A building structural-performance monitoring system using RFID tag with sensors

Transcription

1 icccbe 21 Nottingham University Press Proceedings of the International Conference on Computing in Civil and Building Engineering W Tizani (Editor) A building structural-performance monitoring system using RFID tag with sensors Akinori Tani, Motoki Ugaji & Yuichiro Yamabe Department of Architecture, Graduate School of Engineering, Kobe University, Japan Abstract To develop a conventional structural performance monitoring system, the authors have performed fundamental tests using an alternative system of a RFID tag with sensors, and the effectiveness of proposed system is verified, because RFID tags with sensors are under development in Japan. Results obtained in preceding studies demonstrate that the proposed systems can identify changes of natural periods by Fast Fourier Transform (FFT) analysis using observed response acceleration and/or strain data. In this study, response s are calculated using Fourier integration and response accelerations measured by sensors. In Fourier integration, the Butterworth filter, which cuts low-frequency contents, is used as a high-pass filter. Calculated response s coincide with observed response s measured by laser meters when adequate parameters of the Butterworth filter such as the cut off frequency and the degree of the filter are used. These results show that the proposed system can be applied to structural performances monitoring based on observed accelerations and calculated s. Keywords: structural-performance, monitoring, RFID, acceleration sensor, response 1 Introduction Recently, changes to circulation-type production systems are strongly demanded from environmental perspectives in building construction fields. It is necessary to identify structural performance properly at each stage of a building s life cycle to use structures for a long time. Based on identified results, appropriate structural repairs and anti-seismic retrofit methods can be planned. However, in Japan, structural performance monitoring systems have not been adopted in buildings. Aiming at ubiquitous and conventional structural performance monitoring systems, some application systems using wireless sensor networks have been applied to performance monitoring systems of buildings (Center for Embedded Network Sensing HP (27), Kurata et al. (24), and Paek et al. (25)). Regarding other ubiquitous information systems, radio frequency identification (RFID) tags are used widely in various fields. Recently, RFID tags with sensors have come onto the market in Japan. However, these tags have no memory and the data collected by sensors are transmitted immediately to a controller. In order to reduce communication loads, built-in memory is considered indispensable because high-speed sampling is required to perform structural performance monitoring against earthquake and wind loadings. To apply RFID tags with sensors to structural performance monitoring systems, sensors of acceleration,, strain, pressure, and so on are considered useful. Although RFID tags with

2 these sensors and built-in memory are now under development in Japan, it is considered that such tags with sensors and built-in memory present great possibilities for use as devices for use in structural performance monitoring systems. Regarding fundamental studies of RFID tags with acceleration sensors and built-in memory, the authors performed shaking table tests using two-story and four-story specimens (Tani et al., 27a, b, 28 a, b). In those studies, an alternative system (Microstrain, 29) which has almost identical performance to RFID tags with sensors and built-in memory was used. In this study, calculations of response s are performed using Fourier integration of response acceleration data observed in shaking table tests of a two-story specimen. Calculation results are compared with response s measured by laser meters. Based on those results, the applicability of RFID tags with acceleration sensors are verified and discussed along with accuracy of identification results related to structural characteristics (Ugaji et al., 29). 2 Outline of the RFID tag system with sensors The RFID tag is an electronic device using radio frequency identification technologies. Figure 1 depicts a general configuration of an RFID tag with sensors and built-in memory. Regarding the data transmission method, a semi-passive type is considered suitable, because battery is not used in data transmission and is only used to activate sensors. Thus, the battery consumption is expected to be low. Battery Sensor Transceiver Memory IC Chip RFID Tag Antenna Computer Antenna Controller Transceiver Figure 1. General configuration of RFID tag with sensor and built-in memory. 3 Outline of experimental systems 3.1 Experimental systems In Japan, it is difficult to apply RFID tags to structural performance monitoring systems immediately because almost all RFID tags with sensors and built-in memory are under development. Here, an alternative system (Microstrain, 29) which has almost identical performance to the RFID tag with sensors and built-in memory is used. Data transfers are performed using a wireless LAN. Details of the alternative system are presented in Table 1. This alternative system has 12-bit analog digital (A/D) converters with sensors to which voltage outputs can be attached. Table 2 presents specifications of an acceleration sensor named Sensor K and a servo-type accelerometer. Table 1. Details of the alternative system of RFID tags with sensors and built-in memory. Communication Input and Supply Channels of Memory Size A/D Converter Frequency Voltage Analog Input 2.4 GHz 2 Mbyte 12 bit 3 V 3 Table 2. Specifications of the acceleration sensor (Sensor K) and Servo-type Accelerometer. Sensor Range of Detection Sensitivity Error Supply Voltage Sensor K ±2 g 6 mv/g ±.1% V Accelerometer ±1.7 g 3 mv/g ±.5% -7 V

