15-SMART HEALTH MONITORING OF RECYCLED AGGREGATE CONCRETE….doc
长沙福元路湘江大桥第 2 联方案比选与施工图设计
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长沙福元路湘江大桥第
联方案比选与施工图设计
长沙
福元路
湘江
大桥
方案
施工图
设计
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长沙福元路湘江大桥第 2 联方案比选与施工图设计,长沙福元路湘江大桥第,联方案比选与施工图设计,长沙,福元路,湘江,大桥,方案,施工图,设计
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THEME 2 STRUCTURAL MONITORING AND DAMAGE DETECTIONsmart infrastructure initiatives. There is thus a need to explore the application of smart sensors and actuators in monitoring the properties and health conditions, especially the long term per-formance, of RAC structures and develop associ-ated damage and health monitoring strategies. 2. BACKGROUND In this section, related background information on concrete health monitoring techniques using piezoelectric materials is reviewed. The back-ground information on recycled aggregate concrete (RAC) is given in the report by Qiao (2010). Although RAC could be applied in civil infra-structures, concrete structures are strong in compression but weak in tension which is likely to cause cracking, aging and deterioration, especially for the structures made of RAC. Consequently, effective health monitoring techniques are needed to assess the condition and damage of RAC struc-tures during their service life so that the economic and human life loss can be avoided. There are many nondestructive methods for inspecting con-crete structures, such as radiography, acoustic emission, visual inspection, thermal field, etc. But the limitations of these techniques, including accu-racy, costs, manoeuvrability, in situ capability, etc., make them difficult and/or incapable of being ap-plied to in situ structural health monitoring. Piezoelectric material, Lead zirconate titanate (called PZT), is a kind of smart materials that has been utilized for detecting the defects in concrete structures in recent years. The PZT patches are small, lightweight and inexpensive, which can be SMART HEALTH MONITORING OF RECYCLED AGGREGATE CONCRETE IN BRIDGE APPLICATIONS Pizhong Qiao, Wei Fan, Fangliang Chen Department of Civil and Environmental Engineeirng, Washington State Unviersity, Pullman, WA 99164-2910, USA E-mail: Abstract: In this study, damage and health condition of recycled aggregate concrete (RAC) are evaluated using embedded smart piezoelectric sensors/actuators. The development of damage detection and health monitoring techniques using smart piezoelectric aggregates is studied. The piezoelectric patches are en-closed in the cement modules to form the so-called “smart aggregates”. The smart aggregates are then em-bedded in concrete beams to serve as either the actuators or sensors, and the elastic wave propagation-based technique is developed in this study to detect the damage (crack) in the RAC beams and monitor the degradation of RAC beams due to the freeze/thaw (F/T) conditioning cycles. The damage detection results and elastic modulus reduction monitoring data demonstrated that the proposed smart piezoelectric technology and associated damage detection and health monitoring techniques are capable of identifying damage and monitoring degradation of the RAC materials. 1. INTRODUCTION Concrete is one of the most widely used artifi-cial materials in construction, and the consumption of cement and concrete is maintained at a rapid rate of increase. To produce the granular aggregates in concrete, not only a lot of natural resources of stone or rock materials are needed, but also the ecological environment is adversely impacted. On the other hand, when concrete structures reach the limit of their service life, a large amount of old constructions need to be dismantled in addition to destructive effects of natural disasters, leading to a plenty of concrete waste. As a sort of waste pro-duced by demolishing old buildings, concrete waste will result in serious environment pollution and vast resource extravagance if it is not reutilized or recycled. Thus, recycling concrete wastes can lead to reduction in valuable landfill space and savings in natural resources. There is also a grow-ing need to utilize the recycle aggregates to replace the natural aggregates as good quality gravel sources are increasingly becoming exhausted. Recycled aggregates usually present greater porosity and water absorption, lower density, and lower strength than natural aggregates. With ad-vancement of sensor and wireless communication technologies, it is now becoming more viable to monitor and assess the condition of the transporta-tion structures made of the recycled aggregate con-crete (RAC). The embedded piezoelectric sensors and actuators in the RAC structures should be ca-pable of monitoring the properties and conditions (including damage), especially the long term per-formance, of the RAC structures, contributing to 115was embedded in a cement module, and it was then embedded into concrete structures. By detecting the equivalent circuit parameters, it was shown that the monitoring of temperature and stress could be achieved simultaneously. Yang et al. (2008) employed the structural me-chanical impedance extracted from the PZT elec-tromechanical (EM) admittance signature as the damage indicator. A comparative study on the sen-sitivity of the EM admittance and the structural mechanical impedance to the damages in a concrete structure was conducted. Their results showed that the structural mechanical impedance was more sensitive to the damage than the EM admittance and it was thus a better indicator for damage detec-tion. Shin et al. (2008) presented the application of PZT patches for the strength gain monitoring of concrete. The applicability of the conventional structural mechanical impedance sensing tech-nique, which is normally used for damage detec-tion, was extended to early age concrete monitor-ing. 2.2 Elastic Wave-based Method Wu and Chang (2006a,b) used the high fre-quency transient stress waves to detect the debond-ing damage and its location in a reinforced concrete beam based on a built-in piezoelectric sensors and actuators in a pitch-catch mode. Three types of tests were conducted: debonding tests in reinforced concrete beams, tensile tests on reinforcement bars, and bending tests of reinforced concrete beams. Song et al. (2008) developed the so-called “smart aggregate” based on piezoceramic actua-tors/sensors. The proposed smart aggregate was made by embedding a waterproof piezoelectric patch with lead wires into a small cement block. The smart aggregates were then mounted in the desired locations in the concrete molds before the casting of the concrete structures took place. The smart aggregates were used to perform three major tasks: early-age concrete strength monitoring, im-pact detection, and structural health monitoring. The concrete strength development was monitored by observing the high frequency harmonic wave response of the smart aggregates. The impact on the concrete structure was detected by observing the open-circuit voltage of the piezoceramic patch in the smart aggregates. For the structural health monitoring purposes of concrete, a smart aggre-gate-based active sensing system was designed, and the wavelet packet analysis was considered as a signal-processing tool to analyze the sensor sig-nal. A damage index based on the wavelet packet used as both actuators and sensors by using their piezoelectric effect. The PZT-based active damage detection methods basically include two types: (1) Impedance-based method; and (2) Elastic wave-based method. In the following, a brief review on damage detection methods of concrete using the above two methods is provided. 2.1 Impedance-based Method The Impedance-based method utilizes high-frequency structural excitations, typically higher than 20 kHz (Park et al., 2006) and employs the bonded or embedded PZT patches to capture the changes in mechanical impedance of a structure. Based on the changes in the impedances obtained by the PZT patches, the damages in the structure can be located and identified. Due to its distinct advantages, the electromechanical impedance me- thod has emerged as a powerful health monitoring technique. Soh et al. (2000) conducted structural health monitoring for the destructive load testing of a prototype reinforced concrete bridge. A surface-bonded self-sensing PZT patch was used to identify the local damage region in its vicinity, in the form of a conductance signature. Tseng and Wang (2004) used the impedance technique to detect the presence of damage and monitor its progression in concrete. Smart PZT transducers were bonded to the structures to ac-tively provide the local excitation and simultane-ously sense the structural dynamic response in high frequency band. The frequency-dependent electric admittance signatures of the piezoelectric trans-ducer were compared with the baseline signatures to determine the status of structural health. The damage was quantified by the root-mean-square deviation (RMSD) index. Two sets of experimen-tal test were performed: one for a concrete beam with progressive damage on the surface, and the other for a concrete beam with progressive damage located in the depth of the specimen. Experimental result showed that the impedance method could effectively detect the presence of incipient damage in concrete beams located at a distance of 360 mm away from the PZT patch. The impedance method could also identify the damage and monitor its progression on the surface as well as in the depth of concrete beams. The progression of damage led to the continuous increase in the RMSD index. Wen et al. (2007) embedded the PZT ceramics into concrete blocks for structural health monitor-ing using the equivalent circuit parameters. After covered with a layer of rubber, the disc-like ce-ramic element which worked in thickness mode 116Figure 1. Fabrication process of smart aggregate. Figure 2. Plastic mold for fabrication of smart aggregate. 4. DAMAGE DETECTION AND HEAL-TH MONITORING TECHNIQUES In this study, the elastic wave propagation-based technique is adopted to develop damage de-tection and health monitoring techniques for con-crete embedded with smart aggregates. 4.1 Damage Detection Technique In order to detect the damage inside the con-crete, the signal energy Es of the stress wave is analysis was used to determine the structural health status. Their preliminary study demonstrated that the multi-functional smart aggregates had the po-tential to be applied to the comprehensive monitor-ing of concrete structures from their earliest stages to their entire lifetime. Sun et al. (2006) used the surface-bonded PZT patches for structural health monitoring of a prism concrete beam. From the velocity of Rayleigh waves and longitudinal waves, the dynamic modulus of elasticity and dynamic Poissons ratio of the con-crete were obtained. Then, the effect of uniaxial compressive stress and the resulting internal crack-ing of the concrete on the amplitude of the wave-forms received by piezoceramic sensors was inves-tigated. The results confirmed that the piezoceramic sensors and corresponding ultrasonic wave methods had the potential to monitor the cracking and long-term deterioration of concrete structures. Yan et al. (2009) proposed a smart aggregate-based active sensing approach for structural health monitoring of a concrete shear wall structure. To evaluate the damage status, the front surface of the shear wall was divided into nine sub-domains. Then, a sweep sinusoidal signal from 100 Hz to 10 kHz was sent by the smart aggregate actuator. A wavelet-packet-based damage index matrix was proposed to evaluate the damage status in different sub-domains. The experimental results showed that the proposed smart aggregate-based approach effectively evaluated the damage status in different areas and was capable of detecting the precaution-ary point to predict the structural failure. 3. FABRICATION OF SMART AGGRE-GATES The concept of smart aggregates (Song et al., 2008) was adopted in this study. The smart aggre-gates are small concrete cylinders (about ” in diameter and ” in thickness) with embedded rec-tangular PZT patches. The size of the PZT patches used in this study is 12.7 12.7 mm (0.5 0.5 in.). The PZT patches were first coated with an epoxy waterproof layer. The epoxy-coated PZT patches were then cast in cement modules to form the so-called “smart aggregates” (see Fig. 1). These cy-lindrical modules were made from a mixture of cement, sand and water (cement: sand: water = 1: 1.5: 0.48 in weight), and they were cast using a plastic mold (see Fig. 2). The smart aggregates were later embedded into concrete samples to serve as both actuators and sensors for active health monitoring. investigated. The signal energy Es is defined as 117different time or F/T cycles so that the health con-dition of concrete can be monitored and assessed. 5. EXPERIMENTAL PROGRAM A total of eight concrete prismatic samples with dimensions of 76.2 101.6 406.4 mm (3 4 16 in.) were cast. Three “smart aggregates” were mounted in the mold before casting, and a concrete beam sample with the embedded smart aggregates are shown in Fig. 3. In this study, the four beam samples were made of recycled aggre-gate concrete (RAC), and the other four serving as a reference were made of natural aggregate con-crete (NAC). All the beam specimens were cured in water at the room temperature for 28 days. Three smart aggregates (two were about 25.4 mm E2(1 )+bedded in each concrete sample. The placements of the three smart aggregates are shown in Fig. 3. sFigure 3. Concrete beam specimen and placement of embedded smart aggregates (unit: 1 in. = 25. 