Environmental Risk Mitigation via Deep Learning Modeling of Compressive Strength in Green Concrete Incorporating Incinerator Ash

Green concrete uses incinerator ash or lightweight ash as a substitute for cement. It retains the properties of conventional concrete. Initial laboratory tests have determined the optimum mix design, weight variation, and compressive strength. Defined as an environmentally friendly material, green concrete reduces pollution or improves environmental conditions during production. This study incorporates incinerator ash, a toxic byproduct of waste disposal, into concrete production through a phased laboratory and numerical approach. A database for deep learning modeling was created using Convolutional Neural Networks (CNNs) and the Multi-Verse Optimizer (MVO) algorithm. After evaluating the efficiency and structure of the deep learning model through MATLAB coding, the focus shifted to analyzing the sensitivity of the input parameters on the output parameter using MATLAB for coding, training, and evaluation. The initial results indicate a significant effect of incinerator ash on the compressive strength of concrete. In addition, the deep learning modeling results show that the regression coefficient (R) of 90% reflects the accuracy and efficiency of the deep learning model for the current mix design. The error index, which is also reported, shows that the applied deep learning modeling method achieves optimal performance, with an average error of 0.14. The sensitivity analysis results of the introduced optimal model show that among the five input parameters, cement weight (W) has the greatest influence on compressive strength, as indicated by the statistical group distances from the baseline, percentage values, and average values.