, such kind of indicators are the most worldwide diffused between owners and practitioners (Denysiuk et al. 2016). Several researchers tried to calibrate service-life curves in terms of condition ratings with respect to bridge age on the basis of available data sets (McCarten 2004). One of the most mentioned problems associated to the use of a condition rating-based BMS is the need of having trained inspectors to reduce subjectivity in the judgments: an interesting opinion paper was developed by Vanderzee (2004) evidencing, how future BMS will tend to substitute a subjective process with an objective one by changing the tool for the damage assessment from the classical visual survey approach to more sophisticated mixed NDT/SHM solutions. Condition states are also not able to provide a clear structural safety judgment, since are only based on a qualitative assessment of a visual condition of a component/bridge without taking into account quantitative data and making calculations. Also the loading side is not accounted for. For this reason they have to be coupled with safety indicators in a process of multi-variate optimization, taking also into account restoration costs. Some methods and models were proposed over years in literature: among others, Neves and Frangopol (2005) proposed a model that integrates the current practice in bridge management systems based on visual inspections (condition index) with structural assessment (safety index) during the lifetime of existing structures. The proposed model allows the consideration of uncertainties in the performance deterioration process, times of application of maintenance actions, and in the effects of maintenance actions on the condition, safety, and life-cycle cost of structures by defining all parameters involved in the model as random variables. The condition-based deterministic approach represents therefore the basic operational technical indicator for the assessment of damage to bridges in the framework of a BMS. However, the judgment is

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often characterized by uncertainties, which suggest substituting the deterministic approach with a more refined probabilistic one. Real situations can in fact be classified as intermediate situations between a finite set of possible condition states. Hence, taking into account uncertainties allows to partially reduce subjectivity in judgment. Several researchers dealt with these topics, proposing methods and approaches for a probabilistic damage assessment of bridge components or structures. Sun and Huang (2014) presented a multi-index comprehensive evaluation model for bridge condition assessment based on the use of trapezoidal function to determine the membership of each index, with a fuzzy logic inference system. In the determination of evaluation index weights, subjective and objective combination weighting method was presented, wherein, objective weighting based on inspection data and subjective weighting based on expert evaluation, which combines the actual situation reflected by inspection data with the reliable experience of expert. The influence of extent of index damage was considered to correct the weights to reflect the time-varying degradation characteristics of bridge durability. Faber and Sorensen (2002) and Sloth et al. (2002) proposed the use of Bayesian networks for the probabilistic characterization condition indicators for individual bridge components and bridges, formulated in terms of time dependent conditional probabilities, i.e. the conditional probability of the bridge being in a state requiring maintenance given an observed condition of the bridge. The modeling of the quality of observation results combined subjective and quantitative information within the framework of Bayesian statistics. Bayesian networks provide a powerful basis for the representation of the consequence inducing event scenarios in terms of causal relations between uncertain basic variables thus facilitating an integral tool for the assessment of risk taking into account common cause effects. Tarighat and Miyamoto (2010) applied Bayesian network theory with the aim to find the current condition of a concrete bridge deck considering uncertainty and imprecision in visual inspection results. Bayesian Network is capable of relating causes and effects in a certain problem by their joint probabilities. Since the condition rating of a concrete bridge deck is a parameter, which shows the current state of the condition related to the symptoms, the authors used a Bayesian Network to propose a method or model for probabilistic condition rating assessment.

Figure 3: Bayesian networks for a probabilistic damage assessment based on visual survey outcomes. Regarding research indicators, several metrics have been proposed in the last decades with the aim to quantitatively rank priorities of retrofit in a rational way. Zhu and Frangopol (2012) proposed a review of the most valuable scientific technical indicators, also taking into account their time-dependency. Among the various proposal, main research technical indicators are: Structural reliability: the safe condition is the one in which the failure of the investigated component/system does not occur (Kong and Frangopol 2010). For a structural component with resistance r and load effect s, its performance function is g = r – s, and the probability that this component fails is Pf = P[g<0]; hence reliability is the complement to unit of the failure probability. In mathematical terms, considering a Gaussian distribution of the performance indicator, reliability can be quantified with the calculation of the β index. Ghosn et al. (2016) presented a review of the reliability-based performance criteria used to calibrate design and evaluation codes and standards for assessing the strength, serviceability, and fatigue resistance of structural components. The review showed that large differences exist in the target reliability levels adopted for evaluating the strength of various types of structural members and materials. Cumulative probability of failure: The cumulative probability of failure up to time t is the probability of failure within a period of time (i.e., up to time t) (Okasha and Frangopol 2010). Survivor function: The survivor function is the complement of the cumulative probability of failure (Leemis 1995). The survivor function up to time tf, S(tf) is defined as the probability that a component or system survives up to time tf, or is functioning at time tf. Hazard function: The hazard function, h(t), provides a measure of the instantaneous failure rate of a structural component (Ramakumar 1993), and is defined as the conditional probability that given a component has survived until time t it will fail in the time interval t + dt. Structural redundancy: the availability of system warning before the occurrence of structural collapse (McCarten 2016). Several studies have been performed in presenting measures of quantifying

