Improving the resilience of post-disaster water distribution systems using a dynamic optimization framework

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Improving the resilience of water distribution systems (WDSs) to handle natural disasters (e.g., earthquakes) is a critical step towards sustainable urban water management. This requires the water utility to be able to respond quickly to such disaster events and in an organized manner, to prioritize the use of available resources to restore service rapidly whilst minimizing the negative impacts. Many methods have been developed to evaluate the WDS resilience, but few efforts are made so far to improve resilience of a post-disaster WDS through identifying optimal sequencing of recovery actions. To address this gap, a new dynamic optimization framework is proposed here where the resilience of a post-disaster WDS is evaluated using six different metrics. A tailored Genetic Algorithm is developed to solve the complex optimization problem driven by these metrics. The proposed framework is demonstrated using a real-world WDS with 6,064 pipes. Results obtained show that the proposed framework successfully identifies near-optimal sequencing of recovery actions for this complex WDS. The gained insights, conditional on the specific attributes of the case study, include: (i) the near-optimal sequencing of recovery strategy heavily depends on the damage properties of the WDS, (ii) replacements of damaged elements tend to be scheduled at the intermediate-late stages of the recovery process due to their long operation time, and (iii) interventions to damaged pipe elements near critical facilities (e.g., hospitals) should not be necessarily the first priority to recover due to complex hydraulic interactions within the WDS

Resilience Assessment of the Built Environment of a Virtual City

L'abstract è presente nell'allegato / the abstract is in the attachmen

Decision Making under Uncertainty for Design of Resilient Engineered Systems

Designing resilient engineered systems that can sense and withstand adverse events and recover from the effects of the adverse events is increasingly seen as an important goal of engineering design. This paper proposes a value-driven design for resilience (VD2R) framework in order to enable the assessment of system resilience and the optimization of decision variables (or design characteristics) that maximize the value of the system for a firm. The VD2R framework possesses three unique features that allow system resilience and value to be addressed in a theoretically founded and explicit way. First, it assesses the time-dependent resilience of an engineered system by explicitly modeling the redundancy, robustness, and restoration of the system. This assessment captures the stochastic behavior of degradation and restoration and their impact on system resilience. Second, it encompasses a value model that links time-dependent system resilience to a design firm\u27s future profit. Third, the VD2R framework offers an efficient optimization method to solve high-dimension, mixed-integer decision-making models. The proposed framework is demonstrated with a case study, where the resilience of a series-parallel system is modeled and its design characteristics optimized

Assessing current and future impacts of climate-related extreme events. The case of Bangladesh

Extreme events and options for managing these risks are receiving increasing attention in research and policy. In order to cost these extremes, a standard approach is to use Integrated Assessment Models with global or regional resolution and represent risk using add-on damage functions that are based on observed impacts and contingent on gradual temperature increase. Such assessments generally find that economic development and population growth are likely to be the major drivers of natural disaster risk in the future; yet, little is said about changes in vulnerability, generally considered a key component of risk. As well, risk is represented by an estimate of average observed impacts using the statistical expectation. Explicitly accounting for vulnerability and using a fuller risk-analytical framework embedded in a simpler economic model, we study the case of Bangladesh, the most flood prone country in the world, in order to critically examine the contribution of all drivers to risk. Specifically, we assess projected changes in riverine flood risk in Bangladesh up to the year 2050 and attempt to quantitatively assess the relative importance of climate change versus socio-economic change in current and future disaster risk. We find that, while flood frequency and intensity, based on regional climate downscaling, are expected to increase, vulnerability, based on observed behaviour in real events over the last 30 years, can be expected to decrease. Also, changes in vulnerability and hazard are roughly of similar magnitudes, while uncertainties are large. Overall, we interpret our findings to corroborate the need for taking a more risk-based approach when assessing extreme events impacts and adaptation - cognizant of the large associated uncertainties and methodological challenges -

Future Roles for Autonomous Vertical Lift in Disaster Relief and Emergency Response

System analysis concepts are applied to the assessment of potential collaborative contributions of autonomous system and vertical lift (a.k.a. rotorcraft, VTOL, powered-lift, etc.) technologies to the important, and perhaps underemphasized, application domain of disaster relief and emergency response. In particular, an analytic framework is outlined whereby system design functional requirements for an application domain can be derived from defined societal good goals and objectives

Establishing a frame of reference for measuring disaster resilience

Due to the increasing occurrence of disruptions across our global society, it has become critically important to understand the resilience of different socio-economic systems, i.e., to what extent those systems exhibit the ability both to resist a disruption and to recover from one once it occurs. In order to characterize this ability, however, one must be able to quantitatively measure the relative level of resilience that a given system displays in response to a disruptive event. Such a measurement should be easily understandable and straightforward to implement, but it should also utilize a consistent frame of reference so that one can properly compare the relative performance of different systems and assess the relative effectiveness of different resilience investments. With this in mind, this paper presents an improved approach for measuring system resilience that supports better decision making by providing both consistency and flexibility across different contexts. The theoretical basis for the approach is developed first, and then its advantages and limitations are illustrated in the context of several different practical examples