Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
An Automated Machine Learning-Based Approach for Structural Novelty Detection Based on SHM
One major goal of structural health monitoring (SHM) is to detect, and possibly locate, quantify or predict damage on structures. Without detailed knowledge of structural mechanical behavior, data analysis is a complex task and operational monitoring is often limited to the use of more or less arbitrary thresholds. Data-driven techniques, which rely on a statistical analysis of data, have encountered a growing interest over the past two decades. In parallel, SHM is now increasingly considered for several types of structures with the development of low-cost sensors and IoT.
In this context, this paper proposes an approach based on multiple automated machine learning-based models for novelty detection and location in monitoring data. This study focuses on the monitoring of large structures with multiple sensors. For each sensor, multiple regression models (based on neural networks) are generated using the same training set, with various input data: internal temperature, environmental conditions, or data from other sensors deployed on the structure. Anomalies are then identified in the dataset based on residuals between model outputs and in situ data. For a given sensor, residuals of all models are then compiled to produce an anomaly indicator.
This paper presents some of the results obtained on data acquired from the monitoring of a large concrete bridge. Some anomalies are simulated and added to the dataset to demonstrate the detection performance of the proposed approach.
An Automated Machine Learning-Based Approach for Structural Novelty Detection Based on SHM
One major goal of structural health monitoring (SHM) is to detect, and possibly locate, quantify or predict damage on structures. Without detailed knowledge of structural mechanical behavior, data analysis is a complex task and operational monitoring is often limited to the use of more or less arbitrary thresholds. Data-driven techniques, which rely on a statistical analysis of data, have encountered a growing interest over the past two decades. In parallel, SHM is now increasingly considered for several types of structures with the development of low-cost sensors and IoT.
In this context, this paper proposes an approach based on multiple automated machine learning-based models for novelty detection and location in monitoring data. This study focuses on the monitoring of large structures with multiple sensors. For each sensor, multiple regression models (based on neural networks) are generated using the same training set, with various input data: internal temperature, environmental conditions, or data from other sensors deployed on the structure. Anomalies are then identified in the dataset based on residuals between model outputs and in situ data. For a given sensor, residuals of all models are then compiled to produce an anomaly indicator.
This paper presents some of the results obtained on data acquired from the monitoring of a large concrete bridge. Some anomalies are simulated and added to the dataset to demonstrate the detection performance of the proposed approach.
An Automated Machine Learning-Based Approach for Structural Novelty Detection Based on SHM
Lecture Notes in Civil Engineering
Pellegrino, Carlo (Herausgeber:in) / Faleschini, Flora (Herausgeber:in) / Zanini, Mariano Angelo (Herausgeber:in) / Matos, José C. (Herausgeber:in) / Casas, Joan R. (Herausgeber:in) / Strauss, Alfred (Herausgeber:in) / Manzini, Nicolas (Autor:in) / Mar, Ndeye (Autor:in) / Schmidt, Franziska (Autor:in) / Bercher, Jean-François (Autor:in)
International Conference of the European Association on Quality Control of Bridges and Structures ; 2021 ; Padua, Italy
Proceedings of the 1st Conference of the European Association on Quality Control of Bridges and Structures ; Kapitel: 134 ; 1180-1189
12.12.2021
10 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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