achilles · •2009-2013 futurenet • led by birmingham with loughborough, nottingham, trl, bgs,...

ACHILLESLong-term Deterioration of Linear Infrastructure

Monday 04 February 2019

Friends’ House

London, UK

ACHILLES – an EPSRC Programme Grant

Agenda

1500 Welcome, background and introduction

Prof Stephanie Glendinning (Newcastle)

1520 Modelling of weather-driven deterioration

Prof Neil Dixon (Loughborough)

1550 Improving inputs to models

Prof David Toll (Durham)

1610 Asset behaviour and performance

Prof William Powrie (Southampton)

1625 Forecasting and decision support at network scale

Prof Darren Wilkinson (Newcastle)

1640 Discussion and next steps

Prof Stephanie Glendinning (Newcastle)

1700 end of meeting


Who we are and how we get here

EPSRC funding support - Stakeholders/project partners increasingly embedded

• 2004-2009 BIONICS• Research facility led by Newcastle with Loughborough, Leeds, Bristol, Durham, Dundee,

Nottingham Trent, BGS…

• A large group of partners including HA (now HE), NR, MottM, Skanska, CIRIA…

• Constructed a full scale embankment testing facility that continues to deliver data/insights

• 2005- 3-year start-up funding for CLIFFS• A national network led by Loughborough

• Connected a large group of people interested in climate impact forecasting for slopes spanning multiple disciplines

• 2009-2013 FUTURENET• Led by Birmingham with Loughborough, Nottingham, TRL, BGS, HR Wallingford

• Evaluated the look of the UK transport network in the 2050s

• 2013-2017 iSMART• Led by Newcastle, with Loughborough, Queen’s Belfast, Southampton, BGS, Durham

• Delivered insights into deterioration of transport infrastructure geotechnical assets

• PLUS MANY OTHER RELATED PROJECTS

• 2018- ACHILLES – a programme grant building on this previous work

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Context• The UK’s transport infrastructure is one of the most heavily used in the world

• The UK rail network takes 50% more daily traffic than the French network

• The M25 between junctions 15 and 14 carries 165,000 vehicles per day

• London Underground: Europe's largest metro subway system but also the oldest

• Much of the rail network is over 100 years old

• Not just transport assets

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00:30One incident near Birmingham New Street resulted in a total of 4900 delay minutes along the network for the next 12 hours

After Jaroszweski et al. (2015), Meteorological Applications


Model couples hydrological model SHETRAN with FLAC Models the influence of meteorological parameters and climate (change) on slope behaviour

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Evidence at the asset scale – numerical modelling

Long-term deterioration modelling

• Following equilibrium, deterioration continues at slower rate

• Influence of extremes will become more significant as FoS approaches 1


Evidence of material scale deterioration

Evidence from both the laboratory and field

• Soil water retention

• laboratory investigations

• from field monitoring

• Permeability/hydraulic conductivity

• field investigations

• permeability functions

• Strength

• water content

• suction

• Cracking

• at the micro-scale and effect of freeze-thaw

• at the macro-scale (cracking)

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Current approaches to asset management

Network Rail – Earthworks technical strategy

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Asset deterioration – ACHILLES programme

Generalised deterioration model for transport earthworks (adapted from Thurlby, 2013).

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Modelling approach: Key findings from iSMART• There is conclusive evidence for seasonal ratcheting progressive failure mechanisms

in constructed slopes

• However, it remains challenging to model this seasonal ratcheting mechanism

• Use of an unsaturated framework is critical

• Key input parameters are: • high permeability near surface layer (measured in the field)• Soil water retention curves (SWRC)• stiffness distribution• strength behaviour• cracking

• Non-local strain minimises mesh dependency

• Weather represented using two approaches: Pore pressure cycling and coupling with ‘weather generator’ to account for current and future climates

• Able to produce ‘deterioration’ curves and investigate effects of design

• Our models can replicate measured pore water pressures in a slope and weather driven progressive failure – the approach has been validated

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The near-surface condition and pore water pressures

• It critically controls the rate at which an earthwork responds to weather

• Near-surface permeability measurements for earthworks sparse in the literature

• Detailed information from Newbury to compare simulations, including parametric study to define near-surface layersM

