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This is a Systematic Review study (also classified as: Mixed-Methods).

October 19, 2025Open Access

Strategies for bridge maintenance using BIM: an analysis of methodologies and tools

Key Points

BIM supports preventive, data-driven maintenance strategies for bridges, leading to reduced operating costs and increased efficiency.
Damage visualization and 3-D geometric modeling were the most frequent themes, appearing in a total of 13 studies within the reviewed literature.
A systematic literature review identified barriers to adoption, including interoperability issues and high transitional costs associated with LiDAR technology.
Future research should focus on developing standardized performance metrics and improving interoperability for mainstream BIM adoption in bridge maintenance.

Abstract

Ageing bridge stocks and rising traffic loads in Latin America and worldwide demand cost-effective maintenance strategies. Building Information Modelling (BIM) and its convergence with digital-twin, IoT and AI techniques have shown promise, yet their adoption for bridge upkeep remains fragmentary. This review aimed to (i) synthesise current scientific evidence on BIM-based bridge maintenance, (ii) classify methodologies and tools through a domain taxonomy, and (iii) identify research gaps that hinder large-scale implementation. A PRISMA-guided systematic literature review was conducted in Scopus and Web of Science (search cut-off = 1 February 2024). Inclusion criteria targeted peer-reviewed, open-access studies (2020–2024) that applied BIM to the maintenance of existing bridges. Twenty-five articles met the criteria and were appraised with Mixed-Methods Appraisal Tool (MMAT 2018). Seven dominant research themes were identified, with damage visualization (7 studies) and 3-D geometric modelling (6) being the most frequent, followed by information exchange/management (4). Specifically, LiDAR and photogrammetry enabled sub-centimetre models; Convolutional Neural Networks (CNN) and You Only Look Once (YOLO) algorithms reached mean average precision up to 0.91 for crack detection. Digital-twin workflows reduced operating costs while requiring higher upfront investment. A seven-domain taxonomy and a cost–technology comparison table is proposed. Key barriers reported include IFC 4.3 interoperability, high LiDAR costs ( > 10% of annual budgets), limited visual-programming skills, and cybersecurity concerns in cloud-IoT integrations. BIM supports preventive, data-driven bridge maintenance and has been linked to lower operating costs in several studies; mainstream adoption requires IFC 4.3 based interoperability, targeted training, and open-standard workflows. Future research should focus on standardised performance metrics, edge-AI monitoring and blockchain-secured data exchange.

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