A machine learning and game theory-based approach for predicting creep behavior of recycled aggregate concrete-Reference-Cited by-同舟云学术

A machine learning and game theory-based approach for predicting creep behavior of recycled aggregate concrete

Published:2022-12 Issue: Volume:17 Page:e01653
ISSN:2214-5095
Container-title:Case Studies in Construction Materials
language:en
Short-container-title:Case Studies in Construction Materials

Author:

Feng Jinpeng,Zhang Haowei,Gao Kang,Liao Yuchen,Yang Jie,Wu Gang

Publisher

Elsevier BV

Subject

Materials Science (miscellaneous)

Reference59 articles.

1. Evaluation of global energy performance of building walls integrating PCM: numerical study in semi-arid climate in Morocco;Salihi;Case Stud. Constr. Mater.,2022

2. Policy and management of carbon peaking and carbon neutrality: a literature review;Wei;Engineering,2022

3. Future directions for the application of zero carbon concrete in civil engineering – a review;Wasim;Case Stud. Constr. Mater.,2022

4. The carbon footprint of normal and high-strength concrete used in low-rise and high-rise buildings;Fantilli;Case Stud. Constr. Mater.,2019

5. Recycling and reuse of construction and demolition waste: from the perspective of national natural science foundation-supported research and research-driven application;Xu;Case Stud. Constr. Mater.,2022

Cited by 19 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Comparative study on the prediction of the unconfined compressive strength of the one-part geopolymer stabilized soil by using different hybrid machine learning models;Case Studies in Construction Materials;2024-12

2. Machine learning-based prediction method for drying shrinkage of recycled aggregate concrete;Journal of Building Engineering;2024-11

3. Machine learning models to predict sewer concrete strength exposed to sulfide environments: unveiling the superiority of Bayesian-optimized prediction models;Multiscale and Multidisciplinary Modeling, Experiments and Design;2024-08-21

4. Interpretable Machine Learning Models for Prediction of UHPC Creep Behavior;Buildings;2024-07-07

5. A Creep Model of Steel Slag–Asphalt Mixture Based on Neural Networks;Applied Sciences;2024-07-03