Sustainable Material Properties Driven by the Data Using Mathematical and Machine Learning Optimization Techniques

Sustainable material development is becoming more data-driven with mathematical modeling and machine learning incorporating properties such as durability, strength and environmental impact into the equation. This research is devoted to a hybrid approach to predicting the design of environmentally responsible materials using optimization techniques. Conventional methods often include trial and error experiments or linear modeling, which take time, are not as accurate and do not account for complex and nonlinear relationships between material parameters. They usually don't allow optimization of more than one conflicting objective (e.g. strength vs. carbon footprint) at the same time. To solve these problems, this paper proposes a Multi-Objective Optimization framework (Genetic Algorithms (GA) - Machine Learning (ML) Regression Models). The ML model is used to learn from past datasets of materials to predict performance indicators and the GA is used to explore the design space to simultaneously find the compositions of materials that meet the design criteria of sustainability and performance. This methodology is applied for optimize the eco-concrete mix design in construction industry, with regard to reduce embodied CO? emissions with the same mechanical strength requirements. The proposed system significantly improves environmental and structural metrics, offering a data-driven pathway to sustainable material innovation.