On August 27, 2025, the FHWA hosted a webinar titled “NJDOT’s Pilot Program for Internally Cured High Performance Concrete for Bridge Decks.” NJDOT Project Manager and Infrastructure Preservation CIA team lead Samer Rabie presented the department’s internally cured concrete (ICC) initiative.
The webinar highlighted NJDOT’s work as a case study for more than 300 participants nationwide, enabling agencies to learn from New Jersey’s experience with ICC and consider applications in their own states. After Mr. Rabie’s presentation, attendees asked questions about the EPIC2 initiative, including advice on how to achieve even water distribution, the expected life span of High Performance Internally Cured Concrete (HPIC) bridge decks, and whether internal curing techniques could be applied to other types of concrete.
Webinar Presentation
Transverse early-age cracking
As part of Round 6 of the Every Day Counts (EDC) initiative, NJDOT began implementing Ultra High Performance Concrete (UHPC) for Bridge Preservation and Repair, with plans to institutionalize its use in the upcoming bridge design manual. UHPC’s low water-cement ratio and high use of supplementary cementitious materials (SCMs) increase durability and extend service life, but also raise the risk of transverse early age cracking. This cracking results from autogenous shrinkage, when the cement consumes too much internal water, creating capillary stresses.
Cracks in UHPC bridge decks require costly, time-intensive sealing that must be reapplied every five to ten years, significantly increasing life-cycle costs. To address this issue, FHWA launched the Enhancing Performance with Internally Cured Concrete (EPIC2) initiative under EDC-7. Internal curing uses pre-wetted lightweight fine aggregate (LWFA) to supply additional moisture, improving water distribution and offsetting capillary stresses during the curing process. More than 30 years of studies show that internal curing enhances durability, lowers costs, and reduces waste.
Over 180 EPIC2 Bridge Decks are in service according to FHWA
To date, more than 15 states have deployed internal curing on over 180 bridge decks. NYSDOT, an early adopter of HPIC, reported a 70 percent reduction in early-age cracking with no added cost compared to conventional HPC or UHPC decks. NYSDOT has since mandated internal curing for all continuous bridges and bridge decks statewide. In May 2024, Mr. Rabie participated in a New York State peer exchange on the EPIC2 initiative in Albany.
NJDOT launched its HPIC implementation plan by reviewing existing research, assessing resources and mix plants, and conducting extensive coordination—internally with subject matter experts and divisions, and externally with LWFA suppliers, producers, and contractors. NJDOT also conducted risk evaluations and identified candidate bridges for potential pilot projects.
To support implementation, NJDOT secured a $125,000 STIC Incentive Grant, which funded the purchase of centrifuge apparatuses, staff training, and third-party lab support. The centrifuges measure LWFA moisture content, replacing the traditional “paper towel method,” in which pre-wetted aggregate is weighed, dried manually with industrial-grade paper towels until no moisture remains, and then oven-dried before an assessment is made of surface and absorbed moisture. While the centrifuge approach requires specialized equipment and training, it is significantly faster, less labor-intensive, and more accurate. NJDOT will phase in this method as staff gain experience.
NJDOT has identified 11 candidate bridges for HPIC pilot projects: one under construction, eight in design, and two in concept development. The active pilot—North Munn Avenue over I-280 in East Orange—features twin bridge decks, one built with UHPC and the other with HPIC, enabling a direct comparison under similar conditions.
Twin bridge deck pilot at North Munn Avenue over I-280 in East Orange
Alongside pilot projects, NJDOT is developing materials and construction guide specifications for HPIC. These include substituting 30–50 percent of total fine aggregate with LWFA, establishing a formula to measure absorbed LWFA moisture, and targeting a water content equal to 7 percent of the volume of cementitious materials. Aside from these adjustments, HPIC batching mirrors current UHPC practices.
Early HPIC bridge decks are expected to carry added upfront costs: approximately $50,000 for new mix design, trial batches, and test slabs to validate the process before construction, plus a 20–40 percent increase in unit production costs. Mr. Rabie noted that costs should decrease as specifications are refined, experience grows, and economies of scale take effect. While initial expenses may be higher, HPIC is projected to deliver substantially lower life-cycle costs, primarily by reducing resealing, which can cost around $100,000.
NJDOT’s next steps include a concrete plant outreach program in fall 2025, followed by HPIC workshops and centrifuge training in winter 2025/2026. The department will also continue to assess potential pilot projects through 2025–2026 and monitor the performance of active HPIC bridge deck projects.
Q&A
Q.Will HPIC extend the expected 25-year life span of a bridge deck?
A. The study is assessing how much maintenance HPIC bridge decks require over a 25-year lifespan. Preliminary findings suggest HPIC decks may require only about one-third the maintenance of conventional decks. NJDOT’s Bureau of Research, Innovation, and Information Transfer (BRIIT), in partnership with Rutgers University, is conducting a separate study evaluating how HPIC could extend overall service life. Early findings from NYSDOT suggest HPIC bridge decks may last up to 75 years.
Q.In South Carolina, we have faced difficulties achieving a uniform distribution of moisture for our pre-wetted lightweight fine aggregate using conventional methods like sprinklers. Do you have any suggestions on ways to fix this issue?
A. Some states have tried alternative methods for wetting LWFA. In Louisiana, for example, large bins are filled with water—like a small pool—and the aggregate is soaked for a set period to ensure uniform moisture distribution, rather than using sprinklers.
Q.Can internal curing be used on conventional concrete or is it just for HPC and UHPC?
A. Internal curing could technically be applied to conventional Class A concrete, but it is generally unnecessary. Class A concrete already contains higher water content, reducing its susceptibility to autogenous cracking. UHPC, being relatively moisture starved, benefits most from internal curing.
Q.Does NJDOT have set shrinkage limits?
A. Shrinkage is assessed project-by-project. After crack mapping is completed, a percentage of shrinkage is calculated, but there is no set limit.
A recording of the FHWA webinar is available here.
For more about HPIC and EPIC2, read the NJDOT Tech Transfer Q&A article with Samer Rabie and Jess Mendenhall.
In summer 2025, the FHWA Every Day Counts (EDC)-7 Strategic Workforce Development (SWD) team hosted the Careers in Gear Summer Series—a webinar series highlighting innovative workforce development programs and success stories from across the country.