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Model calibration and validation

• Developed a methodology to allow the influence of meterological parameters and climate on a slope to be investigated

• Model makes use of coupling between SHETRAN and FLAC with Two Phase Flow

• Modelling approach calibrated using Newbury Cutting and Take and Bolton Centrifuge tests


Model validation of seasonal ratcheting

• Kaolin slope (modelled/experimental [Take and Bolton 2011])

• Magnitude and nature of mid-slope and toe displacements are very good

• This is great news as progressive failure begins at toe

• And it is supported by independent observations

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Numerical models of seasonal ratcheting

• Demonstrates simplistic, transient factor of safety method for two scenarios;• Again, shows significance of wet years on the performance of a slope compared to

gradual deterioration under continued seasonal cycles.


Fast-Track Modelling – Cut Slopes

• Newbury Cutting

• Climate Study

• Intervention / Maintenance Study

• Geometric Study

• Embankment Study

Smethurst and Clarke (N.D.)

Newbury Cutting (A34)

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ACHILLES fast-track modelling • High permeability near surface

• Evidence from the field shows that cut slope near surface high permeability zone develop rapidly

• Vegetation rooting mechanical contribution to soil strength• Prior models account for vegetation root influence on suction

generation and effective stress but NOT root influence on strength

• Used to investigate effects of different remediation strategies• Slope regrading

• Toe drainage

• Shear key at toe

• Soil nails

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Fast track modelling work: Future• Embankment model assumptions

• Road

• Impermeable pavement at crest

• Assume drains at road edges effectively remove runoff

• High relative strength and stiffness of fill (replicate high quality compaction)

• Minimised spatial heterogeneity

• Rail

• Permeable crest - Ballasted

• None existent drainage

• Lower relative strength and stiffness of fill (replicate end tipped construction)

• Increased heterogeneity

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Deterioration inputs to models

Evidence from the laboratory and field

• Soil water retention• laboratory investigations

• from field monitoring

• Permeability/hydraulic conductivity• field investigations

• permeability functions

• Strength• water content

• suction

• Cracking• at the micro-scale and effect of freeze-thaw

• at the macro-scale (cracking)

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Soil water retentionField and lab experiments

• wetting and drying cycles

• progressive loss of suction for same water content

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Hydraulic conductivityLarge test programme to characterise hydraulic conductivity

• in the top 1.5 m several orders of magnitude observed

Unsaturated soil strength• cycling of wetting and drying leads to progressive loss of strength

Soil cracking and deterioration

Observations: micro (lab: env SEM) vs macro (field scale crack measurement)

• cracks persist and soil deteriorates


Key messages

• Rate of change (SWRC and strength) is non-linear - greatest change observed after primary drying. Subsequent rate of change and magnitude is lower BUT cycling effect is continued through extreme events

• Macro-scale cracking increasing exposure and influence – it renews and perpetuates W/D cycle effect – deterioration at nano to macro scale.

• W/D is a pre-cursor to the initiation of progressive failure - causing the soil at

the near surface of an engineered clay slope to reduce in strength without any

change in external load.

Implications for slope condition (stability) assessment

• the need for non-stationarity of soil parameters and ground model, with

changes occurring both seasonally and gradually over time.

• there is a need for new constitutive soil model(s) that can account for soil deterioration due to wetting and drying

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New investigations of deterioration processes

Material-scale testing, modelling, performance and mitigation

• Experimental testing at a range of scales

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10-7 10-6 10-410-510-8 10-110-210-3 101 100 1000

Imaging Element testing Large scale laboratory

Field testing

Laboratory and Synchrotron X-ray computed tomography

to observe porosity and fracture networks supported

by mercury and helium porosimetry

Investigate effects of environmental cycles (dry-wet and thermal stress cycles). Triaxial testing and tension testing with suction

measurements

Larger scale flow-based lysimeter

testing

Field-based wetting

experiments to determine

infiltration rates

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Asset behaviour and performance

Field site data

• Long-term monitoring to include deterioration and reaction to extremes

• Field-scale experiments to study particular phenomena

• Monitoring campaigns to determine heterogeneity with time and space

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A new site – A flood embankment in clay

