Artificial intelligence in social sectors: Designing an innovative Climate-Resilient Road Maintenance System (CEEMS)

The safety and effectiveness of transport networks, critical to social and economic development, depend on Road Maintenance Systems (RMS). The resilience of road infrastructure is increasingly at risk as climate change enhances extreme weather events, especially in developing countries. To overcome this difficulty, this research suggests the Climate-Resilient Road Maintenance System (CRRMS). This cutting-edge method combines climate adaptation techniques with Artificial Intelligence (AI) to improve road maintenance. By capturing temporal relationships in road conditions, CRRMS integrates Adaptive Black-winged kite optimized-Self-Attentive Graph Neural Network (ABW-SAGNN) with sensor inputs and road weather data to estimate maintenance requirements. Preprocessing methods like z-score normalization and Fourier Transform are used on the raw sensor and meteorological data to guarantee high-quality input for the model; significant features are extracted to better understand how climate variables affect road conditions, such as road surface temperature, humidity levels, and precipitation rates. A specially designed simulation program that simulates real-world situations and tests different climate change projections is used to assess the system. It examines the effects of climatic factors and harsh weather on road infrastructure. In key performance measures like resilience to extreme weather events, resource allocation efficiency, and maintenance prediction accuracy, CRRMS performs better than standard models in Mean Absolute Error (MAE) (0.03), Mean Standard Error (MSE) (0.02), and (0.98). The findings show that CRRMS could accurately predict repair requirements, reduce costs, and lessen congestion by taking proactive measures. The strategy emphasizes that AI could be used to solve important issues with road infrastructure, create more climate-resilient transport systems, advance long-term sustainability, and enhance community safety in the face of climate evaluation.