TECHNOLOGY FOR INFRASTRUCTURE INTELLIGENCE AND RESILIENCE : A REVIEW

Abstract

Modern civil infrastructure faces significant challenges related to inefficient manual inspections, vulnerability to natural hazards, and limitations in realtime monitoring and predictive maintenance. This study synthesizes findings from five key technological domains—3D point cloud, resilience integration in transportation asset management, multimodal deep fusion, digital twin, and selfsensing concrete—to evaluate their transformative potential for infrastructure management. Using a multidisciplinary methodology that combines systematic literature review, quantitative–qualitative data analysis, advanced computational modeling, and multimodal data integration, this research develops a holistic framework for datadriven monitoring and decisionmaking. The analysis reveals substantial improvements in inspection accuracy, early damage detection, maintenance efficiency, and disaster risk mitigation, achieving conditionassessment accuracy of 90–95%, maintenance cost reductions of up to 30%, and accelerated postdisaster inspections through highprecision 3D reconstruction. Synergies across technologies—such as integrating digital twins with selfsensing concrete signals and point cloud data—enable adaptive, predictive, end-to-end structural health monitoring. However, the study also identifies challenges including large data requirements, high computational costs, multimodal integration complexity, and technological gaps in developing regions. Overall, the findings highlight the strategic role of advanced technologies in developing intelligent, resilient, and sustainable infrastructure, while offering recommendations for data standardization, advanced AI adoption, and largescale implementation for future transportation networks.