and Structures An experimental investigation on the health

Technical Note

Journal of Intelligent Material Systemsand Structures1–7� The Author(s) 2015Reprints and permissions:sagepub.co.uk/journalsPermissions.navDOI: 10.1177/1045389X14566525jim.sagepub.com

An experimental investigation on thehealth monitoring of concretestructures using piezoelectrictransducers at various environmentaltemperatures

Dujian Zou1, Tiejun Liu1, Chaofeng Liang2, Yongchao Huang1, FuyaoZhang1 and Chengcheng Du1

AbstractThe application of piezoelectric transducers in in situ health monitoring of concrete structures has been widely investi-gated. However, previous experimental studies were normally performed in an isothermal room environment, whichoffers insufficient considerations for temperature variations that real engineering structures experience in practical mon-itoring cases. In this article, the temperature effects on smart aggregate–based monitoring results are treated by per-forming active structural health monitoring on two plain concrete specimens at various temperatures. Experimentalresults show that the amplitude of the monitoring signal increases with temperature, with low-frequency signals beingmore sensitive to temperature variation while high-frequency signals less temperature dependent. This research demon-strates the necessity of temperature compensation in smart aggregate–based monitoring techniques.

Keywordshealth monitoring, concrete structures, smart aggregate, environmental temperature

Introduction

The past 100 years have seen the growing use of con-crete as a major construction material. While the dete-rioration and aging of concrete structures are inevitable(Richardson, 2002), the durability of such structureshas raised a lot of concerns in recent years. One typicalproblem caused by insufficient durability is the inade-quate in-service safety, especially the service perfor-mance of concrete structures in extreme events suchas earthquakes, hurricanes, and impacts. As a result,structural health monitoring (SHM) has appeared asan effective solution to evaluate the structural integrityand damage degree during their service stage (Changet al., 2003).

Traditional health monitoring methods are mostlybased on the evaluation of changes in dynamic charac-teristics of structures such as modal frequency, modeshape, and damping ratio (Doebling et al., 1996; Luet al., 2013; Maalej et al., 2010; Peairs et al., 2004; Zouet al., 2014a). When the damage of structure is substan-tial, these methods can be useful to determine the occur-rence of damage in structure. However, they normallygive insufficient information to accurately locate the

damages. To further determine the nature of the dam-age, non-destructive (NDT) monitoring methods suchas acoustic approaches, X-ray, and Gamma ray appearas more powerful tools as they claim to be able to iden-tify the crack size and location in a specific structuralmember (Balageas et al., 2000; Giurgiutiu and Cuc,2005; Kupke et al., 2001).

Early researches have shown that environmentaltemperature and moisture could introduce significantnoise in monitoring stage (Peeters et al., 2001; Sohn,2007). Generally they can affect the monitoring processin three aspects. First, the mechanical properties ofconcrete are temperature and moisture dependent. Anincrease in moisture content of concrete will cause anincrease in elastic modulus of concrete, and the

1Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen,

China2Department of Civil Engineering, Shaoxing University, Shaoxing, China

Corresponding author:

Tiejun Liu, Shenzhen Graduate School, Harbin Institute of Technology,

Shenzhen 518055, China.

Email: [email protected]

at PENNSYLVANIA STATE UNIV on May 10, 2016jim.sagepub.comDownloaded from

http://jim.sagepub.com/

damping capacity of concrete structures could be signif-icantly improved by the existence of dry–wet interfacein concrete (Wu et al., 2012; Xia et al., 2006). This indi-cates that the dynamic characteristics of structurescould change obviously only due to temperature ormoisture variation. Second, as most structures are stati-cally indeterminate structures, the difference in thermalexpansion will induce extra temperature stress in con-crete. Third, the temperature and moisture variationcould affect the performance of the sensors used inmonitoring, and they may not work as well as expectedwhen experiencing lots of dry–wet and temperaturerise–fall cycles. To reduce the influence of environmentnoise on monitoring, some innovative signal processingmethods such as Wavelet, Wavelet Packages, andHilbert–Huang Transformation (HHT) have been pro-posed and used to increase the signal-to-noise ratio(Hera and Hou, 2004; Huang et al., 1998). In addition,temperature compensation measures are also of use toreduce the monitoring error of sensors. A typical exam-ple is the widely used resistance strain gage.

With the rapid development of smart materials, newsensors with better performance become available.They are said to be ‘‘better’’ as they can be used toidentify some specific damages and locate them byusing different monitoring methods such as passive andactive methods. Smart aggregate (SA), shown in Figure1, has been applied in health monitoring of concretestructures by a number of researchers (Liao et al.,2011; Liu et al., 2013; Song et al., 2008; Yan et al.,2009). However, its temperature-dependent characteris-tics have rarely been studied. Song et al. (2008) pro-posed a concrete early-age strength monitoring methodusing SAs and verified its potential in this usage. Thismonitoring is accurate and effective in room environ-ment and for small volume concrete. However, the situ-ation may be different when monitoring large massconcrete such as a bridge pier, in which the concreteexperiences a sharp temperature rise during the first1–3 days (Kim et al., 2001). This temperature rise isinduced by hydration heat, and the highest temperaturecan be easily over 50 �C. Furthermore, the operatingambient temperature of the SAs being used in real-timemonitoring of concrete structures, such as interfacedebonding detection (Xu et al., 2013) and damage identi-fication (Liao et al., 2011; Yan et al., 2009), also changesperiodically every day. Thus, it is necessary to investigatethe temperature response of SA in various temperatureconditions. This article looks into the influence of hightemperature on a typical function unit of monitoring sys-tems using SAs. Both temperature rise and fall processeswere considered. Experimental results show that theamplitude of sensing signals is sensitive to temperaturevariation which indicates that temperature compensationmeasures are essential for monitoring systems using SAs,especially for those with significant operating ambienttemperature changes.

Active monitoring principles

In a SA-based monitoring system, SAs are normallyembedded into concrete to form an actuator/sensormatrix (see Figure 2), which aims to provide overalldamage evaluation and location. During the monitor-ing process, some SAs act as actuators to producestress/ultrasonic wave signals, while the rest are used assensors to capture these wave signals after they travelfor a certain distance. A representative function unit inthe matrix consists of a pair of SAs embedded in con-crete, in which one is employed as an actuator and theother one as a sensor.

The monitoring system for evaluating p-wave ampli-tude variation is shown in Figure 3. This system has

Figure 1. Structure of smart aggregate.

Figure 2. Embedded smart aggregates matrix.

2 Journal of Intelligent Material Systems and Structures



been used in many previous experimental studies (Liuet al., 2013; Song et al., 2008; Zou et al., 2014b), inwhich the operating temperature was kept constantand thus its influence ignored. A signal emission deviceis used to generate harmonic voltage signals, and thesesignals are amplified by a power amplifier to drive theSA actuator. Mechanical vibration signals, in the formof stress wave, are produced as a result of the inversepiezoelectric effect. These stress vibrations are thencaptured by the other SA sensors during their travelingin concrete, and they will be again converted into vol-tage signals to be acquired by an oscilloscope.

Experimental investigations

In this test, the influence of temperature variation on afunction unit (a SA pair embedded in concrete) wasstudied. Two plain concrete specimens (S-1 and S-2),with the dimension of 100 mm 3 100 mm 3 400mm, were cast. The mix mass ratio of the used con-crete is 1.00:0.34:1.55:1.95:0.1 (cement:water:sand:sto-ne:fly ash). The stone size is 5–20 mm and sand size0.25–0.5 mm. The dimension of the Lead ZirconateTitanate (PZT) patch in a SA is 15 mm 3 15 mm3 0.3 mm, and its d33 (piezoelectric strain constant) is670 pC N21. SAs were embedded at the end of con-crete specimens (Figure 4), with a distance of 300 mm.

As previous studies show, the water content in con-crete has a significant effect on the monitoring perfor-mance of SA (Liu et al., 2013; Zou et al., 2014c). It isnot a consideration of this test, thus, to avoid this unex-pected noise; concrete specimens were first dried in anoven at 80 �C for a week to ensure no water is containedin both specimens (see Figure 5). Six temperature casesare considered in this test, that is, 30 �C, 40 �C, 50 �C,60 �C, 70 �C, and 80 �C. Specimens are kept in the ovenat constant temperature for 1 day in each case. Thetemperature evolution scheme is shown in Figure 5, inwhich both fall and rise periods were designed in orderto compare the monitoring performance of SAs duringcooling and heating processes. For each temperature

case, the aforementioned active monitoring method wasperformed, in which input harmonic signals, with fre-quencies of 1, 10, 100, and 1000 Hz and constant ampli-tude of 90 V, were employed to drive the SA actuator.

