Design of sustainable mortar incorporating construction and demolition waste through adaptive experiments accelerated by machine learning

Traditional approaches to designing sustainable construction materials are slow, resource-intensive, and heavily reliant on trial and error. In this study, we present an integrated framework that combines experiments, machine learning (ML), and Bayesian optimization (BO) to drastically accelerate the materials design. A key component of the framework is an ML model that predicts 28-day compressive strength from mixture composition and early-age mechanical properties, which allows nearly 10x acceleration of classical BO-driven design cycles. We demonstrate the potential of this new framework on the designing sustainable mortars containing construction and demolition waste with minimal ordinary Portland cement (OPC) contents and late-age compressive strength satisfying standard requirements. Following the model guidance, we predict, fabricate, and test new mixtures with a lower OPC content and compressive strength above . Experimental fabrication and testing of the optimized mixtures confirmed the accuracy of the ML predictions while correlations between mixture components and compressive strength agree with published literature. • Develop novel framework combining ML and Bayesian optimization to accelerate sustainable CDW mortar design. • Predict 28-day strength from 3-day strength using XGBoost model, which enables a 10x faster design optimization cycle. • Optimized mixtures achieve 50% OPC reduction while maintaining a target compressive strength (no less than 40 MPa). • Water-to-cement ratio identified as key parameter for strength optimization in CDW mortars.