4 mm). One RAC beam was tested for damage detec-tion, and a saw-cut damage with different depths was created in the beam to mimic the crack type damage and varying magnitude of damage. In addition, two beams each for the NAC and RAC were conditioned in the freeze-thaw (F/T) machine (Fig. 4) to accelerate age the material, and their MOE (elastic modulus) were monitored at every 60 F/T cycles and up to the maximum of 300 F/T cy-Cs ESt dt()2 (1) =203.2 mm (8 in.) from the beam ends) were em-cles. Thus, based on the change of measured TOF, the reduction of the MOE can be obtained at the (2) (1 in.) from the left and right ends of the beam, and one was located at the center span of the beam, i.e., Es=where S(t) is the signal energy density distribution in time domain measured from the embedded smart aggregates. It is expected that with the increase of the damage magnitude (e.g., crack depth), the captured stress wave energy level will be decreased. As an attempt to quantitatively investigate the extent of damage, the first shear wave package is investi-gated. This wave package obviously travels from one actuator at one beam end to a sensor at the other beam end in a straight line. The time of flight (TOF) of the wave package can be easily identified by the time interval between the peaks of the exci-tation signal energy and response signal energy. The speed of shear wave inside the concrete can be predicted by Cs=where Cs is the speed of shear wave; E is the Youngs modulus of the concrete; is the density of the concrete; is the Poissons ratio. From the speed of shear wave, the TOF of the first shear wave package can be predicted by TOF l C= / (3) where l is the given distance between the actuator and sensor. By comparing the TOF of concrete with different magnitudes of damage (e.g., crack depths), the damage in the concrete can be quanti-tatively assessed. 4.2 Health Monitoring Technique The health condition of concrete is monitored by evaluating the change of the Youngs modulus of concrete over time (or at the different freeze/thaw (F/T) cycles in this study). In order to monitor the change of the modulus of elasticity (MOE), the same test procedure as in the damage detection technique is adopted. The TOF of the 1st shear wave package is measured to calculate the MOE reduction caused by the aging or the F/T accelerated conditioning. Based on Eqs. (2) and (3) and assuming that the Poissons ratio and the density of the concrete keep unchanged during the F/T cycling process, the following relationship can be established between the TOF and the Youngs modulus of the concrete samples: TOF11 (4) 118into a laptop for damage detection analysis. The experimental setup is shown in Fig. 7. St t( ) 0.5(1 cos(2 100 10 / 4.5)= sin(2 100 10 )t04510 tFigure 7. Experimental setup for damage detection of a RAC concrete beam in Smart Struc-tures Lab at WSU. The original signals from healthy and damaged RAC were shown in Fig. 8. As shown in Fig. 8, the signal captured by the SA3 is a combination of stress wave response from PZT sensor and elec-336 (5) both the compressive wave and shear wave are generated, it is anticipated that the captured shear wave is dominating in terms of signal magnitude. An Agilent 33120A function generator was used to generate the tone burst excitation signal (see Fig. 6). The excitation signal was a 4.5 cycles 100 kHz sine wave windowed by a Hanning window, as shown in Eq. (5) and Fig. 6. A power amplifier was used to amplify the excitation signal in order to drive the PZT actuator inside the smart aggre-gate. A HP 54603B oscilloscope was used to cap-ture the response signal generated by the PZT sen-sors at the sampling frequency of 2 MHz. The captured response signal data was then transmitted Figure 4. Freezing and thawing machine. 6. RESULTS AND DISCUSSION 6.1 Damage Detection of RAC Beam with Embedded Smart Aggregates To illustrate the potential of damage detection using smart aggregates, one RAC beam sample embedded with smart aggregates was cut at the quarter span to create a crack-type damage (see Fig. 5) and tested in laboratory. A crack notch with different depths (i.e., 12.7 mm (0.5 in.), 25.4 mm (1.0 in.), 38.1 mm (1.5 in.), 50.8 mm (2.0 in.), 63.5 mm (2.5 in.) was artificially-induced in the con-crete beam by saw-cutting. Figure 6. 