Explainable Machine-Learning Model for Rapid Damage Assessment of CFST Columns after Close-In Explosion-Reference-Cited by-同舟云学术

Explainable Machine-Learning Model for Rapid Damage Assessment of CFST Columns after Close-In Explosion

Published:2024-06 Issue:3 Volume:38 Page:
ISSN:0887-3828
Container-title:Journal of Performance of Constructed Facilities
language:en
Short-container-title:J. Perform. Constr. Facil.

Author:

Li Jie¹,Pang Yanfen²,Wang Kansheng³,Zhang Xuejie⁴,Wang Ning⁵

Affiliation:

1. Associate Professor, Tianjin Key Laboratory of Civil Structure Protection and Reinforcement, Tianjin Chengjian Univ., Tianjin 300384, China.

2. Postgraduate Student, School of Civil Engineering, Tianjin Chengjian Univ., Tianjin 300384, China.

3. Teaching Assistant, Hebei Univ. of Water Resources and Electric Engineering, Cangzhou 061001, China.

4. Associate Professor, Tianjin Key Laboratory of Civil Structure Protection and Reinforcement, Tianjin Chengjian Univ., Tianjin 300384, China (corresponding author).

5. Lecturer, College of Civil Engineering and Architecture, Wenzhou Univ., Tianjin 300384, China.

Publisher

American Society of Civil Engineers (ASCE)

Link

https://ascelibrary.org/doi/pdf/10.1061/JPCFEV.CFENG-4592

Reference40 articles.

1. Dynamic progressive buckling of circular and square tubes

2. Machine learning model for predicting structural response of RC columns subjected to blast loading

3. Residual strength of blast damaged reinforced concrete columns

4. Chen T. Q. and C. Guestrin. 2016. “XGBoost: A scalable tree boosting system.” In Proc. 22nd ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining 785–794. San Francisco: Association for Computing Machinery.

5. Failure Analysis and Damage Assessment of RC Columns under Close-In Explosions

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