Real-Time Monitoring of Far-Field Concrete Cracks Using Distributed Acoustic Sensing

Posted on October 23, 2025November 5, 2025

Presenter: Yao Wang

Organization: Stevens Institute of Technology

INFRASTRUCTURE

Abstract:

Monitoring cracks is critical for the safety and quality of construction and operation of civil infrastructure. Distributed fiber optic sensors have been utilized to monitor near-field cracks but are insensitive to far-field cracks. This paper presents an approach for real-time monitoring of far-field cracks based on distributed acoustic sensing.

The approach was implemented into a concrete highway bridge, and the performance of the approach was evaluated using a computational model for multi-physics simulations. The results showed that the approach was able to accurately detect and locate far-field cracks six meters away from fiber optic cables with appropriate threshold and temperature compensation. The configurations of the sensing system, such as gauge length, channel spacing, and sampling rate, exhibited significant impacts on crack monitoring results and localization performance.

The capability of real-time monitoring of far field cracks advances the construction and operation of infrastructure.

Mr. Yao Wang is a Ph.D. student in the Department of Civil, Environmental, and Ocean Engineering at Stevens Institute of Technology, advised by Professor Yi Bao. His research focuses on structural health monitoring using advanced acoustic sensing technologies, including Distributed Acoustic Sensing, Acoustic Emission, and Guided Wave. He integrates experiments, multi-physics finite element modeling, and machine learning to investigate wave propagation, signal processing, and sensing configuration optimization for damage detection in civil infrastructure.

Presentation Slides:

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

Posted on October 23, 2025November 6, 2025

Presenter: Md Tufajjal Hossain

Organization: New Jersey Institute of Technology

SAFETY

Abstract:

Heavy traffic volumes, frequent lane merges, toll plazas, and complex interchanges often create conditions for sudden and forceful vehicular stops, known as harsh braking (HB). Traditional safety studies rely on historical crash records, a reactive approach that delays countermeasures. Since HB events are continuously captured by connected-vehicle telematics, their spatial and temporal patterns offer a proactive surrogate for identifying crash-prone roadway segments. Therefore, this study evaluates the potential of harsh braking (HB) events as a surrogate measure of crash risk on New Jersey interstate highways. More than 8.5 million Drivewyze telemetry records and 45,000 police-reported crashes from July to December 2024 were analyzed. HB events were identified by a deceleration threshold of 6 ft/sec² (approximately 0.2g) and spatially matched to one-mile highway segments along with crash data. Descriptive analysis revealed strong spatial clustering of HB events and crashes along high traffic volume corridors such as I-95, I-80, I-78, and I-287, particularly near toll plazas and complex interchanges. Seasonal patterns showed HB counts peaking in late fall, coinciding with higher traffic congestion and adverse weather conditions. Statistical modeling using Negative Binomial (NB) and Zero-Inflated Negative Binomial (ZINB) regressions demonstrated a positive and significant relationship between HB events and crash counts. In the preferred ZINB model, the HB coefficient was 0.01 (p = 0.03), indicating that each additional HB event was associated with roughly a 1 % increase in expected crash frequency per segment. Although the per-event effect was modest, segments with repeated HB activity exhibited substantially elevated crash risk; for instance, an increase of 10 HB events correspond to an expected crash frequency of about 10 % higher. These findings demonstrate that crowdsourced telematics can serve as a practical, proactive tool for highway safety management, supporting early detection of high-risk locations and guiding countermeasures such as improved signage, targeted enforcement, and geometric enhancements before crash records accumulate.

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:

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

Posted on October 23, 2025November 5, 2025

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:

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

Posted on October 23, 2025November 5, 2025

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.