Design and Optimization of Smart Engineering Systems for Sustainable Infrastructure Development in Urban Environments

Abstract

The rapid pace of urbanization over the last few decades has driven substantial demand for intelligent, sustainable urban infrastructure systems, resulting in problems such as overconsumption of energy resources, increased traffic congestion, inefficient resource use, and growing air pollution. In this study, a smart engineering framework for designing and optimizing sustainable urban infrastructure systems using Internet of Things (IoT), artificial intelligence (AI), and optimization-based engineering approaches is presented. It combines smart monitoring systems with data analysis and prediction capabilities, along with highly energy-efficient operational models, to improve infrastructure performance. To test the feasibility of the proposed smart engineering approach in transportation, energy, and water infrastructure systems, a simulation-based method was used. Multi-objective optimization algorithms were incorporated to reduce operational costs, energy consumption, and carbon emissions while maximizing the system performance. The proposed smart engineering system achieved a 28.4% reduction in energy consumption, 31.7% decrease in traffic delay, and 24.6% improvement in resource utilization efficiency. In addition, carbon emissions were reduced by approximately 19.3%, while overall operational reliability increased to 94.8%. These results suggest that incorporating smart engineering systems into sustainable infrastructure design is highly effective in promoting urban resilience, economic viability, and environmental sustainability, offering valuable guidance for urban planners, policy-makers, and engineers in planning and constructing future-generation smart cities. The result of this study also emphasizes the critical roles of data-based optimization and real-time monitoring in achieving long-term, sustainable city development goals.