AI-Driven Thermal Radiations Monitoring System for Sustainable Infrastructure

Abstract

Managing thermal efficiency in urban infrastructure is a critical component of environmental sustainability. Excessive thermal radiation, often resulting in "Urban Heat Islands," leads to increased energy consumption and structural degradation. This paper proposes an AI-driven monitoring system designed to detect and analyze thermal radiation patterns in real-time. By utilizing Machine Learning (ML) algorithms, specifically Random Forest and Convolutional Neural Networks, the system predicts heat accumulation and optimizes cooling interventions. The study utilizes secondary data and simulated datasets to demonstrate how automated systems can identify structural fatigue caused by heat stress. Experimental data indicates that AI-mediated monitoring can improve thermal energy efficiency by 18.4%, directly supporting United Nations SDG 11 and SDG 13. This research discusses the technical architecture, mathematical modeling, and the future of smart, heat-resilient cities.