## 3 Health Monitoring and Test Results
Then, the failure could be found at the interface of the beam of the existing RC frame and the retrofitted RC wall. The failure of the interface of the beam and the wall was also resulting in the failure of boundary columns. Piezoelectric-based sensing was used to perform the SHM of the tested RC wall after specific drift during the test. The energy-based damage index, given by Equation (2), was used to quantify the severity of structural damage. For the excitation stress wave signal of the PPT actuator, a swept sine wave with a wide frequency range provided a favorable excitation for monitoring purposes because it contained low to high-frequency response signals [10]. Since, the 60 Hz AC electrical power in the lab strongly interfered with the data acquisition system at repeated frequencies such as 60 Hz, 120 Hz, 180 Hz,... Therefore, the swept sine wave excitation could not be used for this test. Instead of the swept sine wave, single frequency sinusoidal waves with frequencies of 100 Hz, 1 kHz and 5 kHz and with a duration of 10 s were used as excitation stress waves for the PPT actuator in this health monitoring experiment. An NI (National Instrument, Inc.) system was adopted to send out the stress wave of the PPT actuator and to receive the transmitted stress wave from the PPT sensor. The  
Figure 8: Loading history of the reversed cyclic loading test. excitation signals that were generated from the function generator were 10 V (peak to peak) and were amplified by the power amplifier up to 100 V. The sampling frequency of the signals received by each PPT sensor channel was 50 kHz, obtained using an NI-DAQ-6259 data acquisition system. For each specimen, when a specified drift ratio is reached under cyclic loading, the PPTs are used to conduct SHM and to locate damage in the specimen. Figure 11 shows the concept of local damage detection; the wall panel is divided into three regions (path) and each region has a main actuator. The severities of damage in the difference regions are compared and serious damage is located. First, actuator A1, as depicted in Figures 7 and 11, is used to generate a stress wave with frequencies of 100 Hz, 1 kHz and 5 kHz. Sensors S1 and S4, which are embedded in the wall panels and beams at a middling height, receive the stress waves. The damage index in Equation (2) is calculated and the damage state in each of the propagation paths from A1 to S4, A2-S5 and A3-S6, is identified. Comparing the damage indices that are obtained upon excitation by A1, A2 and A3 reveals the severity of damage in each path and allows serious damage to be located. In SHM using a piezoelectric-based sensing system, the arrangement of actuators and sensors may strongly influence the obtained values of the damage index. Since the damage status of a structure member between an actuator and sensor differs, then damage index is affected by the locations of sensor and actuator. Therefore, the proposed method is better suited to diagnosing local damage to a structure as in this test than to diagnosing the overall damage to the entire structure. For example, in this investigation, actuator A2 was installed in the middle of the foundation and sensor S2 was installed in the center of the wall panel. The signal that was excited by A2 and received by S2 can be used to detect the local damage status of the lower center of the wall panel. Although the sensitivity limit and resolution of the sensing system may be influenced by the propagation distance of the excitation wave, the test results demonstrate that the proposed damage index reflects the severity of structural damage. Figure 9: Failure mode of the specimen RCW1. Cracking patterns of the specimen RCW1 at (**a**) drift 0.25%, (**b**) drift 0.5%, (**c**) drift 0.75% and (**d**) final failure mode at drift 1.0%. Since the results obtained from the excitation frequency of 5000 Hz is similar to 1000 Hz, the work only demonstrates the results obtained from the excitation frequencies of 100 Hz and 1000 Hz.