ABSTRACT
Motorway bridges constitute one of the most crucial parts of transportation networks, with proper life-cycle cost (LCC) estimation being of paramount importance for cost-efficient projects. This paper introduces a probabilistic framework for concrete bridge LCC estimation based on dynamic Bayesian Network (DBN) modelling that stakeholders can consult in projects’ preliminary stages for proper decision-making. Firstly, probability distributions of material quantities and construction costs for the superstructure and foundation are estimated. Subsequently, the maintenance costs of deck expansion joints are illustratively presented, as they are considered vulnerable bridge elements with frequent repairs. The proposed DBN contains continuous and time-dependent variables thoroughly registered over multiple time stages. The DBN is formulated using actual records of 60 concrete bridges’ construction, inspections, condition and maintenance from the Egnatia Odos motorway in Greece. The presented approach is an effective tool for bridge management enhancement owing to stochastic dynamic interdependencies among parameters and continuous information updating.
