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.

Due to a considerable amount of information required to support the decision-making processes, an increasing number of infrastructure owners use computerized management systems. Bridges, being complex and having significant impact on society, have often been the foundation for the development of these systems. In order to manage bridges effectively, condition prediction models are incorporated to the core of decision-making processes. Many of developed and applied stochastic prediction models show certain limitations. The impact of these limitations on deterioration pre­dictions cannot be objectively evaluated without direct comparison of prediction results. Hence, several stochastic pre­diction models based on condition ratings obtained from visual inspections of bridge decks are compared in this article. Models are described and implemented on the data of around 1100 reinforced concrete bridge decks from the ‘Infraes­truturas de Portugal’, a state owned Portuguese general concessionaire for roadways and railways. The statistical analy­sis of different models revealed significant deviations, particularly in higher condition ratings. Results indicate limited prediction capability of a simple homogeneous Markov chain model when compared with time- and space-continuous models, such as the gamma process model.

For long-span bridges as well as statically indeterminate frame structures it is essential to implement efficient and realistic prediction models for the long-term processes of concrete creep, shrinkage, and steel relaxation. In order to systematically study the main influential factors in bridge deflection measurements a probabilistic analysis can be per­formed. Due to the associated computational costs such investigations are limited. The predictions based on the highly scattered input parameters are associated with uncertainties. There is interest in alternative prediction models decou­pled from complex analytical and computationally expensive numerical models, using measured structural responses. A gamma process is an example of such an alternative method. This process is suitable for capturing evolving structural response quantities and deterioration mechanisms like crack propagation, corrosion, creep, and shrinkage, as reported in Ohadi and Micic (2011). The objective of this paper is to illustrate the use of gamma process approaches for the pre­diction of the creep and shrinkage performance of prestressed concrete bridges. The presented approaches incorporate uncertainties and make predictions more reliable with the help of structural health monitoring (SHM) data. The creep-shrinkage response of a prestressed box girder bridge serves for the calibration and evaluation of the considered gamma process approaches.

The corrosion of reinforcement caused by chloride ingress significantly reduces the length of the service life of reinforced concrete bridges. Therefore, the condition of bridges is periodically inspected by specially trained engineers regarding the possible occurrence of reinforcement corrosion. Their main goal is to ensure that the structure can resist mechanical and environmental loads and offer a satisfactory level of safety and serviceability. In the course of assessment, measuring the chloride content, through which corrosion could be anticipated and prevented, presents a possible alternative to visual inspections and corrosion tests that can only indicate already existing corrosion. It is hard to determine the cost-effectiveness and actual value of chloride content measurements in a simple and straightforward way. Thus, the main aim of the paper was to study the value of newly gained information, which is obtained when a chloride content in reinforced concrete bridges is measured. This value was here analyzed through the pre-posterior analysis of the cost of measurement and repair, taking into account different types of exposure and material properties for a general case. The research focus was set on the initiation phase in which there are no visible damages. A relative comparison of costs is presented, where the cost of possible reactive/proactive repair was compared with the maximum cost of measurement, while the measurement is still cost effective. The analysis showed a high influence of the initial probability of depassivation on the maximum cost of the cost-effective measurement, as well as a nonreciprocal relation of the minimum cost of cost-effective reactive repair with the measurement accuracy.

The accuracy of forecasting models for the prediction of an infrastructure's deterioration process plays a significant role in the estimation of optimal maintenance, rehabilitation, and replacement strategies. Numerous approaches have been developed to overcome the limitations of existing forecasting models. In this article, a direct comparison is made between different models using the same input data to derive conclusions of their distinct performance. The models selected for the comparison were Markov, semi‐Markov, and hidden Markov models together with artificial neural networks (ANNs), which have been reported in literature as reliable deterioration prediction models. A quality of fit was performed to measure how well the observed data corresponded to the predicted values, and therefore objectively compare the performance of each model. The results demonstrated that the most accurate prediction was accomplished by the ANN model. Nevertheless, all models presented differences with respect to typical values of concrete decks life expectancy, which is attributed to the inherent difficulties of the database. Additionally, the problem of the visual inspection subjectivity was also regarded as one of the potential causes for the found deviations. Therefore, this article also discusses the shortcomings of current condition assessment practices and encourages future bridge management systems to replace the classical methods by more sophisticated and objective tools.

Within the management of structures, nonformal assessments where the condition of a structure is evaluated on the basis of visual inspections are the most common type of evaluation. This is because not all of the structures in huge infrastructure networks can be assessed in great depth due to cost constraints. The objective of a structural condition assessment is to determine the current state and estimate the future performance of a structure with a maximum degree of accuracy and a minimum of effort. There is therefore a need for advanced methodologies and predictive deterioration models for the assessment of structures/structural elements over time. Data collected during visual inspections should mainly provide information about the most serious problems and suggest the most suitable scheme for the extension of the performed inspection via monitoring, additional on-site measurements and/or laboratory tests. The objective of this contribution is to sketch out the principles of structural evaluation associated with chloride diffusion processes and to feature several levels of structural assessment, starting with simple but conservative methods and progressing to more sophisticated ones. The article proposes a "guideline" which is aimed in particular at existing concrete/reinforced concrete structures, such as bridges, located in the mild European climate in places where deicing salts are applied. K E Y W O R D S chloride ion ingress modeling, concrete structures, levels of assessment, quantification of input parameters 1 | INTRODUCTIONDegradation processes affecting structural materials, such as chloride ion ingress, concrete carbonation, and the subsequent corrosion of reinforcement, are limiting factors for the service life of reinforced concrete structures and/or structural elements. The service life of a structure is determined by the quality of its design, construction, workmanship, and maintenance, and can change during operation due to load effects, the aging of materials and environmentally caused degradation. As the proper safety level of the structure needs to be ensured during its whole service life, owners and operators of existing structures face complex decisions regarding maintenance and repair strategies and/or possible replacement. 1-3 Furthermore, minimizing the overall cost by optimizing inspection, maintenance and repair work is a key task within this decision-making process.According to Rücker et al., 4 assessment procedures can be classified into three groups: (a) nonformal assessment; (b) model-based assessment; and (c) measurement-based assessment. Since they vary in sophistication, these procedures can be divided into several assessment levels, where experience-based nonformal assessment constitutes the lowest level and the fully probabilistic approach is the highest.Discussion on this paper must be submitted within two months of the print publication. The discussion will then be published in print, along with the authors' closure, if any, approximately nine months after the print publica...

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The performance indicators should, by its definition, allow capturing the life-cycle degradation processes affecting maintenance plans or the remaining lifetime. The qualitative or quantitative performance indicators are obtained through visual inspections, non-destructive tests or monitoring systems. After their quantification and the comparison with the respective performance goals and thresholds, a Quality Control plan should be accomplished. The COST TU1406 Action aims to uniform the European performance indicators, systemize the knowledge on the Quality Control plans for bridges, establish quality specifications and finally to develop the guideline and recommendations for the assessment of performance indicators. This contribution focuses on the current work of the first Working Group, WG1, where the first step is a collection of the key performance indicators at a European level. First those key performance indicators which capture mechanical and technical properties and its degradation behavior are assessed, while the further consideration reflect on the natural aging, quality of the material, service life design methods, and sustainable, environmental, economic and social based indicators.