Development of an Optimized Condition Estimation Model for Bridge Components Using Data-Driven Approaches-Reference-Cited by-同舟云学术

Development of an Optimized Condition Estimation Model for Bridge Components Using Data-Driven Approaches

Published:2023-06 Issue:3 Volume:37 Page:
ISSN:0887-3828
Container-title:Journal of Performance of Constructed Facilities
language:en
Short-container-title:J. Perform. Constr. Facil.

Author:

Chang Taeyeon¹^ORCID,Lee Gyueun¹^ORCID,Chi Seokho²^ORCID

Affiliation:

1. Graduate Student, Dept. of Civil and Environment Engineering, Seoul National Univ., 1 Gwanak-Ro, Gwanak-Gu, Seoul 08826, Republic of Korea. ORCID: .

2. Professor, Dept. of Civil and Environment Engineering, Seoul National Univ., 1 Gwanak-Ro, Gwanak-Gu, Seoul 08826, Republic of Korea; Adjunct Professor, The Institute of Construction and Environmental Engineering (ICEE), 1 Gwanak-Ro, Gwanak-Gu, Seoul 08826, Republic of Korea (corresponding author). ORCID: .

Publisher

American Society of Civil Engineers (ASCE)

Subject

Safety, Risk, Reliability and Quality,Building and Construction,Civil and Structural Engineering

Link

https://ascelibrary.org/doi/pdf/10.1061/JPCFEV.CFENG-4359

Reference43 articles.

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2. Bansal S. 2018. “Data science trends on Kaggle.” Accessed April 1 2022. https://www.kaggle.com/code/shivamb/data-science-trends-on-kaggle/notebook.

3. Use of Recursive Partitioning to Predict National Bridge Inventory Condition Ratings from National Bridge Elements Condition Data

4. Using Classification Trees for Predicting National Bridge Inventory Condition Ratings

5. Estimating the Future Condition of Highway Bridge Components Using National Bridge Inventory Data

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