Featuring real-world examples and conversations with skilled trades professionals, program leaders, and other industry innovators, the series spotlighted practical strategies to strengthen the construction workforce—and help build the infrastructure of tomorrow.
Dates and Times
July 23 | 1:00-2:00 PM: Training Success Stories A webinar hosted by the Federal Highway Administration (FHWA) Every Day Counts-7 Strategic Workforce Development (SWD) team featuring short videos and real-world examples of training programs that are making a difference.
The speakers included:
Marguerite Givings (Wisconsin Department of Transportation)
Rich Granger (DriveOhio)
Liam Murphy (Teaching the Autism Community Trades)
Charlie McCullough (Indiana Constructors Inc)
Marjani Rollins (Caltrans)
Airton Kohls (University of Tennessee)
August 6 | 1:00-2:00 PM: Fireside Chat on Youth Development Programs A dynamic fireside chat exploring how youth development programs are building pathways into transportation and skilled trades careers, with insights from leaders driving innovative workforce initiatives across the country.
The speakers included:
Lisa Rose (Mineta Transportation Institute)
Rich Granger (DriveOhio)
Dr. Stephanie Ivey (University of Memphis Southeast Transportation Workforce Center)
Video Recording: 2025 Research Showcase Lunchtime Edition
On May 14, 2025, the NJDOT Bureau of Research, Innovation, and Information Transfer hosted a Lunchtime Tech Talk! webinar, “Research Showcase: Lunchtime Edition 2025”, featuring four presentations on salient research studies. As these studies were not shared at the 26th Annual Research Showcase held in October 2024, the webinar provided an additional opportunity for the over 80 attendees from the New Jersey transportation community to explore the wide range of academic research initiatives underway across the state.
The four research studies covered innovative transportations solutions in topics ranging from LiDAR detection to artificial intelligence. The presenters, in turn, shared their research on assessing the accuracy of LiDAR for traffic data collection in various weather conditions; traffic crash severity prediction using synthesized crash description narratives and large language models (LLMs); non-destructive testing (NDT) methods for bridge deck forensic assessment; and traffic signal detection and recognition using computer vision and roadside cameras. After each presentation, webinar participants had an opportunity to ask questions to the presenters.
Presentation #1 –Assessing the Accuracy of LiDAR for Traffic Data Collection in Various Weather Conditions by Abolfazl Afshari, New Jersey Institute of Technology (NJIT)
Mr. Afshari shared insights from a joint research project between NJIT, NJDOT, and the Intelligent Transportation Systems Resource Center (ITSRC), which evaluated the accuracy of LiDAR in adverse weather conditions.
LiDAR (Light Detection and Ranging) is a sensing technology that uses laser pulses to generate detailed 3D maps of the surrounding area by measuring how long it takes for laser pulses to return after hitting an object. It offers high resolution and accurate detection, regardless of lighting, making it ideal for traffic monitoring in real-time.
The research study began in response to growing concerns about LiDAR’s effectiveness in varied weather conditions, such as rain, amid its increasing use in intelligent transportation systems. Mr. Afshari stated that the objective of the research was to evaluate and quantify LiDAR performance across multiple weather scenarios and for different object types—including cars, trucks, pedestrians, and bicycles—in order to identify areas for improvement.
To conduct the research, the team installed a Velodyne Ultra Puck VLP-32C LiDAR sensor with a 360° view on the Warren St intersection near the NJIT campus in Newark. Mr. Afshari noted that newer types of LiDAR sensors with enhanced capabilities may be able to outperform the Velodyne Ultra Puck during adverse weather. They also installed a camera at the intersection to verify the LiDAR results with visual evidence. The research team used data collected from May 12 to May 27, 2024.
The researchers obtained the weather data from Newark Liberty Airport station and utilized the Latin Hypercube Sampling (LHS) method to identify statistically diverse weather periods for evaluation and maintain a balance between clear and rainy days. They selected over 300 minutes of detection for the study.
The study area for the LiDAR detection evaluation
To evaluate how well the detection system performed under different traffic patterns, they divided the study area into two sections. The researchers used an algorithm for the LiDAR to automatically count the vehicles and pedestrians entering these two areas, then validated the LiDAR results by conducting a manual review of the video captured from the camera.
The research team found that, overall, the LiDAR performed well, though there were some deviations during rainy conditions. During rainy days, the LiDAR’s detection rate decreased for both cars and pedestrians, with the greatest challenges occurring in accurately detecting pedestrians. On average, the LiDAR would miss nearly .8 pedestrians and .7 cars per hour during rainy days, around 30 percent higher than on clear days.
Key limitations of the LiDAR detection identified by the researchers include: maintaining consistent detection of pedestrians carrying umbrellas or other large concealing objects, identifying individuals walking in large groups, and missing high-speed vehicles.
Mr. Afshari concluded that LiDAR performs reliably for vehicle detection but pedestrian detection needs enhancement in poor weather conditions, which would require updated calibration or enhancements to the detection algorithm. He also stated the need for future testing of LiDAR on other weather conditions such as fog or snow to further validate the findings.
Q. Do you think the improvements for LiDAR detection will need to be technological enhancements or just algorithmic recalibration?
A. There are newer LiDAR sensors available, which perform better in most situations, but the main component to LiDAR detection is the algorithm used to automatically detect objects. So, the algorithmic calibration is the most important aspect for our purposes.
Q. What are the costs of using the LiDAR detector?
A. I am not fully sure as I was not responsible for purchasing the unit.
Presentation #2 – Traffic Crash Severity Prediction Using Synthesized Crash Description Narratives and Large Language Models (LLM) by Mohammadjavad Bazdar, New Jersey Institute of Technology
Mr. Bazdar presented research from an NJIT and ITSCRC team effort focused on predicting traffic crash severity using crash description narratives synthesized by a Large Language Model (LLM). Predicting crash severity provides opportunities to identify factors that contribute to severe crashes—insights that can support better infrastructure planning, quicker emergency response, and more effective autonomous vehicle (AV) behavior modeling.
Previous studies have relied on traditional methods such as logit models, classification techniques, and machine learning algorithms like Decision Tree and Random Forests. However, Mr. Bazdar notes that these approaches struggle due to limitations in the data. Crash report data often contains numerous inconsistencies and missing values for varying attributes, making it unsuitable for traditional classification models. Even if you get a good result from the model, it cannot be used to reliably identify contributing factors because of all the data that is excluded.
