Category Archives: Research Showcase

Harsh Braking as a Surrogate for Crash Risk: A Segment-Level Analysis with Connected Vehicle Telematics

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Presenter: Md Tufajjal Hossain

Organization: New Jersey Institute of Technology

SAFETY

Abstract:

None 

Md. Tufajjal Hossain is a Ph.D. student in Transportation Engineering at the New Jersey Institute of Technology (NJIT). His research focuses on traffic flow modeling, intelligent transportation systems, and AI-driven traffic safety analysis. His recent work includes developing real-time incident detection models using crowdsourced Waze data and designing a data-driven framework for optimal Safety Service Patrol route identification based on historical crash data. He also explores crash severity prediction using large language models to enhance roadway safety analytics. At NJIT, he serves as a Teaching Assistant and has contributed to NJDOT-funded research at the Intelligent Transportation Systems Research Center. He is the recipient of the 2025 ITSNJ Outstanding Graduate Student Award and the Best Poster Award at the 2024 ITSNJ Annual Meeting, recognizing his academic excellence and contributions to advancing intelligent and data-driven transportation systems. 

Presentation Slides:

To be added.

Beyond the Clock: Sustainable Solutions for Returned Ready-Mix Concrete

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Presenter: Mohamed Mahgoub

Organization: New Jersey Institute of Technology

INFRASTRUCTURE

Abstract:

A significant portion of ready-mixed concrete, estimated at around 3% of total production, is returned to plants for disposal each year due to issues such as slump loss during transport, surplus production, and strict adherence to the 90-minute discharge time limit set by ASTM C94 and referenced in ACI 318-19. While this rule aims to preserve concrete quality, it often leads to the rejection of truckloads, particularly in congested urban areas, thereby increasing costs, waste generation, and environmental impacts.

To address this challenge, research funded by the Ready Mixed Concrete (RMC) Research & Education Foundation and Portland Cement Association (PCA) examined the effects of extending discharge time on durability and performance. Concrete mixtures, representative of field practice, were prepared and tested at intervals up to 150 minutes, with properties such as air content, slump, temperature, compressive strength, freeze-thaw resistance, and surface resistivity evaluated.

The findings revealed that extending discharge time to 150 minutes had no significant adverse effect on fresh or hardened properties, suggesting that current specifications are overly conservative and could be revised to reduce unnecessary waste, costs, and environmental burdens. 

Mohamed Mahgoub, PhD and PE, is an NJIT Associate Professor and Concrete Industry Management Program Director. He is also a Fellow of ACI. He is an expert in bridge rehabilitation, inspection, rating, design and analysis. Dr. Mahgoub received his Master’s Degree from McMaster University in Hamilton, Ontario, Canada and his doctorate from Carleton University, Ottawa, Canada. Prior to joining NJIT, he was the lead bridge engineer for the Chicago consulting firm Alfred Benesch & Company, working on bridge design for the Michigan DOT. His personal experience includes: highway bridge analysis and design, rehabilitation and construction, and scour analysis. Dr. Mahgoub has designed several bridges in Michigan, Illinois, Wisconsin and Pennsylvania. He has also performed several bridge inspections and load rating in several big cities in Michigan. He was in charge of performing annual scour analyses of all primary and secondary bridges in Calhoun County, MI. After joining NJIT, Dr. Mahgoub was involved in research of several construction material projects for several associations, companies, and state institutions. He was also involved in RAC research. Dr. Mahgoub has served as a member in organizations such as ASCE, PCI, ICRI, and ACI. Dr. Mahgoub has been appointed as the vice president of the local New Jersey ACI Chapter, has been selected as a judge for their annual award, and is also the advisor of NJIT ACI Student Chapter. Dr. Mahgoub has more than 20 technical and scientific publications and presentations to his credit. Dr. Mahgoub has been also serving as a panelist for the NSF and NRC. 

Presentation Slides:

To be added.

Multi-Agent Large Language Model Framework for Code-Compliant Infrastructural Design

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Presenter: Jinxin Chen

Organization: Stevens Institute of Technology

STRATEGIC WORKFORCE DEVELOPMENT

Abstract:

Current structural design practices for infrastructure projects rely on time-intensive manual calculations and code compliance verification, creating bottlenecks in project delivery and potential for human error. This research presents a multi-agent Large Language Model (LLM) framework that automates code-compliant infrastructural design while maintaining interpretability and verifiability which is essential requirements for infrastructure applications.

The framework employs specialized LLM agents coordinated through task distribution: a Task Dispatcher routes design queries to dedicated Design and Evaluation agents, which interface with deterministic calculation tools programmed according to structural design codes. An Expert Consultation agent enables iterative refinement through natural language interaction, supporting the design optimization process. Case studies on reinforced concrete beam design demonstrated exceptional performance: 97% accuracy compared to industry-standard software (SAP2000), 90% time reduction compared to traditional methods, and complete transparency through step-by-step calculations with explicit code references. Statistical validation across 30 design cases showed Mean Absolute Percentage Error below 3% for critical structural parameters.

The framework’s modular architecture enables adaptation to various infrastructure applications by incorporating different design codes and specialized calculation modules. Engineers can specify requirements in natural language while receiving compliant solutions with detailed explanations, facilitating rapid design iteration and supporting workforce development through transparent educational content. This research demonstrates an approach to infrastructure design automation that preserves engineering judgment while eliminating routine calculation tasks. The framework represents a significant step toward preparing the engineering workforce for AI-enhanced infrastructure development. 

Jinxin Chen is a PhD candidate in Civil Engineering at Stevens Institute of Technology, specializing in the integration of artificial intelligence and machine learning into structural design and infrastructure applications. His research focuses on developing automated design tools that enhance workforce efficiency while maintaining engineering rigor and code compliance. Mr. Chen has authored multiple peer-reviewed publications on computational methods in structural engineering, including work on machine learning for ultra-high-performance concrete and AI-assisted design frameworks. His current research on large language model frameworks for infrastructure design aims to streamline project delivery while supporting knowledge transfer and workforce development in the engineering profession. 

Presentation Slides:

To be added.