ABSTRACT
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 ([Formula: see text]) and behavior have been done. The results of 229 concrete samples made by oil palm shell ([Formula: see text]) as a lightweight aggregate ([Formula: see text]) were used to develop models for predicting the [Formula: see text] of the high-strength lightweight aggregate concrete ([Formula: see text]). To this end, gene expression programming ([Formula: see text]), adaptive neuro-fuzzy inference system ([Formula: see text]), artificial neural network ([Formula: see text]), and multiple linear regression ([Formula: see text]) are employed as machine learning ([Formula: see text]) and regression methods. The water-to-binder ([Formula: see text]) ratio, ordinary Portland cement ([Formula: see text]), fly ash ([Formula: see text]), silica fume ([Formula: see text]), fine aggregate ([Formula: see text]), natural coarse aggregate ([Formula: see text]), [Formula: see text], superplasticizer ([Formula: see text]) contents, and specimen age are among the nine input parameters used in the developed models. The results show that all [Formula: see text]-based models efficiently predict the [Formula: see text]'s [Formula: see text], which comprised [Formula: see text] agricultural wastes. According to the results, the [Formula: see text] model outperformed the [Formula: see text] and [Formula: see text] 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.