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redundancy for structural design or assessment. However, no agreement has been reached on redundancy measures yet. Redundancy can be defined through the reliability index β, by calculating the ratio between the reliability index β of the intact system in a generic time instant and the difference of reliability indexes calculated for the intact and damaged system at the same time instant: this difference can be interpreted as the availability of system warning before failure (Anitori et al. 2013). Structural vulnerability: vulnerability is one of the key measures used to capture the essential features of damage tolerant structures. Based on the study of Frangopol and Curley (1987), probabilistic measure of vulnerability was proposed by Lind (1995), defined as the ratio of the failure probability of the damaged system to the failure probability of the intact system, clearly a function of the loading acting on a structure. The value of vulnerability is 1.0 if the probabilities of failure of the damaged and intact systems are the same. Lind (1995) also defined damage tolerance as the reciprocal of the vulnerability. Structural robustness: in its original definition, robustness is thought as the ability of a structure to resist progressive collapse under sudden local damage (Anitori et al. 2013). In other words, tolerance to damage from the extreme or accidental loads (Saydam and Frangopol 2011); however, and based on the definition proposed by Baker et al. (2008): “…robustness is taken to imply tolerance to damage from extreme loads or accidental loads, human error and deterioration.”, recently the concept of robustness has been extended by some researchers to the case of systems under deterioration occurring progressively because of ageing and environmental effects. For instance, Biondini (2009) proposed a time-dependent measure of robustness intended to quantify the susceptibility to damage increases at any point of time during the structure service-life. On the other hand, Cavaco et al. (2013, 2016) proposed a time-independent measure of robustness with the aim to quantify the susceptibility to damage in the whole service life of the structure. Both definitions are different and of application to different aspects in the life-cycle management of existing structures. According to Ghosn (1998) and Liu (2000), robustness is defined as the capability of the system to continue to carry load after the failure of one main member in its damaged condition. Frangopol and Curley (1987) and Biondini et al. (2008) proposed a probabilistic measure of robustness as the ratio between system failure probability of the undamaged system and the system failure probability assuming one impaired member. Structural risk: indicator defined as the combined effect of chances and consequences (C) of some failure or disaster in a given context, i.e. R = Pf x C (Bakker and Klatter 2012; Ellis et al. 2016). Structural resilience: indicator defined as the time needed for recovering the initial functionality after the occurrence of a hazardous event. Also for scientific/research indicators, a stochastic approach is required for characterizing uncertainties in the definition of the variables used for calculating the indicators. Several researchers evidences the importance of using a probabilistic approach when dealing with the assessment of bridge performances: Bjerrum et al. (2002, 2004) showed how the Danish Road Directorate is applying probability-based bridge management as a part of the maintenance management of older or deteriorated bridges. It was also shown that there is a legal justification for applying probabilistic techniques in the safety evaluation. Table 1 shows safety requirements for the Ultimate Limit State specified as formal yearly probability of failure and relative reliability indexes. Some applications on aged steel bridges are provided by O’Connor et al. (2004), demonstrating how plastic modelling by equilibrium based finite element formulations in combination with probabilistic modelling can provide an effective tool for the assessment and management of existing structures. Jensen et al. (2004) evidenced also how probabilistic approaches can be used also at network level with significant improvement of quality of BMS decision making process. An interesting fully probabilistic reliability analysis taking into account also deterioration forecasts and synthetizing results in terms of different reliability indexes (calculated for fatigue, SLS permanent load combination, SLS frequent load combination, flexural strength and shear stability) through the use of radar charts was carried out by Strauss et al. (2014). Table 1: Safety requirements for the Ultimate Limit State specified as formal yearly probability of failure Pf and the corresponding reliability index β.

Given the substantial differences between operational and scientific technical indicators, many researchers tried to find correlations between them. Anitori et al. (2014), as example, analyzed potential

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relationships between robustness and condition ratings of existing bridges, with the aim to correct rating with data derived by robustness assessment to make it dependent on the system behavior. The study focused on multi-beam bridges deteriorated by corrosion of prestressing steel and lead to the definition of a corrective factor based on reliability index measures, and taking into account the relative importance of the member under inspection in the overall bridge safety. Deco and Frangopol (2010) proposed a condition-based approach describing lifetime deterioration of RC bridge decks. The study evidenced how the combined use of condition and reliability indices is a powerful tool, especially when it is applied to RC bridge decks under corrosion. Furthermore, in the case of RC decks under corrosion, the correlation between condition and reliability was demonstrated. Other types of indicators could be mentioned as not pure operational/scientific technical indicators, but more as based on statistical theories applied on pure technical indicators. Among others, condition indexing based on the concept of apparent age can be associated to operational indicators, whereas the credibility index can be proposed on the scientific ones. The condition index based on the concept of the apparent age was proposed by Zonta et al. (2008), based on the underlying theory that the Apparent Age of a standard element is the most likely age of the element given its condition state, assuming theoretical age distributions that are consistent with the normal deterioration model adopted for the element. With this approach, it is clear that the size of an element impacts the perception of degradation, and this is accounted for by introducing exponential weights proportional to the size. To estimate the age distribution of whatever combination of elements, as in the case of a bridge, authors extended the meaning of these exponent weights, also considering the different psychological impact that different types of element have on the overall perception of the bridge deterioration. Biondini et al. (2010) proposed the use of adaptation of a statistical estimator proposed by Grandori et al. (1998) to the case of the deterioration modeling of bridge structures. In particular, the effect of the epistemic uncertainty associated with deterioration modeling on the bridge service life prediction can be assessed through the calculation of the credibility indicator, able to compare two models and decide which one is the most reliable, both in qualitative and quantitative terms.

3.2. LITERATURE REVIEW ON NON-TECHNICAL INDICATORS

Technical indicators are usually considered also for making deterioration forecasts and thus define probable future deterioration scenarios for an infrastructural asset. Given a certain damage condition, a bridge owner can define the optimal restoration strategy to be carried out for extending the service life of an aged structure. In some cases, more than one solution can be developed, thus evidencing the need of identifying the best option. Hence, the selection of the best solution needs a set of indicators to be assessed and then compared for rationally support a choice. It is evident that performance indicators associated for the damage assessment can be used for quantifying the effectiveness of each retrofit solution. However, when realizing a restoration intervention, the execution of the different work phases implies a series of social, environmental and economic consequences that often are the most impacting in the decision making process. In the following, results of the review performed in the field of non-technical indicators are provided. Some researchers proposed a way for taking into account such different aspects in a unique indicator usually called “Sustainability index” (Hendy and Petty 2012), where the concept of sustainability can be viewed as the a way of life characterized by a sustainable development, i.e. development that meets the needs of the present without compromising the ability of future generations to meet their own needs (Maier et al. 2012). The authors used this index for comparing different solutions for a new bridge project. The rating of the attributes generates a visual summary graphic and an overall sustainability index score as shown in Figure 4. The graphical plot (named spider/radar diagram) serves as a convenient summary of the relative impact of each attribute. The overall index score ranges from zero to one; the higher the index, the greater the potential for the bridge design to be improved to give a more overall sustainable performance, but a high score does not mean that the bridge is poor in terms of sustainability. A score of zero corresponds to all points lying at the center of the spider’s web. A score of one corresponds to all points lying on the boundary. The contribution of each individual impact to the overall rating can be weighted. The sustainability index described by the authors gives equal weighting to the five top-level headings of economy, society, environment, climate change and resources.

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Figure 4: Application of a sustainability index to alternative bridge projects.

