Loading...

Seismic Risk and Resilience of a System of Systems: Analysis of a Virtual City

Lesani Shadbad, Ali | 2022

79 Viewed
  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 55708 (09)
  4. University: Sharif University of Technology
  5. Department: Civil Engineering
  6. Advisor(s): Mahsoli, Mojtaba
  7. Abstract:
  8. The fundamental objective of this study is to present seismic hazard, risk and resilience assessment of a virtual city using sampling and agent-based simulation in two levels of refinement. In this context, hazard is the exceedance probability of ground shaking intensity, risk is exceedance probability of measures such as loss, and resilience is the ability to quickly recover after a hazard event. This study utilizes the Rtx risk and resilience assessment framework established at the Center for Infrastructure Sustainability and Resilience Research (INSURER) at Sharif University of Technology. This framework employs multiple interacting probabilistic models to quantify the risk and resilience. The aforementioned framework and probabilistic models have previously been developed, and the present study aims at modifying and further developing the models to consider two levels of refinement in modeling: the building level and the sub-block level. Using sub-block reduces the computational cost despite leading to reasonably accurate results, which is crucial for large-scale community-level analyses. To evaluate the accuracy at the sub-block level, the developed models are applied in a comprehensive testbed comprising a virtual community, entitled “INSURER City,” with a population of 125,390, a portfolio of 17,418 residential, commercial, and other buildings, electric power network, water network, transportation system, the healthcare system. Only the residential and commercial buildings along with the healthcare system are considered in present study. The abovementioned framework uses Monte Carlo sampling to propagate uncertainties in the analysis. In this framework, hazard and risk are quantified using scenario sampling which is specific implementation of Monte Carlo sampling. Each sample in scenario sampling analysis comprises a scenario with a designated duration in which several events may occur based on the occurrence rate of the sources of hazard. Each event begins with probabilistic simulation of the corresponding hazard event wherein the occurrence, magnitude, rupture location, and rupture area are randomly predicted, and thus the ground motion parameters are evaluated at the desired sites. Thereafter, the risk models quantify the damage incurred by buildings and infrastructure components, casualties, and loss. At the end of each scenario, the desired parameter, i.e., the maximum intensity in the hazard analysis and the total loss in the risk analysis, is calculated. In addition, this study quantifies the community resilience by integrating hazard models, risk models, and recovery models in a Monte Carlo sampling analysis. First, risk models evaluate the initial post-hazard state of the community. Subsequently, the recovery process of the community to normal conditions is simulated using agent-based modeling in which recovery models predict the duration and cost of each operation. In this simulation, agents are decentralized autonomous decision-making entities that are responsible for the recovery of a designated set of infrastructure components. To carry out repair and recovery operations, agents make decisions on priorities based on their behavioral parameters, the information they collect from the environment and their interaction with other agents, and available resources. Recovery operations continue until the community reaches its pre-incident status, and the costs incurred by the community as a result of various consequences are quantified and accumulated. The costs are broken down into various categories comprising direct economic costs, indirect economic costs, direct social costs, and indirect social costs. Direct economic costs include the cost of building inspection, repair mobilization, and repair. Indirect economic costs consist of temporary shelter cost and business income loss. Direct social costs cover the direct cost of treatment of injuries, fatalities, and search-and-rescue. Eventually, indirect social costs stem from the indirect cost of injuries and fatalities and life quality reduction due to homelessness. Consequently, the accumulation of all aforementioned costs at the end of each sample in Monte Carlo sampling leads to the total cost incurred by the community and the recovery time. Through repeated sampling, the probability distribution of the total community cost is obtained. The community resilience measure is then formulated as a function of the total community cost as the demand parameter, and the gross regional product of the community, which represents the capacity to cope with that demand. The primary novelty of this study lies in providing the basis for risk and resilience analysis at two levels of refinement. Using sub-block allows the large-scale analysis of large communities with many buildings. Furthermore, this study is the first to use the Rtx framework for risk and resilience assessment of an entire city. The proposed framework yields a reproducible community resilience model along with its software implementation in Rtx, a computer program for reliability, risk, and resilience analysis that is freely downloadable at rtx.civil.sharif.edu.

  9. Keywords:
  10. Hazards ; Risk ; Recovery ; Resilience ; Infrastructure ; Probabilistic Modeling ; Monte Carlo Sampling ; Agent Based Simulation ; Infrastructure Resilience ; Seismic Resilience

 Digital Object List

 Bookmark

No TOC