Optimizing sustainability and resilience of composite construction materials using life cycle assessment and advanced artificial intelligence techniques

The need for sustainable and resilient composite construction materials that can cope with increasing environmental and structural demands of modern construction is becoming urgently critical. The proposed model will handle the lack of sustainability and mechanical performance of the existing approaches. Specifically, they are not capable of dynamically adapting up to the changing environmental conditions and the intrinsic complexity of optimizing the material properties for the composites. To overcome these constraints, the present study develops an innovative multimethod framework by integrating the implementation of several state-of-the-art optimization and machine-learning techniques in order to enhance the sustainability and resilience of composite materials. The work is initialized by proposing a Multiple Objective Genetic Algorithm (MOGA) with dynamic fitness functions for the optimization of material designs, by balancing environmental impacts with mechanical performance in real time. This approach, hence, fits different environmental conditions and material requirements at the same time while importantly enhancing the design stage itself. At the same time, Gaussian Process Regression is the method that enables future LCA outcome prognoses undertaken using RL; it is possible to deal with the sustainability prediction as uncertain, and hence it is incorporated in the ongoing process of material optimization. In this way, RL will adaptively optimize processing parameters for the manufacturing of composites: both material resilience and goals regarding sustainability are realized through self-learning. Finally, a hybrid Particle Swarm Optimization (PSO) and Simulated Annealing (SA) algorithm is introduced to probe and polish the solution space for composite material designs to leap over local optima hurdles. The overall improvement in the integrated attributes is 15% of the carbon footprint decrease, 20% in the tensile strength, and 12% decrease in energy consumption during processing. This study exemplifies one of the outstanding novel designs of composite materials, offering dynamism, adaptiveness, and robustness in enhancement of sustainability and resilience parameters in the process.