3.2.1. ENVIRONMENTAL INDICATORS

Regarding environmental indicators, many parameters, including cost, energy consumption, the use of non-renewable resources, environmental impacts, traffic disruption, durability and the reuse or recycling of components and materials have to be considered (Wallbank et al. 1999). In order to make sense of all these different factors, ways of assessing the level of sustainability of a bridge and of comparing the levels of alternative proposals are required. Life Cycle Assessment (LCA) is a technique for assessing the environmental impacts and burdens associated with an item over its entire life – cradle to grave – from winning the raw materials to final disposal (Wallbank 2002). LCA concentrates on environmental aspects – resource use, ecological consequences and human health – and does not normally address economic or most social aspects. The LCA methodology is defined by the standards DIN EN ISO 14040 and DIN EN ISO 14044 and is the only internationally standardized method for the ecological assessment of product systems (Klöpffer & Grahl 2009). To account for the environmental impacts of the life-cycle of bridges (Beck et al. 2012), the following environmental indicators were proposed: Primary Energy Demand: separated into renewable and non-renewable; measured in MJ. Primary energy demand is the quantity of energy directly withdrawn from the hydrosphere, atmosphere or geosphere. For fossil fuels and uranium, this is the amount of resource withdrawn expressed in their energy equivalent. For renewable resources, the energy- characterized amount of biomass consumed is described. For hydropower, Primary Energy is based on the amount of energy that is gained from the change in the potential energy of the water (i.e. from the height difference) (Kreißig & Kümmel 1999). Abiotic depletion: measured in kg Sb- equivalents. The depletion of abiotic resources has been one of the impact categories taken into account in the environmental impact assessment. However, abiotic resource depletion is one of the most debated impact categories because there is no scientifically “correct” method to derive characterization factors (van Oers and Guinee 2016). The problem of depletion of abiotic resources can still be defined in different ways, such as a decrease in the amount of the resource itself, a decrease in world reserves of useful energy/exergy, or an incremental change in the environmental impact of extraction processes at some point in the future (Guinee et al. 2002; Heujungs et al. 1997). The impact category of “abiotic depletion” is calculated by multiplying LCI results, extractions of elements and fossil fuels (in kg) by the characterization factors (ADPs in kg antimony equivalents/kg extraction) (van Oers and Guinee 2016). Global Warming Potential: given in kg CO2- equivalents. The Global Warming Potential describes the mechanism of the greenhouse effect: short-wave radiation emitted by the sun comes into contact with the earth’s surface and is partly absorbed (which leads to direct warming) and partly reflected as infrared radiation. The reflected part is absorbed by so-called greenhouse gases in the troposphere, and is re-radiated in all directions, including back to earth, which causes a warming effect at the earth’s

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surface. Greenhouse gases that are considered to be caused or increased anthropogenically are for example carbon dioxide, methane and chlorofluorcarbons (CFCs) (Kreißig & Kümmel 1999). Ozone Depletion Potential: measured in kg R11- equivalents. Ozone is produced in the stratosphere by disassociation of oxygen atoms that are exposed to short wave UV light. This leads to the formation of the ozone layer in the stratosphere. Despite of its minimal concentration, the ozone layer is essential for life on earth: Ozone absorbs short wave UV radiation, which causes changes in growth or a decrease in harvest crops (disruption of photosynthesis), the generation of tumors (skin cancer and eye diseases) and decrease of sea plankton, and releases the radiation in longer wavelengths. Anthropogenic emissions such as fluorine-chlorine-hydrocarbons (CFCs) and nitrogen oxides (NOX) deplete ozone and cause the hole in the ozone layer (Kreißig & Kümmel 1999). Human/Terrestrial Toxicity: measured in kg 1.4-DB equivalents. The Human Toxicity Potential (HTP) is a quantitative toxic equivalency potential (TEP) that has been introduced previously to express the potential harm of a unit of chemical released into the environment. HTP includes both inherent toxicity and generic source-to-dose relationships for pollutant emissions. The overall HTP score of an emissions profile is obtained by multiplying the release of each chemical by the equivalency factor and then adding the resulting numbers (McKone and Hertwich 2001). A different normalizing chemical is used for carcinogens and non-carcinogens. Acidification Potential: measured in kg SO2- equivalents. Acidification of soils and waters originates predominantly through the transformation of air pollutants such as sulphur dioxide and nitrogen oxide into acids (H2SO4 und HNO3). This leads to a decrease in the pH-value of rainwater and fog. This “acid rain” harms ecosystems; forest dieback is the most well-known impact. Other damaging effects are nutrients being washed out of soils, an increased solubility of metals into soils, or damage to buildings and building materials (for example metals and natural stones are corroded or disintegrated at an increased rate) (Kreißig & Kümmel 1999). Eutrophication Potential: measured in kg PO4- equivalents. Eutrophication is the enrichment of nutrients in a defined place, either aquatic or terrestrial. It is caused by air pollutants, waste water and fertilization in agriculture. In water, eutrophication causes an accelerated algae growth, which in turn, prevents sunlight from reaching the lower depths. This leads to a reduction of photosynthesis and oxygen production. Additionally, more oxygen is needed for the decomposition of the dead algae. Both effects lead to a decreased oxygen concentration in the water, which can eventually lead to fish dying and to anaerobic decomposition. Dying waters are the visible consequence of this phenomenon. In terrestrial ecosystems, on eutrophicated soils possible consequences are an increased susceptibility of plants to diseases and pests, same as is a degradation of plant stability. If the eutrophication exceeds the amounts of nitrogen necessary for maximum plant growth, this can lead to an enrichment of nitrate, this again causing increased nitrate content in groundand drinking water (Kreißig & Kümmel 1999). Photochemical Ozone Creation Potential: measured in kg C2H4-equivalents. Despite playing a protective role in the stratosphere, at ground-level, ozone is classified as a damaging trace gas. Photochemical ozone production in the troposphere, commonly known as summer smog, is suspected to cause damages on vegetation and materials. In addition, high concentrations of ozone are toxic to humans. Ground-level ozone is produced by combination of radiation from the sun and the presence of nitrogen oxides and hydrocarbons. Such hydrocarbons are emitted by incomplete combustion, in conjunction with petrol (storage, turnover, refueling etc.) or from solvents (Kreißig & Kümmel 1999). The production of waste has also to be quantified: Gervasio et al. (2012) presented a study in which the potential impacts due to the production of construction and demolition waste are then considered by the impact categories listed above for the case of highway composite bridges. Summarizing, environmental impacts, in terms of greenhouse gas emissions and waste production have to be estimated for all activities occurring during the life cycle of a bridge. Specific software programs containing suitable dataset for the LCA analysis (e.g. SimaPro 7, 2008) can be used for the assessment of such environmental indicators.

