Explainable artificial intelligence-based compressive strength optimization and Life-Cycle Assessment of eco-friendly sugarcane bagasse ash concrete

Investigations on the potential use of sustainable sugarcane bagasse ash (SCBA) as a supplementary cementitious material (SCM) in concrete production have been carried out. The paper employs model agnostic eXplainable Artificial Intelligence (XAI) to develop interpretable models for the compressive strength of SCBA concrete. A dataset of SCBA concrete, comprising of 2616 data points, was first established. Then, black box machine learning models were employed for predicting compressive strength of SCBA concrete. White box modelling using model agnostic XAI explanations for the influence of different input parameters on the compressive strength of SCBA concrete was then used for the local and global interpretations of influencing parameters. Based on the relative influence of features on the compressive strength of SCBA concrete, a nonlinear model equation was thus developed. Optimization of SCBA concrete mixes was done and it was found that a compressive strength of 40 MPa could be achieved for water to binder content ranging from 0.42 to 0.48 with low cement content. Additionally, the Life-Cycle Assessment (LCA) was carried out, and finally, it was proposed that SCBA concrete has potential for becoming an alternate eco-friendly building material. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.