Performance-based engineering for multiple hazards: the role of structural reliability and risk assessment

Buildings, bridges and other civil infrastructure facilities are designed by current codes and standards using provisions that invariably are prescriptive in nature. While facilities so designed usually possess adequate levels of safety under design-basis events, other environmental or man-made events may cause them to suffer damage or loss of function, leading to economic losses, with uncertain impacts on the building occupants, owners and the community that they serve. The new paradigm of performance-based engineering enables structural engineers to achieve more reliable and informative prediction of civil infrastructure behavior and control of performance across a range of hazards. When supported by a risk-informed decision framework founded on structural reliability principles, performance-based engineering provides stakeholders with a structured framework for thinking about performance objectives, uncertainty, and how public safety and socio-economic well-being may be threatened by the failure of civil infrastructure to perform under a spectrum of hazards

An approximate dynamic programming approach to food security of communities following hazards

Food security can be threatened by extreme natural hazard events for households of all social classes within a community. To address food security issues following a natural disaster, the recovery of several elements of the built environment within a community, including its building portfolio, must be considered. Building portfolio restoration is one of the most challenging elements of recovery owing to the complexity and dimensionality of the problem. This study introduces a stochastic scheduling algorithm for the identification of optimal building portfolio recovery strategies. The proposed approach provides a computationally tractable formulation to manage multi-state, large-scale infrastructure systems. A testbed community modeled after Gilroy, California, is used to illustrate how the proposed approach can be implemented efficiently and accurately to find the near-optimal decisions related to building recovery following a severe earthquake.Comment: As opposed to the preemptive scheduling problem, which was addressed in multiple works by us, we deal with a non-preemptive stochastic scheduling problem in this work. Submitted to 13th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP13 Seoul, South Korea, May 26-30, 201

Load Duration and Probability Based Design of Wood Structural Members

Methods are presented for calculating limit state probabilities of engineered wood structural members, considering load duration effects due to stochastic dead and snow load. These methods are used to conduct reliability studies of existing wood design criteria. When realistic load processes are considered, it is found that the importance of load duration and gradual damage accumulation has been somewhat overstated. One possible probability-based design method that should be useful in future code development work also is presented

Performance-Based Engineering of Constructed Systems

This paper offers a synthesis of specification-based versus performance-based civil engineering and articulates the committee’s progress in framing and articulating: “what is performance-based civil engineering?”, “how can we objectively define performance?” and, “what are the issues that should be recognized along our way to performance-based engineering and their possible resolutions

Dynamic Risk Assessment of Resilient Infrastructure Systems under Uncertain Conditions

This paper proposes an adaptive risk management for civil infrastructure system in a dynamic stochastic environment, aimed at improving the ability of the system to adapt to changing conditions in the future. The proposed methodology is developed based on a rolling-horizon (RH) approach to (a) increase computational efficiency, (b) reduce uncertainties in the prediction of evolving conditions in the future, and (c) implement over an uncertain or infinite time horizon. The proposed RH-based adaptive risk management is applied to a decision problem where a hypothetical residential community in Kathmandu, Nepal is exposed to earthquake hazard as well as multiple evolving conditions. The results show that the proposed risk management significantly reduces the uncertainties in the prediction of the dynamic conditions and mitigates seismic risk to the community over time

Computational environment for modeling and enhancing community resilience: Introducing the center for risk-based community resilience planning

The resilience of a community is defined as its ability to prepare for, withstand, recover from and adapt to the effects of natural or human-caused disasters, and depends on the performance of the built environment and on supporting social, economic and public institutions that are essential for immediate response and long-term recovery and adaptation. The performance of the built environment generally is governed by codes, standards, and regulations, which are applicable to individual facilities and residences, are based on different performance criteria, and do not account for the interdependence of buildings, transportation, utilities and other infrastructure sectors. The National Institute of Standards and Technology recently awarded a new Center of Excellence (NIST-CoE) for Risk-Based Community Resilience Planning, which is headquartered at Colorado State University and involves nine additional universities. Research in this Center is focusing on three major research thrusts: (1) developing the NIST-Community Resilience Modeling Environment known as NIST-CORE, thereby enabling alternative strategies to enhance community resilience to be measured quantitatively; (2) developing a standardized data ontology, robust data architecture and data management tools in support of NIST-CORE; and (3) performing a comprehensive set of hindcasts on disasters to validate the data architecture and NIST-CORE