Cost–benefit analysis (CBA) is considered an effective evaluation method for fostering optimal decision making and ranking of road infrastructures over decades. This paper introduces a comprehensive systematic literature review focusing on CBA’s methodological perspective for identifying its current modelling approaches and scrutinizing their key features and encompassed tools. Fifty-six relevant studies were extracted from Web of Science, Scopus and Google Scholar from 2005 until 2020. Initially, the bibliometric analysis presents an overall illustration of the most significant CBA concepts. The descriptive statistics determined eight distinct modelling categories used for CBA implementation, each encompassing three different modelling approaches for capturing the data risk assessment (deterministic or probabilistic), CBA’s parameters interactive behavior (static or dynamic) and the considered economies (microeconomic or macroeconomic). In-depth content analysis led to the interpretation of the current status of extant models and the identification of three main knowledge gaps: the absence of the CBA’s inputs updating into a probabilistic environment, the deficiency of a dynamic interdependent framework and the necessity of homogenous cost datasets for road projects. Future research directions and a conceptual framework for modelling CBA into a microeconomic, probabilistic and dynamic environment are proposed providing decision-makers with new avenues for more reliable CBA modelling.

Long-term deterioration of concrete bridges is a natural process that requires prudent maintenance actions throughout the bridge’s life-cycle. Nowadays, there is an ongoing effort to simulate such processes into practical models. One primary element for the model’s accuracy is the datasets used for its development. The gap between underestimated or overestimated and actual values could be narrowed by utilizing real-world datasets on bridge deterioration and rehabilitation obtained from systematic inspections over time in similar environments. Therefore, the present study aims to develop an empirical probabilistic model for precisely predicting the bridge’s future performance and suitably implementing maintenance strategies that facilitate sustainable management during bridge service life based on real data. Actual records of 72 concrete bridges from motorways in Northern Greece were collected, documenting different detected defect types, condition states, and associated maintenance costs over time. Two discrete-time Markov-chain models for the bridge’s superstructure and substructure were produced, allowing for the prediction of maintenance costs that align with the given structural condition throughout its operational life. A Chi-square test demonstrated the model’s applicability to similar datasets. This enables bridge managers to obtain a comprehensive overview of the bridge’s longitudinal performance and maintenance expenditures and adopt economically sustainable solutions for the bridge’s management.

Pavement infrastructure deteriorates progressively under the combined influence of multiple uncertainty-related factors making accurate deterioration prediction essential for effective Pavement Management Systems (PMSs). Probabilistic modelling approaches have received increasing attention for capturing uncertainty and variability of pavement deterioration and have been examined across several review studies. However, a systematic review that comprehensively investigates and evaluates the existing probabilistic pavement performance prediction models (PPPMs) has yet to be established. Accordingly, this study presents a systematic meta-review that synthesizes current knowledge, identifies key research gaps and proposes a conceptual framework for PPPM aligned with current research needs. Primarily, the analysis was based on a systematic literature review (SLR) of ten review papers identified in the Web of Science (WoS) between 2010 and 2026. The in-depth content analysis revealed five PPPM categories: Markov-chain, Bayesian approaches, fuzzy models, grey theory and logistic regression dominating the probabilistic PPPM field. The findings highlight a shift towards hybrid, multi-parameter, and duration-based models for effectively representing pavement deterioration over time. Thus, an adaptive conceptual framework of a Dynamic Bayesian Network (DBN) is proposed that integrates the dynamic interdependencies of multiple pavement performance indicators (PPIs), the dynamic updating of model parameters and the impact of extreme events on PPPM.