3 3.2 Shaking table system and specimen Figure 1(a) shows a shaking table test system. This system has an 8 8 mm shaking table with excitation performance of 1 g to 18.6 kgf weight in a single direction. In Figure 1(a), the alternative system of RFID tags and the sensor K are also shown. Figure 1(b) portrays details of a two-story specimen. Floors and columns of the specimen consist of steel and aluminum plates. response acceleration and data at each floor and the shaking table are measured using the sensor K and laser meters, respectively. Response acceleration data on the shaking table are also measured using the servo-type accelerometer. Acceleration Sensor (Sensor K) (Unit: mm) Laser Displacement Meter Column (Aluminum :2 3 2) Steel Plate Vibration Direction V-Link Angle Bolt M5 (a) Shaking table system and sensor system Figure 2. Shaking table system and two-story specimen. (b) Two-Story Specimen. 4 Measurement of acceleration and calculation method of 4.1 Measurement of response acceleration In shaking table tests, absolute acceleration and data of the shaking table and each floor are measured using sensors (Sensor K) and laser meters. Four earthquake wave data are used as earthquake inputs: El Centro NS (194), Taft EW (1952), Hachinohe NS (1968), and JMA Kobe NS (1995). Measurements of sensors are started before the shaking table system is started, and are terminated after the shaking table system is terminated because the measurement system sensors can not be synchronized with the shaking table test system. Regarding a superpositioning method, origins of data collected by sensors are shifted horizontally to conform to clock times of maximal data measured by the servo-type accelerometer and Sensor K on the shaking table. The sensors sampling Interval is.3125 s (32 Hz). The sampling interval of the shaking table test system, measuring by servo-type accelerometers and laser meters are.1 s. The duration is 2 s. Figure 3 depicts the relative accelerations from the second floor to the foundation for Taft EW and Hachinohe NS, calculated as differences of the measured absolute response accelerations. Acceleration (gal) Acceleration (gal) (a) El Centro NS (b) Hachinohe NS Figure 3. Relative acceleration of the top of the second floor from the foundation.

4 4.2 Calculation method of response s Regarding an integrating method of acceleration data, Fourier integration is performed in the frequency domain. Generally, response s are calculable by the integration of measured acceleration data twice. However, integration results often become strange because the sensors dynamic characteristics affect the observed data. To modify these phenomena, filtering operations are used. Here, the Butterworth filter is used as a high-pass filter as shown in Eq. (1). 2N { } 2 ( ) = 1 1+ ( ω / ω) H ω. (1) c In Eq. (1), ω, ω c, N, and H(ω) respectively denote the frequency, cut-off frequency, degree, and amplitude of the filter. This filter has the following characteristics: (i) Phase characteristics are nearly flat. (ii) Data are passed without changes if the frequency is greater than ω c. (iii) Data after the filtering operation become small according to the filter shape determined by ω c and N. (iv) The filter shapes change steeply when the degree of filter N becomes large. Integration results are considered to be affected by the shape of the filter. Therefore, the cut-off frequency ω c is determined in accordance with the following procedures. (a) The degree of the filter is assumed to be 4 (N=4). (b) Cut-off frequency ω c is changed from.1 to 2 with.1 pitches. The response s are calculated using Fourier integration method and the assumed filter parameters. (c) Calculated results are compared with those measured by laser meters, and errors to absolute maximal s are calculated in four earthquake inputs using Eq. (2). Maximal data of integrated result Errors = 1 (%) (2) Maximal data measured by laser meter (d) These errors of absolute s of the shaking table and the second floor, and relative s from the first to second floor are calculated by Eq. (2), respectively. The arithmetic means of these errors are calculated to each earthquake input, and the cut-off frequency is determined when the arithmetic average of these three errors becomes smallest, as portrayed in Figure 4. Error (%) Frequency (Hz) Figure 4. Calculation errors and determination of the optimal cut-off frequency. (shaking table) (top of ) Relative (1st to ) Arithmetic average Minimal error (.51 Hz) In accordance with procedures described above, the cut-off frequency ω c becomes.51 Hz, and the round number.5 Hz is used as the cut-off frequency of the Butterworth filter. A flowchart of Fourier integration is presented in Figure 5. Using the Fourier integration method and the Butterworth filter (ω c =.5 Hz, N=4), the absolute response acceleration data measured by sensors are integrated, and the absolute response s are calculated. Figure 6 portrays calculation results of absolute s at the second floor in case of Taft EW and Hachinohe NS. Figure 7 portrays calculation results of relative s at the second floors from the foundation in case of Taft EW and Hachinohe NS, calculated as differences from the second floor to the foundation. Calculation errors are presented in Table 3.

