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A state-space model (SSM) integrating physical parameters is proposed and developed in this work, to describe the increase of global average temperature and the subsequent changes in regional climate and hydrology. This SSM approach aims at providing updated and improved forecasts, based on observations and using Bayesian inference, and at facilitating flexible engineering decision-making schemes. Global climate model simulations are used for informing the distribution of the parameters of the SSM. The case study of the Colorado River Basin serves as a preliminary application of the method, to forecast changes in the upper basin natural flow. The method projects that the post-2000 low flow volume will continue, or become even lower on average, although such projections are subject to large uncertainty. Given the increasing need of climate projections in the design, operation, and management of infrastructure, the SSM approach can serve as a useful tool, informed by historical records, to facilitate engineering applications. 

Hot temperatures drive excessive energy use for space-cooling in built environments. In a building, a system operator could save costs by making better decisions under the uncertainties associated with urban temperature and future energy demands. In this paper, we assess the impact of urban weather modeling on energy cost, using a value of information (VoI) analysis, in a day-ahead (DA) electricity market. To do that, we combine two probabilistic models: (a) a model for forecasting urban temperature and (b) a model for forecasting hourly net electric load of a building given ambient urban temperature. We then quantify the impact of better urban weather modeling by propagating the uncertainty from the temperature model to the load forecasting model. We perform a numerical case study on residential building prototypes located in the city of Pittsburgh. The result indicates that using a better weather model could save 4.34-8.22% of the electricity costs for space-cooling. 

We assess the Value of Information (VoI) for inspecting components in systems managed by multiple agents, using game theory and Nash equilibrium analysis. We focus on binary systems made up by binary components which can be either intact or damaged. Agents taking maintenance actions are responsible for the repair costs of their own components, and the penalty for system failure is shared among all agents. The precision of inspection is also considered, and we identify the prior and posterior Nash equilibrium with perfect or imperfect inspections. The VoI is assessed for the individual agents as well as for the whole set of agents, and the analysis consider series, parallel and general systems. A negative VoI can trigger the phenomenon of Information Avoidance (IA), where rational agents prefer not to collect free information. We discuss whether it is possible that the VoI is negative for one or for all agents, for the agents with inspected or uninspected components, and for the total sum of VoIs. 

When the operation and maintenance (O&M) of infrastructure components is modeled as a Markov Decision Process (MDP), the stochastic evolution following the optimal policy is completely described by a Markov transition matrix. This paper illustrates how to predict relevant features of the time evolution of these controlled components. We are interested in assessing if a critical state is reachable, in assessing the probability of reaching that state within a time period, of visiting that state before another, and in returning to that state. We present analytical methods to address these questions and discuss their computational complexity. Outcomes of these analyses can provide the decision makers with deeper understanding of the component evolution and suggest revising the control policy. We formulate the framework for MDPs and extend it to Partially Observable Markov Decision Processes (POMDPs). 

We investigate how sequential decision making analysis can be used for modeling system resilience. In the aftermath of an extreme event, agents involved in the emergency management aim at an optimal recovery process, trading off the loss due to lack of system functionality with the investment needed for a fast recovery. This process can be formulated as a sequential decision-making optimization problem, where the overall loss has to be minimized by adopting an appropriate policy, and dynamic programming applied to Markov Decision Processes (MDPs) provides a rational and computationally feasible framework for a quantitative analysis. The paper investigates how trends of post-event loss and recovery can be understood in light of the sequential decision making framework. Specifically, it is well known that system’s functionality is often taken to a level different from that before the event: this can be the result of budget constraints and/or economic opportunity, and the framework has the potential of integrating these considerations. But we focus on the specific case of an agent learning something new about the process, and reacting by updating the target functionality level of the system. We illustrate how this can happen in a simplified setting, by using Hidden-Model MPDs (HM-MDPs) for modelling the management of a set of components under model uncertainty. When an extreme event occurs, the agent updates the hazard model and, consequently, her response and long-term planning. 

The value of information (VoI) provides a rational metric to assess the impact of data in decision processes, including maintenance of engineering systems. According to the principle that “information never hurts”, VoI is guaranteed to be non-negative when a single agent aims at minimizing an expected cost. However, in other contexts such as non-cooperative games, where agents compete against each other, revealing a piece of information to all agents may have a negative impact to some of them, as the negative effect of the competitors being informed and adjusting their policies surpasses the direct VoI. Being aware of this, some agents prefer to avoid having certain information collected, when it must be shared with others, as the overall VoI is negative for them. A similar result may occur for managers of infrastructure assets following the prescriptions of codes and regulations. Modern codes require the probability of some failure events be below a threshold, so managers are forced to retrofit assets if that probability is too high. If the economic incentive of those agents disagrees with the code requirements, the VoI associated with tests or inspections may be negative. In this paper, we investigate under what circumstance this happens, and how severe the effects of this issue can be.