computational methods for smart structures and materials

Development of low cost and innovative smart

materials for highways and civil engineering

structures

D. G. GouliasDepartment of Civil and Environmental Engineering, University ofMaryland, USA

Abstract

With the increasing cost of infrastructure monitoring and maintenanceand the limited funding for these activities the engineering community islooking towards the development of smart materials. Today there are severalexamples of innovative smart materials requiring however a) increased high-tech knowledge for their installation monitoring and interpretation of data, andb) significant expense for their installation in large scale projects, such ashighways and bridges.

This paper presents the result of a project undertaken for investigatingthe development of simple and low cost sensor for concrete and asphaltmixtures. Several configurations of metallic grids were imbedded into concreteand asphalt beams that were exposed to different stress and strain levels, andtemperature differentials. The input and output voltage of these grid sensorswere monitored in relation to the applied external stimuli. The results indicatedthat such a simple and low cost sensing devices can be used with theconventional asphalt and concrete mixtures for developing smart civilengineering materials and structures. In addition to the advantages related tosmart materials, (real time monitoring, ability to identify current condition ofmaterials and structures; possibility of predicting future conditions; examiningbehavior and response; and cognitive interpretation of the external excitationfactors), such sensors are low cost, easy to install and could be combined withthe current reinforcement of the structural elements for additional savings andmonitoring capabilities. The methodology of this study can be used indeveloping simple and low cost materials for other engineering materials andapplications.

Computational Methods for Smart Structures and Materials, C.A. Brebbia & A. Samartin (Editors) © 2000 WIT Press, www.witpress.com, ISBN 1-85312-816-3

34 Computational Methods for Smart Structures and Materials II

1 Introduction

Historically, monitoring of the infrastructure condition and behavior isconsidered as a separate, almost independent, activity from the infrastructureconstruction. The development of smart highway and civil engineering materialswill permit the use of the construction material as the sensor. The potential ofintegrating sensing capabilities to construction materials will: i) permit real timethree-dimensional monitoring of the condition and behavior of materials andstructures; ii) monitoring of the environmental characteristics on the surface andthroughout the structure; iii) examine the impact of vehicles and loads on thepavements and structures (for example the real time effects of combined movingdynamic loads, the influence and distribution of tire pressure); iv) collect trafficcharacteristics throughout the pavement surface, including vehicles loadscharacteristics, spatial distribution and weaving information, speed, tirepressure); and v) provide communication to the users on the status of theinfrastructure and the environmental and traffic conditions ahead. Thus smartmaterials could be the key component of the Intelligent Transportation System(ITS) architecture.With the recognition of the potential benefits of smart materials in civilengineering infrastructure and the availability of emerging technologies severalstudies were undertaken for their development. Examples include the use offiber and/or fiber optic sensors, piezo-electric ceramic composite sensors, smartalloy sensors, and other [1, 2, 3,4]. While the development of these technologiesare complex, promising, and challenging, they often address a single aspect ofthe infrastructure monitoring needs/functions and do fail to recognize i) theinfrastructure construction context (including the harsh environment ofconstruction and the required level of expertise and training by field personnel),ii) sensor/design simplicity and robustness; and iii) system cost/benefits over thedesign life of the structure [5].In this study the possibility of using a simple and low cost sensor was examined.In addition to the advantages related to smart materials, (real time monitoring,ability to identify current condition of materials and structures; possibility ofpredicting future conditions; examining behavior and response; and cognitiveinterpretation of the external excitation factors), such sensors are easy to installand could be combined with the current reinforcement of the structural elementsfor additional savings and monitoring capabilities.

2 Experimental investigation

In order to provide the sensing capabilities to both Portland cement concrete andasphalt mixtures a copper wire sensor grid was embedded in 18 x 6 x 4.75 inchasphalt and concrete beams. Initially several configuration of the sensor gridwere examined and tested with these construction materials. The final gridconfiguration for the sensor was composed of 24 inch gauge copper wire thatwas woven on a loom frame, Figure 1. The intersection points of the wire werewelded using a solder containing 40% tin and 60% lead. The sensor grid was 16x 3 in., containing 15 wires one inch apart, and 7 wires at 0.5 inch apart in the


Computational Methods for Smart Structures and Materials II 35

other direction. The metal wire grid was extended throughout both ends of thebeams and in the proximity of the lower portion of their thickness where highertensile stresses are expected from the flexural testing. The wires of the sensorwere connected to a DC power supply, and the applied load, deformation, andinput and output voltage of the sensor were monitored with the use of a dataacquisition system, Figure 2 and 3. The beams were tested using a close loopmaterial testing device (MTS), in three point flexural loading conditions with0.05 in./min. loading rate.The concrete and asphalt mixtures were designed using local materials and mixdesign practice. A trap rock aggregate and silica sand was used with an AC-10asphalt binder in order to produce the asphalt mixture. The aggregate and theasphalt binder were heated to a temperature of 340°F and blended at 280°F. Theasphalt beams were compacted in three layers. After the lower layer wascompacted the sensor grid was laid out on its surface. The second layer ofasphalt was then place and compacted, followed by the final layer of material.The uniform compaction of the asphalt layers was monitored using densitymeasurements. The Portland cement concrete mix was designed according toACT 211, with a 3 inch slump. The water cement ratio and the design strengthwas 0.41 and 6000 psi respectively.With the gradual increase of the applied load an increased level of stress anddeformations was generated in the asphalt and concrete beams. The response ofthe sensor was monitored throughout this testing. As deformation of the beamstook place the sensor grid was deformed (stretched) with an increase in lengthand reduction in the cross section. Such deformation is proportional to thestretching action (the applied stress/deformation), affecting thus its electricresistance. The resistance of the sensor is directly proportional to the length ofthe sensor and inversely proportional to the cross sectional area. While thesensor grid was elongated by the induced stress, the cross sectional areadecreased with a combined effect on the sensor resistance. Such reduction inresistance will produce a voltage change at the end of the sensor. Therelationship is described from the ohms law:

Figure 1: Grid configuration of the wire based sensor


36 Computational Methods for Smart Structures and Materials //

V = IR

where V is the voltage, R the resistance of the sensor, and 1 the electric current.Upon failure of the beam a significant drop in the output voltage of the sensor isexpected or a drop to zero when the resistance of the sensor brakes.

3 Results and sensor response

The testing results on the asphalt and concrete beams indicated the sensitivity ofthe sensor to detect changes in the applied load and/or deformations duringloading. The four parameters that were monitored during testing, (input andoutput sensor voltage, applied load and deformation) are presented in Figures 4,5, 6, 7, for one of the tested concrete beams. As it can be seen from the resultsthe load was increased up to the failure of the beams. The deformation of thebeams was monitored in the mid section using a wood strip glued at the bottomand with two LVDT's (Linear Variable Differential Transducers), one on eachside. For this concrete beam the input voltage provided by the power supply forthe sensor was kept constant at 7.423V, and the output voltage changed withincreasing load and deformation. The load was increased up to failure of thespecimen at about 7,800 psi, and the maximum vertical deformation was 0.028inch immediately before failure. Similar response was obtained from the testingof the remaining concrete and asphalt beams. However the slop of the sensoroutput voltage drop was dependent on the magnitude and the rate of the appliedloading and the material characteristics (stiffness), indicating that the sensorresponds in relation to the characteristics of the applied load and the materialthat is been used with.

4 Conclusion

The results of this study indicate that this simple and low cost sensor ($2.00 forthe metal grid, and $1.50 for welding) is responsive to the excitation from theapplied loads. Also the sensor response is sensitive and dependent to thecharacteristics of the applied load and the construction material that is been usedwith. The sensor could be used to monitor: i) the level of stress and the amountof deformation/deflection of materials used in buildings, highways, bridges andother infrastructure components; ii) the degradation of materials and thepresence of cracks and failures, since these conditions will effects the responseof the sensor; iii) truck weights in highways so as to eliminate excessive damageto the pavement structure; and other.

Future plans include the investigation of the effects of different shapeand characteristics of the sensor, an evaluation of the temperature effects on thesensor response, effects of bonding between sensor and construction material,and the sensor response to dynamic and moving wheel loads.


Computational Methods for Smart Structures and Materials II

PROJECT LAYOUT-CONCRETE SAMPLES

37

Figure 2: Testing setup

Figure 3: Concrete beam with sensor and testing setup



VERTICAL DEFORMATION Vs. TIMEConcrete Sample #1

f> 8 10 12 14 16 IK 20 22 24TIME (sec)

Figure 4: Vertical deflection at mid-span beam length

LOAD Vs. TIMEConcrete Sample #1

10 12 14 16 18 20 22 24TIME (sec)

Figure 5: Applied load versus loading time


Computational Methods for Smart Structures and Materials II

INPUT VOLTAGE Vs. TIMEConcrete Sample #1

39

,,«

3=• 7.42

12TIME (sec)

Figure 6: Power supply input voltage

OUTPUT Vs TIMEConcrete Sample #1

Figure 7: Sensor output voltage



References

[1] Fuhr P. L., Ambrose T.P., and Snyder D.M., Stress Monitoring in ConcreteUsing Embedded Optical Fiber Sensors, ASCE, Journal of StructuralEngineering, Vol. 119, PP 2263-2269, NY, July 1993.

[2] Chen P.W. and Chung D.L., Carbon-Fiber-Reinforced Concrete As AnIntrinsically Smart Concrete For Damage Assessment During DynamicLoading, Journal of The American Ceramic Society, Vol. 78, PP 816,March 1995.

[3] Simonsen H. D., Paetsch R., and Dunphy J. R., Fiber Bragg Grating SensorDemonstration In Glass-Fiber Reinforced Polyester Composite, FirstEuropean Conference On Smart Structures And Materials, SPIE Vol. 1777,pp73-76, Glasgow, England, May 1992.

[4] Orrell P.R., and Leach A.P., Fiber-Optic Distributed Temperature Sensing,First European Conference On Smart Structures And Materials, SPIE Vol.1777, ppl51-155, Glasgow, England, May 1992.

[5] Proceedings Smart Pavement Conference, American Society of TestingMaterials, Dallas, Texas, December 1993.


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