100 kHz 4.5 cycles Hanning windowed tone burst. Figure 5. Artificially-induced crack notch in the RAC beam sample. The wave propagation tests were conducted using the smart aggregates embedded at the two ends (one serving as actuator, and the other as sen-sor) for damage detection. A stress wave was gen-erated by the embedded smart aggregate at one end (e.g., SA1 as shown in Fig. 5), and the response signal was captured by the smart aggregate at the other end (e.g., SA3 as shown in Fig. 5). Since the in-plane dimension of the thin square PZT patch actuator in its plane is much larger than in its thickness, the major effect of PZT actuation is per-pendicular to the beam length direction. Although 119In order to detect the damage (i.e., the crack-type notch with different depths in this study) in-side the RAC beam, the signal energy Es of the stress wave in Eq. (1) is investigated. The signal energy density distribution in time domain for the same RCA beam but with different crack notch depths is shown in Fig. 11. 1000 RAC-0.5in. RAC-1.0in. RAC-1.5in. RAC-2.0in. RAC-2.5in.400 RAC-Healthy RAC-0.5in. RAC-2.0in.Time (x10-4 s)Figure 11. Stress wave signal energy density. As shown in Fig. 11, with the increase of the notch depth, the captured stress wave energy level vestigate the extent of the damage, the first shear wave package (approximately from 200 s to 250 s) was investigated. This wave package obvi-ously travels from the actuator SA1 to the sensor SA3 directly in a straight line (see Fig. 5). The time of flight (TOF) of the wave package was eas- Original signal Fitted exponential curvesignal energy. The measured time of flight (TOF) is 193 s. The speed of shear wave inside the RAC beam is predicted by Eq. (2) as Cs RAC-Healthy RAC-1.5in. RAC-2.0in.value from Youngs modulus test was obtained (2.885 x 106 psi); is the density of RAC, r = 2,400 kg/m3; is the Poissons ratio, which is as-sumed to be 0.15. From the speed of shear wave, the time of flight of the first shear wave package is predicted as TOF l C s= =/ 0.3556/1898.7 187 106s (7) The predicted (187 s) and the measured (193 200012345ily identified by the time interval between the peaks of the excitation signal energy and response =+ 1898.7 m/s (6) where Cs is the speed of shear wave; E is the Youngs modulus of the RAC (in this study, the E2(1 )tromagnetic interference (EMI) caused by high voltage excitation signal. In order to eliminate the effect of EMI, an exponential function is used to fit the electrical charge release part of the signal curve. The stress wave response signal is then re-stored by cancelling out the EMI part of the signal from the original signal. The curve fitting is illus-trated in Fig. 9. The restored stress wave response signal is shown in Fig. 10. RAC-Healthy800506000-50-100 RAC-1.0in. RAC-1.5in.-150 RAC-2.5in.-200012345Time (x10-4s)Figure 8. Original signals captured by smart ag-gregate SA3. is decreasing. As an attempt to quantitatively in-500-50-100-150-200012345Time (x10-4 s)Figure 9. Original signal and fitted exponential curve. 60 RAC-0.5in. RAC-1.0in.40 RAC-2.5in.20from the compression test, and E = 19.889 GPa 0-20-40-6012345Time (x10-4 s)Figure 10. Restored stress wave signal from the saw-cut RAC beam.120during the F/T cycling process, the relationship between the TOF and the Youngs modulus of the RAC samples in Eq. (4) was used to monitor (or measure) the change of the Youngs modulus over the number of F/T cycles. First, the original signal from SA3 is col-lected. Then, the same stress wave signal restora-tion technique as described for damage detection in Section 6.1 is adopted to restore the stress wave signal. The TOF of the 1st shear wave package in the healthy sample is then identified. The delay of the 1st wave package between 60 F/T cycles (or other higher F/T cycles) and 0 cycles is esti-mated via a cross-correlation technique. Finally, the ratio between the TOF from healthy (0 cycles) sample and conditioned samples (at 60, 120, 180, process. As an example, the data from one RAC sam-ple is shown in Fig. 13. The TOFs of the healthy and conditioned (at 60 F/T cycles) samples and their corresponding Youngs modulus (normalized by the Youngs modulus in the healthy state) are RAC and NAC samples from 0 to 300 cycles (at every 60 cycles) will be finished in the near future (by May 31, 2010), and the degradation rate be-tween the RAC and NAC beam samples will be compared. As shown in Table 1, the normalized