Version 1
: Received: 23 September 2024 / Approved: 23 September 2024 / Online: 23 September 2024 (11:03:43 CEST)
How to cite:
Kardoulias, A.; Arailopoulos, A.; Seventekidis, P. From Binary to Multi-Class: Neural Networks for Structural Damage Classification in Bridge Monitoring Under Static and Dynamic Loading. Preprints2024, 2024091753. https://doi.org/10.20944/preprints202409.1753.v1
Kardoulias, A.; Arailopoulos, A.; Seventekidis, P. From Binary to Multi-Class: Neural Networks for Structural Damage Classification in Bridge Monitoring Under Static and Dynamic Loading. Preprints 2024, 2024091753. https://doi.org/10.20944/preprints202409.1753.v1
Kardoulias, A.; Arailopoulos, A.; Seventekidis, P. From Binary to Multi-Class: Neural Networks for Structural Damage Classification in Bridge Monitoring Under Static and Dynamic Loading. Preprints2024, 2024091753. https://doi.org/10.20944/preprints202409.1753.v1
APA Style
Kardoulias, A., Arailopoulos, A., & Seventekidis, P. (2024). From Binary to Multi-Class: Neural Networks for Structural Damage Classification in Bridge Monitoring Under Static and Dynamic Loading. Preprints. https://doi.org/10.20944/preprints202409.1753.v1
Chicago/Turabian Style
Kardoulias, A., Alexandros Arailopoulos and Panagiotis Seventekidis. 2024 "From Binary to Multi-Class: Neural Networks for Structural Damage Classification in Bridge Monitoring Under Static and Dynamic Loading" Preprints. https://doi.org/10.20944/preprints202409.1753.v1
Abstract
Structural Health Monitoring (SHM) plays a vital role in ensuring the health status of a wide range of structures, such as bridges, buildings and large infrastructure in general. The advantages of this process can be further enhanced by incorporating more numerical and statistical approaches into traditional methods, such as Finite Element Analysis and Machine Learning. In this study a bridge structure is examined, and neural networks are trained with data derived from finite element analyses under static loads and dynamic excitations. Initially, a binary classification problem is addressed, where numerically trained classifiers are tasked with identifying whether the structure is in a healthy state or not. This category is further divided into two subcategories, depending on the extent of the damage present in the structure. Subsequently, a multi-class classification problem is defined, where three different damage classes of the same extent are considered, and the trained network is required to distinguish between them. Finally, conclusions are drawn from the results of the study regarding the model error parameter, the impact of the damage size, as well as the types of neural networks and training data used.
Keywords
Structural Health Monitoring (SHM); FEM; Machine Learning (ML); Artificial Neural Networks (ANNs); Deep Neural Networks (DNNs); Convolutional Neural Networks (CNNs)
Subject
Engineering, Civil Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