To address this challenge, the research team explored the effectiveness of generating consistent and informative crash descriptions by converting structured parameters into synthetic narratives, then leveraging large language models (LLMs) to analyze and predict crash severity based on these narratives. Since LLMs can parse through different terminologies and missing attributes, it allows researchers to analyze all available data, and not the minority of crash data that has no inconsistencies or missing variables.
The research team used BERT, an Encoder Model LLM, to analyze over 3 million crash records from January 2010 to November 2022 for this study. Although crash reports often contain additional details, the team exclusively utilized information regarding crash time, date, geographic location, and environmental conditions. Additionally, they divided crash severity into three categories: “No Injury,” “Injury,” and “Fatal.”
The narratives synthesized by BERT include six sentences, with each sentence describing different features of the crash, such as time and date, speed and annual average daily traffic (AADT), and weather conditions and infrastructure. BERT then tokenizes and encodes the narrative to generate contextualized representations for crash severity prediction.
They also found that a hybrid approach—using BERT to tokenize crash narratives and generate crash probability scores, followed by a classification model like Random Forest to predict crash severity based on those scores—performed best. An added benefit of the hybrid model is that it produces comparable, if not better, results than the BERT model, in hours rather than days.
In the future, Mr. Bazdar and the research team plan to enhance their model by integrating spatial imagery, incorporating land use and environmental data, and utilizing decoder-based language models, hoping to achieve more effective results.
Q. How does your language model handle missing data fields?
A. The model skips missing information completely. For example, if there is a missing value for the light condition, the narrative will not mention anything about it. In traditional models, a report missing even one variable would have to be discarded. However, with the LLM approach, the report can still be used, as it may contain valuable information despite the missing data.
Q. What percentage of the traffic reports were missing data?
A. The problem is that while a single value like light condition, may be missing in only a small percentage of crash reports, a large portion—nearly half—of crash reports have some missing data or inconsistency.
Presentation #3 – Forensic Investigation of Bridge Backwall Structure Using Ultrasonic and GPR Techniques by Manuel Celaya, PhD, PE, Advance Infrastructure Design, Inc.
Dr. Celaya described his work performing non-destructive testing (NDT) on the backwall structure of a New Jersey bridge, utilizing Ultrasonic Testing (UT) and Ground Penetrating Radar (GPR).
The bridge in the study, located near Exit 21A on I-287, was scheduled for construction; however, NJDOT had limited information about its retaining walls. To address this, NJDOT enlisted Dr. Celaya and his firm, Advanced Infrastructure Design, Inc. (AID), to assess the wall reinforcements—mapping the rebar layout, measuring concrete cover, and detecting potential cracks and voids in the backwalls.
The team used a hand-held GPR system to identify the presence, location, and distribution of reinforcement within the abutment wall. The GPR device collects the data in a vertical and horizontal direction, indicating the distance of reinforcement like rebar and its depth of penetration. This information was needed to ensure that construction on the bridge above would not impact the abutment walls.
SAFT images of the bridge abutment produce by the Ultrasonic Testing
They also employed Ultrasonic Testing (UT), a method that uses multiple sensors to transmit and receive ultrasonic waves, allowing the team to map and reconstruct subsurface elements of the bridge wall. The system captures a detailed cross-sectional view of acoustic interfaces within the concrete using a grid-based measurement pattern, ensuring precise and reliable data collection. Additionally, they used IntroView to evaluate the UT data and produce Synthetic Aperture Focusing Technique (SAFT) images to illustrate and identify anomalies within the concrete.
AID also conducted NDT to assess the depth of embedded bolts in the I-287 bridge abutments using GPR scans, but aside from detecting steel rebar reinforcements, no clear signs of the bolts were found. However, the UT results offered valuable insights, revealing that the embedded bolts in the west abutment wall were deeper than those in the east abutment.
Q. What was the process workflow like for the Ultrasonic Testing?
A. It is not that intuitive compared to Ground Penetrating Radar. With GPR, you can clearly identify structures on the site. However, with UT, there has to be post-processing analysis in the office, it cannot be attained in the field. This analysis takes time and requires a certain level of expertise.
Presentation #4 – Traffic Signal Phase and Timing Detection from Roadside CCTV Traffic Videos Based on Deep Learning Computer Vision Methods by Bowen Geng, Rutgers Center for Advanced Infrastructure and Transportation
Mr. Geng shared insights from an ongoing Rutgers research project that evaluates traffic signal phase and timing detection using roadside CCTV traffic video footage, applying deep learning and computer vision techniques. Traffic signal information is essential for both road users and traffic management centers. Vehicle-based signal data supports autonomous vehicles and advanced Traffic Sign Recognition (TSR) systems, while roadside-based data aids Automated Traffic Signal Performance Measures (ATSPM) systems, Intelligent Transportation Systems (ITS), and connected vehicle messaging systems.
While autonomous vehicles can perceive traffic signals using on-board camera sensors, roadside detection relies entirely on existing infrastructure such as CCTV traffic footage. Mr. Geng noted that advancements in computer vision modeling provides a resource-efficient tool for improving roadside traffic signal data collection, compared to other potential solutions like infrastructure upgrades, which would be costly. For the study, the researchers decided to develop and implement methodologies for traffic signal recognition using CCTV cameras, and evaluate the effectiveness of different computer vision models.
Most previous studies have concentrated heavily on vehicle-based traffic signal recognition, while roadside-based TSR has received relatively limited attention, with some previous studies using vehicle trajectory to determine traffic signal status. Furthermore, early research relied on traditional image processing techniques such as color segmentation, but more recent studies have shifted toward a two-step pipeline using machine learning tools like You Only Look Once (YOLO) or deep learning-based end-to-end detection methods. Both the two-step pipeline and end-to-end detection approaches have their advantages and drawbacks. The two-step pipeline uses separate models for detection and classification, requiring coordination between stages and creating slower process speeds, but making it easy to debug. In contrast, end-to-end detection is faster and more streamlined but more difficult to debug.
Real-time traffic signal detection using the research model
In this study, the researchers adopted three different methodologies; two using the two-step pipeline, and one using an end-to-end detection model. All three models employed YOLOv8 for object detection; however, they differed in color classification methods. The researchers used video data from the DataCity Smart Mobility Testing Ground in downtown New Brunswick, across five signalized intersections.
