Bridging the gap between data generation and utilization: a social network-based framework for highway infrastructure management

The increasing volume of data generated within the construction sector, particularly in highway infrastructure projects, often lags in effective utilization, resulting in inefficiencies that hinder informed decision-making. This study investigates the gap between data generation and its use in the post-construction phase of National Highway Authority of India (NHAI) projects. To address the gaps, a structured social network-based framework, operationalized through adjacency matrix construction and density analysis using Gephi software (version 0.10.1), was employed to analyze connections among 22 data, 12 information, 5 knowledge, and 10 decision attributes. These attributes were categorized as active, inactive, or missing pathways to identify inefficiencies and opportunities for improved data integration. The analysis revealed critical gaps in infrastructure management: as-built documentation exhibited only 29.41% active utilization with 41.18% missing paths; sensor data showed 42.86% missing paths; and emergency response planning demonstrated 38.89% active utilization alongside 38.89% missing paths. Conversely, user behavior trends achieved 83.33% active utilization, demonstrating effective operational integration. The framework identified that 48.2% of raw data pathways, 58.3% of information pathways, 49.0% of knowledge pathways, and 54.0% of decision pathways are actively utilized. Advanced technologies such as predictive analytics, real-time monitoring systems, and centralized documentation through digital twins and Building Information Modeling (BIM) are proposed to transform inactive and missing pathways into active ones, thereby enhancing data-driven decision-making. The framework offers adaptability for diverse infrastructure contexts and highlights strategies for improving resource allocation, maintenance planning, and transparency. Despite challenges from data quality variations and organizational readiness, addressing these data gaps is critical to ensuring resilience, efficiency, and long-term success in infrastructure management.