Dr. Mahdi Ghafoori

Dr. Mahdi Ghafoori

Title:

  • Assistant Professor

Contacts:

mghafoori@caad.msstate.edu
Office: (662) 325-8305
132 Howell Building

Overview

Summary:

Dr. Ghafoori is an Assistant Professor in the Building Construction Science and Data Science program at Mississippi State University. He is a civil engineer and data scientist, holding a Doctor of Philosophy (Ph.D.) in Engineering and Applied Sciences with a focus on civil engineering, and computer science from the University of Colorado. His research focuses on applying data science and artificial intelligence techniques, such as pattern recognition and machine learning, to enhance decision-making and address key challenges in the construction industry.

Research interests:

Data-Driven Infrastructure and Asset Management
Building sustainability and energy efficiency
Grid-interactive buildings and demand-side management
Construction Safety Enhancement through Wearable Sensor and Monitoring Technologies

Publications

Conference Proceeding

  • Assessment of Physical Demand in Bridge Rehabilitation Work by Physiological Status Monitoring.  . Pages 783--791. 2023
  • Multi-Objective Maintenance Optimization Model to Minimize Maintenance Costs While Maximizing Performance of Bridges.  . Pages 735--743. 2023
  • Physiological Metrics Across Construction Activity.  . Pages 792--800. 2023
  • Building Optimization Model for Minimizing Operation and Maintenance Costs.  . Pages 422--431. 2022
  • Estimating Electricity Consumption of Buildings Using Information Theory and Machine Learning Methods.  . Pages 432--440. 2022
  • Multi-Objective Optimization Model to Minimize Upgrade and Utility Costs of Large Existing Buildings.  . Pages 648--657. 2022
  • Optimizing selection of building materials and fixtures to reduce operational costs.  . 2019

Journal Article

  • Electricity peak shaving for commercial buildings using machine learning and vehicle to building (V2B) system. Applied Energy. Volume 340, Page 121052. 2023
  • Heart rate modeling and prediction of construction workers based on physical activity using deep learning. Automation in Construction. Volume 155, Page 105077. 2023
  • Innovative Optimization Model for Planning Upgrade and Maintenance Interventions for Buildings. Journal of Performance of Constructed Facilities. Volume 36, Issue 6, Page 04022051. 2022
  • Simulation-based optimization model to minimize equivalent annual cost of existing buildings. Journal of Construction Engineering and Management. Volume 148, Issue 2, Page 04021202. 2022
Architecture

(662) 325-2202

Building Construction Science

(662) 325-8305

Interior Design

(662) 325-0530

Dean's Office

(662) 325-5150