Optimization and predictive performance of fly ash-based sustainable concrete using integrated multitask deep learning framework with interpretable machine learning techniques.

ABSTRACT: Concrete strength prediction is of great relevance for construction safety and quality assurance; however, these methods often trade-off their accuracy or interpretability, especially when it comes to the use of supplementary cementitious materials like fly ash in process. This study aims to build an interpretable, highly accurate model for predicting the compressive and tensile strength of concrete with a hybrid approach based on gradient boosting (XGBoost), deep neural networks (DNNs), and optimization via AutoGluon Process. The model is put into a multitask learning (MTL) framework that includes mix design variables, environmental factors, and non-destructive testing (NDT) data samples. The interpretation of model predictions is accomplished through SHAP and LIME to quantify global and

SUBMITTER: Nandurkar BP 

PROVIDER: S-EPMC12370992 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature