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In recent times, there has been an increase in the occurrence of disasters caused by a rapid increase in the aging of infrastructure. A structural health monitoring system can be utilized to detect severe damages in infrastructure and prevent accidents. In a previous study, the authors developed a method for damage detection in bridges using monitoring systems. However, the operational cost of the proposed method was high due to the requirement of multiple expensive accelerometers. The aim of this study is to reduce the number of accelerometers used in the aforementioned method by determining the optimal installation locations of the accelerometers. As a result of this study, optimal accelerometer installation locations for bridge damage detection, have been proposed.

Impact Assessment of Bridge Damage Detection Based on Deep Learning According to Number and Location of Accelerometer Installations