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Research Interests
Research Interests
Risk Assessment of Transportation Infrastructure subjected to Climate Change and Emerging Technology
Applications of Artificial Intelligence for Infrastructure Performance Prediction and Maintenance
Network-Level Risk Analysis of Transportation Systems
Geospatial Modeling and Data-Driven Approaches for Optimizing Traffic Management and Road Services
Research Activities
Research Activities
FUTURE PROOFING TRANSPORT INFRASTRUCTURE FROM CLIMATE CHANGE &
TRANSPORTATION TECHNOLOGY DISRUPTIONS
Classification of Coastal Flood Hazard to Bridges in Nova Scotia
Abstract:
Coastal areas around the world are deeply affected by climate change, leading to significant vulnerabilities in transportation systems, particularly bridges. Bridge failures can drastically disrupt transportation networks, resulting in major economic and social consequences. The Nova Scotia Flood of 2023 serves as a grim reminder of climate change's toll on the Atlantic coastal regions of Canada, where extreme weather events like flooding are increasingly common and intense. Such events cause greater riverbed erosion and scour at bridge foundations, significantly raising the risk of their collapse. This study introduces a risk-based approach to evaluate the vulnerability of bridges in Nova Scotia's coastal areas, focusing specifically on abutment scour. It uses statistical modeling to connect downscaled climate data from global models with local streamflow under various climate scenarios. The study also calculates the changing probability of bridge failure over time, considering factors like foundation depth and increased scour from climate change. Furthermore, it expands to a network-wide scale, assessing how potential bridge failures could affect travel times, traffic flow, and congestion. This comprehensive evaluation aims to understand the wider effects on traffic mobility and network connectivity. Amid limited resources for bridge management and rising climate risks, this research provides critical insights for making informed decisions on bridge climate adaptation strategies, ensuring the continued safety and functionality of transportation networks in the face of Canada's evolving climate.
COMPARATIVE ANALYSIS OF BUILDING WITH SHEAR WALL & DIAGRID STRUCTURES
Abstract:
The rapid growth of urban population, the lack of spaces in the cities and the high cost of land have already forced the developers to focus on the high rise buildings. As the height of the building increases, the lateral load resistant system become more important than gravity load resistant system. That’s why it is important to define lateral load resistant system in high rise buildings. So the lateral load resistant systems such as shear wall and diagrid are introduced since they are better in terms of cost, aesthetic and performance. However, the diagrid structural system has become more popular these days due to its efficiency and aesthetic look provided by the unique geometric configurations of the system.
In this study, a comparative analysis has been done on the buildings with different lateral load resisting systems. Five different building model of G+9 story building has been modeled with shear wall and diagrid structure to compare their performance. The design is analyzed for seismic zone V and medium soil condition as per IS 1893:2016 using finite modelling software. The building is kept the same except for the lateral load resisting system. From the patterns of the results, it was concluded that building models with the combination of shear wall and diagrid module has better performance in term of the maximum story displacement, story stiffness, story drift, base shear, and time period.
IMPACT OF COVID-19 ON ROAD TRANSPORTATION IN MARYLAND, USA
Abstract
The COVID-19 pandemic caused business shutdown worldwide because of which there was a significant reduction in roadway traffic movement and vehicle miles travelled. This, in turn resulted in lower gasoline sales. This paper assesses the economic impact of reduced vehicle miles of travel due to COVID-19 and determines the impact on the revenue generated in the highway trust fund due to reduced gasoline taxes collected in Maryland, USA. An economic loss assessment is performed using gasoline sales and vehicle miles of travel. The effect of declining Annual Average Daily Traffic (AADT) between 2018-2020 is analyzed for the 23 Maryland Counties and Baltimore city, to identity Counties that were significantly impacted due to the declining AADT. Finally, a machine learning model using artificial neural network is performed to predict the significance of various Counties subjected to declining AADT during COVID-19. The results are useful for assessing the economic impact of COVID-19 on roadway improvement projects owing to reduced gasoline sales.
A Machine Learning Approach to Traffic Congestion Hotspot Identification and Prediction
Abstract
Travel-time delays due to recurring congestion result in productivity loss, likelihood of accidents, and environmental pollution due to greenhouse gas emission. The National Highway Traffic Safety Administration in the United States has listed several driver assistance technologies that are now common in most of the newer vehicles. While these technologies can help reduce the likelihood of traffic related accidents, they can do very little to reduce recurring congestion prevalent in urban areas. Recurring congestion during rush hours is prevalent, for example, along Interstate 95 and Capital Beltway 495 in the Baltimore-Washington area. Such congestion enhances the likelihood of crashes. Previous approaches to hotspot identification are primarily theoretical which limits their practical applicability. This paper develops a Machine Learning approach by way of a geospatial and neural network integration to predict traffic congestion hotspots during rush hour. The approach uses live traffic sensor data. A case study from Maryland is presented. The result shows top hotspot segments across Maryland. Using a snapshot of hotspots at eight different time periods, the likelihood of hotspot locations is predicted using an artificial neural network. The research can serve as a valuable tool for traffic congestion hotspot identification and travel-time prediction.
wctr_paper_traffic_congestion_v2.pptx
Resilient Roads in Challenging Terrain: A Case Study of Siddhartha Highway in Nepal
Abstract
Nepal, a country known for its diverse and challenging topography, relies heavily on a robust road infrastructure network to connect its remote regions and urban centers. Amongst the many critical road sections in the country, Siddhartha Highway, a vital transportation artery in Nepal, traverses diverse terrains and experiences a high volume of traffic, making it a critical focal point for research and development. This research paper aims to evaluate and compare the suitability of flexible and rigid pavement solutions for different sections of Siddhartha Highway, focusing on road safety and durability. The paper commences by identifying the prevailing challenges faced on Siddha Baba Road section, characterized by potholes, faulting in rigid pavements, alligator cracking, rutting, inadequate drainage, and recurring landslides. For each problem, potential remedies are proposed, ranging from full-depth patching to slope stabilization measures. The analysis underscores the need for comprehensive infrastructure upgrades to enhance road safety and traffic management. To address the choice between flexible and rigid pavement solutions, we factor in the terrain conditions and accident records along the highway. The flexible pavement solution is economical and can be reinforced as traffic grows, while the rigid pavement offers longevity and better resistance to landslides and severe environmental conditions. The study concludes by recommending a combination of pavement types, suggesting flexible pavement for Siddhababa Road section and rigid pavement for other sections of Siddhartha Highway. This strategy offers an optimal balance between economics and safety in enhancing the road network's overall performance. This research paper contributes to the field of transportation engineering by providing valuable insights into the selection of pavement types that can effectively address road challenges in hilly and mountainous regions, while prioritizing safety, durability, and economic feasibility.
REVIEW ACTIVITIES
REVIEW ACTIVITIES
ASCE Journal of Structural Design and Construction Practice
Transportation Research Board (TRB) Annual Meeting
Canadian Society for Civil Engineering (CSCE)'s Annual Conference
Geo-Congress 2026, Salt Lake City, Utah
GeoMandu – A Nepal Geotechnical Society (NGS) International Conference Series
IEEE AFRICON Conference 2023
Innovations in Education Conference 2024
SUPERVISOn & COLLABORATION
SUPERVISOn & COLLABORATION
Dr. Cancan Yang, Associate Professor, McMaster University, CA (PhD Supervisor)
Dr. Wael El-Dakhakhni, Professor, McMaster University, CA (PhD Co-Supervisor)
Dr. Hao Yang, Associate Professor, McMaster University, CA
Dr. Sonia Hassini, Assistant Professor, McMaster University, CA
Dr. Anil K. Bachu, Assistant Professor, IIT Patna, IN (MTech Supervisor)
Dr. Manoj K. Jha, Adjunct Associate Professor, University Of Maryland Global Campus, USA
Dr. Imrose Muhit, Senior Lecturer, Teesside University, U.K.
Dr. Arvind Kumar Jha, Associate Professor, IIT Patna, IN
Dr. Junan Shen, Professor, Georgia Southern University, USA
Dr. Shalini Rankavat, Associate Professor, Shiv Nadar University, IN
Dr. Mingsai Xu, Assistant Professor, Xiamen University, CH
Dr. Haifeng He, Postdoctoral Fellow, McMaster University, CA
UBMS Research Group, Bridge Management System, IN
Mr. Will Crocker, Structures Assets Management Engineer, Nova Scotia Public Works, CA
Mr. Ankit Mahajan, Assistant Professor, Chandigarh University, IN
Mr. Santosh Pandey, Structural Engineer, Kathmandu Metropolitan, NP (BTech Supervisor)
Dr. Hellon Ogallo, Engineering Team Leader, Maryland State Highway Administration, USA
Ms. Jing Li, PhD Scholar, McMaster University, CA
Mr. S.K. Varma, PhD Scholar, Shiv Nadar University, IN
Mr. Nishesh Chhetri, M.S. Student, University of Alaska Fairbanks, USA
Mr. Riyadul Hashem, PhD Student, Auburn University, USA
Mr. Uday Shankar Yadav, PhD Candidate, University of Massachusetts, USA
Mr. Rupesh Yadav, PhD Student, University of Colorado Denver, USA
Ms. Rajani Gautam, M.S. Student, Florida International University, USA
Mr. Naresh Bhatta, Undergraduate Student, Kathmandu University, NP
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