Muhammad Zohaib Sarwar, PhD

I am a computational engineer and data scientist specializing in predictive modeling and AI-driven solutions for infrastructure and transportation systems. Currently a Postdoctoral Fellow at the Norwegian University of Science and Technology (NTNU), I leverage expertise in structural dynamics and machine learning to develop intelligent monitoring and predictive maintenance systems for critical infrastructure. My work integrates advanced data analytics, time-series modeling, and cloud-based IoT platforms to enable real-time condition assessment of bridges, rail networks, and other complex engineering systems.

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Muhammad Zohaib Sarwar

Research

My research interests include structural health monitoring, AI-based damage detection, and railway condition monitoring. I develop predictive models integrating civil engineering with advanced machine learning.

Projects

  • Predictive Maintenance for Railways (2023–Present): CI/CD workflow for predictive models of railway wheel wear (Python, Azure).
  • Intelligent Concrete Drying System (2022–2023): Sensor fusion model for concrete curing optimization (IoT, ML).
  • Autonomous UAV for Bridge Inspection (2018–2019): Automated defect detection platform using UAVs and computer vision.

Publications

Clustered Federated Learning for Population-Based Structural Health Monitoring
Cheema M.A., Sarwar M.Z., Salvo Rossi P., Cantero D.
IEEE Internet of Things Journal, 2025

Numerical benchmark applied to four data-driven methods for road bridge damage detection from passing vehicles responses
Cantero D., Sarwar M.Z., Malekjafarian A., Corbally R., Makki Alamdari M., Cheema P., Noh H.Y., Liu J., Aggarwal J.
Archives of Civil and Mechanical Engineering, 2024

Computationally-efficient structural health monitoring using graph signal processing
Cheema M.A., Sarwar M.Z., Gogineni V.C., Cantero D., Salvo Rossi P.
IEEE Sensors Journal, 2024

Probabilistic autoencoder-based bridge damage assessment using train-induced responses
Sarwar M.Z., Cantero D.
Mechanical Systems and Signal Processing, 2024

Vehicle-assisted bridge damage assessment using probabilistic deep learning
Sarwar M.Z., Cantero D.
Measurement, 2023

Deep autoencoder architecture for bridge damage assessment using responses from multiple vehicles
Sarwar M.Z., Cantero D.
Engineering Structures, 2021

Instant bridge visual inspection using a UAV by image capturing and geo-tagging system and deep convolutional neural network
Saleem M.R., Park J.W., Lee J.H., Jung H.J., Sarwar M.Z.
Structural Health Monitoring, 2021

Multimetric event-driven system for long-term wireless sensor operation for SHM applications
Sarwar M.Z., Saleem M.R., Park J.W., et al.
IEEE Sensors Journal, 2020

Bridge displacement estimation using a co-located acceleration and strain sensor
Sarwar M.Z., Park J.W.
Sensors, 2020

Conferences

  • Sarwar, M.Z.; Cantero, D. (2022). Data-driven bridge damage detection using multiple passing vehicle responses. IABMAS 2022, Barcelona, Spain.
  • Sarwar, M.Z.; Cantero, D. (2021). Unsupervised deep learning-based bridge damage detection using a fleet-sourcing concept. SHMII-10, Porto, Portugal.
  • Park, J.W.; Sarwar, M.Z. (2018). Ultra low-power smart wireless sensor network with event-based operation. SMAR 2018, Spain.

Technical Reports

Academic Activities

  • Research Mentor for PhD researcher, NTNU Electronic Engineering (2025).
  • Supervised MSc thesis on ML in structural engineering, NTNU (2023).
  • Teaching Assistant, Structural Dynamics, Chung-Ang University (2018).
  • Undergraduate project mentor, Chung-Ang University (2017).
  • Reviewer for journals: IEEE Transactions on Industrial Informatics, Mechanical Systems and Signal Processing, IEEE Sensors Journal, Measurement, Sensors MDPI, Scientific Reports.
  • International reviewer, Swiss National Science Foundation (SNSF).

Education