Global Trends in Predicting Pavement Distress: A Systematic Review - Perkasa, Gerda; Soetjipto, Jojok Widodo; Nurtjahjaningtyas, Indra (ASTONJADRO, 2026)
Road pavement deterioration has become increasingly complex due to growing traffic loads, the impacts of climate change, and limited maintenance budgets. In response, the present study explores how recent global advances in pavement condition prediction, particularly Dynamic Bayesian Networks (DBN) and Artificial Intelligence (AI) can be adapted to the Indonesian context. A Systematic Literature Review (SLR) was conducted following the PRISMA 2020 protocol, covering publications from 2020 to 2025 across international databases (Scopus, Web of Science, ScienceDirect, IEEE Xplore, Google Scholar) as well as with national sources (ASTONJADRO, Rekayasa Sipil, Jurnal Media Publikasi Terapan, Jurnal Syntax). The initial search yielded 200 articles, which were screened by title, abstract, and full-text reviews, resulting in a final selection 52 articles. The review classifies prediction methods into four key categories: index-based approaches (IRI, PCI, SDI), which are practical yet insufficient for capturing temporal dynamics; probabilistic models (Markov Chain, Bayesian Network, DBN), which enable the modelling of uncertainty; AI-based methods, which provide high accuracy but offer limited interpretability; and hybrid models combining probabilistic methods with AI to enhance reliability. The study synthesizes global trends and the Indonesian context, indicating that DBN holds notable potential for supporting road maintenance policies when underpinned by robust time-series data and cross-institutional integration. The review further recommends the development of DBN–AI hybrid models and the enhancement of local data infrastructure as strategic priorities for future research and policy in Indonesia.