Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Post-Earthquake Damage Assessment of Building Based on Deep Learning
Evaluation of the severity of structural damage caused by earthquakes is time-consuming using labor-intensive approaches. Deep learning methods have been increasingly used to detect and classify image damages, whereas few studies have been applied to assess the level of damaged structural images such as no, light, moderate, or severe damages under earthquakes. The goal of this paper is to propose an approach to automatically categorize the structures using convolutional neural networks (CNNs). Two pre-trained CNNs, namely InceptionV3 and Xception were employed. Adaptive moment estimation algorithm (Adam) was adopted to optimize the parameters of deep learning models. A gradient-weighted class activation mapping (Grad-CAM) was applied for locating damages. The testing results highlighted that InceptionV3 and Xception algorithms showed a high performance with 86.67% and 88.33% accuracy, respectively. The Grad-CAM located successfully the actual damages to structures. It depicts the ability to use CNNs for the assessment of structural damages using images.
Post-Earthquake Damage Assessment of Building Based on Deep Learning
Evaluation of the severity of structural damage caused by earthquakes is time-consuming using labor-intensive approaches. Deep learning methods have been increasingly used to detect and classify image damages, whereas few studies have been applied to assess the level of damaged structural images such as no, light, moderate, or severe damages under earthquakes. The goal of this paper is to propose an approach to automatically categorize the structures using convolutional neural networks (CNNs). Two pre-trained CNNs, namely InceptionV3 and Xception were employed. Adaptive moment estimation algorithm (Adam) was adopted to optimize the parameters of deep learning models. A gradient-weighted class activation mapping (Grad-CAM) was applied for locating damages. The testing results highlighted that InceptionV3 and Xception algorithms showed a high performance with 86.67% and 88.33% accuracy, respectively. The Grad-CAM located successfully the actual damages to structures. It depicts the ability to use CNNs for the assessment of structural damages using images.
Post-Earthquake Damage Assessment of Building Based on Deep Learning
Lecture Notes in Civil Engineering
Reddy, J. N. (Herausgeber:in) / Wang, Chien Ming (Herausgeber:in) / Luong, Van Hai (Herausgeber:in) / Le, Anh Tuan (Herausgeber:in) / Le, Luong V. (Autor:in) / Nguyen, Nhi V. (Autor:in) / Nguyen, Liem C. (Autor:in) / Luu, Cong Q. (Autor:in) / Tran, Uyen H. P. (Autor:in) / Nguyen, Tan-No (Autor:in)
The International Conference on Sustainable Civil Engineering and Architecture ; 2023 ; Da Nang City, Vietnam
Proceedings of the Third International Conference on Sustainable Civil Engineering and Architecture ; Kapitel: 162 ; 1515-1522
12.12.2023
8 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Post-Earthquake Damage Assessment—Case Study of the Educational Building after the Zagreb Earthquake
| DOAJ | 2021
Post-earthquake damage assessment of buildings – procedure for conducting building inspections
| DOAJ | 2021
GIS Loss Estimation and Post-Earthquake Assessment of Residential Building Damage
| British Library Conference Proceedings | 2002
Post-earthquake damage assessment of buildings
| British Library Conference Proceedings | 2002