3.2.2. SOCIAL INDICATORS

Regarding social indicators, two main subcategories can be found: economically quantifiable and not- quantifiable indicators. For quantifiable ones, the most significant social effect caused by bridge maintenance is usually disruption and delay to the travelling public. Delays during maintenance works to bridges on busy roads can be significant. The resulting costs can outweigh the construction costs many times. Reducing the delay costs can provide a substantial improvement in the level of sustainability of the work (Wallbank et al. 1999). It is interesting to notice that Koch et al. (2001) estimate the user costs due to traffic delays and lost productivity to be more than ten times the direct cost of maintenance, repair, and rehabilitation. User costs are estimated as the product of additional travel time and the value of time. A review of 5

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user cost models can be found in Thoft-Christensen (2006). The first general definition of social road user costs (RUC) was formulated by Daniels et al. (1999): RUC = VOC + VOT + AC where VOC is the vehicle operating cost component and includes the costs of fuel, tires, engine oil, maintenance, and depreciation, VOT is a function of the hourly wage rate and AC is the accident costs (fatal accidents, non-fatal injury accidents, property damage accidents). VOT is in most cases the most relevant component and must be differentiated when dealing with passengers cars or trucks. Also number of vehicle accidents can be considered a social indicator for assessing the impact of a restoration project on the safety of users. Some studies dealt with the calibration of models taking into account main bridge parameters. Among others, Thompson (2002) presented a user annual accident count model, having the following expression: Annual accident count = 0.001 × (C1 + C2×lanes×length + C3×Narrowness×ADT) where the variables and constants are as defined in Table 2. Lounis and Daigle (2010) in the evaluation of social indicators, considered average accident costs and the normal accident rate are taken from statistics published by Transport Canada (1994, 2006) and Statistics Canada (2003): data taken for this example are presented in Table 6. Considering restoration operations, a work zone is a defined area on a road where maintenance works will be carried out (Gao and Zhang 2014). It will disrupt normal traffic flow and reduce the vehicle capacity of a bridge. Due to the presence of a work zone, some or all road lanes of a bridge will be temporarily closed and the traveler will have to either slow the speed, or idle in a queue, or find a detour route. Considering the lack of statistical data on accident in work zones, a rule of thumb of three times the normal accident rates is used as suggested by Walls & Smith (1998). Table 2: Example of main parameters of an accident model, calibrated on US data.

Bai et al. (2010) presented a framework for comprehensive estimation of user cost for bridge management, synthesizing the existing state of practice of user cost estimation and techniques to address a number of considerations in such estimation. Different methodologies were presented to compute the different user costs associated with detour, delay and safety during normal operations of a bridge or during rehabilitation or reconstruction workzones. In addressing key considerations, the authors recognized that during an intervention, a given bridge user may detour for more than one reason, and thus not accounting for this situation may lead to double counting of user costs, which was never explicitly considered in the literature. Zinke et al. (2012) presented a practical case study of highway overpass deconstructed and replaced by a new structure calculating social impacts in terms of increased travel time for users and fuel costs. Other more conceptual social indicators can be used: among others, Pandey et al. (2006) proposed the use of the Life Quality Index (LQI) for the assessment of the impact of road maintenance programs on the quality of life in the Netherlands. The LQI is equivalent to a multi-attribute utility function being consistent with the principles of rational decision analysis. It is further refined to consider the issues of discounting of life years, competing background risks, and population age and mortality distribution. Rackwitz et al. (2005) expanded the LQI framework and applied it to determine optimal safety levels in civil engineering infrastructures. Maes et al. (2003) applied LQI for optimising the life-cycle cost of structures. With reference to economically not-quantifiable social indicators, aesthetic impact, prestige and historic value and political implications can be considered as relevant social qualitative indicators. One important point is in fact that bridges are often located directly in the urban space or are seen as prestigious landmarks. Therefore, their aesthetic impact and acceptance is another important performance aspect (Bonenberg A. 2010). Furthermore, historic bridges can constitute a part of the

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cultural heritage and hence, preservation can become an important aspect. Dette and Sigrist (2011) proposed an aesthetic indicator for concrete bridges called time of unsatisfactory appearance (RTUA), that is defined as the fraction of the service lifetime in which the condition of the concrete surface is below a certain aesthetic threshold and thus the appearance is impaired. Other visual quality characteristics can be considered as aesthetic indicators: a review of these can be found in Smardon and Hunter (1983), FHWA (1988) and Rahman (1992). In the following some examples: Visual Pattern Elements: These are the primary visual attributes of objects as follows: Color refers to consistencies between bridge colors, hues, values, and chrome with those of its environment; Form is the virtual mass, bulk, or shape of a bridge, and refers to the compatibility between these attributes and those of the environment; Line refers to the compatibility of bridge edges, bands, and introduced silhouette lines with its environment; Texture refers to the compatibility between bridge surface textural grain, density, and regularity of pattern. The overall term landscape compatibility has often been used to indicate how well the bridge fits into the overall landscape on the basis of visual pattern elements – color, form, line, and texture Visual Pattern Character: refers to the visual contrast between a bridge and its visual environment (setting). Two objects may have similar visual pattern elements but may exhibit very different visual characters. Visual characters are scale, dominance, diversity, continuity, and variety. Scale Contrast: the extent to which the bridge blends into its environment on the basis of its size relative to the sizes of other features in its environment. Spatial Dominance: similar to scale contrast but pertains to a larger dimension; is the extent to which the bridge elements would be dominant in views of larger landscape and cityscape. An excellent rating (low spatial dominance) is one where the bridge does not dominate; a poor rating (high spatial dominance) is one where the bridge features too prominently in the composition of the landscape. Diversity: a function of the frequency, variety, and intermixing of the visual pattern elements of the bridge with its setting. Also termed as setting contrast (the extent to which project’s visual pattern elements contrast with or blends in with its existing natural or man-made background). Continuity: is the uninterrupted flow of pattern elements in a landscape and the maintenance of visual relationships between landscape components that are immediately connected or related. Variety: the richness/diversity of physical objects and interrelationships within the landscape. Visual quality: This is simply the excellence of the viewing experience. While this may be a subjective measure, there is generally consensus regarding sights that have high visual quality (city skylines, waterfalls, fall leaf colorations, etc.). Visual quality may be assessed using one of several approaches (i) determining whether (and possibly, the extent to which) an area is designated a site of natural history (parks, scenic rivers, etc.), (ii) using opinion surveys of viewers, (iii) using indicators of visual quality – vividness, intactness, unity – all three of which must be high for a high visual quality. From the above list of visual character and quality attributes, the bridge manager can select a set of performance measures to evaluate alternative bridge projects on the basis of visual quality. Care should be taken to avoid choosing performance measures that overlap (Patidar et al. 1991). Regarding politics, any clear indicator was found in literature. However, some suggestions are herein reported on how to define it in a consistent way. In the STSM researcher opinion, the aim of a political indicator is to try to quantify benefits associated to a decision in terms of improvement of the social consensus: hence, when dealing with the comparison of different solutions, the one characterized by the highest consensus is the best one. For the assessment of this indicator, the most suitable tool is represented by opinion polls via interviews or other ways: the key issue in this context is to define a proper sample of citizens to be queried, reflecting the real distribution of population potentially interested/afflicted in case of adoption of a specific decision.

