Life-cycle cost analysis of deteriorating structures has to consider not only time-varying resistance and loading affecting the reliability of these structures but also maintenance interventions applied during their lifetime. Finding the optimum maintenance scenario for a deteriorating structure is a complex process involving the selection of maintenance interventions and their application times associated with minimum life-cycle cost. There is a close relationship between maintenance intervention cost and the effect of the intervention on system reliability. However, this relationship is not included in modern structural management systems since costs of interventions are prescribed as fixed values independent on their effects on system reliability. Neglecting the interactions among maintenance interventions, their effects on structural performance, and corresponding costs leads to significant limitations of modern management systems for civil infrastructures. In this paper, maintenance intervention cost...

The prioritization of maintenance interventions on aging structures is one of the most important decision processes in the management of existing civil infrastructure. This process has to consider the uncertainties in loads, resistances, and maintenance interventions over a given time horizon. A computational program for probabilistic maintenance optimization in which the reliability index is the quantitative measure of performance in the presence of uncertainties and the present value of expected cumulative cost is the objective to be minimized has been developed by the writers. Using this program, time-varying uncertainties associated with reliability deterioration of structures and effects of maintenance interventions on the system reliability can be captured efficiently. The aim of this study is to provide insight in the methodology for probabilistic maintenance optimization of aging structures in which the optimum solution is found by minimizing the present value of expected cumulative maintenance an...

Fatigue life in critical parts of structure components, which is subjected to variable amplitude loading, is a very complex subject. When a fatigue life is influenced by these load variations, accelerations and/or retardations in crack growth rate can occur. Thus, an accurate prediction of fatigue life requires an adequate evaluation of these stress interaction effects. There are many different indexes for calculating fatigue damage at critical locations. The crack severity index based on crack growth is one of the methodologies implemented for modern military aircrafts to define mission severity relative to a known reference usage. But, this index cannot consider stress interaction. In this study, a novel index was proposed to evaluate an effect for stress interaction by using entropy concept, and fatigue life prediction methodology was developed by using entropy index of stress interaction and crack severity index of effective stress.

The purpose of a bridge maintenance strategy is to make effective decisions by evaluating current performance and predicting future conditions of the bridge. The social cost because of the rapid increase in the number of decrepit bridges. The current bridge maintenance system relies on traditional man-power-based methods, which determine the bridge performance by employing a material deterioration model, and thus shows uncertainty in predicting the bridge performance. In this study, a new type of performance degradation model is developed using the actual concrete deck condition index (or grade) data of the general bridge inspection history database (1995–2017) on the national road bridge of the bridge management system in Korea. The developed model uses the long short-term memory algorithm, which is a type of recurrent neural network, as well as layer normalization and label smoothing to improve the applicability of basic data. This model can express the discrete historical degradation indices in continuous form according to the service life. In addition, it enables the prediction of bridge performance by using only basic information about new and existing bridges.

The reliability of civil infrastructure is time variant. This is due to strength deterioration and time-variant mechanical and environmental loadings. Demands for keeping civil infrastructure in functional and reliable states require maintenance interventions. Limited financial resources require optimal maintenance strategies. These strategies have to consider the effects of uncertainties not only on the structural reliability deterioration but also on the maintenance interventions. In this paper, a simulation-based method for predicting reliability index profiles of deteriorating bridges associated with both time- and performance-controlled maintenance interventions is introduced. The method provides a computational procedure which can handle various types of maintenance interventions in a consistent manner. This method also includes an advanced cost model which takes into account the interaction between costs of maintenance interventions and their effect on system reliability. Examples are presented dem...

A deterioration model plays an important role to predict the valid total maintenance cost for sustainable maintenance of bridges. In the current state-of-the-art, the deterioration model has regression parameters as a probabilistic process by an initially determined mean and standard deviation, called an existing model. However, the existing model has difficulty to predict maintenance costs accurately, because it cannot reflect an information based on structural damage at an operational stage. In this research, updating the probabilistic deterioration model is presented for the prediction of pre-stressed concrete I-type (PSCI) girder bridges using a particle filtering technique which is an advanced Bayesian updating method based on big data analysis. The method enables predicting maintenance cost fitted in the current structural status, which includes the recent information by inspection with bridge-monitoring. The method is adapted in the Mokdo Bridge which is currently being used for evaluating the efficiency of maintenance cost by effects on updated probabilistic values with two different scenarios. As the result, it is shown that the proposed method is effective in predicting maintenance costs.