The model achieved an overall accuracy of 84.7 percent, with certain signal colors detected more accurately than others. Mr. Geng shared that the research team was satisfied with these results. They see potential for the model to be used to support real-time traffic signal data logging and transmission for ATSPM and connected vehicle messaging system applications.
Q. How many cameras did you have at each intersection?
A. For each intersection we had two cameras facing two different directions. For some intersections, we had one camera facing north and another facing south, or one facing east and the other facing west.
Q. What did you attribute to the differences in color recognition?
A. There was some computing resource issue. Since we are trying to implement this in real-time, there are difficulties balancing accuracy with possible latency issues and processing time.
The Federal Highway Administration’s Local Aid Support team in the Office of Transportation Innovation and Workforce Solutions will be holding a national webinar on October 19, 2023 for those interested in learning more about this year’s winning entries in the 2023 Build a Better Mousetrap National Recognition Program for Transportation Innovation.
Winners were announced at the National Local and Tribal Technical Assistance Program Association’s Annual Meeting in Columbus, Ohio this summer. New Jersey’s “Route 71 Over Shark River Road Diet” was this year’s Bold Steps Award Winner in the national competition.
Build a Better Mousetrap celebrates innovative solutions for challenges that local and tribal transportation workers encounter. These innovations can range from the development of tools and equipment modifications to the implementation of new processes that increase safety, reduce cost, and improve efficiency of our transportation system.
Gerald Oliveto, P.E., from the New Jersey Department of Transportation will give a presentation about the Route 71 bridge rehabilitation and road diet project. More information about this award-winning project, recipient of this year’s “Bold Steps” Award, can be found here and here.
Mr. Oliveto will be among the presenters during the national webinar. Below is a full list of the 2023 BABM Award recipients during the webinar.
Innovative Project Award – “The Mobile Unit Sensing Traffic (MUST) Device” – a device specifically designed to monitor traffic, detect dangerous events, and provide real-time warning messages to users along rural roads. Presenter: HollyAnna Littlebull, formerly Confederated Tribes and Bands of the Yakama Nation. Associate Director of the Northwest Tribal Technical Assistance Program (TTAP) Center, University of Washington.
Bold Steps Award – “Route 71 Over Shark River Road Diet” – a road diet project that preserves an old historic bridge while improving safety and saving money. Presenter: Gerald Oliveto, New Jersey Department of Transportation
Smart Transformation Award – “Solar-powered Remote Cameras” – providing more accurate and immediate information on road conditions that assists with emergency response while requiring less maintenance. Presenter: Matthew Beyer, St. Louis County, Minnesota, Public Works Department
Pioneer Award – “Safe Sightings of Signs and Signals (SSOSS) Software” – an automated process for identifying and addressing obstructed traffic signals saving time and money while increasing data accuracy. Presenter: Matthew Redmond, City of Walnut Creek, California
Registration – The national webinar is scheduled for Thursday, October 19, 2023, 2.00 PM and 4.00 PM Eastern. The FHWA has provided this link to learn more about the BABM Award winners event and receive a Zoom Government Meeting link to access the event.
Since 2019, the FHWA Every Day Counts (EDC) Innovation, Crowdsourcing for Advancing Operations, has been supporting the adoption of crowdsourced data and tools to advance transportation operations across 35+ States and their local agencies to improve traffic incident, road weather, work zone, traffic signal, traveler information, and emergency management, along with a host of other ITS and TSMO practices.
The Crowdsourcing Innovation Team in collaboration with the ITS Joint Program Office (JPO) Professional Capacity Building (PCB) Program delivered this introductory Crowdsourcing course, one in series of webinars, featuring State and local practitioner perspectives.
On July 18, 2023, Sal Cowan, NJDOT’s Senior Director of Mobility served as one of the course instructors for Traveler Information and Traffic Incident Management, the third session in a webinar series targeted to transportation professionals with an interest in or responsibility for the management and operations of roadway systems. Mr. Cowan delivered instruction on how crowdsourcing can be used to enhance traveler information. He shared examples of how some leading state transportation agencies (e.g., Virginia, Arizona, Kentucky, Pennsylvania) are using various crowdsourcing platforms for communicating traveler information. Mr. Cowan then spoke at greater length about New Jersey’s Travel Information Systems, highlighting the state’s initiatives for Commercial Vehicle Notifications, 511 Platforms and Voice Assistant Systems, and Crowdsourced Data, among other topics.
Mr. Cowan was joined by two other featured speakers and the event’s host, Ralph Volpe, EDC-6 Crowdsourcing Program Co-Lead, who moderated the capacity-building webinar.
Vaishali Shah, AEM Corporation, Support Lead for the FHWA EDC-5/6 Crowdsourcing Innovation, gave an introduction to the Traffic Incident Management topic and described the components and challenges of State and local TIM systems. She shared several examples of how crowdsourced data is being used to enhance Traffic Incident Management (TIM) around the U.S..
Mr. Cowan explained the rationale for crowdsourcing to improve traveler information and shared examples of how its being used in select states, including New Jersey.Ms. Shah explained how crowdsourcing applications were being used to enhance TIM and shared some examples of innovative state and local deployments nationally.
John Parker, Pennsylvania Turnpike Commission (PTC), Senior Traffic Operations Project Manager, then described the PTC’s Traffic Incident Management and Traveler Information initiatives. In his talk, he described various examples of data-sharing providers and partnerships, touching upon technology platforms, dashboard features, operating challenges, and new partnering opportunities being considered by the PTC and the state of Pennsylvania to enhance crowdsourcing for TIM and Traveler Information.
More information on this webinar training event can be found here, including a recording of the webinar, the presentation, transcript, and the question and answers that closed out the training event.
The Transportation Curriculum Coordination Council (TC3)’s mission is to develop and maintain a quality training curriculum to enhance the competency of the nation’s transportation Construction, Maintenance, and Materials technical workforce. TC3 is a state-based initiative adopted as a Technical Service Program within AASHTO.
The TC3 Online Video Library contains playlists of instructive videos on Construction, Maintenance, Materials and Traffic and Safety. TC3 has a library of more than 250 online training modules covering a variety of topics in the three primary disciplines.