3.2.3. ECONOMIC INDICATORS

Regarding economic indicators, their use is aimed on one hand to assess economic efficiency of alternative bridges rehabilitation projects. Robert et al. (2004) analyzed the role of economic analysis in engineering and political decisions regarding transportation investments. The focus of their study was on use of computer systems (referred to as “analysis tools,” or simply “tools”) for performing systematic economic analysis of U.S. transportation investments. A wide variety of tools exist for economic analysis of transportation investments, and these can be categorized using any number of approaches (Virtala 1997). More generally, cost-benefit analysis of a project can be carried out as an economic (if the project realization will never serve public purposes) or financial (if the building generates revenues) one. In the case of bridge maintenance, projects can generate revenue, so we use economic analysis. Among others, Valuch and Pitonak (2015) presented a review of economic indicators in use:

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Economic payback period: the year in which benefits equalize costs for restoration of bridges. Economic Payback period can be expressed as a time of repayment. Economic net present value: indicator that has an economical result in financial terms, as difference of social benefits and costs. In case of more sophisticated build-operate-transfer contracts, usually developed when dealing with the identification of the best solution among a set of alternative projects, other additional terms related to royalties, business income taxes and earninr reserves must be substracted to the net value (Chang and Chen 2001). Economic internal rate of return: indicator that represents the rate in which an economic net present value is equal to zero. Economic benefit-cost ratio: indicator compares the economic net present value of all the social benefits and costs of the project life cycle and its cost of acquisition. Viewpoint of equity: The viewpoint of equity takes account of the equity invested in the construction period and the total net profit before dividends are given to stockholders in the operating period (Chang and Chen 2001). The total net profit here comes from the statement of cash flows, which considers financing-related items such as loans, interest, stocks, dividends, and so forth. The purpose of this indicator is to serve as a reference for stockholders In this way, the concessionaire will know how long it will take for their investment to be recovered with the total net profit in the operating period. View point of dividends: From the viewpoint of dividends, the equity invested in the construction period and the dividends paid to stockholders in the operating period are considered (Chang and Chen 2001). This viewpoint also provides information to stockholders about the length of time during which the dividends given to stockholders in the operating period can recover the equity investment (by stockholders) in the construction period. Debt coverage ratio: it is a ratio between earnings before interest and taxes (EBIT), including depreciation. However, depreciation is not a real cash outflow (Chang and Chen 2001). It represents the wearing-out of the equipment. Therefore, to present the concessionaire’s available capital to pay debt, depreciation is added back to EBIT. Debt coverage ratio shows the concessionaire’s ability to pay debt. The higher the debt coverage ratio, the better the concessionaire’s debtpaying ability. The debt coverage ratio influences the willingness of banks to loan money to the concessionaire. Generally speaking, a debt coverage ratio at least equal to or larger than 1.0 is acceptable (Brigham et al. 1997). Many researchers deal with another parameter called Life-Cycle Cost (LCC) (Thoft-Christensen 2004; Bakker et al. 2012; Fuchs et al. 2014, Wessels et al. 2014; Lounis and Almansour 2016), i.e. the sum of all the costs that an owner has to sustain during the entire service-life of a bridge structure. Bakker et al. (2016) proposed also a composed indicator called Economic End of Life Indicator (EELI) function of the LCC costs in different conditions. However, in the STSM researcher opinion, this is not a pure economic indicators, but can be considered a synthetic economic indicator in which all technical and non-technical issues can be monetized: in such a way, it might be seen as the main output of a consequence function, i.e. a relationship able to monetize a technical or environmental or social indicator, allowing a subsequent direct and clear comparison. Some examples in fact can be found in Keoleian et al. (2005) that integrated life cycle assessment and cost model was developed to evaluate infrastructure sustainability, and compared alternative materials and designs using environmental, economic and social indicators. Cope et al. (2010) analyzed the influence of interest rate, user cost weight, traffic volume on the relative attractiveness of stainless steel compared to traditional steel, using data from a mid-western state in the United States, calibrating an empirical life-cycle assessment model subsequently tested with a sensitivity analysis. Wang et al. (2014) analyzed shield tunnels with secondary linings, and proposed a maintenance framework based on performance-based design, taking into account strength and LCC as main performance indicators. The same concept can be applied to repair costs, that are actually considered as en economic indicator, whereas might be conceptually classified as the monetization of the technical indicator improvement after the execution of a restoration project.

3.3. ANALYSIS OF POTENTIAL CORRELATIONS BETWEEN TECHNICAL AND SUSTAINABILITY INDICATORS

When dealing with the optimal scheduling of restoration interventions for a bridge asset, it is important to quantify with the same metric different performance indicators, to be able to compare alternative solutions and identifying the optimal one, i.e. that characterized by the lowest overall costs. Hence, it is fundamental to identify potential correlation models between different types of indicators, and trying to express the outcomes in monetary terms. In such a way it is possible to present an objective

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assessment of technical, environmental, social consequences associated to the adoption of a specific restoration protocol. A general scheme is presented in Figure 5. Figure 5: Main steps of the decision making process in a cost-based bridge management system. Given a deterioration state at a generic i-th year or a future deterioration forecast, the decision making process consists in the identification of the best retrofit solution among alternative options on the basis of the assessment of PIs, or the do nothing alternative. Usually the selection is made through the use of multi-objective optimization though multi-criteria decision-making (MCDM) with radar charts (Stipanovic et al. 2016; Stipanovic and Klanker 2016). Among others, analytical hierarchy process (AHP) seems to be an effective tool: Ali et al. (2015) presented a sustainability assessment of two bridges with the use of AHP, which involved evaluation of pairwise comparison of the various categories to assist with evaluation of the sustainability scores. Once identified the best retrofit solution, conditional to the deterioration state present or forecasted in a specific time instant, a bridge owner has to decide if doing the retrofit or postpone in the future years the intervention. In case of do nothing, any cost is sustained, whereas the deterioration state remain unchanged and will be subject to a worsen as soon as time will pass. On the contrary, if the decision is to doing retrofit, several consequences have to be assessed at technical, environmental and social level. In particular, consequence function needs to be used for monetizing impacts associated to the execution of a restoration work. Regarding technical consequences, the retrofit implies an increase of the technical indicator value associated to the deterioration state. This technical improvement can be monetized using consequence functions estimating direct costs for restoration, i.e. a cost model that correlates a technical indicator values associated to different deterioration levels with related costs needed for a restoration intervention. Some studies proposed such types of direct costs models: among others Sobanjo et al. (2002) performed an ANOVA analysis on existing agency data with the aim to calibrate unit maintenance cost functions for different types of bridge elements. Zanini et al. (2016) presented an extensive cost analysis for maintenance and seismic retrofit of typical existing road bridges, based on integrated procedures for assessment of state and seismic vulnerability. Regarding environmental consequences, impacts in terms of content of chemical substances like CO2 have to be quantified with performance indicators, considering direct and indirect items. More generally, environmental indicators should be assessed with reference to technical and social issues: for example, like restoration operations and indirect aspects like emissions associated to travel delays. Both subclasses are clearly influenced by the time duration of the retrofit works, whereas in addition, traffic flows have to be taken into account for the indirect consequences. Some insights are presented by Boulent (2016). Emissions associated to the production of materials needed for the execution of the retrofit protocol have to be considered in the subclass of technical issues, since are strictly related to the designed retrofit solution. Regarding social consequences, travel delays have to be monetized taking into account hourly cost of passengers involved in delays and estimating increased travel time and flows to derive an absolute value. Consumption of vehicles and fuel costs have to be included in social consequence functions. Lastly, accident consequences have to be monetized. Hence, a cost-based approach should also be used as alternative metric for identifying the best retrofit solution, with the use of such kind of consequence functions, estimating total costs of each possible alternative, conditional to a specific deterioration state, and then adopting the best one, i.e. the one characterized by the lowest overall costs.

