World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
49
Citations
10551
World Ranking
5851
National Ranking
2655

Overview

Mani Golparvar-Fard is affiliated with the University of Illinois at Urbana-Champaign in the United States. Their research is situated primarily within the field of Engineering, with a particular focus on Civil and Structural Engineering, as well as Building and Construction. Additional subfields in their work include Geology, Radiological and Ultrasound Technology, and Artificial Intelligence.

The primary topics covered in their research include Infrastructure Maintenance and Monitoring, BIM and Construction Integration, 3D Surveying and Cultural Heritage, Occupational Health and Safety Research, Construction Project Management and Performance, Robotics and Sensor-Based Localization, and Manufacturing Process and Optimization.

Recent notable publications by Mani Golparvar-Fard include:

  • Automated Methods for Activity Recognition of Construction Workers and Equipment: State-of-the-Art Review (2020, Journal of Construction Engineering and Management)
  • Human-object interaction recognition for automatic construction site safety inspection (2020, Automation in Construction)
  • Bridge Inspection with Aerial Robots: Automating the Entire Pipeline of Visual Data Capture, 3D Mapping, Defect Detection, Analysis, and Reporting (2020, Journal of Computing in Civil Engineering)
  • Vision-Based Construction Worker Activity Analysis Informed by Body Posture (2020, Journal of Computing in Civil Engineering)
  • Scan2BIM-NET: Deep Learning Method for Segmentation of Point Clouds for Scan-to-BIM (2021, Journal of Construction Engineering and Management)

Mani Golparvar-Fard has published frequently in several key venues that include:

  • Automation in Construction
  • Journal of Computing in Civil Engineering
  • Journal of Construction Engineering and Management
  • Proceedings of International Structural Engineering and Construction
  • Construction Research Congress 2020

Their collaborative work often involves a number of frequent co-authors, such as:

  • Yoonhwa Jung
  • Amir Ibrahim
  • Fouad Amer
  • Dominic Roberts
  • Shuai Tang

The scientist's work spans several interconnected domains related to the application of advanced technologies and methodologies in construction and civil engineering, with strong emphases on automation, safety, and digital integration within construction processes. Through multiple publications and collaboration, their contributions reflect active engagement with areas such as activity recognition, site safety inspection, 3D mapping, and deep learning for building information modeling.

Best Publications

  • Visual monitoring of civil infrastructure systems via camera-equipped Unmanned Aerial Vehicles (UAVs): a review of related works

    Youngjib Ham;Kevin K. Han;Jacob J Lin;Mani Golparvar-Fard

  • Evaluation of image-based modeling and laser scanning accuracy for emerging automated performance monitoring techniques

    Mani Golparvar-Fard;Jeffrey Bohn;Jochen Teizer;Silvio Savarese

  • Automated Progress Monitoring Using Unordered Daily Construction Photographs and IFC-Based Building Information Models

    Mani Golparvar-Fard;Feniosky Peña-Mora;Silvio Savarese

  • Application of D4AR – A 4-Dimensional augmented reality model for automating construction progress monitoring data collection, processing and communication

    Mani Golparvar-Fard;Feniosky Peña-Mora;Silvio Savarese

  • Visualization of construction progress monitoring with 4D simulation model overlaid on time-lapsed photographs

    Mani Golparvar-Fard;Feniosky Peña-Mora;Carlos A. Arboleda;SangHyun Lee

  • Construction performance monitoring via still images, time-lapse photos, and video streams

    Jun Yang;Man-Woo Park;Patricio A. Vela;Mani Golparvar-Fard

  • Vision-based action recognition of earthmoving equipment using spatio-temporal features and support vector machine classifiers

    Mani Golparvar-Fard;Arsalan Heydarian;Juan Carlos Niebles

  • Automated 2D detection of construction equipment and workers from site video streams using histograms of oriented gradients and colors

    Milad Memarzadeh;Mani Golparvar-Fard;Juan Carlos Niebles

  • Target-free approach for vision-based structural system identification using consumer-grade cameras

    Hyungchul Yoon;Hazem Elanwar;Hazem Elanwar;Hajin Choi;Mani Golparvar-Fard

  • Enhancing construction hazard recognition with high-fidelity augmented virtuality

    Alex Albert;Matthew R. Hallowell;Brian Kleiner;Ao Chen

  • Vision-based material recognition for automated monitoring of construction progress and generating building information modeling from unordered site image collections

    Andrey Dimitrov;Mani Golparvar-Fard

  • Appearance-based material classification for monitoring of operation-level construction progress using 4D BIM and site photologs

    Kevin K. Han;Mani Golparvar-Fard

  • Potential of big visual data and building information modeling for construction performance analytics: An exploratory study

    Kevin K. Han;Mani Golparvar-Fard

  • Integrated Sequential As-Built and As-Planned Representation with D4AR Tools in Support of Decision-Making Tasks in the AEC/FM Industry

    Mani Golparvar-Fard;Mani Golparvar-Fard;Mani Golparvar-Fard;Feniosky Peña-Mora;Feniosky Peña-Mora;Feniosky Peña-Mora;Silvio Savarese;Silvio Savarese;Silvio Savarese

  • Segmentation of building point cloud models including detailed architectural/structural features and MEP systems

    Andrey Dimitrov;Mani Golparvar-Fard

  • Image-Based Automated 3D Crack Detection for Post-disaster Building Assessment

    Matthew M. Torok;Mani Golparvar-Fard;Kevin B. Kochersberger

  • Mapping actual thermal properties to building elements in gbXML-based BIM for reliable building energy performance modeling

    Youngjib Ham;Mani Golparvar-Fard

  • Automated Methods for Activity Recognition of Construction Workers and Equipment: State-of-the-Art Review

    Behnam Sherafat;Changbum R. Ahn;Reza Akhavian;Amir H. Behzadan

  • High-precision vision-based mobile augmented reality system for context-aware architectural, engineering, construction and facility management (AEC/FM) applications

    Hyojoon Bae;Mani Golparvar-Fard;Jules White

  • End-to-end vision-based detection, tracking and activity analysis of earthmoving equipment filmed at ground level

    Dominic Roberts;Mani Golparvar-Fard

  • Four-dimensional augmented reality models for interactive visualization and automated construction progress monitoring

    Mani Golparvar-Fard;Feniosky A. Peña-Mora;Silvio Savarese

Frequent Co-Authors

Feniosky Peña-Mora
Feniosky Peña-Mora Columbia University
Khaled El-Rayes
Khaled El-Rayes University of Illinois at Urbana-Champaign
Silvio Savarese
Silvio Savarese Stanford University
Jules White
Jules White Vanderbilt University
Juan Carlos Niebles
Juan Carlos Niebles Stanford University
David Forsyth
David Forsyth University of Illinois at Urbana-Champaign
Martin Fischer
Martin Fischer Stanford University
Timothy Bretl
Timothy Bretl University of Illinois at Urbana-Champaign
Matthew R. Hallowell
Matthew R. Hallowell University of Colorado Boulder
Billie F. Spencer
Billie F. Spencer University of Illinois at Urbana-Champaign

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