An integrated decision support system for strategic budget/resource allocation in bridge infrastructure management based on digital technologies

Purpose This study aims to develop a digital decision support system (DSS) for strategic budget allocation in bridge infrastructure maintenance, improving lifecycle cost efficiency and sustainability through data-driven planning. Design/methodology/approach The proposed system integrates building information modeling (BIM), a web-based data management platform and a genetic algorithm (GA) to automate and optimize maintenance and repair (M&R) planning. BIM models were created in Autodesk Navisworks and linked via API to a role-based web server. The GA was implemented to generate cost-effective M&R schedules by simulating multiple scenarios and evaluating trade-offs between cost and performance improvement. The system was validated using expert opinion from 12 participants through a structured questionnaire. Findings The expert evaluation indicated significant improvements over traditional methods. The DSS scored high in sustainability (4.69/5), labor savings (4.58/5) and time efficiency (4.41/5), while accuracy (3.89/5) and maintenance knowledge utilization (4.03/5) also showed positive results. The system demonstrated the ability to extend infrastructure lifespan, improve planning efficiency and support proactive budget allocation. Practical implications The DSS can be adapted to other infrastructure types and regional contexts, offering a practical solution for improving maintenance efficiency, transparency and cost control in asset management. Originality/value This study presents an integrated and scalable infrastructure maintenance framework that bridges digital modeling, web-based collaboration and heuristic optimization. Unlike existing tools, the DSS enables real-time data updates, visualization and scenario-based planning, supporting both strategic and operational decisions.