TC3 helps states, local government, and industry save money at a critical time of infrastructure investment through course development, web-based trainings, information, and resource sharing that is available at substantially reduced cost. The TC3’s website has additional resources available here about AASHTO’s Techical Services Program.
On July 10th, the NJDOT Bureau of Research hosted a Lunchtime Tech Talk! webinar, “Advanced Reinforced Concrete Materials for Transportation Infrastructure.” Welcoming remarks were given by Mansi Shah, Manager of the Bureau of Research, who turned over the session to its moderator, Omid Sarmad, a member of the NJDOT Technology Transfer Project Team. The presentation was conducted jointly by the Co-Directors of New Jersey Institute of Technology’s Materials and Structures Laboratory (MATSLAB), Dr. Matthew Bandelt, and Dr. Matthew Adams.
Researchers described the durability issues for concrete including corrosion, shrinkage, salt scaling, and freeze-thaw cycles.
Transportation infrastructure systems must resist conditioning from the natural environment and physical demands from service loading to meet the needs of users across the state. Deterioration leads to costly and timely durability and maintenance challenges. This presentation provided a background on the state-of-the-art of advanced reinforced concrete materials that are being investigated to improve reinforced concrete transportation infrastructure. The duo, both Associate Professors at the New Jersey Institute of Technology, spoke about the team’s research conducted to assess the mechanical properties and long-term durability of these systems.
Dr. Bandelt opened the presentation with an overview of the MATSLAB where the work was conducted, and the motivation which led to the project. The demand for the research was initiated by the various durability issues that exist in concrete, in particular corrosion, shrinkage, salt scaling, and freeze-thaw cycles. These issues are exacerbated in New Jersey due to the climate and the large-scale adoption of concrete throughout the state. A variety of different concretes were evaluated in the project, such as Ultra-High Performance Concrete (UHPC), Engineered Cementitious Composite (ECC) and a Hybrid Fiber Reinforced Concrete (HyFRC), each having its own unique mechanical properties.
Researchers described a multi-physics time-dependent modeling framework that considers the structural response, materials ingress and electrochemical reactions.
The experimental testing program involved mechanical testing, corrosion testing, testing in freezing environments, and shrinkage testing. Corrosion testing of ductile and normal concrete systems used a chloride ponding test method with exposure to an aggressive environment for over one year. Various steel reinforcing bars were studied, and systems were tested in uncracked and pre-cracked conditions. Freeze-thaw and salt-scaling experimental activities were conducted, using mixes that were commonly used by NJDOT. Drying shrinkage behavior of the ductile and normal concrete systems was also investigated. Dr. Bandelt and Adams developed a numerical modeling approach to simulate the corrosion behavior of ductile concrete systems to understand the long-term performance. The results of the durability testing showed that UHPC had the best performance across the board, and that ductile concrete systems had improved durability.
The professors then described their life-cycle cost modeling methodology, which was completed to assess the costs of a representative bridge-deck made with normal reinforced concrete. There are primarily two ways to evaluate service life; experimental evaluation which describes the physical testing of materials is accurate and intuitive, while numerical evaluation is more cost efficient, time efficient, and more easily extrapolated to various scenarios. There are gaps however in numerical modeling, mainly the lack of inclusion of cracks, corrosion behavior, and boundary conditions. The team sought to develop a framework to simulate the long-term durability of a select group of materials under the combined effects of mechanical loading and environmental conditioning.
The research showed that their framework was effective in service life evaluation, and that most importantly, UHPC bridge deck experienced slower deterioration under the same traffic load and environmental conditions. The reinforced UHPC beams and reinforced UHPC bridge decks exhibited excellent resistance to chloride penetration and corrosion propagation according to the modeling results. The structural deteriorations of the reinforced UHPC systems were also significantly slower compared to that of reinforced normal strength concrete systems. The study also showed that chloride induced corrosion performance is affected by the initial damage pattern, which depends on the structure and loading conditions. This means that it becomes important to consider the structural configuration, traffic loading conditions, and climate characteristics to assess the long-term durability of an advanced reinforced concrete system.
Afterwards, Dr. Bandelt and Adams both participated in a Q&A with the audience.
Q. UHPC seems to be advancing in the bridge industry. What are the biggest challenges looking forward on the rehabilitation of bridge decks?
A. Yes, it’s advancing quite rapidly. The FHWA has a website where you can see all the projects where UHPC was deployed, and if you plot the number of projects over time, you’ll see nearly an exponential growth. Part of that is due to the fact that there is a lot of research going on, and a lot new standards coming out. Organizations like AASHTO and ACI have released a lot of design guidance that has helped spur adaptation.
Still, the biggest challenge is getting new people used to using these design methods. As we move past some of that, I think we’ll see adoption continue to increase. UHPC may not be the right solution for every project, but there are many beneficial uses for which it will be the most appropriate tool to achieve long lasting sustainability.
Q. Regarding the resilience of concrete: Are advanced reinforced concretes better able to handle the freeze/thaw cycles that could be outcomes of climate change? If so, do you have any modeling projection to show how it fairs in comparison to regular concrete?
A. We haven’t done any specific modeling in comparison to traditional concrete in relation to climate change, but in general these systems are more resilient. They simply perform better; as you saw in our research, after 300 cycles we saw virtually no damage from freeze/thaw cycles in the system. When you see that level of performance in these accelerated tests which are quite aggressive, you can extrapolate that these advanced reinforced concretes will simply perform better.
Q. Why did the HyFRC showed much higher free shrinkage than HPC? Is the HyFRC mix design different from HPC other than fibers?
A. The mix design of the HyFRC is a bit different. One thing in particular is that even though it has those blended fibers, it has a significantly higher water to cement ratio. So because it has more water, it is a bit more prone to drying shrinkage. With UHPC that turns out to be less of a concern because it’s much stronger and is not as susceptible.
Q. Could your modeling adjust relative humidity to a more wet and hot climate in the future?
A. Yes, absolutely. The case study we looked up was in New Jersey, but we can modify that to be in any setting so you can see where it would be geographically advantageous to use certain systems.
Q. Can you explain more about the deterioration we saw in slide 66?
Video Recording of Lunchtime Tech Talk!, Advanced Reinforced Concrete Materials for Transportation.