Results and discussions

Figure 6 shows the original sensing signals of S-1 withan input frequency of 1 Hz. The inconsistency of start-ing and ending times of these signals is due to the non-simultaneous manual capture with an oscilloscopewhich makes the figure a bit messy. It is observed inFigure 6 that the amplitude of sensing signals is verysensitive to temperature variation either in the fall orrise period. After a fast Fourier transform (FFT) filterwas used for de-noising purpose, their amplitudes wereextracted and analyzed in Figure 7. It can be seen that,in both specimens and with all the excitation signals(1, 10, 100, and 1000 Hz), the development of signalamplitude shows a similar trend, that is, their ampli-tude increases with temperature and vice versa. It alsocan be seen that the amplitude of low-frequency signalsis significantly greater than that of high-frequency sig-nals, which is consistent with previous experimentalinvestigations (Liu et al., 2013; Song et al., 2008; Zouet al., 2014c).

To further illustrate the influence of temperaturevariation on sensing signal amplitude, the ratio of eachsignal amplitude to that at 30 �C of the second tem-perature cycle is plotted in Figure 8. During these twocooling processes, the average signal amplitudes of S-1are seen to be sharply decreased by 94% (1 Hz), 87%(10 Hz), 72% (100 Hz), and 58% (1000 Hz) when thetemperature drops from 80 �C to 30 �C and that of S-2are decreased by 96% (1 Hz), 86% (10 Hz), 68%(100 Hz), and 53% (1000 Hz) correspondingly.Compared with the temperature cooling processes, thefollowing heating processes seem to have more consis-tent effect on signal amplitude. An overall averageincrease of 1372% (1 Hz), 623% (10 Hz), 246%(100 Hz), and 148% (1000 Hz) can be seen while thetemperature rises from 30 �C to 80 �C, and the corre-sponding values of S-2 are recorded as 2717% (1 Hz),709% (10 Hz), 257% (100 Hz), and 141% (1000 Hz).It should be noted that the maximal average amplitudedecrease, induced by a temperature fall of 10 �C,reaches 52% (1 Hz), 46% (10 Hz), 38% (100 Hz),and 32% (1000 Hz) in S-1 and 63% (1 Hz), 53%(10 Hz), 45% (100 Hz), and 37% (1000 Hz) in S-2.Correspondingly, the maximal average amplitude increasefor a 10 �C rise is 125% (1 Hz), 82% (10 Hz), 44%(100 Hz), and 40% (1000 Hz) in S-1 and 213% (1 Hz),103% (10 Hz), 46% (100 Hz), and 45% (1000 Hz) in S-2.

As a 10 �C temperature change is common in a day,these results provide evidence for the temperature-dependent monitoring behavior of SA, and it is advised

Figure 3. Schematic diagram of the monitoring system.

Zou et al. 3



that, for in situ health monitoring of concrete structuressuffering from evident temperature variation, the influ-ence of temperature variation on sensing signal shouldbe taken into consideration. In addition, the influenceof temperature on signal amplitude is weakened byhigher frequency excitation signals. Therefore, high-frequency excitation signals may be preferred for the insitu monitoring concrete structures. On the other hand,it should be pointed out that some measured signalamplitudes at 100 and 1000 Hz are abnormal and threesignal amplitudes obtained at 40 �C are smaller thanthose obtained at 30 �C during two cooling processes.This may be mainly induced by the environmentalnoises, whose effects on monitoring signals of 100 and1000 Hz tend to be dominating as the response signalamplitudes decrease significantly.

The reasons why signal amplitude is temperaturedependent are worth thinking of. First, it is generallyaccepted that the amplitude of signal traveling in con-crete is inverse to its elastic modulus (Song et al., 2008).While it has been proved that the elastic modulus ofconcrete decreases as the temperature increases (Xiaoand Konig, 2004), it is possible that the increase in tem-perature will give rise to an amplitude rise as well.Second, the physical characteristics of PZT are tem-perature dependent. A monotonic increasing relation-ship between the PZT strain coefficient (d33) andtemperature was found in the range of 0–100 �C(Zhuang et al., 1989). Finally, as can be seen fromFigure 1, SAs are coated by epoxy resin and marble.The expansion coefficient of epoxy resin is around60 3 1026 m/ �C, which is significantly greater thanthat of marble (5 3 1026 m/ �C). During a synchro-nous temperature rise process, non-uniform expansionstrain occurs between epoxy and marble, which willinduce a pre-tightening force on the PZT patch, and itmakes the load transfer between patch and marble

more effective. All in all, the aforementioned three fac-tors contribute to an amplitude increase during a tem-perature rise. However, it may be difficult to generallyderive an accurate formula to describe the temperature-dependent behavior of SA due to the diversity in micro-structures of SAs and concrete specimens; these includethe variation in encapsulation layer (epoxy) thicknessand the intrinsic discreteness in concrete and marblematerial, and these variations partially lead to the sig-nal differences for S-1 and S-2.