5 absolute response acceleration data in time domain Fast Fourier Transform Finite Fourier cosine coefficient in frequency domain Finite Fourier sine coefficient in frequency domain Filtering operation using Butterworth Filter Inverse Fast Fourier Transform Fourier Integration Fourier Integration Response velocity in time domain Figure 5. Flowchart of Fourier integration. Inverse Fast Fourier Transform Response Displacement in time domain (a) Taft EW (b) Hachinohe NS Figure 6. Calculation results of absolute s at the top of the second floor (a) Taft EW (b) Hachinohe NS Figure 7. Calculation results of relative s from the first to second floor. -1 Table 3. Calculation errors of response s using Fourier integration method. of the shaking table at the Relative from 1st to of the shaking table at the Relative from 1st to Taft EW Hachinohe NS (cm) (cm) Error (%) El Centro NS JMA Kobe NS (cm) (cm) Error (%) Discussion and Conclusions Aiming at ubiquitous structural performance monitoring systems of buildings, the calculation method

6 of response s using measured absolute acceleration data and Fourier integration method is proposed. Integration results are verified in comparison with response s measured by laser meters. The obtained results suggest the following conclusions. 1) response s can be calculated using acceleration data measured by sensors and Fourier integration methods with filtering operations. Regarding filtering operations, the Butterworth filter used in this study is also effective when appropriate parameters such as the cutoff frequency and the degree of the Butterworth filter are determined. 2) As for parameters of the Butterworth filter, the degree of the filter is fixed to 4. The cut-off frequency giving the minimal arithmetic average of calculation errors is determined as the optimal cut-off frequency. The proposed optimizing method of the cut-off frequency is proved to be effective, and errors of integration results are distributed from.528 to 1.7%. 3) As for the applicability to the structural performance monitoring system, changes of predominant periods can be detected by Fourier analysis using acceleration data measured by sensors. This method can detect the deterioration of the objective building totally. On the other hand, response s calculated by Fourier integration method can detect damages based on maximal response s and/or residual s. This method can detect damages in each floor and those of structural members such as columns, braces, shear walls and so on. 4) In order to apply proposed calculation method of response s to actual building structures, it is necessary to verify the applicability of obtained parameters of the Butterworth filter in this study. It is also necessary to establish the determination method of appropriate parameters of the Butterworth filter applicable to actual buildings. 5) Based on results obtained in this study, RFID tags with acceleration sensors and built-in memory are considered to be applicable to structural performance monitoring systems of actual buildings based on measured response acceleration data and calculated response s. Furthermore, damage detection methods for buildings using response acceleration and data must be developed to improve structural performance monitoring of buildings. References CENTER FOR EMBEDDED NETWORK SENSING Home Page, 27: projects.htm. KURATA, N., SPENCER, B.F. and RUIZ-SANDOVAL, M., 24. Building Risk Monitoring Using Wireless Sensors. Proc. of the 13th World Conference on Earthquake Engineering, CD & DVD-ROM, Paper No. 146, pp.1-11, Vancouver, Canada. MICROSTRAIN INC. HP, PAEK, J., CHINTAIAPUID, K., CAFFEREY, J., GOVINDAN, R. and MASRI, S., 25. A Wireless Sensor Network for Structural Health Monitoring: Performance and Experience. Proc. of the Second IEEE Workshop on Embedded Networked Sensors (EmNetS-II), 1-1, Sydney, Australia. TANI, A, MURAKAMI, M, YAMABE, Y., 27a. Fundamental tests on structural health monitoring systems by RFID tag with acceleration sensors. Proc. of the 2nd International Symposium on Improvement of Structural Safety for Building Structures (ISSBS 6), 28-39, Kobe, Japan. TANI, A., MURAKAMI, M., YAMABE, Y., 27b. Fundamental tests on structural health monitoring systems by RFID tag with acceleration sensors Verification on applicability to structural health monitoring system by 4-story specimen. Proc. of the 3rd International Symposium on Improvement of Structural Safety for Building Structures (ISSBS 7), 85-94, Daegu, Korea. TANI, A., UGAJI, M. and YAMABE, Y., 28a. Fundamental Tests on Structural Health Monitoring Systems by Using RFID Tag with Wire Strain Gage. Proc. of the 4th International Symposium on Improvement of Structural Safety for Building Structures (ISSBS 8), pp , Harbin, China. TANI, A., YAMABE, Y., MURAKAMI, M. and UGAJI, M., 28b. Fundamental Tests on Structural Health Monitoring Systems by Using RFID Tag with Sensors. Proc. of 14th World Conference on Earthquake Engineering, DVD-ROM, Paper ID:11-149, pp. 1-8, Beijing, China. UGAJI, M., TANI, A. and YAMABE, Y., 29. A Health Monitoring System of Building Structures by RFID Tag with Sensors Calculation of Displacements by Integration of Acceleration. Proc. of Annual Meeting of Architectural Institute of Japan, Kinki Branch, Structural Division, Vol. 49, (in Japanese)