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Dr. Kamran Latif

Assistant Professor (Civil)

Overview:

Dr. Kamran Latif serves as an Assistant Professor in the Civil Engineering Department at the National University of Technology (NUTECH), Islamabad, Pakistan. He earned a Ph.D. in Civil Engineering from Hanyang University, South Korea, specializing in Advanced Infrastructure Systems. His doctoral research addressed deep learning-based activity recognition of excavators using multi-stream input data. His research interests encompass Building Information Modeling (BIM), Digital Twin, construction automation, construction management, artificial intelligence applications, geotechnical and tunneling engineering, and risk management. He has published research articles in international journals, including the ASCE Journal of Computing in Civil Engineering and Tunneling and Underground Space Technology. At NUTECH, he teaches undergraduate and postgraduate courses and contributes to research innovation and academic development.

Experience:

  • Assistant Professor, Department of Civil Engineering, NUTECH (2025-Present)
  • PhD Research Scholar, Hanyang University, South Korea (2021-2025)

Qualification:

  • Ph.D. Civil Engineering (MS. Leading to Ph.D. Civil Engineering)                 2025

Hanyang University, Seoul, South Korea

  • B.Sc. Mining Engineering                                                                                     2015

UET Lahore, Punjab, Pakistan

Taught Courses:

I am teaching the following courses in the current semester.

  1. Soil Mechanics (Undergrad)
  2. Geotechnical Engineering (Undergrad)
  3. Construction Project Management and Control (Postgraduate)

Research:

JOURNAL PAPERS

  1. Cho, HS., Latif, K., A Sharafat., & Seo, J. (2025). Multi-Modal Excavator Activity Recognition Using Two-Stream CNN-LSTM with RGB and Point Cloud Inputs. Applied Sciences in Special Issue AI-Based Machinery Health Monitoring https://doi.org/10.3390/app15158505
  2.  
  3. Latif, K., A Sharafat., & Seo, J. (2023). Digital twin-driven framework for TBM performance prediction, visualization, and monitoring through machine learning. Applied Sciences in Special Issue Application of Geographic Information System and Building Information Modelling II. https://doi.org/10.3390/app132011435
  4.  
  5. Kim, I.-S.; Latif, K.; Kim, J.; Sharafat, A.; Lee, D.-E. & Seo, J. (2022) Vision-based activity classification of excavators by bidirectional LSTM. Applied Sciences in Special Issue Application of Geographic Information System and Building Information Modelling II. https://doi.org/10.3390/app13010272
  6.  
  7. Sharafat, A., Khan, M.S., Latif, K., Tanoli, W. A., Park, W., & Seo, J. (2021). BIM-GIS-Based Integrated Framework for Underground Utility Management System for Earthwork Operations. Applied Sciences in Special Issue Application of Geographic Information System and Building Information Modelling). https://doi.org/10.3390/app11125721
  8.  
  9. Sharafat, A., Latif, K., & Seo, J. (2021). Risk Analysis of TBM Tunneling Projects Based on Generic Bow-Tie Risk Analysis Approach in Difficult Ground Conditions. Tunnelling and Underground Space Technology. https://doi.org/10.1016/j.tust.2021.103860
  10.  
  11. Sharafat, A., Khan, M.S., Latif, K., & Seo, J. (2022). BIM-Based Tunnel Information Modelling Framework for Visualization, Management, and Simulation of Drill-and-Blast Tunnelling Projects. ASCE Journal of Computing in Civil Engineering https://doi.org/10.1061/(ASCE)CP.1943-5487.0000955
  12.  

CONFERENCE PAPERS

  1. Munir, M., Khan, U., Suhail, S. A., Latif, K., & Yousaf, H. N. (2025). Developing a Composite Index to Measure the Integration of BIM, IPD, and Lean Construction: Analysis and Framework. Research Corridor Journal of Engineering Science, 2(2), 19-27. International Conference on Innovating for a Sustainable Future: Global Challenges and Solutions
  1. Latif, K., Sharafat, A., Deng, T., Park, S., & Seo, J. (2024). Digital Twin for Excavator-dump Optimization based on Two—stream CNN-LSTM and DES. Korean Society of Civil Engineering CONVENTION CONFERENCE & CIVIL EXPO.

 
  1. Deng, T., Sharafat, A., Latif, K., Lee, S., & Seo, J. (2024). AI-Based digital Twin for Real-time Monitoring of Dump Truck Productivity Using IoT and Deep Learning Transformer Model for License Plate Recognition. Korean Society of Civil Engineering CONVENTION CONFERENCE & CIVIL EXPO.

 
  1. Sharafat, A., Latif, K., Deng, T., Park, S., & Seo, J. (2023). Virtual Reality enabled digital twin for smart design of Tunnel Boring Machine (TBM). Korean Society of Civil Engineering CONVENTION CONFERENCE & CIVIL EXPO.

 
  1. Latif, K., Sharafat, A., Deng, T., Park, S., & Seo, J. (2023). Value-added activity recognition of excavator through machine learning (ML). Korean Society of Civil Engineering CONVENTION CONFERENCE & CIVIL EXPO.

 
  1. Deng, T., Sharafat, A., Lee, S., Latif, K., & Seo, J. (2023). Automatic Earthwork Productivity Measurement based on IOT and Passive RFID for Low Light Harsh Construction Environment. Korean Society of Civil Engineering CONVENTION CONFERENCE & CIVIL EXPO.

 
  1. Sharafat, A., Latif, K., Tanoli, W. A., & Seo, J. (2022). Framework for design optimization and assembly process simulation of Tunnel Boring Machine (TBM) based on Digital Twin and Virtual Reality. 22nd International Conference on Construction Applications of Virtual Reality (CONVR)

 
  1. Sharafat, A., Latif, K., Park, S., & Seo, J. (2022). Digital Twin-Driven Optimization of Blast Design for Underground Construction. Korean Society of Civil Engineering CONVENTION CONFERENCE & CIVIL EXPO.

 
  1. Latif, K., Sharafat, A., Park, S., & Seo, J. (2022). Digital Twin-Based Hybrid Approach to Visualize the Performance of TBM. Korean Society of Civil Engineering CONVENTION CONFERENCE & CIVIL EXPO.

 
  1. Latif, K., Sharafat, A., & Seo, J. (2021). Predicting TBM advanced rate in difficult ground conditions based on the artificial neural network using different training functions. Korean Society of Civil Engineering CONVENTION CONFERENCE & CIVIL EXPO.

 
  1. Sharafat, A., Latif, K., Park, S., & Seo, J. (2021). Risk assessment of TBM advanced rate in the difficult ground using Event Tree Analysis (ETA). Korean Society of Civil Engineering CONVENTION CONFERENCE & CIVIL EXPO.

 
  1. Sharafat, A., Latif, K., Khan, M.S., & Seo, J. (2020). Development of BIM-IFC Standard Data Model Framework for Rock Support of Drill-and-Blast Tunnelling Projects. Korean Society of Civil Engineering CONVENTION CONFERENCE & CIVIL EXPO.

 

 

Projects:

  • Project title: Digital Twin Framework for Drill-and-Blast Tunnelling Projects
    Supporting organization: Ministry of Land, Infrastructure and Transport, Republic of Korea.
  • Project title: Digital Twin Framework for Civil Engineering
    Supporting organization: National Research Foundation of Korea.
  • Intelligent control platform and big data-based optimal route navigation (C-Map) technology (Development of intelligent construction equipment control technology

Supporting organization: Ministry of Land, Infrastructure and Transport, Republic of Korea.