State

Deterioration state

ith - year

Action

Do nothing

Do Retrofit

Impact

No impacts

Costs

Identify the best retrofit solution

based on Technical

Environmental Social

Economic Indicators

(Radar chart)

No costs

Technical Environmental

Social Consequences

(Costs)

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The advantage of using a cost-based approach with respect to a qualitative MDCD approach is that the selection of the best retrofit solution is that the decisional process is characterized by a clearly understandable metric; however, qualitative indicators like aesthetics are difficult to be monetized. On the other hand, the use of MDCD/AHP methods all different types of indicators without using a common metrics, but using dimensionless values subsequently weighted: in STSM researcher opinion, the definition of weights is the main drawback of such kind of methods, and in most of the cases results in a vague comprehension for an owner. A cost-based approach seems more useful, especially when dealing with many bridges: qualitative issues, like aesthetics, can be in fact taken into account only when dealing with relevant or historical bridges and not for ordinary structures.

3.4. LITERATURE REVIEW ON MAIN DETERIORATION MODELS FOR TECHNICAL INDICATORS

In a BMS framework and in any quality control plan, one of the key components is the deterioration model. For an optimal maintenance scheduling, there is in fact a need of reliable deterioration forecasts. Some methodological considerations were developed on how to design a comprehensive framework for the forecasting over time of maintenance costs for bridges belonging to a roadway network. In the field of maintenance optimization for existing bridges, different types of deterioration models can be used. Deterioration models allow to forecast the time evolution of the condition state of a bridge over its service life. Two main categories can be identified (Akgul 2010): deterministic and probabilistic deterioration models. Deterministic models do not incorporate the probabilistic nature of the deterioration of a structural element. These models rely solely on historical statistical data on the condition of a structural element in order to predict the future condition of the element. Statistical data must be available in order to best-fit a regression line or curve on such existing data. The models can be categorized as linear or polynomial models. This kind of models is clearly basic and over years was replaced by more refined probabilistic deterioration models, mainly based on Markov processes. Markov processes allows to model uncertainties in deterioration forecasts. The basic concepts of a Markov process are those of a state and a transition. Markov process is a stochastic process distinguished by Markovian property that states that knowledge of the present state is sufficient to predict the future stochastic behavior of the process: future states or performances are thus independent from their past history. Markov processes can be applied to qualitative indicators (i.e. discrete) like condition ratings or quantitative (i.e. continuous) indicators like measurements continuously derived from a SHM. Four main categories of Markov processes can be generally defined: discrete-time and state (i.e. the classic Markov chain), discrete-time and continuous-state, continuous-time and discrete-state, and continuous-time and state. Kallen and van Noortwjik (2006) presented a study focused on the statistical estimation of parameters in various types of continuous-time Markov processes using bridge condition data in the Netherlands. The parameters in these processes were transition intensities between discrete condition states. These intensities may depend on the current state and on the age of the structure. Authors also discussed the influence of the inspector on the condition process. Orcesi and Cremona (2006) presented the results of the analysis of the dataset of the French concrete bridge stock and statistically derived Markov transition matrix coefficients to be used for making deterioration forecasts. Recently Orcesi et al. (2016) proposed a method for incorporating climate change impacts and traffic growth by adding to the classical Markov deterioration model further degradation matrices associated to externalities able to worsen bridge conditions. Matsumura et al. (2006) proposed three different method for deriving deterioration curves: The first is an approach based on the theoretical analysis of deterioration phenomena and the experimental data to estimate the length of each deterioration period. The process of analyzing the deterioration phenomena is essential and very useful, but the real deterioration process is affected by many factors such as environmental conditions or characteristics of material, and it is difficult to estimate the deterioration process only from the experimental data. The second approach is the statistical approach based on the analysis of the inspection data. Since the inspection data is supposed to reflect various many conditions those have affected the speed of deterioration, it is desirable to establish deterioration curves based on the existing inspection data. But, since the inspection has been intended to find severe damages or deteriorations that need prompt repair or rehabilitation, it is difficult to know when deteriorations started from the existing inspection data. Nakajima et al. (2008) presented the results of 20 years of visual inspections on bridges of a Japanese national highway with special attention on “coating conditions” and “rust & corrosion” and calibrated a deterioration model focusing on regression analysis and deterioration speed.

