A machine learning-based genetic programming approach for the sustainable production of plastic sand paver blocks

Plastic sand paver blocks (PSPB) provide a sustainable alternative by reprocessing plastic waste and decreasing reliance on environmentally hazardous materials such as concrete. They promote waste management and environmentally favorable building practices. This paper presents a novel method for estimating the compressive strength (CS) of plastic sand paver blocks based on gene expression programming (GEP) techniques. The database collected from the experimental work comprises 135 compressive strength results. Seven input parameters were involved in predicting the CS of PSPB, namely, plastic, sand, sand size, fiber percentage, fibre length, fibre diameter, and tensile strength of the fibre. Simplified mathematical expressions were used to figure out the CS. The results of GEP formulations showed that they were better in line with the experimental data, with R² values for CS of 0.89 (training) and 0.88 (testing). The models' performance was evaluated using sensitivity analysis and statistical checks. The statistical evaluations show that the actual and predicted values are closer together, which lends credence to the GEP model's capacity to forecast PSPB CS. The sensitivity analysis showed that sand size and fibre percentage contribute more than 50% of the CS in PSPB. In addition, the results demonstrate that the proposed models are accurate and have a robust capacity for generalization and prediction. This research can improve environmental protection and economic benefit by enhancing the reuse of PSPB in producing green ecosystems.