This is a primary study about the temperature effecton health monitoring of concrete structures using SAs.The above experimental investigation has demonstratedthat the temperature strongly affects the SA-basedmonitoring system. However, some limitations shouldbe stated as well. The main challenge in this researchwould be how to analyze wave propagation path in aspecimen with certain boundary conditions. The workpresented did not look into this problem, and the

Figure 4. Concrete specimen and locations of SAs (mm).

Figure 5. Experimental temperature history.




boundary conditions were not treated. Waves of eachfrequency are thus assumed to follow the same propa-gating path and experience the same degree of superpo-sition at the sensor location. This seems reasonablewhen all the tests were performed under the same test-ing conditions except the variation in temperature. Few

researchers have reported the development of elasticmodulus of concrete material in daily temperaturerange. Therefore, it is hard to quantitatively analyzethe influence of elastic modulus variation induced bytemperature on the wave superposition. It is worthlooking into wave propagation in detail and taking

Figure 7. Absolute signal amplitude at various temperatures: (a) 1, (b) 10, (c) 100, and (d) 100 Hz.

Figure 6. Amplitude history of sensing signals of S-1 with 1 Hz excitation frequency: (a) first temperature fall period and (b) firsttemperature rise period.

Zou et al. 5



boundary conditions into consideration in the future;this can be achieved by employing wave separationtechniques or by using longer specimens. In addition,the temperatures used in this test are apparently higherthan those of a concrete structure experienced in nor-mal service. Small temperature range and low tempera-ture level are more helpful for in situ SHM of concretestructures.

Conclusion

In this article, the SA-based concrete monitoring resultsare presented and the temperature effects are analyzed.Conclusions could be summarized as follows:

(1) Environmental temperature has a significantimpact on the monitoring amplitude.Specifically, the signal amplitude increases withtemperature, and the low temperatures (30 �C–50 �C) have less influence on the monitoringsignals compared with that of high tempera-tures (60 �C–80 �C). However, it still can beclearly observed that the temperature effectson signal amplitude are evident in the tempera-ture range of 30 �C–40 �C.

(2) A maximal variation in signal amplitude isobserved to be larger than 30% under the tem-perature variation of 10 �C. This will lead to anunexpected monitoring error for SA-based insitu health monitoring of concrete structures.Therefore, temperature compensation measure-ments are recommended for SA-based in situmonitoring. In addition, high-frequency excita-tion signals are less affected by temperature.Hence, they are favored for monitoring struc-tures which experience environmental tempera-ture variations.

Declaration of conflicting interests

The authors declared no potential conflicts of interest withrespect to the research, authorship, and/or publication of thisarticle.

Funding

The work is sponsored by the Program for National ScienceFoundation for Excellent Young Scientists under Grand51422804, New Century Excellent Talents in University underGrant NCET-13-0171, National Nature Science Foundationof China under Grants 51308322 and 51178154, ChinaPostdoctoral Science Foundation under Grants 2013M530049and 2014T70085, and Program of Shenzhen Science andTechnology Plan under Grant ZDSY20120613125318342.

References

Balageas D, Bourasseau S, Dupont M, et al. (2000) Compari-

son between non-destructive evaluation techniques and

integrated fiber optic health monitoring systems for com-

posite sandwich structures. Journal of Intelligent Material

Systems and Structures 11(6): 426–437.Chang PC, Flatau A and Liu SC (2003) Review paper: health

monitoring of civil infrastructure. Structural Health Moni-

toring 2(3): 257–267.Doebling SW, Farrar CR, Prime MB, et al. (1996) Damage

Identification and Health Monitoring of Structural and

Mechanical Systems from Changes in their Vibration Char-

acteristics: A Literature Review. Los Alamos, NM: Los

Alamos National Laboratory.Giurgiutiu V and Cuc A (2005) Embedded non-destructive

evaluation for structural health monitoring, damage detec-

tion, and failure prevention. Shock and Vibration Digest

37(2): 83–105.Hera A and Hou Z (2004) Application of wavelet approach

for ASCE structural health monitoring benchmark stud-

ies. Journal of Engineering Mechanics 130(1): 96–104.Huang NE, Shen Z, Long S, et al. (1998) The empirical mode

decomposition and the Hilbert spectrum for nonlinear and

non-stationary time series analysis. Proceedings of the

Figure 8. Relative signal amplitude at various temperatures: (a) S-1 and (b) S-2.




