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Articles | Volume XLVIII-1-2024
https://doi.org/10.5194/isprs-archives-XLVIII-1-2024-937-2024
https://doi.org/10.5194/isprs-archives-XLVIII-1-2024-937-2024
16 May 2024
 | 16 May 2024

Smart Bridge Damage Assessment through Integrated Multi-Sensor Fusion Vehicle Monitoring

Aminreza Karamoozian, Masood Varshosaz, Amirhossein Karamoozian, Huxiong Li, and Zhaoxi Fang

Keywords: Bridge Damage Assessment, Integrated Vehicle Monitoring, Probabilistic Deep Learning, Multi-Sensor Fusion

Abstract. This study explores the efficacy of vehicle-assisted monitoring for bridge damage assessment, emphasizing the integration of diverse sensor data sources. A novel method utilizing a deep neural network is proposed, enabling the fusion of fixed sensors on bridges and onboard vehicle sensors for damage assessment. The network offers scalability, robustness, and implementability, accommodating various measurement types while handling noise and dynamic loading conditions. The main novel aspect of our work is its ability to extract damage-sensitive features without signal preprocessing for future bridge health monitoring systems. Through numerical evaluations, considering realistic operational conditions, the proposed method demonstrates the capability to detect subtle damage under varying traffic conditions. Findings underscore the importance of integrating vehicle and bridge sensor data for reliable damage assessment, recommending strategies for optimal monitoring implementation by road authorities and bridge owners.