Probabilistic Machine-Learning Methods for Performance Prediction of Structure and Infrastructures through Natural Gradient Boosting-Reference-Cited by-同舟云学术

Probabilistic Machine-Learning Methods for Performance Prediction of Structure and Infrastructures through Natural Gradient Boosting

Published:2022-08 Issue:8 Volume:148 Page:
ISSN:0733-9445
Container-title:Journal of Structural Engineering
language:en
Short-container-title:J. Struct. Eng.

Author:

Chen Shi-Zhi¹,Feng De-Cheng²^ORCID,Wang Wen-Jie³,Taciroglu Ertugrul⁴

Affiliation:

1. Associate Professor, School of Highway, Chang’an Univ., Xi’an, Shaanxi 710064, China.

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

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

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

Publisher

American Society of Civil Engineers (ASCE)

Subject

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

Link

https://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29ST.1943-541X.0003401

Reference63 articles.

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4. Optimal DCFP bearing properties and seismic performance assessment in nondimensional form for isolated bridges

5. Optimal sliding friction coefficients for isolated viaducts and bridges: A comparison study

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