Royal Society of London Series A: Mathematical, Physical

and Engineering Sciences 454(1971): 903–995.Kim JK, Kim KH and Yang JK (2001) Thermal analysis of

hydration heat in concrete structures with pipe-cooling sys-tem. Computers & Structures 79(2): 163–171.

Kupke M, Schulte K and Schuler R (2001) Non-destructivetesting of FRP by dc and ac electrical methods. Composites

Science and Technology 61(6): 837–847.Liao WI, Wang JX, Song G, et al. (2011) Structural health

monitoring of concrete columns subjected to seismic exci-tations using piezoceramic-based sensors. Smart Materials

and Structures 20(12): 125015Liu T, Huang Y, Zou D, et al. (2013) Exploratory study on

water seepage monitoring of concrete structures usingpiezoceramic based smart aggregates. Smart Materials and

Structures 22(6): 065002.Lu W, Teng J, Xu Y, et al. (2013) Identification of damage in

dome-like structures using hybrid sensor measurementsand artificial neural networks. Smart Materials and Struc-

tures 22(10): 105014.Maalej M, Chhoa CY and Quek ST (2010) Effect of cracking,

corrosion and repair on the frequency response of RCbeams. Construction and Building Material 24(5): 719–731.

Peairs DM, Park G and Inman DJ (2004) Improving accessi-bility of the impedance-based structural health monitoringmethod. Journal of Intelligent Material Systems and Struc-

tures 15(2): 129–139.Peeters B, Maeck J and De Roeck G (2001) Vibration-based

damage detection in civil engineering: excitation sourcesand temperature effects. Smart Materials and Structures

10(3): 518–527.Richardson M (2002) Fundamentals of Durable Reinforced

Concrete. London: Taylor & Francis.Sohn H (2007) Effects of environmental and operational

variability on structural health monitoring. PhilosophicalTransactions of the Royal Society A: Mathematical, Physi-

cal and Engineering Sciences 365(1851): 539–560.

Song G, Gu H and Mo Y (2008) Smart aggregates: multi-

functional sensors for concrete structures-a tutorial and a

review. Smart Materials and Structures 17(3): 033001.Wu S, Chen X and Zhou J (2012) Influence of strain rate and

water content on mechanical behavior of dam concrete.

Construction and Building Materials 36: 448–457.Xia Y, Hao H, Zanardo G, et al. (2006) Long term vibration

monitoring of an RC slab: temperature and humidity

effect. Engineering Structures 28(3): 441–452.Xiao J and Konig G (2004) Study on concrete at high tem-

perature in China—an overview. Fire Safety Journal 39(1):

89–103.Xu B, Zhang T, Song G, et al. (2013) Active interface debond-

ing detection of concrete-filled steel tube with piezoelectric

technologies using wavelet packet analysis. Mechanical

Systems and Signal Processing 36(1): 7–17.Yan S, Sun W, Song G, et al. (2009) Health monitoring of

reinforced concrete shear walls using smart aggregates.

Smart Materials and Structures 18(4): 047001.Zhuang ZQ, Haun MJ, Jang SJ, et al. (1989) Composition

and temperature dependence of the dielectric, piezoelectric

and elastic properties of pure PZT ceramics. IEEE Trans-

actions on Ultrasonics, Ferroelectrics and Frequency Con-

trol 36(4): 413–416.

Zou D, Liu T and Qiao G (2014a) Experimental investigation

on the dynamic properties of RC structures affected by the

reinforcement corrosion. Advances in Structural Engineer-

ing 17(6): 851–860.Zou D, Liu T, Huang Y, et al. (2014b) Feasibility of water

seepage monitoring in concrete with embedded smart

aggregates by P-wave travel time measurement. Smart

Materials and Structures 23(6): 067003.Zou D, Liu T, Qiao G, et al. (2014c) An experimental study

on the performance of piezoceramic based smart aggre-

gate in water environment. IEEE Sensors Journal 14(4):

943–944.

Zou et al. 7



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and Structures An experimental investigation on the health