A. Basically what we did was look at tensile strains throughout a bridge area. The colors coincide with different levels of tensile strain. We counted up areas that were in different sections, and based on the percent area that we saw that was damaged, and we would use a multiplier to create a rating system.
To view a copy of the presentation, please click here.
On April 26, 2023, the NJDOT Bureau of Research hosted a Lunchtime Tech Talk! webinar, “Research Showcase: Lunchtime Edition!”. The event featured three important research studies that NJDOT was not able to include in the NJDOT Research Showcase virtual event held last October. The Showcase serves as an opportunity for the New Jersey transportation community to learn about the broad scope of academic research initiatives underway in New Jersey.
Video Recording: 2023 Research Showcase Lunchtime Edition
The three research studies explored issues at the intersection of transportation and the environment and the advancement of sustainable transportation infrastructure. The presenters, in turn, shared their research on the design and performance evaluation results of harvesting energy through transportation infrastructure; the properties of various materials used in roadway design treatments to effectively quantify and mitigate stormwater impacts of roadway projects; and analytical considerations inherent in estimating road surface temperatures to inform the development of a winter weather road management tool for NJDOT. After each presentation, webinar participants had an opportunity to pose questions of the presenter.
Presentation #1 – New Design and Performance Evaluation of Energy Harvesting from Bridge Vibration by Hao Wang, Associate Professor, Civil and Environmental Engineering, Rutgers Center for Advanced Infrastructure and Transportation (CAIT)
Dr. Wang noted that energy harvesting converts waste energy into usable energy that is clean and renewable for various transportation applications. Energy harvesting projects can be large scale (solar or wind energy solutions) or micro-scale (providing power for lighting, self-powered sensor devices, and wireless data transfer).
In this project, the large scale application considered the use of photovoltaic noise barriers (PVNBs) which integrate solar panels with noise barriers to harvest solar energy. His research developed energy estimation models at the project- and state-level for a prototypical design installation of noise barriers.
In his presentation, Dr. Wang focused on the micro-scale application that employed piezoelectric sensors on bridge structures. He noted that piezoelectric energy harvesting can be achieved by compression or vibration. He explained that traffic and winds cause roadway bridges to vibrate. This movement subjects the piezoelectric sensors to mechanical stresses or changes in geometric dimensions which create an electric charge.
Piezoelectric energy harvesting is affected by the material, geometry design of the transducer, and external loading. Instead of embedding sensors in pavements, the researchers sought to attach the sensors to the bridge structure imposing less impact on the host structure and increasing the ease of installation. They developed and evaluated new designs of piezoelectric cantilevers to create a range of resonant frequency to match with bridge vibration modes.
Multiple degree-of-freedom (DOF) cantilever designs were tested in the laboratory, and in full-scale tests. The goal was to customize the design to maximize power outputs resulting from bridge vibrations. Multiple cantilever design options were examined with adjustable masses. Simulation models were developed for estimating energy harvesting performance and to facilitate the optimization of mass combinations through quantitative models.
The researchers used finite element models to simulate the effect, and assessed the model in the laboratory to manage the voltage output of various designs. Bridges have multiple vibration frequencies under different vibration modes, on the bridge structure and the span. A full-scale bridge test was conducted using the Rutgers-CAIT Bridge Evaluation and Accelerated Structural Testing lab (BEAST) to give sample voltage outputs from cantilevers.
Future research will be needed to explore the effect of loading speed that takes into consideration the variable speeds on a bridge something that was not captured in the laboratory testing.
Findings of the research included: multiple degree of freedom (DOF) cantilevers can generate considerable energy when resonant frequencies match vibrational frequencies of the bridge structure; finite element modeling can predict resonant frequencies of multiple-DOF cantilevers as validated by experiments and ensures that numerical models can be used to explain the relationship between resonant frequency and mass combination for optimized design; and the proposed cantilever designs and optimization approach can be used for piezoelectric energy harvesting considering a variety of vibration features from bridges under different external conditions.
Dr. Wang responded to questions following his presentation:
Q. How far below the asphalt are the sensors placed and how often do they need to be replaced? A. For this project, the installation in this phase used a magnetic fixture to attach the cantilever to the girder. The installation procedure was easy for this phase. For a field installation, we will need to consider more thoroughly the mount and durability but did not need to address this during this phase and we do not have real-world data now to share about that.
Q. Would the vibrations be amplified with the cables? A. The cables on the real bridge – if we attached to the cable the vibrations would be less, which is why we attached them to the girder.
Presentation #2 – Impacts of Vegetation, Porous Hot Mix Asphalt, Gravel and Bare Soil Treatments on Stormwater Runoff from Roadway Projects by Qizhong (George) Guo, Professor, Civil and Environmental Engineering, Rutgers Center for Advanced Infrastructure and Transportation
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Dr. Guo described the effect of increased impervious coverage in urban locations that leads to increased surface water runoff. Transportation agencies are required to assess and mitigate the stormwater runoff impacts of roadway projects. The project explored the effect on runoff of use of gravel, vegetation, porous Hot Mix Asphalt (HMA), and bare soil. Areas where these materials would be used include the roadway right-of-way, medians, and beneath guiderails.
Variables explored in lab testing included subsoil hydraulic conductivity, rainfall intensity and rainfall duration. The researchers used the Curve Number (CN) method for estimating direct runoff from rainstorms. Lab testing involved a column of soil with little lateral flow and limited depth to the level representing the water table. To apply lab findings to field conditions, the regression equation of Curve Number versus the infiltration rate obtained from the laboratory measurements can be applied after replacing the laboratory-measured infiltration rate with the field-measured subsoil hydraulic conductivity or assigned hydrologic soil groups.
This research resulted in Curve Numbers for bare soil and vegetation similar to the established CNs for dirt (including right-of-way) and open space (lawns, fair condition). The estimated CNs for gravel were significantly smaller than the established CNs for gravel (including right-of-way). The research resulted in CNs for porous HMA but no comparison can be made as there is no established CN for this material. The project could help NJDOT in seeking approval of the Curve Numbers for gravel and porous HMA from regulatory agencies. In addition, the study affirmed the use of pervious surfaces and the effectiveness of stormwater runoff reduction to restore natural hydrology.
