Supervised machine learning driven analysis of compressive strength in concrete incorporating sustainable sludge with SHAP and PDP analyses

Due to the increasing demand for ready-made clothing, wastewater treatment facilities are producing large amounts of textile effluent sludge (TES), which is often dumped as backfill and contributes to groundwater and land contamination. Compressive strength (CS), a vital property of concrete, is usually measured through experimental tests that are both costly and time-consuming. To promote eco-friendly construction, this study investigates the mechanical, microstructural, environmental, and machine learning (ML) characteristics of concrete containing various TES percentages. The significance of this research lies in integrating TES as a partial cement and aggregate replacement with ML-based compressive strength prediction, providing a rapid cost cost-effective alternative to traditional experimental testing. Random forest (RF), linear regression (LR), extreme gradient boosting (XGB), and support vector regression (SVR) models were developed using 253 datasets from previous studies, with the RF model achieving superior performance with R² values of 0.927 for training and 0.915 for testing. Analysis revealed that 15% TES replacement reduced compressive strength by 25.21% after 28 days while decreasing the environmental footprint by 13.90%, indicating an optimal balance between sustainability and performance. SEM observations confirmed increased porosity and ettringite formation in TES-modified concrete, correlating with the slight reduction in strength. Overall, this study presents an innovative, eco-friendly concrete solution that transforms hazardous TES into a valuable construction material while enabling reliable ML-based property prediction.