A Markov process model of sizing the capacity of reserved storage yard in intermodal container terminal

The intermodal container terminal is an important node linking the routes of shipping and China Railway Express. This paper proposes a Markov process model to explore the optimal capacity of the reserved storage yard in the intermodal container terminal. The flow balance equations are formulated to calculate the steady-state probability. The total cost is taken as the performance measure. Numerical analysis manifests that the railway preference rate and the strategy with and without transfer obviously affect the optimum capacity of the reserved storage yard. Some useful insights for the management of the intermodal container terminal are discussed.

A Probabilistic Model for Optimal Bridge Inspection Interval

The US Department of Transportation and Federal Highway Administration require routine inspections to monitor bridge deterioration. Typically, bridge inspections are conducted every 24 months. This timeframe was determined solely based on engineering judgment. The objective of this study is to develop probabilistic models to forecast bridge deterioration and statistically determine the optimal inspection intervals. A two-dimensional Markov process model that considers the current condition of a bridge, and number of years that the bridge has been in that condition, is created to predict future bridge conditions based on historical data. Using the forecasting model, a statistical process is developed to determine the optimal inspection intervals. The proposed methodology in this study is implemented, utilizing a dataset consisting of information about deterioration conditions of more than 17,500 bridges in the state of New York from 1992 to 2018. The outcomes of the statistical analysis indicate that the typical 24-month inspection interval is considerably pessimistic, and not necessary for all bridges currently in condition 5 or higher. However, the 24-month interval is too optimistic and risky for bridges currently in condition 4 or lower. The outcomes of this study help bridge owners and transportation agencies assign maintenance resources efficiently, and invest the millions of dollars currently allocated for unnecessary inspections in much-needed infrastructure development projects.

Developing A Semi-Markov Process Model for Bridge Deterioration Prediction in Shanghai

The performance of urban bridges will deteriorate gradually throughout service life. Bridge deterioration prediction is essential for bridge management, especially for maintenance planning and decision-making. By considering the time-dependent reliability in the bridge deterioration process, a Weibull distribution based semi-Markov process model for urban bridge deterioration prediction was proposed in this paper. Historical inspection records stored in the Bridge Manage System (BMS) database in Shanghai since 2004 were investigated. The Weibull distribution was used to characterize the bridge deterioration behavior within each condition rating (CR), and the semi-Markov process was used to calculate the bridge transition probabilities between adjacent CRs. After that, the service life expectancy of urban bridges, the transition probabilities of the deck system and the substructure, and the future CR proportion change caused by deterioration was predicted. The prediction results indicate that the life expectancy of concrete beam bridges is about 77 years. The decay rate of the deck system is the fastest among three major parts, and the substructure has a much longer life expectancy. It suggests that the overall prediction accuracy of the semi-Markov model in network-level is better than the regression analysis method. Furthermore, the proportion of bridges in intact condition will gradually decrease in the next few decades, while the percentage of bridges in the qualified and bad state will increase rapidly. The prediction results show a good agreement with the actual deterioration trend of the urban bridges in Shanghai. In order to alleviate the pressure of bridge maintenance in the future, it is necessary to adopt a more targeted preventive maintenance strategy.

Testing for the Diffusion Matrix in a Continuous-Time Markov Process Model with Applications to the Term Structure of Interest Rates*

For each component in the diffusion matrix of a d-dimensional diffusion process, we propose a test for the parametric specification of this component. Overall, d(d−1)/2 test statistics are constructed for the off-diagonal components, while d test statistics being for the main diagonal components. Using theories of degenerate U-statistics, each of all these test statistics is shown to follow an asymptotic standard normal distribution under null hypothesis, while diverging to infinity if the component is misspecified over a significant range. We obtain new empirical findings by applying our tests to evaluate a variety of three-factor affine term structure models in modeling the volatility dynamics of monthly U.S. Treasury yields.

Reduction of Seismic Risk of Infrastructure via Daily Management Works

<p>During recent years, the possibility that damage at the time of earthquake could change depending on the deterioration condition of infrastructure has been noted through analytical calculations. Faced with such a possibility, management policy should be optimized by internalizing the external elements of earthquake damage, evaluating the appropriateness of management policy for infrastructure, and optimizing the system. In this study, the deterioration process for infrastructure was modelled using the Markov process model, and a methodology to determine the optimal management policy is proposed by considering the two risks: i) the risk that infrastructure fails because of deterioration independent of external factors such as an earthquake, and ii) the risk of infrastructure failure at the time of earthquake, which changes due to deterioration of the infrastructure. In an example of the application, it is demonstratively shown that the optimal management policy would change in the case where earthquake risk is not considered.</p>

Variability in an Optimal Infrastructure Management Policy by Internalization of Seismic Risk

During recent years, the possibility that damage at the time of earthquake could change depending on the deterioration condition of infrastructure has been noted through analytical analyses. Faced with such a possibility, management policy should be optimized by internalizing the external elements of earthquake damage, evaluating the appropriateness of management policy for infrastructure, and optimizing the system. In this study, the deterioration process for infrastructure was modelled using the Markov process model, and a methodology to determine the optimal management policy is proposed by considering the two risks: i) the risk that infrastructure fails because of deterioration independent of external elements such as an earthquake, and ii) the risk that changes due to deterioration fails the infrastructure at the time of earthquake. Using an example of the application the following two points are demonstratively shown: i) the optimal management policy would change in the case in which earthquake risk is not considered, and ii) the optimal management policy would change depending on the earthquake occurrence probability in the case in which earthquake risk is considered.