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

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

In this paper, the SHapley Additive exPlanation (SHAP) is utilized in conjunction with the ensemble machine learning (EML) model to study the creep behaviors of recycled aggregate concrete (RAC) for the first time. Five typical EML models, such as Random Forest (RF), Adaptive Boost Machine (AdaBoost...

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Bibliographic Details
Main Authors: Jinpeng Feng, Haowei Zhang, Kang Gao, Yuchen Liao, Jie Yang, Gang Wu
Format: Article
Language:English
Published: Elsevier 2022-12-01
Series:Case Studies in Construction Materials
Subjects:
Recycle aggregate concrete
Low-carbon
Creep prediction
SHAP
Ensemble learning
Bayesian optimization
Online Access:http://www.sciencedirect.com/science/article/pii/S2214509522007859

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