Youngs modulus decreases as the number of F/T cycles increases, demonstrating that the RAC sample degrades with the F/T conditioning cycles and the smart aggregates and associated wave-based health monitoring technique are capable of monitoring degradation process in the RAC beams. Figure 13. Original signals of RAC sample with 0 cycles and 60 cycles. s) TOFs show a close agreement, confirming that the first wave package is the shear wave propagat-ing from SA1 to SA3 directly. The total signal energy of the first shear wave package normalized by the signal energy at healthy state is shown in Fig. 12. It is shown that the shear wave energy captured by SA3 generally decreases with the increase of the notch crack depth in the RAC beam. When the notch depth reaches 38.1 mm (1.5 in.), there is a significant drop of the signal energy because the notch ap-proaches the propagation path of the 1st wave package. Hence, the signal energy of the 1st shear wave package can be used as an index to indicate the existence and roughly the extent of the dam-age. 240, and 300 F/T cycles) is used to indicate the Youngs modulus change during the F/T cycling 1.0listed in Table 1. The complete series of tests of 0.20.00.0 0.5 1.0 1.5 2.0 2.5Notch depth (in.)Figure 12. Normalized signal energy of the 1st wave package with different notch (crack) depths. 6.2 Health Monitoring of RAC Beams with Embedded Smart Aggregates In this section, the two RAC beam samples with embedded smart aggregates were conditioned in an F/T conditioning machine (see Fig. 4), and their health condition in term of the Youngs modulus (see Eq. (4) was monitored with the em-bedded smart aggregates by the time of flight (TOF). The smart aggregates were used to monitor the Youngs modulus change of the concrete sam-ples at every 60 cycles till 300 cycles. In order to monitor the Youngs modulus change, the same experimental setup and test procedure as in the damage detection in Section 6.1 were adopted. The TOF of the 1st shear wave package was measured to calculate the Youngs modulus reduction caused by the freeze and thaw cycling process. From Eqs. (2) and (3) and assuming that Poissons ratio and the density of the RAC beams keep unchanged 121In summary, the findings in the development Freeze and thaw cycles ACKNOWLEDGEMENTS This research was partially supported by the Transportation Northwest (TransNow Regional Center)/USDOT (Contract No. 652781) and Wash-ington State Department of Transportation (WSDOT). The recycled aggregates used in this study were donated by Central Pre-Mix Concrete Co. of Spokane, WA (Craig L. Matteson), and their generosity is gratefully acknowledged. REFERENCES Park, S., Ahmad, S., Yun, C.-B., and Roh, Y. (2006). “Multiple crack detection of concrete structures using impedance-based structural health monitoring techniques,” Experimental Mechanics, 46: 609618. Qiao, P.Z. (2010). “Seismic performance and smart health monitoring of concrete with recycled aggregate, Part I: Smart health monitoring of concrete with recycled aggregate,” Draft Final Research Report to Transportation Northwest (TransNow)/USDOT, Univ. of Washington, Seattle, WA. 64 pages. Shin, S.W., Qureshi, A.R., Lee, J.Y., et al. (2008). “Piezoelectric sensor based nondestructive ac-tive monitoring of strength gain in concrete,” Smart Materials and Structures, 17(5). Soh, C.K., Tseng, K.K.H., Bhalla, S., et al. (2000). “Performance of smart piezoceramic patches in health monitoring of a RC bridge,” Smart Materials and Structures, 9(4):533-542. Song, G.B., Gu, H.C., and Mo, Y.L. (2008). “Smart aggregates: multi-functional sensors for con-crete structures - a tutorial and a review,” Smart Materials and Structures, 17(3). Sun, M.Q., Staszewski, W.J., Swamy, R.N., et al. (2008). “Application of low-profile piezoce-ramic transducers for health monitoring of concrete structures,” NDT & E International, 41(8):589-595. Tseng, K.K. and Wang, L. (2004). “Smart piezo-electric transducers for in situ health monitor-ing of concrete,” Smart Materials and Struc-tures, 2004.13(5):1017-1024. Wen, Y.M., Chen, Y., Li, P., et al. (2007). “Smart Normalized TOF (10-6 s) Modulus niques using embedded smart piezoelectric aggre-Youngs spread application of recycled concrete in transpor-gates resulted from this study promote the wide-Table 1. TOFs and change of Youngs modulus. of damage detection and health monitoring tech-Samples 0 181 1.0 tation construct
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