PSI - Issue 64

ScienceDirect Available online at www.sciencedirect.com ScienceDirect Structural Integrity Procedia 00 (2023) 000 – 000 Available online at www.sciencedirect.com Structural Integrity Procedia 00 (2023) 000 – 000 Procedia Structural Integrity 64 (2024) 557–564 Available online at www.sciencedirect.com ScienceDirect

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2452-3216 © 2024 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of SMAR 2024 Organizers 10.1016/j.prostr.2024.09.307 2452-3216 © 2024 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of SMAR 2024 Organizers 2452-3216 © 2024 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of SMAR 2024 Organizers * Corresponding author. Tel.: +49-308-1044-4486. E-mail address: eshwar-kumar.ramasetti@bam.de 1. Introduction Structural Health Monitoring (SHM) systems are used to monitor the health of engineering structures such as bridges, turbines, and large buildings based on data acquired from different kinds of sensors (Sonbul and Rashid, 2023). The life span of the structures is shortened due to the short-term and long-term damages caused to the structures by environmental changes and mechanical factors, which makes the monitoring process pivotal aspect for structures. Keywords: Structural Health Monitoring; Artifical Intelligence; Machine Learning; SPP100plus; Nibelungen bridge. 1. Introduction Structural Health Monitoring (SHM) systems are used to monitor the health of engineering structures such as bridges, turbines, and large buildings based on data acquired from different kinds of sensors (Sonbul and Rashid, 2023). The life span of the structures is shortened due to the short-term and long-term damages caused to the structures by environmental changes and mechanical factors, which makes the monitoring process pivotal aspect for structures. * Corresponding author. Tel.: +49-308-1044-4486. E-mail address: eshwar-kumar.ramasetti@bam.de SMAR 2024 – 7th International Conference on Smart Monitoring, Assessment and Rehabilitation of Civil Structures Development of generic AI models to predict the movement of vehicles on bridges Eshwar Kumar Ramasetti*, Ralf Herrmann, Sebastian Degener and Matthias Baeßler Federal Institute for Materials Research and Testing (BAM), Unter den Eichen 87, Berlin 12205, Germany Abstract For civil, mechanical, and aerospace structures to extend operation times and to remain in service, structural health monitoring (SHM) is vital. SHM is a method to examining and monitoring the dynamic behavior of essential constructions. Because of its versatility in detecting unfavorable structural changes and enhancing structural dependability and life cycle management, it has been extensively used in many engineering domains, especially in civil bridges. Due to the recent technical developments in sensors, high-speed internet, and cloud computing, data-driven approaches to structural health monitoring are gaining appeal. Since artificial intelligence (AI), especially in SHM, was introduced into civil engineering, these modern and promising methods have attracted significant research attention. In this work, a large dataset of acceleration time series using digital sensors was collected by installing a structural health monitoring (SHM) system on Nibelungen Bridge located in Worms, Germany. In this paper, a deep learning model is developed for accurate classification of different types of vehicle movement on the bridge from the data obtained from accelerometers. The neural network is trained with key features extracted from the acceleration dataset and classification accuracy of 98 % was achieved. SMAR 2024 – 7th International Conference on Smart Monitoring, Assessment and Rehabilitation of Civil Structures Development of generic AI models to predict the movement of vehicles on bridges Eshwar Kumar Ramasetti*, Ralf Herrmann, Sebastian Degener and Matthias Baeßler Federal Institute for Materials Research and Testing (BAM), Unter den Eichen 87, Berlin 12205, Germany Abstract For civil, mechanical, and aerospace structures to extend operation times and to remain in service, structural health monitoring (SHM) is vital. SHM is a method to examining and monitoring the dynamic behavior of essential constructions. Because of its versatility in detecting unfavorable structural changes and enhancing structural dependability and life cycle management, it has been extensively used in many engineering domains, especially in civil bridges. Due to the recent technical developments in sensors, high-speed internet, and cloud computing, data-driven approaches to structural health monitoring are gaining appeal. Since artificial intelligence (AI), especially in SHM, was introduced into civil engineering, these modern and promising methods have attracted significant research attention. In this work, a large dataset of acceleration time series using digital sensors was collected by installing a structural health monitoring (SHM) system on Nibelungen Bridge located in Worms, Germany. In this paper, a deep learning model is developed for accurate classification of different types of vehicle movement on the bridge from the data obtained from accelerometers. The neural network is trained with key features extracted from the acceleration dataset and classification accuracy of 98 % was achieved. © 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of SMAR 2024 Organizers Keywords: Structural Health Monitoring; Artifical Intelligence; Machine Learning; SPP100plus; Nibelungen bridge.