Damage detection, aimed at preventing overall structural failure and enabling planned repair and rehabilitation, has been a significant research focus in recent years. Data-driven structural health monitoring through structural response analysis constitutes the core of contemporary studies. This approach utilises artificial intelligence tools, particularly artificial neural networks, to eliminate the need for complex preprocessing of time-series data and achieve more accurate results compared to traditional structural health monitoring and damage detection methods. In this study, an unsupervised deep neural network (convolutional autoencoder) is proposed to reconstruct input data and employ the reconstruction error as a damage-sensitive feature. The findings demonstrate that the proposed model achieves highly accurate damage detection across various structural conditions, despite being trained solely on healthy structural data. Moreover, the model exhibits favourable performance in terms of the number of training parameters and computational effort. Finally, to validate the effectiveness of the model, the results are compared with those of similar studies, demonstrating superior accuracy.
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Allahyari Pargu,O , Nasirpour,G , Asgharzadeh-Bonab,A and Babapour,A . (2026). Data-driven and unsupervised damage detection in frame structures using deep neural networks. Science and Technology in Mechanical Engineering, 4(2), 65-81. doi: 10.22034/stme.2025.532643.1142
MLA
Allahyari Pargu,O , , Nasirpour,G , , Asgharzadeh-Bonab,A , and Babapour,A . "Data-driven and unsupervised damage detection in frame structures using deep neural networks", Science and Technology in Mechanical Engineering, 4, 2, 2026, 65-81. doi: 10.22034/stme.2025.532643.1142
HARVARD
Allahyari Pargu O, Nasirpour G, Asgharzadeh-Bonab A, Babapour A. (2026). 'Data-driven and unsupervised damage detection in frame structures using deep neural networks', Science and Technology in Mechanical Engineering, 4(2), pp. 65-81. doi: 10.22034/stme.2025.532643.1142
CHICAGO
O Allahyari Pargu, G Nasirpour, A Asgharzadeh-Bonab and A Babapour, "Data-driven and unsupervised damage detection in frame structures using deep neural networks," Science and Technology in Mechanical Engineering, 4 2 (2026): 65-81, doi: 10.22034/stme.2025.532643.1142
VANCOUVER
Allahyari Pargu O, Nasirpour G, Asgharzadeh-Bonab A, Babapour A. Data-driven and unsupervised damage detection in frame structures using deep neural networks. STME. 2026;4(2):65-81 (In Persian). doi: 10.22034/stme.2025.532643.1142
Science and Technology in Mechanical Engineering