Research Foundation of Education Bureau of Human Province; National Nature Science Foundation of China [52178108, 52478128]; Natural Science Foundation of Hunan Province [2023JJ50180, 2024JJ5033]; Science and Technology Innovation Program of Hunan Province [2022RC1181]; Shenzhen Science and technology planning Project [GJGJZD20220517141800001]; [23B0575]
机构署名：
本校为第一且通讯机构
院系归属：
土木工程学院
摘要：
Traditional physical-driven modal methods are inappropriate for damage diagnosis of long-span flexible structures with complex mechanical behaviour. This study develops a deep Convolutional Neural Network-based damage diagnosis method for in-service bridges by using dynamic responses under moving loads. The dynamic responses were collected from the critical points on the girders of a cable-stayed bridge specimen under vehicle loading. These collected data was transformed into images based on Gramian Angular Field and Markov Transition Field (MTF). A deep learning algorithm based on VGG-19 was ...
