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The state of engineering systems changes in time due to the effect of gradual (e.g. corrosion, fatigue) and shock deterioration (e.g. earthquakes, floods, and tornados). At specified moments, for example, after a shock, decision-makers might wish to know the state of the system to take the optimal management action. Different data acquisition strategies such as inspections and continuous structural health monitoring (SHM) can help in the definition and prediction of the system state over time. The acquisition of information comes at a cost that must be balanced by the benefit it brings in terms of risk reduction. The value of information from Bayesian decision analysis quantifies the benefit provided by such information. This article proposes a formulation to compute the value of information of inspection and continuous SHM for degrading engineering systems. In the proposed formulation, the information collected before a given time is used to improve the prediction of the effects of gradual and shock deterioration processes and the future probability of failure. This article investigates the case study of a two-span reinforced concrete bridge degrading under the effect of chemical reactions and seismic actions.

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Over the past 15 years, there has been substantial research performed to improve the safety of tank cars carrying hazardous materials. This has included a series of full-scale tank car impact tests and the development of impact/puncture models capable of evaluating the performance of existing and novel tank car designs under specific impact conditions. In parallel, statistical information was developed on tank car performance based on data from past accidents. However, such data cannot predict the performance of novel tank car designs involving new materials and configurations. This paper presents an innovative analytical framework to address this gap by simulating railroad tank cars behaviors under various train accident and rolling stock features. The finite element method-based simulation model is developed to generate impact forces on railcars after the initiation of train derailments. The results of the modeling were compared, validated, and calibrated with the real-world train derailment to assure the practicality of the analytical tool. This research is novel as it is the first quantitative, analytical methodology that can accurately and practically assess the physical behaviors of railcar movements and impact forces in train derailments. The results of this study provide new and important information for tank car performance in train derailments, which contributes to the literature in structural modeling of rail tank cars. It also specifies an approach to building a bridge between analytical methods and real-world scenarios and data for estimating tank car conditional probability of release (a frequently used metric for hazardous materials transportation safety).