Compressive strength prediction of high-strength oil palm shell lightweight aggregate concrete using machine learning methods

Promoting the use of agricultural wastes/byproducts in concrete production can significantly reduce environmental effects and contribute to sustainable development. Several experimental investigations on such concrete's compressive strength (f(c)) and behavior have been done. The results of 229 concrete samples made by oil palm shell (OPS) as a lightweight aggregate (LWA) were used to develop models for predicting the f(c) of the high-strength lightweight aggregate concrete (HS - LWAC). To this end, gene expression programming (GEP), adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), and multiple linear regression (MLR) are employed as machine learning (ML) and regression methods. The water-to-binder (W/B) ratio, ordinary Portland cement (OPC), fly ash (FA), silica fume (SF), fine aggregate (Sand), natural coarse aggregate (Gravel), OPS, superplasticizer (SP) contents, and specimen age are among the nine input parameters used in the developed models. The results show that all ML-based models efficiently predict the HS - LWAC's f(c), which comprised OPS agricultural wastes. According to the results, the ANN model outperformed the GEP and ANFIS models. Moreover, an uncertainty analysis through the Monte Carlo simulation (MCS) method was applied to the prediction results. The growing demand for sustainable development and the crucial role of eco-friendly concrete in the construction industry can pave the way for further application of the developed models due to their superior robustness and high accuracy in future codes of practice.