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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,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施工图学 生 姓 名：田强学号：200718030404班级：桥 2007-4 班专业（全称） ：土木工程专业（桥梁工程方向）指 导 教 师：李传习张玉平2011 年 6 月长沙市福元路湘江大桥第 2 联方案比选与施工图设计长沙福元路湘江大桥第 2 联方案比选与施工图设计说明一、工程概况福元路湘江大桥位于银盆岭大桥、三汊矶大桥居中偏北位置，距上游银盆岭大桥约 2.9km左右，距下游三汊矶大桥约 2.7km 左右。福元路湘江大桥西端接线道路为长望路，起于银杉路，东端接线道路为盛世路，止于芙蓉北路，工程建安费约 9.42 亿元。工程全长约 3574.89m，其中越江段约 1430m，按双向六车道城市主干路设计，并设置有双向人行道，桥梁主跨宽度 38.5m，标准宽度为 31.5m，其中单侧人行道宽度 3.5m。桥梁荷载等级为公路-I 级。 大桥设主、 副通航孔， 主通航孔跨度 3210m， 副通航跨度 85m， 通航净高 10.0m。长沙市区地震基本烈度为 6 度，按七度设防。本人设计为长沙市福园路湘江大桥第 2 联，梁桥全长 120m，双向四车道，并设置双向人行道，其中单侧人行道宽度 1.5m。本桥采用 40m 跨预应力混凝土连续箱梁。二、设计规范及技术标准（一） 、设计规范1 JTJ 001-03，公路工程技术标准S北京：人民交通出版社，20032 JTGD62-2004，公路钢筋砼及预应力砼桥涵设计规范S北京：人民交通出版社 20043 JTGD60-2004，公路桥涵设计通用规范S北京：人民交通出版社，20044 JTJ 024-2004，公路桥涵地基与基础设计规范S北京：人民交通出版社，2004（二） 、技术标准1、.设计荷载：公路 I 级； 人群荷载：3.0KN/。2、桥梁全长：120m。3、桥面宽度：净 14+21.5m 人行道。4、设计洪水频率：百年一遇。5、通航等级：无。三、施工要点施工时除严格遵守中华人民共和国交通部颁标准公路桥涵施工技术规范 、 公路工程质量检测评定标准及本桥施工招标技术规范的有关要求，同时，施工前应认真阅读各具体设计图纸，领会设计意图，并采取必要的复核措施。另外，施工时尚应注意：1、混凝土必须仔细研究确定施工工艺和选用材料，进行混凝土最佳配合比设计与试验，控制质量，控制标准和检测方法，并严格执行；为保证全桥颜色一致，建议采用同一厂家同一品牌的水泥。2、钢材普通钢筋、预应力钢材和锚具设计技术指标进行购买。3、预应力管道埋设所有预应力管道必须采用波纹管，预应力管道必须设置塑料内衬管才可以浇注混凝土，管道与管道间的连接及管道与锚具喇叭管的连接应确保其密封性，应选择与锚具相匹配的塑料波纹管，其产品需经过行业主管部门的批准。所有预应力管道的定位必须准确牢固，管道轴线必须与垫板垂直，严格按照图纸设置钢筋定位。纵向预应力管道位置的偏差不得大于 1cm.4、下部结构施工前应全面检查各桩基坐标，墩、台及垫石各控制点高程，经确认无误后方可进行施工，桩位应严格按照桩位平面图放样。施工时应注意各墩台处支座的布置情况，确保支座位置、规格准确无误。支座垫石顶面必须水平。盖梁顶的支座垫石顶面高程施工前应认真核对无误后，方可施工。6、浇注桩基混凝土， 尤其是水下混凝土时， 应保证导管埋入混凝土有足够深度， 并要连续进行，避免发生断桩事故，并防止孔壁坍塌事故的发生。7、施工承台时注意墩身钢筋的预埋，预埋时保证钢筋的定位准确，钢筋接头位置应相互错开。 THEME 2 STRUCTURAL MONITORING AND DAMAGE DETECTION 115SMART 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 smart 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 116used 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 was 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 117analysis 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. Figure 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 investigated. The signal energy Es is defined as 1182( )sES tdt= (1) 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 2(1)sEC =+ (2) 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 /sTOFl 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: 11sTOFCE (4) Thus, based on the change of measured TOF, the reduction of the MOE can be obtained at the different 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 (1 in.) from the left and right ends of the beam, and one was located at the center span of the beam, i.e., 203.2 mm (8 in.) from the beam ends) were em-bedded in each concrete sample. The placements of the three smart aggregates are shown in Fig. 3. Figure 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-cles. 119 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 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 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 into a laptop for damage detection analysis. The experimental setup is shown in Fig. 7. 33( )0.5(1 cos(2100 10 /4.5)sin(2100 10 )S ttt= 6045 10t (5) Figure 6. 100 kHz 4.5 cycles Hanning windowed tone burst. Figure 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- 120tromagnetic 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. 012345-200-150-100-50050Signal captured by SA3 (mV)Time (x10-4s) RAC-Healthy RAC-0.5in. RAC-1.0in. RAC-1.5in. RAC-2.0in. RAC-2.5in. Figure 8. Original signals captured by smart ag-gregate SA3. 