Welcome to NTIRI!

Welcome to the North Texas Initiative for Resilient Infrastructure, where innovation meets resilience for a stronger tomorrow. Explore our cutting-edge research, transformative projects, and community-driven solutions and education that shape the future of infrastructure resilience in North Texas and beyond.

NEWS

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NTIRI Members Awarded the U.S. Department of Labor Grant

Dr. Shahandashti, along with Dr. Roy, Dr. Yin, and Dr. Balderama, has been awarded the U.S. Department of Labor Susan Harwood Training Grant administered by the Occupational Safety and Health Administration (OSHA) for FY 2025. NTIRI members will develop training and educational materials for resilience workers involved in debris removal and clean-up, focusing on hazard awareness, avoidance, and control. The training will also inform resilience workers of their rights and employers of their responsibilities under the Occupational Safety and Health Act.

International Collaboration Spotlight

News Highlight
Dr. Hajibabaei

University of Innsbruck’s researcher and former visiting scholar at NTIRI, Dr. Hajibabaei, presented the collaborative research at EWRI 2021 USA and CCWI 2024 Italy

Dr. Mohsen Hajibabaei, former visiting scholar at NTIRI from the University of Innsbruck, presented collaborative research with NTIRI faculty at the EWRI 2021 conference in the United States and the CCWI 2024 conference in Italy.


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Alumni Spotlight

Dr. Pooya Darghiasi

Dr. Darghiasi presented Machine Learning approach to enhance transportation systems

Dr. Pooya Darghiasi presented his work at the ASCE International Conference on Transportation and Development (ICTD) 2025 in Glendale, Arizona. Dr. Darghiasi’s research focuses on using machine learning to enhance transportation systems. His poster, titled “Enhanced Road Surface Temperature Prediction Using Random Forest Model and NWS Weather Forecast Data,” highlights the development of a predictive model for road surface temperatures. This model, which utilizes the Random Forest algorithm and data from the National Weather Service (NWS), was found to be more accurate than other models such as linear regression, SVM, and KNN. The study identified ambient temperature and relative humidity as the most influential factors for predicting road surface temperature, demonstrating the practical application of this research in supporting effective winter maintenance efforts and improving road safety.

Dr. Sumaya Sharveen

Dr. Sharveen Advances Seismic Resilience in Road Infrastructure

Dr. Sharveen recently presented innovative research focused on improving the seismic resilience of concrete road networks through advanced optimization frameworks. Her first study introduces a risk-averse decision-making model that integrates Monte Carlo simulations, traffic modeling, and a customized simulated annealing algorithm to guide seismic rehabilitation planning under uncertainty. In a second study, she unveiled a computationally efficient framework that utilizes topological surrogates and genetic algorithms to significantly reduce processing time while maintaining decision quality. These contributions provide critical tools for transportation agencies to make informed, resource-conscious decisions in seismically active regions.

News Highlight
Dr. Sooin Kim

Dr. Kim joins Wayne State University as an Assistant Professor

NTIRI alumni Dr. Sooin Kim joined Wayne State University as an Assistant Professor after graduating from the University of Texas at Arlington. Alumni Dr. Kim contributed to several National Science Foundation (NSF) funded research projects led by NTIRI’s members Dr. Shahandashti, Dr. Yasar, and Dr. Makhmalbaf.


University Profile

Student Highlight

Mr. Sandesh Adhikari

UTA Researcher Presents at Pipelines Conference 2025

Sandesh Adhikari, a Ph.D. student, recently presented his research at the Pipelines 2025 Conference. His study, “Assessing the Impact of Network Topology on Seismic Resilience in Gas Pipeline Networks,” explores how different pipeline configurations affect resilience under earthquake hazards. By generating and analyzing 100,000 synthetic networks, he compared tree-like and loop-like topologies using robustness, reliability, and vulnerability metrics. The findings show that loop-like networks generally maintain higher serviceability and robustness, while tree-like structures are more vulnerable. This research provides valuable insights for strengthening gas pipeline systems in seismically active regions.

News Highlight
Mr. Sushil Bhatta

UTA Researcher Presents AI-Driven Infrastructure Research at ASCE ICTD 2025

Ph.D. student Sushil Bhatta presented three innovative studies at the ASCE International Conference on Transportation and Development (ICTD) 2025 in Glendale, Arizona. Under the mentorship of Dr. Mohsen Shahandashti, Mr. Bhatta’s research focuses on applying artificial intelligence and data-driven techniques to improve transportation infrastructure resilience. His presentations showcased a deep learning approach for land cover classification to enhance slope failure predictions, an automated method for assessing slope conditions using street view and satellite imagery, and a machine learning model for accurately forecasting road surface temperatures to support winter maintenance efforts.