PSI - Issue 64

Ayaho Miyamoto et al. / Procedia Structural Integrity 64 (2024) 464–475 Author name / Structural Integrity Procedia 00 (2019) 000–000

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The acceleration sensor was bonded to the underside of the rear wheel spring and was coated with waterproof epoxy resin to protect the sensor over a long period of time. The three-axis acceleration sensor used was positioned so that its X, Y and Z axes were aligned with the direction of travel of the bus, the direction perpendicular to the direction of travel and the vertical direction, respectively. Analog vibration data obtained from the three-axis acceleration sensor in the form of acceleration response were converted to digital data via a data logger and saved in the computer as Excel file data automatically (Yabe (2021)) . Fig. 3(c) shows a package box for automatic data collection system conjunction with a GPS sensor which has two operating functions; detecting the bus approaching the target bridges with judging the ups or downs of road and triggering for start of measuring acceleration of bus vibration (see Table 4). In the long-term monitoring that has been conducted on the bus routes in Ube-city, attempts were made to evaluate systematically the influence of bus operating conditions such as weather, the number of oncoming vehicles, the number of persons on the vehicle and vehicle speed on “characteristic deflection”. Fig. 4 shows a hardware configuration of automatic data collecting system in the bus for extracting “characteristic deflection” which is relatively free from the influence of dynamic disturbances due to such factors as the unevenness of the road surface and a structural anomaly parameter. As shown in Fig. 4, in this system, GPS is used for pinpointing bus’s position by GPS sensor data and detecting ups or downs of a road. If the bus is over the target bridge, this system automatically measures acceleration data with position data. It is expected that the automatic data collection system will be able to realize a relatively low-cost (rational) bridge management strategy (Yabe (2021)) . The “characteristic deflection” is affected by various external disturbances such as the bus operating conditions as mentioned before (Miyamoto et al. (2019)) . Consequently, “characteristic deflection” is inevitably subject to variation. An attempt was made, therefore, to determine changes over time in “characteristic deflection” obtained from the bus based monitoring system by applying the moving average method, assuming that as the number of samples, N , increases, variations due to external disturbances such as bus operating conditions converge to a single value according to the central limit theorem. The moving average method is the method of calculating the average of data in data section (segment; the number of data sets to be averaged) by calculating averages for incrementally shifted subsections. In the previous studies (Yabe et al. (2015)) , the simple moving average method, which is one of the commonly used moving average methods, was used to process in “characteristic deflection” data. As an example, Fig. 5 shows the relationship between the number of data sections(segments) and the standard deviations of the corresponding “characteristic deflections” obtained by applying the moving average method to data subsets in the data section(segment) (Miyamoto et al. (2015)) . As shown in Fig. 5, as the number of data sets increases, the standard deviation becomes incrementally smaller. After the number of data sections reaches a certain level, the standard deviation does not change significantly and converges. This is thought to have shown that various external disturbances (error factors)

Fig. 4. Configuration of an automatic data collecting system in the Ube-city bus (hardware) (Yabe (2021)).