Year: 2025
Type: review
Study design: Systematic Review
Abstract: 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.
Source: Advances in Civil Engineering
DOI: 10.1155/adce/2211031
Authors: Aashish Khadka, Jacobus Daniel van der Walt, Markus Pahlow
Institution: University of Canterbury
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,760
Funder: University of Canterbury
OpenAlex ID: W4414657080