Xiaoying Zhuang1,2,*, Shuai Zhou3,4
CMC-Computers, Materials & Continua, Vol.59, No.1, pp. 57-77, 2019, DOI:10.32604/cmc.2019.04589
Abstract Advances in machine learning (ML) methods are important in industrial engineering and attract great attention in recent years. However, a comprehensive comparative study of the most advanced ML algorithms is lacking. Six integrated ML approaches for the crack repairing capacity of the bacteria-based self-healing concrete are proposed and compared. Six ML algorithms, including the Support Vector Regression (SVR), Decision Tree Regression (DTR), Gradient Boosting Regression (GBR), Artificial Neural Network (ANN), Bayesian Ridge Regression (BRR) and Kernel Ridge Regression (KRR), are adopted for the relationship modeling to predict crack closure percentage (CCP). Particle Swarm Optimization (PSO) More >
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