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The third approach is the one based both on the knowledge of experts and on the field data, by assuming deterioration curves based on the theoretical and experimental findings on the deterioration phenomena and the knowledge of experts (Lee et al. 2008), and adjusting them by reflecting the inspection data (Kim et al. 2008, Powers and Hinkeesing 2014). Also NDT techniques or probing results can be used for improving reliability of existing deterioration models: Crespi et al. (2010) evidencing the difficulty of draw up a deterioration model for a medium-long time forecast, proposed a method to draw up deterioration curves using available concrete carbonation tests on structures of the considered network, allowing to simulate future deterioration and to obtain a maintenance program in a medium-long term scenario. Usually Markov chain model is used to solve a wide range of practical problems: this process bases on the definition of the yearly transition matrix, which contains probability values associated to changes of condition state, usually derived from past data sets or numerical model results. Deterioration models usually are defined for technical performance indicators, expecially with reference to condition rating systems, but also with quantitative indicators. Several authors dealt with the calibration of deterioration models for main bridge elements on the basis of data derived by past visual inspections using condition rating systems: Kaito and Abe (2004) presented a method is advantageous in the application under incompleteness information, requiring only the two series of deterioration ratings and their inspected dates from which deterioration rate can be calculated. Fraher et al. (2010) presented an improved method for predicting NBI condition ratings based on a translation tool between bridge health state indexes and Pontis NBI ratings. Ferreira et al. (2014) described the degradation and maintenance models adopted in Portoguese roadway bridge management system with Markov coefficients calibrated on the basis of historical data. Thompson (2012) presented a comparison between deterioration models developed in Florida and Virginia using the same basic assumption, to investigate the level of consistency in model parameters and to understand how agency characteristics, policies, and climate can affect deterioration rates for pure Markovian and hybrid Markovian/Weibull models. Usually the attention is focused on data derived from aging bridges, however when a bridge is restored deterioration phenomena restart their action: in such terms, there is the need to quantify a deterioration model also for restorated bridges. Mizutani et al (2014) conducted a statistical deterioration prediction, and proposed the post evaluation method of repair effects. The mixed Markov deterioration hazard model and its hierarchical Bayesian estimation were employed as concrete model and estimation method, and the heterogeneity between deterioration processes before and after the repair was evaluated quantitatively. In addition, through the relative evaluation of deterioration rates, the post evaluation of repair effects was conducted by the authors. Lethanh et al. (2014) extended Markov chains method usually adopted for describing natural ageing phenomena to the case in which also sudden events, like natural hazards, can affect the health state of a bridge. The model allows the consideration of both manifest (natural ageing) and latent (sudden events) deterioration processes. The manifest deterioration process is modeled using discrete jumps between condition states as a Markov chain. The latent deterioration process is modeled by estimating the probability of failure when an object is in each manifest condition state. The probability of failure is estimated using fragility curves. Other researchers proposed deterioration models based on a quantitative prediction of the structural response over time: : Gaal et al. (2002) applied a probabilistic approach focusing on corrosion due to chloride ingress in concrete structures, with the aim to give reliable predictions for the future demand of maintenance. Oh and Kim (2004) proposed method consists of evaluation of field data, derivation of deterioration models, damage prediction, and structural performance evaluation of bridge members. Matsumoto and Frangopol (2006) introduced a condition evaluation method for bridge elements under chloride attack using a quasi-quantitative grading method. Monte Carlo simulation was adopted as a probabilistic problem solver to include all the uncertainties associated with the parameters to predict the future condition states of deteriorating structures. Based on simulations, the probabilities of transition of condition states were determined using the stochastic parameters such as cover depth, diffusion coefficient and surface chloride content. Several maintenance scenarios were taken into account and the optimum lifetime maintenance strategy for highway bridges was selected based on the minimum present values of expected cumulative maintenance cost. Akgul (2010) proposed a probabilistic piecewise linear model based on latin hypercube sampling technique and simulation toolbox as alternative proposal to the Monte Carlo sampling approach proposed by Neves and Frangopol (2005). Some studies were carried out with the aim to try to find a correlation between qualitative/quantitative deterioration modelling strategies: among others, Wada et al. (2008) studied themes for the elaboration of a deterioration prediction model for chloride attack, and quantitative/qualitative structural soundness evaluation, looking to keep them in the good working by analyzing inspection data and taking the local characteristic into account. Recently, Xing et al. (2016) used Bayesian updating of time-dependent performance indicators monitored (e.g. through a SHM) for the prediction of structural lifetime and critical inspection points in time.

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Deterioration models can be developed at system level but also at component level: one interesting issue when dealing with element deterioration models is how to couple them in a unique deterioration tool (Zanini et al. 2016). Also repair actions on a specific component can be viewed in relation with the other ones: Jido et al. (2004) evidenced how often it is necessary to consider such interactions between sub-systems when a repair/replacement policy is discussed, and proposed a methodology to determine the optimal synchronized repair policy for a vertical integrated bridge structure. Kim et al. (2008) presented deterioration models based on Markov chains for six main elements in Korean BMS. Liu and Weissmann (2012) proposed different methodologies for modeling the deterioration of individual elements of a bridge. These individual element deterioration curves are key elements in the development of BMS modules that forecast bridge condition and optimize limited funding. Individual element deterioration curves can also be a key element in the prediction of overall bridge network condition as summarized by indices such as the Bridge Health Index (BHI). Hasan et al. (2014) presented a research where the Markov Process has been used to predict deterioration curves for three different elements of concrete bridges in Victoria, Australia on the basis of historical data from 5000 bridges and culverts in Victoria. Fernandes et al. (2014) described a continuous in time Markov model for characterizing deterioration of telematics equipment in highway infrastructures.

3.5. FORMULATION OF A GENERIC FRAMEWORK FOR MAINTENANCE COSTS FORECASTING

As previously stated, in a BMS framework and more generally in any quality control plan, one of the key components is the deterioration model. Deterioration forecasts play a key role in optimizing resources allocation: Jiang (2004) evidenced how bridge condition predictions affect bridge project selections and their corresponding system benefits aiming to maximize the total expected benefit of the bridge system while a number of constraints are simultaneously satisfied. This optimization process is based on the predicted bridge conditions. Therefore, the accuracy of bridge condition predictions is vital to the effectiveness of bridge project selection. Silva and Fernandes (2006) presented a work aimed to evidence the role of a reliable deterioration model in the probabilistic life-cycle cost assessment and performance of a road bridge. On the basis of the scheme proposed in Figure 5 and a deterioration forecast model for a technical indicator, herein considered a condition rating, classically ranging from 1 (best state) to 5 (worst state), a proposal of how to implement a cost-based forecasting model is briefly presented for a generic bridge structure. In this example condition rating is related to the entire bridge structure, since non-technical consequences can be entirely taken into account starting from the system to network level. The main aim of a cost-based forecasting approach is to quantify over time maintenance costs, taking into account and thus monetizing technical, environmental and social impacts. Once defined the condition rating system, Markov chain model allows to forecast the probability of being in each state conditional to a specific future time instant, given the actual condition state distribution. Assuming a bridge in best conditions, the initial condition state vector is: C(t=0) = [1 0 0 0 0] The following yearly transition matrix was assumed: P(t=1) = [ 0.87055 0.12945 0 0 0 ; 0 0.93303 0.06697 0 0 ; 0 0 0.91700 0.09300 0 ; 0 0 0 0.79370 0.20630 0 ; 0 0 0 0 1 ] Hence, Figure 6 shows the probability distributions in a time interval of 70 years provided with the adopted forecast model. Now, for a generic future year, it is necessary first of all to identify the best retrofit solution in case of bridge in CS1, CS2, CS3, CS4 or CS5. It is evident that the assessment of future social, environmental and economic indicators should require suitable specific forecasts: for example, if an owner is actually (2016) dealing with the assessment of the best retrofit solution to be carried out in 2026, he should make some hypothesis on how traffic flows will change in future (social), the variation of exposure to environmental factors and pollutant agents (environmental), the best discount rate to be taken into account (economic) and other similar issues. In such terms, it is clear that owner must not limit his forecast only to the technical issues, but he has to make reasonable assumptions also to social, environmental and economic factors, and such assumptions are clearly conditional to the time instant to which they are referred.

