Mohammad R. Jahanshahi
Cited by
Cited by
NB-CNN: Deep learning-based crack detection using convolutional neural network and Na´ve Bayes data fusion
FC Chen, MR Jahanshahi
IEEE Transactions on Industrial Electronics 65 (5), 4392-4400, 2018
An innovative methodology for detection and quantification of cracks through incorporation of depth perception
MR Jahanshahi, SF Masri, CW Padgett, GS Sukhatme
Machine vision and applications 24 (2), 227-241, 2013
A survey and evaluation of promising approaches for automatic image-based defect detection of bridge structures
MR Jahanshahi, JS Kelly, SF Masri, GS Sukhatme
Structure and Infrastructure Engineering 5 (6), 455-486, 2009
Evaluation of deep learning approaches based on convolutional neural networks for corrosion detection
DJ Atha, MR Jahanshahi
Structural Health Monitoring 17 (5), 1110-1128, 2018
Unsupervised approach for autonomous pavement-defect detection and quantification using an inexpensive depth sensor
MR Jahanshahi, F Jazizadeh, SF Masri, B Becerik-Gerber
Journal of Computing in Civil Engineering 27 (6), 743-754, 2013
A texture‐based video processing methodology using Bayesian data fusion for autonomous crack detection on metallic surfaces
FC Chen, MR Jahanshahi, RT Wu, C Joffe
Computer‐Aided Civil and Infrastructure Engineering 32 (4), 271-287, 2017
Automated defect classification in sewer closed circuit television inspections using deep convolutional neural networks
SS Kumar, DM Abraham, MR Jahanshahi, T Iseley, J Starr
Automation in Construction 91, 273-283, 2018
Multi-image stitching and scene reconstruction for evaluating defect evolution in structures
MR Jahanshahi, SF Masri, GS Sukhatme
Structural Health Monitoring 10 (6), 643-657, 2011
A new methodology for non-contact accurate crack width measurement through photogrammetry for automated structural safety evaluation
MR Jahanshahi, SF Masri
Smart materials and structures 22 (3), 035019, 2013
Autonomous pavement condition assessment
MR Jahanshahi, FJ Karimi, SF Masri, B Becerik-Gerber
US Patent 9,196,048, 2015
Inexpensive multimodal sensor fusion system for autonomous data acquisition of road surface conditions
YL Chen, MR Jahanshahi, P Manjunatha, WP Gan, M Abdelbarr, SF Masri, ...
IEEE Sensors Journal 16 (21), 7731-7743, 2016
Image-based crack quantification
MR Jahanshahi, S Masri
US Patent 9,235,902, 2016
Deep convolutional neural network for structural dynamic response estimation and system identification
RT Wu, MR Jahanshahi
Journal of Engineering Mechanics 145 (1), 04018125, 2019
Data fusion approaches for structural health monitoring and system identification: past, present, and future
RT Wu, MR Jahanshahi
Structural Health Monitoring 19 (2), 552-586, 2020
3D dynamic displacement-field measurement for structural health monitoring using inexpensive RGB-D based sensor
M Abdelbarr, YL Chen, MR Jahanshahi, SF Masri, WM Shen, UA Qidwai
Smart materials and structures 26 (12), 125016, 2017
Pruning deep convolutional neural networks for efficient edge computing in condition assessment of infrastructures
RT Wu, A Singla, MR Jahanshahi, E Bertino, BJ Ko, D Verma
Computer‐Aided Civil and Infrastructure Engineering 34 (9), 774-789, 2019
Parametric performance evaluation of wavelet-based corrosion detection algorithms for condition assessment of civil infrastructure systems
MR Jahanshahi, SF Masri
Journal of computing in civil engineering 27 (4), 345-357, 2013
Color and depth data fusion using an RGB‐D sensor for inexpensive and contactless dynamic displacement‐field measurement
YL Chen, M Abdelbarr, MR Jahanshahi, SF Masri
Structural Control and Health Monitoring 24 (11), e2000, 2017
Effect of color space, color channels, and sub-image block size on the performance of wavelet-based texture analysis algorithms: An application to corrosion detection on steelá…
MR Jahanshahi, SF Masri
Computing in Civil Engineering (2013), 685-692, 2013
An evaluation of image‐based structural health monitoring using integrated unmanned aerial vehicle platform
MA Akbar, U Qidwai, MR Jahanshahi
Structural Control and Health Monitoring 26 (1), e2276, 2019
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