Synergizing GIS and genetic algorithms to enhance road management and fund allocation with a comprehensive case study approach

• Published in: Scientific reports Vol. 15; no. 1; pp. 4634 - 21
• Main Authors: Mohamed, Ahmed Gouda, Alqahtani, Fahad K., Ismail, ElHassan Reda, Nabawy, Mohamed
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 07.02.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166; 639/705; Algorithms; Asset management; Decision making; Fund allocation; Genetic algorithms; Geographic information system; Geographic information systems; Humanities and Social Sciences; Infrastructure; Maintenance and repair; multidisciplinary; Pavement condition index; Remote sensing; Roads & highways; Science; Science (multidisciplinary); Pavement condition index; Asset management; Geographic information system; Fund allocation; Maintenance and repair
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-025-88760-4

This study identifies a critical knowledge gap, revealing how the deterioration of roads, compounded by extensive usage and additional factors, poses significant risks to the road networks’ functionality. Without a robust fund allocation and prioritization strategy, the extent of this risk may be overlooked, adversely affecting the performance of essential infrastructure elements. Our research introduces an integrated decision-making model for existing road infrastructures to address this gap. This innovative approach combines a Geographic Information System (GIS)-based road management model with a fund allocation prioritization strategy, enhanced by an optimization engine via a genetic algorithm. The primary aim is to precisely determine Maintenance and Repair (M&R) interventions tailored to the condition states, thereby improving the Pavement Condition Index (PCI) of the road segments. The research is structured around three key objectives: (1) develop a detailed GIS-based road management database incorporating inspection data and attributes of road infrastructure for proactive M&R decision-making; (2) efficiently allocate funds to maintain service delivery on deteriorated roads; and (3) pinpoint the optimal type and timing of M&R interventions to boost the condition and performance of the road segments. Anticipated results will provide asset managers with a comprehensive decision support system for executing effective M&R practices.