Interpretable XGBoost-SHAP Machine-Learning Model for Shear Strength Prediction of Squat RC Walls-Reference-Cited by-同舟云学术

Interpretable XGBoost-SHAP Machine-Learning Model for Shear Strength Prediction of Squat RC Walls

Published:2021-11 Issue:11 Volume:147 Page:04021173
ISSN:0733-9445
Container-title:Journal of Structural Engineering
language:en
Short-container-title:J. Struct. Eng.

Author:

Feng De-Cheng¹^ORCID,Wang Wen-Jie²,Mangalathu Sujith³,Taciroglu Ertugrul⁴^ORCID

Affiliation:

1. Associate Professor, Key Laboratory of Concrete and Prestressed Concrete Structures of the Ministry of Education, Southeast Univ., Nanjing 211189, China (corresponding author). ORCID: .

2. Graduate Student, School of Civil Engineering, Southeast Univ., Nanjing 211189, China.

3. Research Scientist, Data Analytics Division, Mangalathu, Mylamkulam, Kottarakara, Kollam, Kerala 691507, India.

4. Professor, Dept. of Civil and Environmental Engineering, Univ. of California, Los Angeles, Los Angeles, CA 90095. ORCID: .

Publisher

American Society of Civil Engineers (ASCE)

Subject

Mechanical Engineering,Mechanics of Materials,General Materials Science,Building and Construction,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29ST.1943-541X.0003115

Reference41 articles.

1. Robust Pixel-Level Crack Detection Using Deep Fully Convolutional Neural Networks

2. Permutation importance: a corrected feature importance measure

3. Chen T. 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. New York: Association for Computing Machinery.

4. Seismic Performance Parameter Quantification of Shear-Critical Reinforced Concrete Masonry Squat Walls

5. Metaresearching Structural Engineering Using Text Mining: Trend Identifications and Knowledge Gap Discoveries

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