Roadway Culvert Asset Management: Systematic Review and Future Research Directions

Culverts are essential for the sustainability and safety of transportation infrastructure. Despite their significance, they are often insufficiently managed, leading to significant socioeconomic and environmental repercussions. This systematic review critically evaluates the current state of roadway culvert asset management (RCAM), focusing on inspection technologies, factors influencing culvert deterioration, and predictive models for condition forecasting. Using the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) methodology, this review synthesizes findings from 86 key studies published between 1988 and 2023, sourced from Scopus, ScienceDirect, and Google Scholar. By employing scientometric mapping research trends and keyword relationships within RCAM are determined. The review identifies critical limitations in existing practices, including a predominant reliance on labor‐intensive and subjective inspections, and the absence of real‐time data integration in predictive models. These gaps lead to delayed interventions, escalating risks of costly infrastructure failures, such as roadway collapses. The integration of cost‐effective advanced sensor technologies and machine learning algorithms for real‐time monitoring and predictive analytics emerges as a transformative solution. This study underscores the urgent need to adopt these technologies in RCAM to enhance infrastructure sustainability, reduce maintenance costs, and mitigate catastrophic failures.