Ala’a R. Al-Shamasneh1, Faten Khalid Karim2, Arsalan Mahmoodzadeh3,*
CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 281-303, 2025, DOI:10.32604/cmc.2025.065748
- 09 June 2025
Abstract Soilcrete is a composite material of soil and cement that is highly valued in the construction industry. Accurate measurement of its mechanical properties is essential, but laboratory testing methods are expensive, time-consuming, and include inaccuracies. Machine learning (ML) algorithms provide a more efficient alternative for this purpose, so after assessment with a statistical extraction method, ML algorithms including back-propagation neural network (BPNN), K-nearest neighbor (KNN), radial basis function (RBF), feed-forward neural networks (FFNN), and support vector regression (SVR) for predicting the uniaxial compressive strength (UCS) of soilcrete, were proposed in this study. The developed models… More >
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