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Figure 6: CS probability distributions forecasted in a time interval of 70 years. Hence, once identified for a future generic time instant the best solution for each condition state of the condition rating system assumed, considering also non-technical aspects, consequence functions allows to monetize related impacts. Technical, environmental and social impacts are thus monetized and summed, providing an overall economic estimate of the impacts associated to the development of the best restoration protocol, given a specific condition state. In the specific case under analysis, given 5 different alternative condition states, for a generic future year, a set of 5 overall cost values (one for each condition state) is derived. Consequence functions can be also defined as probability density functions: in such terms uncertainty in cost-estimates is also taken into account. Consequence functions calibrated for the actual (2016) configuration of new bridge, expressed in terms of bridge replacement cost units are listed in Table 3; mean values are assumed for each subclass whereas a homogeneous variance (% of the mean value) was assumed for all the subclasses. A more refined estimate should be developed assessing for each subclass a specific value with specific sensitivity analyses. Table 3: Technical, environmental and social consequence function main parameters.

Consequence function CS1 CS2 CS3 CS4 CS5 m Technical 0 0,01 0,03 0,4 1 m Environmental 0 0,1 0,15 0,3 0,6 m Social 0 0,1 0,3 2 3 s 0% 10% 15% 30% 50% Assumptions on environmental, social and economic trends in the time-window of interest have to be done. In this specific case study, the assumptions are the following:

- regarding environmental issues, exposed population and environment remain unchanged; - regarding social issues, it is assumed an increase of 30% of traffic flows from year #30,

assuming a more sustainable technology for cars, environmental impacts associated to pollution induced by the increase of traffic flows are compensated with the diffusion of electric vehicles, hence the increase of traffic flows has only impact on delays estimates and not an indirect consequence on environmental impacts;

- regarding economic issues, a discount rate equal to 2% is assumed. On the basis of the consequence functions, assumptions on non-technical forecasts and the deterioration model of the technical indicator, it is possible to calculate expected maintenance costs at each year of the time window of interest, as a weighted sum of overall costs associated to a specific condition state multiplied with the probability of being in that specific condition state. Figure 7 shows the expected overall maintenance costs over time: results clearly evidences the traffic flow improvement assumed by the owner. Figure 8 takes also into account economic issues: the graph shows actualized amounts of economic resources needed for carrying out a retrofit in a generic future year. This kind of results allow owners to consider also the cost-dimension of the problem: in fact, when dealing with bridges that are not characterized by an urgent need of restoration actions in a future year, the use of a cost-based approach can lead to additional suggestions on how scheduling the intervention

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for optimizing resource allocation. For example, considering this case study, assuming as a technical threshold a 50% CS5 probability, a scheduling should be needed at year 35. However, due to the improvement of traffic flows at year 30, the owner if acting at year 35 should sustain a significant improvement of costs. The cost-based approach allows to optimize resource allocation: in fact, on the basis of the case study outcomes, an intervention before year 30, even if not needed in terms of technical needs, could save money and thus optimize allocation of resources. Economic thresholds should additionally be used for taking into account financial needs of the owning company.

Figure 7: Overall maintenance costs forecasted for a time window of 70 years.

Figure 8: Actualized overall maintenance costs forecasted for a time window of 70 years.

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4. SCIENTIFIC DEVELOPMENTS Besides the obligations toward needs of the COST Action described above, some additional work developments emerged during the STSM, mainly on focused on how to take into account natural hazards in the different goals of a quality control plan (WG3). The underlying concept in solving this problem is that damages induced by the occurrence of a hazardous event must be considered as an instantaneous equivalent natural ageing time window, and thus a common metrics when assessing damage should be used. The discussion of such issue with Professor Casas lead to the development of a method for the definition of the optimal time schedule for inspections taking into account natural aging and natural hazards. The work started from the analysis of the results presented by Nasrollahi and Washer (2015) that illustrated a methodology for the estimation of appropriate inspection intervals on the basis of historical condition data collected from 20 years of routine inspections to bridge decks in US. The authors proposed a method for rationalize the frequency of visual inspection in relation to the likelihood of a bridge deteriorating from a good condition to a poor one, on the basis of the probability density functions calibrated on the available historical data set. The authors considered only natural ageing as deterioration factor, whereas our efforts focused on quantify how natural hazards influence these estimates. Hence, once defined a common metric for the damage assessment, on the basis of natural ageing time-in-condition curves and using time dependent fragility curves, it is possible to quantify the reduction of the likelihood inspection time due to the potential occurrence of a natural hazard of a certain magnitude. Three main approaches where developed: a general, a simplified and an emergency management one. The first is based on Monte Carlo sampling of a loading history for a bridge structure subjected to a specific natural hazard, based on the knowledge of the models used for describing the specific phenomenon and the distribution of the inter-arrival times. The main drawback of this general approach is that a huge number of simulations is required and often, even if a probabilistic approach is used for describing the hazard, information on it are scarce and lacking. The general approach can be substituted by a simplified one based on the definition of the hazard function for the site of interest and the calculation of the loading characterized with a certain probability of exceedance in the whole service life of the bridge. This method seems the more suitable to be implemented in a BMS. Lastly, the approach can be used also for prioritizing post-event emergency inspection, expecially with a SHM recording loadings on a bridge structure is present: by the comparison of the recorded intensity loading with thresholds derived from fragility functions, it is possible to derive the most probable state of the bridge with a fixed confidence level, and thus making a rank of the structures mostly in need of a visual inspection. Further efforts will be required for the implementation of a numerical example and an extended description of the proposed approach.

5. FUTURE COLLABORATION The future collaboration with the host institution is guaranteed since during the period spent at UPC I developed very good relationship with Professor Casas. I exchanged with him many ideas and viewpoints on the topics related to our COST TU1406 Action, and we are planning to develop a strict and stable collaboration.

6. FORESEEN PUBLICATIONS/ARTICLES I expect to publish, as a final product of my STSM proposal, a paper in the upcoming Ninth International Conference on Bridge Maintenance, Safety and Management, IABMAS2018, which will take place in Melbourne, Australia on July 9-13, 2018 dealing with the results related to my obligations towards the current needs of the COST TU1406 Action. In addition, I want to better develop ideas and scientific developments discussed with Professor Casas and publish a journal paper on them.

7. ADDITIONAL COMMENTS I really would like to acknowledge the host institution, and particularly Professor Joan Ramon Casas for his suggestions and interesting discussions done during my STSM, which allowed the redaction this work and suggested some interesting ideas to be developed in future months, as future goals of the COST project. I would also thank to Professor Jose Matos and to the entire COST TU1406 Management Committee for gave me this very important opportunity.

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9. ANNEXES

9.1. CONFIRMATION BY THE HOST INSTITUTION ON THE SUCESSFUL EXECUTION OF THE STSM

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