A Flexible Bridge Rating Method Based on Analytical Evidential Reasoning and Monte Carlo Simulation

Abstract EN

Several bridge inspection standards and condition assessment practices have been developed around the globe.

Some practices employ four linguistic expressions to rate bridge elements while other practices use five or six, or adopt numerical ratings such as 1 to 9.

This research introduces a condition rating method that can operate under different condition assessment practices and account for uncertainties in condition assessment by means of the Evidential Reasoning (ER) theory.

The method offers flexibility in terms of using default elements and their weights or selecting alternative set of elements and condition rating schemes.

The implemented ER approach accounts for uncertainties in condition rating by treating the condition assessments as probabilistic grades rather than numerical values.

The ER approach requires the assignment of initial basic beliefs or probabilities, and typically these initial beliefs are assigned by an expert.

Alternatively, this research integrates the Monte Carlo Simulation (MCS) technique with the ER theory to quantitatively estimate the basic probabilities and to produce robust overall bridge condition ratings.

The proposed method is novel to the literature and has the following features: (1) flexible and can be used with any number of bridge elements and any standard of condition grades; (2) intuitive and simple paired comparison technique is implemented to evaluate weights of the bridge elements; (3) the MCS technique is integrated with the ER approach to quantify uncertainties associated with the stochastic nature of the bridge deterioration process; (4) the method can function with limited data and can incorporate new evidence to update the condition rating; (5) the final rating consists of multiple condition grades and is produced as a distributed probabilistic assessment reflecting the condition of the bridge elements collectively.

The proposed method is illustrated with a real case study, and potential future research work is identified.