Following the presentation, Dr. Guo responded to questions asked through the chat feature:
Q. What are preventative measures to avoid porous HMA clogging? A. Sediment source control is needed to prevent dirt and dust from entering the porous HMA. If the area around the pavement is subject to erosion, runoff carries this dirt or sand into the material. If the material becomes clogged, a vacuum is needed to clean it.
Q. Can we disperse runoff in roadway drainage systems as opposed to collection? A. There are several ways to disperse runoff, such as by the use of rain gardens, a horizontal spreader, or use of a stone/gravel strip to spread the runoff.
Some questions were submitted in the Chat and, due to time constraints, were answered by Dr. Guo after the Tech Talk.
Q. We recently had a project meeting during concept development where we suggested porous asphalt for guide rail base. Another team mentioned they would prefer we not use PHMA due to it clogging over time and basically becoming HMA. What research has been done on PHMA effectiveness over time, and what can be done to remedy reduced flow (if it does occur)? A: The clogging of porous hot mix asphalt (PHMA) and other porous pavement varieties is undeniably a significant and pressing issue. Our study for NJDOT did not tackle the problem of clogging, but other researchers have conducted relevant investigations, and more targeted research is anticipated. The most effective method to reduce clogging is by preventing excessive coarse sediment from entering PHMA and other porous pavements. Special care should be taken to maintain the surrounding landscape in order to mitigate soil erosion, and not to apply sand to any of the road surfaces for snow abatement. Alternatively, sediment in the runoff can be captured or filtered using a swale or gravel strip before it enters the PHMA or other porous pavement areas. Implementing a proactive inspection and monitoring system for clogging is also essential.
In cases where PHMA or other porous pavements become clogged, a vacuum street sweeper or regenerative air sweeper can be employed to dislodge and remove the solid materials. However, traditional mechanical sweepers should be avoided, as they may cause the solids to break down or force particulates deeper into the porous spaces, exacerbating the clogging issue in porous pavements.
Q. Did you use the same course stone mix in the NJDOT specs for the course stone non-vegetative surface. I assume you are calling this gravel. A: Yes, the NJDOT construction specifications were adhered to in the design of the laboratory setup for all four land treatment types: gravel, porous hot mix asphalt, vegetation, and bare soil. These specifications can be found in the “Roadway Design Manual (2015)”, “Standard Construction Details (2016)”, and “Standard Specifications for Road and Bridge Constructions (2019)”. Comprehensive details are provided in Table 13 in Appendix A of our Final Report for the research project (FHWA-NJ-2023-004).
Q. What compaction did you use for the porous HMA? We usually use only a small portable tamper machine in the field with about 2 passes. A. In our laboratory, a gyratory compactor was employed for the compaction of the porous HMA samples tested. Two relevant sentences in our Final Report for the research project (FHWA-NJ-2023-004) state: “For the porous asphalt land treatment, cylindrical porous Hot Mix Asphalt (HMA) gyratory samples with a diameter of 6 in and a depth of 4 in were manufactured at Rutgers CAIT Asphalt Pavement Lab. The mix design utilized to manufacture the HMA met the requirements of the Open-graded Friction Course in the Updated Standard Specifications for Road and Bridge Construction (2007).”
Q: Can this report be used to get acceptance of porous HMA by DEP? A: Yes, although further dialogue with NJDEP, NRCS, and other relevant agencies or organizations may be necessary for the ultimate acceptance.
Q: NJDOT Materials lab did a study of various ages of porous HMA in the field and found out that it did not clog over an 8 year period. It appeared to be self-cleaning. A: I appreciate the information you provided. The likelihood of porous HMA clogging is closely related to the volume and size of solids, sediment, or particulates entering it. A minimal amount of fine particulates is unlikely to cause serious or rapid clogging issues in porous HMA. To my knowledge, there is no “self-cleaning” mechanism inherent in porous HMA.
Q: What about the contamination in runoff water which will penetrate in subsoil? A: Contaminants in runoff water should not be allowed to infiltrate the subsoil. Highly contaminated runoff must not directly enter land treatments (LTs), green stormwater infrastructure (GSI), stormwater Best Management Practices (BMPs), or stormwater control measures (SCMs). Instead, these systems will treat mildly contaminated runoff as it passes through them. Consequently, the runoff water will achieve a relatively high level of purity before it infiltrates the subsoil.
Presentation #3 – Practical Considerations of Geospatial Interpolation of Road Surface Temperature for Winter Weather Road Management by Branislav Dimitrijevic, Assistant Professor, Civil and Environmental Engineering, New Jersey Institute of Technology (NJIT) and Luis Rivera, Analyst Trainee, NJDOT Transportation Mobility, Transportation Operations Systems & Support
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Mr. Rivera provided background on NJDOT’s Weather Savvy Road System that addresses the need for proactive winter road maintenance and the wide variation in road conditions throughout the state. There are only 48 stationary Road Weather Information Systems (RWIS) stations across the state in areas that are deemed essential. They provide information on road conditions (wet or dry), and road temperature. The Weather Savvy Road System integrates stationary RWIS and mobile RWIS (MRWIS) to track road conditions in real time, provide data visualization to operators to inform decision-making, and assist in planning road management.
In 2017, NJDOT received a USDOT Accelerated Innovation Deployment grant for implementation of FHWA’s Every Day Counts Round 4 Weather Savvy Roads Integrating Mobile Observations (IMO) innovation. The agency deployed Internet of Things (IoT) and Connected Vehicle technology to improve road weather management. NJDOT installed sensors and dash cameras on 24 fleet vehicles to pick up air temperatures, road temperatures, surface condition, and road grip, and portable PC equipment to analyze and report this information to improve safety for the traveling public and inform decision-making. Road surface temperature is the most indicative measure of road condition.
Dr. Dimitrijevic discussed research undertaken to gather road surface temperatures using Kriging, a geospatial interpolation model. The goal was to discover a way to extrapolate the information collected from the sensors to provide estimated road surface temperatures across the entire road network within NJDOT’s jurisdiction.
The researchers collected data from RWIS/MRWIS and other data available, including land coverage, elevation, etc., that can affect road surface temperatures (RST). They sought to use a Kriging Interpolation and Machine Learning Model to give estimated RSTs over the network to inform planning and evaluation of winter road maintenance efforts. Variability in RST across the analysis region is a big factor. Researchers needed to find a function that fit the variability between the data points, and use that to estimate the parameter value at any particular point.
