Methods and Tools for Natural Hazard Risk Analysis in Eastern Canada: Using Knowledge to Understand Vulnerability and Implement Mitigation Measures

Nastev, Miroslav; Nollet, Marie‐José; Ezz, Ahmad Abo El; Smirnoff, Anna; Ploeger, S.K.; McGrath, Heather; Sawada, M.; Stefanakis, Emmanuel; Parent, Michel

doi:10.1061/(asce)nh.1527-6996.0000209

Cited by 8 publications

(8 citation statements)

References 16 publications

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“…The final functions were generated for a reduced number of combined building classes, 24 instead of 53 building classes common for construction practice in Eastern Canada. The earthquake scenarios used in their development were based on magnitude (M5, M6 and M7) and incorporate five epicentral distances (10,20,30,40 and 60 km) and five NEHRP/NBC site classes (A, B, C, D, E). In terms of the nonlinear structural response and the respective effective damping, the implicit fragility functions for the combined 24 buildings classification scheme were developed for short strong motion duration, magnitude range of M < 5.5, and for medium duration, magnitude range of 5.5 < M < 7.5.…”

Section: Discussionmentioning

confidence: 99%

“…Before generating implicit fragility functions, comparative analyses were conducted to determine which IM represents better the seismic scenario in terms of risk assessment and exhibits less scatter to the magnitude-distance-site class scenarios for a given building class. Examples of the displacement demands at the performance point in terms of Sa(0.3s) and Sa(1.0s) for low-rise pre-code URM buildings are given in Figure 3 for the same magnitude (M5, M6, M7), distance (10,20,30,40 and 60 km) and site class (A, B, C, D, E) seismic scenarios extracted from the ER 2 global database. Each graph on Figure 3 contains 1275 data points.…”

Section: Selecting Single Intensity Measurementioning

confidence: 99%

“…Cumulative damage probabilities to reach slight damage were extracted from the global ER 2 database for low-and medium-code light wood frame building class, W1-l and W1-m. In this example, twenty-five earthquake scenarios were generated for magnitude M6 by permutations of five distances (10,20,30, 40 and 60 km) and five NEHRP/NBCC site classes (A, B, C, D, E) scaled in increasing levels of Sa(1.0s). Cumulative probability for the slight damage state is given in terms of Sa(1.0s) as IM.…”

Section: Fitting Implicit Fragility Curves For Single Immentioning

confidence: 99%

“…The process starts with low IMs yielding response spectral displacement Sd close to zero and the successively for Sd values equally spaced on the logarithmic scale ranging from 10 −2 to 10 3 inches (0.025 cm to 2500 cm) until the ultimate point on the capacity curve is attained. The corresponding earthquake scenarios were generated by permutations of four magnitudes (M5, M6, M7, M8), five distances (10,20,30,40 and 60 km) and five site classes (A, B, C, D, E), for a total of 100 earthquake scenarios. The generated vulnerability functions were then tabulated in a database giving performance points, respective probabilistic damage states and associated IMs.…”

Section: Database Of Damage State Probabilitiesmentioning

confidence: 99%

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Earthquake Magnitude and Shaking Intensity Dependent Fragility Functions for Rapid Risk Assessment of Buildings

et al. 2018

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An integrated web application, referred to as ER 2 for rapid risk evaluator, is under development for a user-friendly seismic risk assessment by the non-expert public safety community. The assessment of likely negative consequences is based on pre-populated databases of seismic, building inventory and vulnerability parameters. To further accelerate the computation for near real-time analyses, implicit building fragility curves were developed as functions of the magnitude and the intensity of the seismic shaking defined with a single intensity measure, input spectral acceleration at 1.0 s implicitly considering the epicentral distance and local soil conditions. Damage probabilities were compared with those obtained with the standard fragility functions explicitly considering epicentral distances and local site classes in addition to the earthquake magnitudes and respective intensity of the seismic shaking. Different seismic scenarios were considered first for 53 building classes common in Eastern Canada, and then a reduced number of 24 combined building classes was proposed. Comparison of results indicate that the damage predictions with implicit fragility functions for short (M ≤ 5.5) and medium strong motion duration (5.5 < M ≤ 7.5) show low variation with distance and soil class, with average error of less than 3.6%.

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Section: Discussionmentioning

confidence: 99%

Section: Selecting Single Intensity Measurementioning

confidence: 99%

Section: Fitting Implicit Fragility Curves For Single Immentioning

confidence: 99%

Section: Database Of Damage State Probabilitiesmentioning

confidence: 99%

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Earthquake Magnitude and Shaking Intensity Dependent Fragility Functions for Rapid Risk Assessment of Buildings

et al. 2018

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“…We reviewed abstracts of papers published in and after 2000 in the journals Natural Hazards Review and Earthquake Spectra and identified articles that presented the experience and concerns of end-users. These studies investigate end-users via interviews and participatory studies, or by presenting frameworks for tools and usability testing [60][61][62][63][64][65][66][67][68][69][70][71][72][73][74][75].…”

Section: Research Articles: Emergency Management Andmentioning

confidence: 99%

Toward Human-Centered Simulation Modeling for Critical Infrastructure Disaster Recovery Planning

Ganji

Miles

2018

2018 IEEE Global Humanitarian Technology Conference (GHTC)

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Critical infrastructure is vulnerable to a broad range of hazards. Timely and effective recovery of critical infrastructure after extreme events is crucial. However, critical infrastructure disaster recovery planning is complicated and involves both domain-and user-centered characteristics and complexities. Recovery planning currently uses few quantitative computer-based tools and instead largely relies on expert judgment. Simulation modeling can simplify domain-centered complexities but not the human factors. Conversely, humancentered design places end-users at the center of design. We discuss the benefits of combining simulation modeling with human-centered design and refer it as human-centered simulation modeling. Human-centered simulation modeling has the capability to make recovery planning simpler and more understandable for critical infrastructure and emergency management experts and other recovery planning decisionmakers.We qualitatively analyzed several resilience planning initiatives, post-disaster recovery assessments, and relevant journal articles to understand experts and decision-makers' perspectives. We propose a conceptual design framework for creating human-centered simulation models for critical infrastructure disaster recovery planning. This framework consists of three constructs: 1) user interaction with design features that end-users interact with, including model parameters assignment, decision-making support, task queries, and usability; 2) system representation that refers to system components, system interactions, and system state variables; and 3) computation core that represents computational methods required to perform processes. Keywords-resilience and recovery planning, critical infrastructure recovery, simulation modeling, human-centered designI.

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Disaster Resilience and Computational Methods for Urban Infrastructures

Eslamian

Maleki

2021

Handbook of Disaster Risk Reduction for Resilience

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Methods and Tools for Natural Hazard Risk Analysis in Eastern Canada: Using Knowledge to Understand Vulnerability and Implement Mitigation Measures

Cited by 8 publications

References 16 publications

Earthquake Magnitude and Shaking Intensity Dependent Fragility Functions for Rapid Risk Assessment of Buildings

Earthquake Magnitude and Shaking Intensity Dependent Fragility Functions for Rapid Risk Assessment of Buildings

Toward Human-Centered Simulation Modeling for Critical Infrastructure Disaster Recovery Planning

Disaster Resilience and Computational Methods for Urban Infrastructures

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