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Abdollah Shafieezadeh
Researcher at Ohio State University
Publications - 132
Citations - 2053
Abdollah Shafieezadeh is an academic researcher from Ohio State University. The author has contributed to research in topics: Computer science & Reliability (statistics). The author has an hindex of 18, co-authored 110 publications receiving 1209 citations. Previous affiliations of Abdollah Shafieezadeh include Utah State University & Georgia Institute of Technology.
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Scenario-based resilience assessment framework for critical infrastructure systems: Case study for seismic resilience of seaports
TL;DR: A probabilistic framework for scenario-based resilience assessment of infrastructure systems will enable port stakeholders to systematically assess the most-likely performance of the system during expected future earthquake events.
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Age-Dependent Fragility Models of Utility Wood Poles in Power Distribution Networks Against Extreme Wind Hazards
TL;DR: In this paper, the authors present a framework for the development of age-dependent fragility curves of utility wood poles that relies on agedependent probabilistic capacity models of wood poles and wind induced demand models.
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REAK: Reliability analysis through Error rate-based Adaptive Kriging
Zeyu Wang,Abdollah Shafieezadeh +1 more
TL;DR: An extension of the Central Limit Theorem based on Lindeberg condition is adopted here to derive the distribution of the number of design samples with wrong sign estimate and subsequently determine the maximum error rate for failure probability estimates.
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Optimal intensity measures for probabilistic seismic demand modeling of extended pile-shaft-supported bridges in liquefied and laterally spreading ground
TL;DR: In this article, a coupled-bridge-soil-foundation model is adopted to perform an in-depth investigation of optimal seismic intensity measures among 26 IMs found in the literature.
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Statistical models for shear strength of RC beam‐column joints using machine‐learning techniques
TL;DR: In this paper, a set of probabilistic joint shear strength models using the conventional multiple linear regression method, and advanced machine learning methods of multivariate adaptive regression splines (MARS) and symbolic regression (SR) were proposed.