Strategic Deployment of Piezoelectric Energy Harvesting in Smart Urban Infrastructure: A Hybrid Qpfrs, M-SWARA, K-Means Clustering, and Promethee Evaluation for Sustainable Advantage

Abstract

This study develops a hybrid multi-criteria decision-making (MCDM) model to evaluate and prioritize kinetic energy harvesting materials for urban infrastructure, a critical area as cities increasingly adopt sustainable technologies to meet energy demands. The integration of Quadripartitioned Fuzzy Rough Sets (QPFRS), MSWARA, K-Means clustering, and PROMETHEE offers a novel approach for addressing the complexities of material selection by balancing factors such as energy efficiency, durability, cost, scalability, and environmental impact. The strategic importance of this research lies in its potential to guide urban planners and policymakers toward making informed decisions that align with long-term sustainability goals and competitive advantages in smart city development. The findings identify BaTiO3 and ZnO as the most suitable materials for large-scale urban projects, offering superior performance in scalability and environmental sustainability, while materials like PZT exhibit strong energy efficiency but pose environmental concerns. By offering a comprehensive, adaptable evaluation framework, this study contributes both methodologically and practically to the growing field of sustainable urban infrastructure, ensuring that material selection not only meets immediate energy needs but also supports long-term urban development strategies.