Newbury - highway cutting in London Clay

BIONICS - Test embankment in intermediate plasticity clay


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Water content 0.3 m

Water content 0.6 m

Water content 0.9 m

Water content 1.5 m

SMD water balance

Neutron Probe A

Neutron Probe B

Neutron Probe C

Long-duration observations - NewburyA

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Soil

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13 years


Forecasting and decision-support

• Investigation of ‘real’ route data (M4 & London-Bristol rail line)

• Consider actual slope geometries and geologies for use in modelling

• Use (surrogate, statistical) models to generate deterioration curves accounting for heterogeneity and uncertainty

• Develop a rational approach to understanding the future behaviour of significant proportions of a network, and prioritisation of investment decisions

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Field scale: asset data for modelling

Rail slope geometry (London-Bristol)

Total rail slopes: 19,506

• 7,903 cut slopes 443 cuttings in London Clay

• 11,603 Embankments 405 embankments constructed from London Clay

[data: Mott MacDonald/NR Lidar data]

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Road slope geometry (M4 London-Bristol)

Total road slopes: 2,039

• 505 cut slopes 26 cuttings in London Clay

• 624 Embankments 24 embankments constructed from London Clay

[data: HAGDMS]

• Aim is to balance the number of modelling to give useful coverage of slopes on the network (our approach covers 86 % of the slopes on the network)


Deterioration state 180 years after cutting construction + NR failed slopes and vulnerability

FoS ≥ 1.3

FoS < 1.3 ≥ 1.2

FoS < 1.2 > 1

FoS ≤ 1

Vulnerability descriptor, bounding lines and failed slopes as per Network Rail Earthworks Technical Strategy 2018

Real data more conservative than model• Site specific failure drivers – not captured

by model?• Lack of high permeability near surface

behaviour and desiccation. This second point to be addressed in ACHILLES

Network Rail data overlain on model results


Dealing with uncertainty/variability/heterogeneity

• Material variability• Properties vary from slope to slope, and from slice-to-slice within a

slope.

• Incorporate material spatial heterogeneity within a slice using spatially correlated Gaussian random fields.

Random field for cohesion (Nuttall, 2013)

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Dealing with uncertainty/variability/heterogeneity

• Material variability

• Include parameter variability• Latin hyper cube methods – Allow efficient coverage of parameter

space

• Statistical methods to derive appropriate ranges of parameters and understand their relative frequency of occurrencefo

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Linking model outputs to financial impacts

• Link models to whole life costs• Investigate effect of maintenance vs remediation on costings led by

John Preston (Southampton University – Transport Economist) – RC3


Hierarchical Bayesian modelling for data synthesis

• Use emulators/surrogates for the numerical codes in order to efficiently characterise network state

• Use hierarchical spatial Bayesian modelling to pool information and correctly propagate uncertainty due to missing data and the future

• Use Monte Carlo ensembles characterising network performance to make probabilistic forecasts and compare cost and risk of different intervention strategies

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Conclusions and ongoing work

• We have considerably advanced the numerical models of climate driven slope failure and their inputs, including a novel deterministic approach to use UKCP09 data.

• We have successfully demonstrated the likely mode of deterioration and failure, and created deterioration curves that reflect these.

• The time to failure is still not correct, but we are working to correct this.

• Further work is also continuing to incorporate more extreme weather events.

• The model can be used to demonstrate that future climate effects have an adverse impact on slope stability.

• We have demonstrated the use of a simplified model in investigate remediation strategies and are working on coupling this with future climate effects

• We are now linking the performance curves to investment and design options and considerations of uncertainty and heterogeneity

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QuestionsFast track modelling

• What types of interventions should we include?

• How could this work complement and extend current work being undertaken for asset owners?

Material scale

• What types of interventions should we include at the material scale?

• What site investigation/lab test data is available that might help gain additional insights?

• How might we use the performance curves to better inform remediation and design decisions?

Asset scale

• What is the evidence for asset deterioration/loss of performance?

• Are there particular data sets that we should be using?

Network scale

• What system/network scale performance data/indicators should we use?

• How might we use the network performance forecasts to better inform investment and operational decisions?

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achilles · •2009-2013 futurenet • led by birmingham with loughborough, nottingham, trl, bgs,...

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