Condition Prediction of Existing Concrete Bridges as a Combination of Visual Inspection and Analytical Models of Deterioration

Ivan Zambon,Jose Matos,Anja Vidović,Alfred Strauss

doi:10.3390/app9010148

Abstract

In most state-of-the-art Bridge Management Systems, structural condition is predicted by a homogeneous Markov chain model that uses condition ratings assigned during visual inspections. Although generally accepted, such an approach exhibits certain shortcomings, one of which is not considering the nature of actual physical phenomena that cause deterioration. To overcome this shortcoming, this article presents a framework that combines both information on condition ratings through the semi-Markov process and knowledge of bridge properties using analytical deterioration models. In this manner, and contrary to current practice, not only are the results of visual inspection taken into account, but also information such as environmental loading, as well as material and structural properties. The presented framework was implemented in the case study bridge, in which the deterioration caused by carbonation-induced corrosion was studied. Along with the implementation in the case study, the article contained a detailed overview of the subject of carbonation-induced corrosion and emphasized issues that require additional research in order to develop the framework into a comprehensive and fully applicable tool for condition prediction. Accounting for its adaptability to other material types and deterioration processes and its consideration of the historic deterioration path, the framework presents a suitable alternative to frameworks presently implemented for condition prediction.

Highlights

Bridges are vital elements of roadway and railway transportation infrastructure and they require timely decision-making to schedule Maintenance, Repair and Rehabilitation (MR&R) activities.To support the decision-making process, an increasing number of computer-aided Bridge ManagementSystems (BMSs) have been developed over the past two decades [1]
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Summary

Introduction

Bridges are vital elements of roadway and railway transportation infrastructure and they require timely decision-making to schedule Maintenance, Repair and Rehabilitation (MR&R) activities.To support the decision-making process, an increasing number of computer-aided Bridge ManagementSystems (BMSs) have been developed over the past two decades [1]. The main goal of the BMS is to establish an optimal operation strategy as a compromise between technical and social factors, such as maximization of network performance, minimization of life-cycle costs, and minimization of the probability of failure [2]. To establish such a strategy, it is essential to comprehend the long-term performance of a bridge, taking into account possible deterioration [3,4]. In cases where condition prediction is purely based on condition ratings, the knowledge of how materials and elements are affected

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