Mohsen Zaker Esteghamati

Assistant Professor

Utah State University

United States

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Mohsen Zaker Esteghamati is an Assistant Professor at Utah State University in the United States. His research areas include the application of machine learning in structural engineering, seismic design, and the evaluation of infrastructure resilience against geohazards. He has recently published articles addressing topics such as the impact of modeling decisions on seismic assessments and the efficiency of explainable data-driven models in estimating structural performance.

Articles (16)

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2025

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2025

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2023

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2022

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2022

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2022

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2020

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2020

Collaborators (8)

Scott Brandenberg

Professor

University of California, Los Angeles

UNITED STATES

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Srishti Banerji

Assistant Professor of Structural Engineering

Utah State University

UNITED STATES

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Thomas Gernay

Associate Professor

Johns Hopkins University

UNITED STATES

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Chukwuebuka Nweke

Assistant Professor

University of Southern California

UNITED STATES

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Roberto Gentile

University College London

UNITED KINGDOM

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Georg Reichard

Professor and Department Head

Virginia Tech

UNITED STATES

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Oladoyin Kolawole

Assistant Professor

New Jersey Institute of Technology

UNITED STATES

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Brennan Bean

Assistant Professor

Utah State University

UNITED STATES

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Mohsen Zaker Esteghamati

Research Interests

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About

Articles (16)

Towards using explainable data-driven surrogate models for treating performance-based seismic design as an inverse engineering problem

Impact of modeling decisions on seismic loss and fragility assessment of steel buildings

Beyond Development: Challenges in Deploying Machine-Learning Models for Structural Engineering Applications

Insights into susceptibility of underground infrastructure to geohazards due to subsurface urban heat Island

A Numerical Investigation of the Performance of Damaged Concrete Barriers Under Sequential Vehicular Impacts

Efficiency and explainability of design‐oriented machine learning models to estimate seismic response, fragility, and loss of a steel building inventory

Evaluating fire resistance of timber columns using explainable machine learning models

Evaluating the impact of higher-mode and inelastic dynamic responses of concrete frames on the performance of seismic intensity measures

Do all roads lead to Rome? A comparison of knowledge-based, data-driven, and physics-based surrogate models for performance-based early design

Sustainable early design exploration of mid-rise office buildings with different subsystems using comparative life cycle assessment

A relational database to support post-earthquake building damage and recovery assessment

A Data-Driven Approach to Evaluate Site Amplification of Ground-Motion Models Using Vector Proxies Derived from Horizontal-to-Vertical Spectral Ratios

A Holistic Review of GM/IM Selection Methods from a Structural Performance-Based Perspective

Estimating seismic demand models of a building inventory from nonlinear static analysis using deep learning methods

INSSEPT: An open-source relational database of seismic performance estimation to aid with early design of buildings

Probabilistic seismic performance and loss evaluation of a multi-story steel building equipped with butterfly-shaped fuses

Collaborators (8)

Scott Brandenberg

Srishti Banerji

Thomas Gernay

Chukwuebuka Nweke

Roberto Gentile

Georg Reichard

Oladoyin Kolawole

Brennan Bean

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