Open Access

ARTICLE

Harnessing Machine Learning for Superior Prediction of Uniaxial Compressive Strength in Reinforced Soilcrete

Ala’a R. Al-Shamasneh¹, Faten Khalid Karim², Arsalan Mahmoodzadeh^3,*

1 Department of Computer Science, College of Computer & Information Sciences, Prince Sultan University, Rafha Street, Riyadh, 11586, Saudi Arabia
2 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
3 Center of Research and Strategic Studies, Lebanese French University, Erbil, 44001, Iraq

* Corresponding Author: Arsalan Mahmoodzadeh. Email:

Computers, Materials & Continua 2025, 84(1), 281-303. https://doi.org/10.32604/cmc.2025.065748

Received 21 March 2025; Accepted 27 April 2025; Issue published 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 in this study were optimized using an optimization technique, gradient descent (GD), throughout the analysis (direct optimization for neural networks and indirect optimization for other models corresponding to their hyperparameters). After doing laboratory analysis, data pre-preprocessing, and data-processing analysis, a database including 600 soilcrete specimens was gathered, which includes two different soil types (clay and limestone) and metakaolin as a mineral additive. 80% of the database was used for the training set and 20% for testing, considering eight input parameters, including metakaolin content, soil type, superplasticizer content, water-to-binder ratio, shrinkage, binder, density, and ultrasonic velocity. The analysis showed that most algorithms performed well in the prediction, with BPNN, KNN, and RBF having higher accuracy compared to others (R² = 0.95, 0.95, 0.92, respectively). Based on this evaluation, it was observed that all models show an acceptable accuracy rate in prediction (RMSE: BPNN = 0.11, FFNN = 0.24, KNN = 0.05, SVR = 0.06, RBF = 0.05, MAD: BPNN = 0.006, FFNN = 0.012, KNN = 0.008, SVR = 0.006, RBF = 0.009). The ML importance ranking-sensitivity analysis indicated that all input parameters influence the UCS of soilcrete, especially the water-to-binder ratio and density, which have the most impact.

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