Predicting the Compressive Strength of Environmentally Friendly Concrete Using Multiple Machine Learning Algorithms

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@Article{yang:2024:Buildings,

• author = "Yanhua Yang and Guiyong Liu and Haihong Zhang and Yan Zhang and Xiaolong Yang",
• title = "Predicting the Compressive Strength of Environmentally Friendly Concrete Using Multiple Machine Learning Algorithms",
• journal = "Buildings",
• year = "2024",
• volume = "14",
• number = "1",
• pages = "Article No. 190",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "2075-5309",
• URL = "https://www.mdpi.com/2075-5309/14/1/190",
• DOI = "doi:10.3390/buildings14010190",
• abstract = "Machine learning (ML) algorithms have been widely used in big data prediction and analysis in terms of their excellent data regression ability. However, the prediction accuracy of different ML algorithms varies between different regression problems and data sets. In order to construct a prediction model with optimal accuracy for fly ash concrete (FAC), ML algorithms such as genetic programming (GP), support vector regression (SVR), random forest (RF), extremely gradient boost (XGBoost), backpropagation artificial neural network (BP-ANN) and adaptive network-based fuzzy inference system (ANFIS) were selected as regression and prediction algorithms in this study; the particle swarm optimisation (PSO) algorithm was also used to optimise the structure and hyperparameters of each algorithm. The statistical results show that the performance of the assembled algorithms is better than that of an NN-based algorithm. In addition, PSO can effectively improve the prediction accuracy of the ML algorithms. The comprehensive performance of each model is analysed using a Taylor diagram, and the PSO-XGBoost model has the best comprehensive performance, with R2 and MSE equal to 0.9072 and 11.4546, respectively.",
• notes = "also known as \cite{buildings14010190}",

}

Genetic Programming entries for Yanhua Yang Guiyong Liu Haihong Zhang Yan Zhang Xiaolong Yang

Citations