Dr. Dimitrijevic discussed the differences between three Kriging methods: Ordinary Kriging and Universal Kriging, the simplest and fastest to calculate; regression Kriging which uses additional factors, besides distance, that will affect RST; and Empirical Bayesian Kriging that uses Bayesian inference to calculate parameters, but also calculates the probability of making an error.
All three Kriging methods assume that for any correlation between a given parameter that you are trying to estimate in a given area, there is a relationship between the values of that parameter at different points that depends on the actual location of the points, or distance between points. The method uses the known value of surrounding parameter points, for example, the road surface temperature at these points, and measures the distance between these points of known parameter value to estimate the parameter (RST) at the unknown point. Kriging assumes a statistical relationship involving the distance between RWIS stations.
Researchers conducted case studies using RST interpolation of stationary RWIS data by driving between RWIS locations, and then expanded the RWIS coverage of mobile sensors during a winter storm event. They found the best results came from combining RWIS and mobile RWIS data. They found Regression Kriging to be helpful for including other factors (the most statistically significant being vegetation type, land cover type, distance to water, and elevation). Increasing the mobile RWIS records reduced the error level, and this finding resulted in a recommendation to increase the number of mobile sensors on NJDOT’s fleet.
Kriging was effective in capturing the spatial variation in the dataset. An error of one degree Fahrenheit still needs to be addressed. The researchers continue to look into solutions in ongoing research which will explore additional interpolation methods, integration of short-term past predictions, and a bi-level interpolation using stationary RWIS data at a regional scale and the mobile RWIS data to make adjustments to the local scale.
The model that performed best was implemented in a web-based map tool that gathers data in real time and refreshes the estimated road surface temperature every 10-15 minutes, providing a map and the ability to download data. When complete, this tool will become part of the toolbox for Operations, Maintenance and Mobility division.
Dr. Dimitrijevic answered questions following his presentation:
Q. How is the dew point and frost point measured by the sensor? A. Dew point is not measured; there are statistical models that calculate readings of air temperature, air humidity and pressure to determine dew point or frost point. Dew point and frost point are the same thing. The term used depends on the temperature.
Q. What other interpolation models, besides Kriging, will you be looking at? A. We are looking at a combination of machine learning and geo-statistical modeling. There is also bi-level modeling that uses one method to regress the regional scale estimate, and another to use the localized readings to adjust the estimates for a local roadway. These methods require more computation time, but we are looking for models that can calculate in real time for tactical management purposes.
The American Association of State Highway and Transportation Officials (AASHTO) Innovative Initiative (AII) program recognized NJDOT’s Sawcut Vertical Curb as one of seven Focus Technologies in 2022. AASHTO held a webinar on Wednesday, April 12, 2023 during which NJDOT practitioners and contractors offered their first-hand experience with implementing the saw-cutting method on their projects successfully. The innovation was also recognized as the NJ’s Build a Better Mousetrap Winner in 2022 and a video description of the innovation can be found here.
Below is a reprint of the AASHTO Innovation Initiative Page that features the Saw Cut Vertical Curb and can be accessed here.
NJDOT’s Saw Cut Vertical Curb is featured as an AASHTO Innovation Initiative.
The AASHTO Manual for Assessing Safety Hardware (MASH) establishes uniform standards for the installation of roadway safety features, including longitudinal barriers. In accordance with the recent MASH standards, the New Jersey Department of Transportation (NJDOT) has updated the installation requirements for guide rails. Per this new requirement, curbs in front of and along guide rail end terminal treatments should be limited to a maximum 2-inch exposure. The typical exposure of existing curbs is four inches. To make guide rail installation MASH compliant, the conventional practice is to remove existing curbs and replace them with 2-inch curbs. The practice typically requires seven days of field operations for the removal, replacement, and curing of concrete. Not only does this timeframe add to labor costs, but also exposes work crews and motorists to work zone traffic for longer periods of time.
NJDOT has developed an innovative method in which the existing curbs can be saw cut to two inches in lieu of removal and replacement. The existing guide rail can remain in place during saw-cutting, while the construction crew can return at a later time to remove and upgrade the guide rail. The saw-cutting approach requires only two days of labor. The process uses a power-driven vertical curb saw fitted with horizontally-oriented, diamond-edge blades or abrasive wheels that are capable of sawing to the required dimensions without causing uncontrolled cracking. The saw is water-cooled, circular, and has alignment guides. The saw is also capable of immediately collecting the slurry produced from cutting the concrete. Traffic control in work areas requires a moving operation set up that includes channelizer barrels and drums, construction signs based on the Manual on Uniform Traffic Control Devices (MUTCD) and DOT standard details, and a truck with a mounted crash cushion.
Do you have to reduce the curb height to make the longitudinal barriers compliant with AASHTO Manual for Assessing Safety Hardware (MASH) requirements?
Join AASHTO for an information-packed webinar with New Jersey Department of Transportation on how saw-cutting is used in curb retrofitting to make longitudinal barrier installations compliant with new requirements in a safer, more cost-effective, and more efficient manner.
The American Association of State Highway and Transportation Officials (AASHTO) Innovative Initiative (AII) program recognized NJDOT’s Sawcut Vertical Curb as one of seven Focus Technologies in 2022. More info about the about the AII award and the Saw Cut Vertical Curb innovation can be found here.
AASHTO’s webinar will be held on Wednesday, April 12, 2023 at 2:00 pm EDT. Register HERE
NJDOT Build a Better Mousetrap winner, Saw Cut Vertical Curb, is a response to a change in standards requiring existing curbing at guide rails to be reduced in height. This innovation increases safety and cost savings.
During this free webinar, participants will engage with NJDOT practitioners and contractors who have first-hand experience in implementing the saw-cutting method on their projects successfully.
Discussion will include:
Benefits of saw-cutting vertical curbs
Implementation considerations
Successes and lessons learned
Resources to get you started
Lead States Team Expert Presenters and Panelists
Gary Liedtka-Bizuga, New Jersey Department of Transportation
Henry Jablonski, New Jersey Department of Transportation
Peter Harry, Jr., ML Ruberton Construction Co., Inc.
Rick Berenato, ML Ruberton Construction Co., Inc.
Click to learn more about the Saw Cut Vertical Curb innovation and the New Jersey Build a Better Mousetrap program.
