A Computational Framework for Concrete Durability, Strength, and Eco-Environmental Issues Optimization Using Genetic Algorithms

This study introduces a computational framework for multiobjective optimization of concrete curing, specifically designed to address practical construction challenges while simultaneously enhancing durability and compressive strength and, crucially, minimizing environmental and economic impacts. Departing from traditional laboratory-centric methods, our approach employs a realistic curing simulation technique (plastic bagging with intermittent water spray) validated against full water immersion, confirming its applicability for field conditions. A genetic algorithm (GA) is at the core of this framework, uniquely integrating empirically derived functions that model concrete properties, including chloride ion permeability for durability and compressive strength, across a wide range of mix compositions and curing parameters (temperature, duration, relative humidity). A key innovation lies in the GA’s ability to holistically optimize not just technical performance but also critical economic and environmental metrics, such as cement reduction potential and water consumption. Our findings highlight optimal curing strategies that achieve superior concrete performance with reduced resource intensity. Interestingly, the framework revealed that prolonged curing beyond 7 days can paradoxically lead to decreased compressive strength, a phenomenon further investigated and attributed to specific surface chemical reactions supported by Fourier transform infrared spectroscopy analysis. The identified 7 day curing duration emerged as the most balanced solution, demonstrating significant advantages in performance and sustainability. This research offers a pioneering tool for civil engineers, enabling data-driven decision-making for designing truly durable, cost-effective, and eco-friendly concrete structures, thereby providing a substantial contribution to sustainable construction management.