Feasibility study and optimization of limestone calcined clay composites for compressive strength using multi-layered explainable artificial intelligence models

Interpretable white box machine learning models to predict the compressive strength of Limestone Calcined Clay (LC 3 ) cementitious composite have been developed and analyzed. For this, a large dataset consisting of more than 4000 numerical instances has been collected from the literature, which has been tuned and normalized. Mainly based on the proportion of the chemical composition of its main ingredients - Limestone (0-31 by %mass of LC 3 ), Calcined Clay (0-70 by %mass of LC 3 ), and Cement (30-100 by %mass of LC 3 ) with a water-to-binder ratio ranging from 0.40 to 0.60, predictive models have been developed for the 28 days compressive strength ( f c ) of LC 3 . The performance of various predictive models is then compared across the dataset, and suggestions are made for their reliability and explainability. The models are further interpretated using multi-layered explanations invoking model-agnostic explainable artificial intelligence (XAI) technique. Optimization and interaction plots have also been generated and shown as three-dimensional (3D) visualization. Percent of calcined clay, cement and the water to binder ratio were found to be the most influencing parameters for the compressive strength of LC 3 . Results of this study indicate that the optimized blends of LC 3 can be expressed with up to 50 % replacement of OPC without any significant compromise in f c , contributing to the development of sustainable cementitious composites. The major advantage of using this framework lies in the model agnostic approach, so different predictive models can be compared in a same manner and thus, this approach is applicable to interpretable forecasting models for binary, ternary, quaternary sustainable cementitious composites.