Saeed Eftekhar Azam

Assistant Professor

University of New Hampshire

United States

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Saeed Eftekhar Azam is an Assistant Professor at the University of New Hampshire, USA. His research focuses on structural health monitoring (SHM) utilizing advanced methods such as machine learning, deep learning, and blockchain technologies. Recent articles include investigations into unsupervised machine learning for bridge SHM, Bayesian smoothing for state estimation, and novel architectures for structural damage detection.

Articles (24)

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2020

Collaborators (9)

Oreste Bursi

Professor

University of Trento

ITALY

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Amin Heidarpour

MONASH UNIVERSITY

AUSTRALIA

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Giuseppe Abbiati

Aarhus University

DENMARK

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alberto pavese

Associate Professor

University of Warwick University Bookshop

ITALY

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Erin Santini-Bell

Professor

University of New Hampshire

UNITED STATES

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Qusai A. Alomari

Research Assistant Professor

University of Nebraska - Lincoln

UNITED STATES

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Ahmed Rageh

Research and Teaching Assistant

University of Nebraska - Lincoln

UNITED STATES

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Daniel G. Linzell

University of Nebraska - Lincoln

UNITED STATES

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Daniel Dias-da-Costa

Associate Professor

University of Sydney

AUSTRALIA

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Saeed Eftekhar Azam

Research Interests

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About

Articles (24)

A novel CNN architecture for robust structural damage identification via strain measurements and its validation via full-scale experiments

Unsupervised machine learning for bridge SHM using FPGA: Proof of concept via full-scale experiments

BIONIB: Blockchain-Based IoT Using Novelty Index in Bridge Health Monitoring

Experimental evaluation of Bayesian smoothing for input and state estimation using computer vision

A Review of Digital Twinning Applications for Floating Offshore Wind Turbines: Insights, Innovations, and Implementation

SHM-Traffic: DRL and Transfer Learning Based UAV Control for Structural Health Monitoring of Bridges With Traffic

Introduction and application of a drive-by damage detection methodology for bridges using variational mode decomposition

An output-only unbiased minimum variance state estimator for linear systems

Minimum variance unbiased Bayesian smoothing for input and state estimation of systems without direct Feedthrough: Mitigating Ill-Posedness of online load identification

System Identification via Unscented Kalman Filtering and Model Class Selection

Weakly supervised crack segmentation using crack attention networks on concrete structures

Leveraging Deep Learning for Robust Structural Damage Detection and Classification: A Transfer Learning Approach via CNN

A linear Bayesian filter for input and state estimation of structural systems

Bridge Health Monitoring Using Proper Orthogonal Decomposition and Transfer Learning

Singular value decomposition and unsupervised machine learning for virtual strain sensing: Application to an operational railway bridge

Evaluating pod-based unsupervised damage identification using controlled damage propagation of out-of-service bridges

Experimental validation and numerical investigation of virtual strain sensing methods for steel railway bridges

A Bayesian smoothing for input‐state estimation of structural systems

A framework for hybrid simulation with online model updating suitable for hard real‐time computing

Fatigue crack detection in welded structural components of steel bridges using artificial neural network

On Bayesian active vibration control of structures subjected to moving inertial loads

Unsupervised Machine Learning for Robust Bridge Damage Detection: Full-Scale Experimental Validation

Auto-regressive model based input and parameter estimation for nonlinear finite element models

Steel railway bridge fatigue damage detection using numerical models and machine learning: Mitigating influence of modeling uncertainty

Collaborators (9)

Oreste Bursi

Amin Heidarpour

Giuseppe Abbiati

alberto pavese

Erin Santini-Bell

Qusai A. Alomari

Ahmed Rageh

Daniel G. Linzell

Daniel Dias-da-Costa

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