012345-200-150-100-50050 Original signal Fitted exponential curveSignal captured by SA3 (mV)Time (x10-4 s) Figure 9. Original signal and fitted exponential curve. 12345-60-40-200204060Stress wave signal(mV)Time (x10-4 s) RAC-Healthy RAC-0.5in. RAC-1.0in. RAC-1.5in. RAC-2.0in. RAC-2.5in. Figure 10. Restored stress wave signal from the saw-cut RAC beam. In 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. 1234502004006008001000Stress wave signal energy Time (x10-4 s) RAC-Healthy RAC-0.5in. RAC-1.0in. RAC-1.5in. RAC-2.0in. RAC-2.5in. 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 is decreasing. As an attempt to quantitatively in-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-ily identified by the time interval between the peaks of the excitation signal energy and response signal 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 1898.7 m/s2(1)sEC =+ (6) where Cs is the speed of shear wave; E is the Youngs modulus of the RAC (in this study, the value from Youngs modulus test was obtained from the compression test, and E = 19.889 GPa (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 6/0.3556/1898.7187 10sTOFl Cs= (7) The predicted (187 s) and the measured (193 121s) 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. 0.00.51.01.52.02.50.00.81.0Normalized signal energy of 1st wave packageNotch 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 during 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, 240, and 300 F/T cycles) is used to indicate the Youngs modulus change during the F/T cycling 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 listed in Table 1. The complete series of tests of 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. 122Table 1. TOFs and change of Youngs modulus. Samples Freeze and thaw cycles TOF (10-6 s) Normalized Youngs Modulus 0 181 1.0 RAC-3 60 188 0.927 7. CONCLUDING REMARKS In the study, the goal is to develop damage de-tection and health monitoring techniques using smart aggregate. The smart aggregates using the PZT patches were fabricated, and they were em-bedded in concrete beams to detect damage (by saw-cut cracks with different depths) and monitor health condition (by the accelerated F/T condition-ing) of the RAC material. The corresponding dam-age (crack) detection and condition assess-ment/monitoring techniques based on the wave propagation were developed and implemented. Based on the experimental evaluation and im-plementation of smart aggregates in damage detec-tion and health monitoring conducted in this study, the following preliminary conclusions are obtained: 1. The damage detection technique using embed-ded smart aggregates show its capability of de-tecting the severity of crack damage in con-crete beams. 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 crack damage. 2. The health monitoring technique using embed-ded smart aggregates also exhibits its capabil-ity of assessing the change of Youngs modulus (aging or degradation) due to the F/T conditioning cycles in concrete beams. 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 able to assess degradation proc-ess in the RAC beams. 3. Though the F/T conditioning is still in process (will be completed by May 31, 2010; the re-sults will be included at the time of confer-ence), it is anticipated that the RAC samples will degrade faster than the NAC beams. The degradation rates of both the RAC and NAC beams will be measured and monitored by the dynamic modulus test (ASTM C215) and the proposed health moni