Mohsen Shahandashti

Assistant Professor

The University of Texas at Arlington

United States

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Mohsen Shahandashti is an Assistant Professor at the University of Texas at Arlington, USA. His research areas include water distribution networks, geotechnical properties, and the impact of COVID-19 on the architecture and construction industries. Recent articles authored by Dr. Shahandashti explore optimal resilience enhancement in infrastructure, machine learning applications for economic forecasting, and the effects of legislative actions on post-disaster reconstruction.

Articles (20)

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2024

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2021

Collaborators (4)

Sooin Kim

Assistant Professor

Wayne State University

UNITED STATES

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Mohsen Hajibabaei

Postdoctoral Researcher & Lecturer

University of Innsbruck

AUSTRIA

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Robert Sitzenfrei

Full Professor

University of Innsbruck

AUSTRIA

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Bahram Abediniangerabi

Assistant Professor

Texas A&M University

UNITED STATES

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Mohsen Shahandashti

Research Interests

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Articles (20)

Graph-Based Warm Solutions for Optimal Resilience Enhancement of Water Distribution Networks

Deep Learning for Exploring the Relationship Between Geotechnical Properties and Electrical Resistivities

Effects of Degradation of Pipes on Seismic Rehabilitation Decision-Making for Water Distribution Networks

Empirical Investigation of the Effect of Anti-Price-Gouging Law on Postdisaster Reconstruction Wages

Forecasting the architecture billings index (ABI) using machine learning predictive models

Identifying Pareto-optimal seismic rehabilitation strategies for water distribution networks considering decision maker’s risk attitudes

Identifying Pareto-optimal seismic rehabilitation strategies for water distribution networks considering decision maker's risk attitudes

Diagnosis of COVID-19 impact on the construction employment: Decline and recovery

Diagnosis of COVID-19 impact on the construction employment: Decline and recovery

Estimating energy savings of ultra-high-performance fibre-reinforced concrete facade panels at the early design stage of buildings using gradient boosting machines

Diagnosing and quantifying post-COVID-19 fluctuations in the architecture billings indices

Characterizing relationship between demand surge and post-disaster reconstruction capacity considering poverty rates

Risk-averse rehabilitation decision framework for roadside slopes vulnerable to rainfall-induced geohazards

Availability Heuristic in Construction Workforce Decision-Making amid COVID-19 Pandemic: Empirical Evidence and Mitigation Strategy

Identifying critical combination of roadside slopes susceptible to rainfall-induced failures

Effects of Network Uncertainty on Seismic Vulnerability Assessment of Water Pipe Networks

Impact of COVID-19 on the Diversity of the Construction Workforce

Deep learning for estimating energy savings of early-stage facade design decisions

Seismic Rehabilitation Optimization of Water Pipe Networks Considering Spatial Variabilities of Demand Criticalities and Seismic Ground Motion Intensities

GIS-Based Data Integration Approach for Rainfall-Induced Slope Failure Susceptibility Mapping in Clayey Soils

Collaborators (4)

Sooin Kim

Mohsen Hajibabaei

Robert Sitzenfrei

Bahram Abediniangerabi

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