Information Fusion and Finite Element Model Simulations for Bridge Condition Prognosis with Conflicting Data-Reference-Cited by-同舟云学术

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Information Fusion and Finite Element Model Simulations for Bridge Condition Prognosis with Conflicting Data

Published:2022-03-07 Issue: Volume: Page:
ISSN:
Container-title:Construction Research Congress 2022
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Author:

Sun Zhe¹²³,Shi Ying¹²³,Tang Pingbo¹²³

Affiliation:

1. Faculty of Architecture, Civil and Transportation Engineering, Beijing Univ. of Technology, Beijing, PR China.

2. Dept. of Civil and Environmental Engineering, Carnegie Mellon Univ., Pittsburgh, PA.

3. Associate Professor, Dept. of Civil and Environmental Engineering, Carnegie Mellon Univ., Pittsburgh, PA.

Publisher

American Society of Civil Engineers

Link

http://ascelibrary.org/doi/pdf/10.1061/9780784483954.046

Reference28 articles.

1. Integrated field and office tools for bridge management;Adams T. M.;Transportation Research Record,2005

2. 3D FEM Analysis of Pounding Response of Bridge Structures at a Canyon Site to Spatially Varying Ground Motions

3. Cao H. Tian Y. Lei J. Tan X. Gao D. Kopsaftopoulos F. and Chang F. K. (2017). “Deformation data recovery based on compressed sensing in bridge structural health monitoring.” Structural Health Monitoring 2017: Real-Time Material State Awareness and Data-Driven Safety Assurance - Proceedings of the 11th International Workshop on Structural Health Monitoring IWSHM 2017 1(174) 888–895.

4. Chen, F.-C., and Jahanshahi, M. R. (2018a). “Video-based crack detection using deep learning and Nave Bayes data fusion. 18.

5. NB-CNN: Deep Learning-Based Crack Detection Using Convolutional Neural Network and Naïve Bayes Data Fusion

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