Interpretable machine learning models for the estimation of seismic drifts in CLT buildings-Reference-Cited by-同舟云学术

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Interpretable machine learning models for the estimation of seismic drifts in CLT buildings

Published:2023-07 Issue: Volume:70 Page:106365
ISSN:2352-7102
Container-title:Journal of Building Engineering
language:en
Short-container-title:Journal of Building Engineering

Author:

Junda Eknara,Málaga-Chuquitaype Christian^ORCID,Chawgien Ketsarin

Publisher

Elsevier BV

Subject

Mechanics of Materials,Safety, Risk, Reliability and Quality,Building and Construction,Architecture,Civil and Structural Engineering

Reference71 articles.

1. Behavior of cross-laminated timber diaphragm connections with self-tapping screws;Sullivan;Eng. Struct.,2018

2. Cross-Laminated timber: a survey on design methods and concepts in practice;Schenk;Civil Eng.,2022

3. The Revision of the Timber Chapter of the Eurocode 8 in Proc;Fragiacomo,2019

4. Chapter 8: Design of Timber Structures in Seismic Design of Buildings to Eurocode 8;Málaga-Chuquitaype,2016

5. Cyclic behaviour of typical metal connectors for cross-laminated (CLT) structures;Gavric;Mater. Struct.,2015

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1. Machine-learning-aided regional post-seismic usability prediction of buildings: 2016–2017 Central Italy earthquakes;Journal of Building Engineering;2024-08

2. Machine-Learning Applications in Structural Response Prediction: A Review;Practice Periodical on Structural Design and Construction;2024-08

3. Probabilistic curvature limit states of corroded circular RC bridge columns: Data-driven models and application to lifetime seismic fragility analyses;Earthquake Spectra;2024-07-25

4. Seismic Response Prediction of Rigid Rocking Structures Using Explainable LightGBM Models;Mathematics;2024-07-21

5. Machine learning techniques for diagrid building design: Architectural–Structural correlations with feature selection and data augmentation;Journal of Building Engineering;2024-06

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