A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Automated damage diagnosis of concrete jack arch beam using optimized deep stacked autoencoders and multi-sensor fusion
A novel hybrid framework of optimized deep learning models combined with multi-sensor fusion is developed for condition diagnosis of concrete arch beam. The vibration responses of structure are first processed by principal component analysis for dimensionality reduction and noise elimination. Then, the deep network based on stacked autoencoders (SAE) is established at each sensor for initial condition diagnosis, where extracted principal components and corresponding condition categories are inputs and output, respectively. To enhance diagnostic accuracy of proposed deep SAE, an enhanced whale optimization algorithm is proposed to optimize network meta-parameters. Eventually, Dempster-Shafer fusion algorithm is employed to combine initial diagnosis results from each sensor to make a final diagnosis. A miniature structural component of Sydney Harbour Bridge with artificial multiple progressive damages is tested in laboratory. The results demonstrate that the proposed method can detect structural damage accurately, even under the condition of limited sensors and high levels of uncertainties.
Automated damage diagnosis of concrete jack arch beam using optimized deep stacked autoencoders and multi-sensor fusion
A novel hybrid framework of optimized deep learning models combined with multi-sensor fusion is developed for condition diagnosis of concrete arch beam. The vibration responses of structure are first processed by principal component analysis for dimensionality reduction and noise elimination. Then, the deep network based on stacked autoencoders (SAE) is established at each sensor for initial condition diagnosis, where extracted principal components and corresponding condition categories are inputs and output, respectively. To enhance diagnostic accuracy of proposed deep SAE, an enhanced whale optimization algorithm is proposed to optimize network meta-parameters. Eventually, Dempster-Shafer fusion algorithm is employed to combine initial diagnosis results from each sensor to make a final diagnosis. A miniature structural component of Sydney Harbour Bridge with artificial multiple progressive damages is tested in laboratory. The results demonstrate that the proposed method can detect structural damage accurately, even under the condition of limited sensors and high levels of uncertainties.
Automated damage diagnosis of concrete jack arch beam using optimized deep stacked autoencoders and multi-sensor fusion
Yang Yu (author) / Jiantao Li (author) / Jianchun Li (author) / Yong Xia (author) / Zhenghao Ding (author) / Bijan Samali (author)
2023
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
Damage‐sensitive feature extraction with stacked autoencoders for unsupervised damage detection
| Wiley | 2021
Jack Arch Bridges - Investigation and Rating
| British Library Conference Proceedings | 1997
| DOAJ | 2023