Presenter: Sk Md Imdadul Islam

Organization: Rowan University

INFRASTRUCTURE

Abstract:

The utilization of plastic has escalated over the past five decades, rising from 15 Mt to 311 Mt where a significant portion is regarded as waste after their end of use life. These waste plastics can be single or composite type, depending on how many polymers/plastics they include. Merely 12% of all plastics are recycled, while rest are disposed of in landfills, subjected to incineration, and some escape the collecting system, ultimately contaminating oceans and environment. To overcome this issue, utilizing waste plastics in flexible pavements looks promising. However, in wet mixing inadequate storage stability, attributed to compatibility issues between the plastic and asphalt is very frequent. This study evaluated the storage stability and performance characteristics of composite plastic modified binders at dosages of 1% to 3%, using polyethylene grafted maleic anhydride (PE-g-MA) and Reactive Elastomeric Terpolymer (RET) stabilizers. Thermal characterization of plastics was carried out using differential scanning calorimetry, showing melting points between 130–155°C, suitable for wet blending applications. Storage stability was evaluated using complex shear modulus separation index and fluorescence microscopy.

The results revealed improvement in storage stability with both stabilizers up to 2% dosage, with RET achieving more uniform and homogeneous plastic dispersion in asphalt binder matrix. Furthermore, rheological properties were evaluated through Superpave performance grading (PG), Multiple Stress Creep Recovery (MSCR), and Linear Amplitude Sweep (LAS). RET increased the high PG of plastic modified binders by up to three grades for HP (HDPE+PP) and two grades for LHP (LDPE+HDPE+PP), while PE-g-MA had a minimal impact on high PG. Both stabilizers maintained the low PG of the base binder, with RET providing greater improvement than PE-g-MA. The rutting performance increased significantly with RET, by enhancing recovery and lowering J_nr, outperforming PE-g-MA. With the addition of plastic, the fatigue performance was degraded. However, the use of stabilizers mitigated this effect, with RET and PE-g-MA enhanced fatigue lives by approximately 1500% and 270%, respectively. Overall, RET stabilizer was more effective than PE-g-MA in improving storage stability and performance characteristics. These findings suggest that composite plastic modified binders with up to 2% dosage combined with stabilizers can offer better storage stability and performance for sustainable pavement applications. 

Sk Md Imdadul Islam is a PhD student in Civil and Environmental Engineering and a graduate research fellow at Center for Research and Education in Advanced Transportation Engineering Systems (CREATES) at Rowan University, conducting research under the guidance of Dr. Yusuf Mehta. His current work, funded by the U.S. Army Engineer Research and Development Center (ERDC) and Cold Regions Research Engineering Laboratory (CRREL), focuses on the “Incorporation of Recycled Plastics into Asphalt Binder and Mixtures,” aligning with his research interests in Pavement Engineering, Sustainability, and Materials & Solid Waste Management. Prior to his doctoral studies, he earned an M.S. in Civil Engineering from The University of Texas at RGV and B.S. in Civil Engineering from the Khulna University of Engineering & Technology. His professional background also includes practical experience as a Project Estimator 1 at Millennium Engineers Group Inc., further fueling his motivation to contribute meaningfully to the field of engineering materials and advanced transportation systems. 

Presentation Slides:

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Presenter: Kaan Ozbay

Organization: New York University

INFRASTRUCTURE

Abstract:

Effective bridge management requires balancing long-term economic efficiency with resilience against risks posed by aging infrastructure and various hazards. Traditional life cycle cost analysis (LCCA) frameworks primarily focus on cost minimization, often underrepresenting risk and uncertainty factors that are critical for sustainable decision-making. To address this gap, our joint team from Rutgers’ RIME Lab and NYU C2SMART center, in collaboration with the NJDOT, developed a highly flexible and customizable Excel-Python integrated decision-support tool, ASSISTME-LCCA, that incorporates multi-objective optimization into the LCCA process. The framework enhances an existing Excel-based LCCA tool with Python-based automation and optimization capabilities, enabling the evaluation of bridge maintenance and rehabilitation strategies under budget constraints.

Using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), the model generates Pareto-optimal solutions that jointly minimize life cycle costs and prioritize bridges with greater susceptibility to risks by maximizing risk scores, which can be customized through weighted parameters to emphasize different risk types. Additional objectives such as Annual Average Daily Traffic (AADT) can also be included in the optimization to align with agency priorities. A case study using representative bridge inventory and condition data demonstrates how the tool produces insights. Results highlight trade-offs between cost efficiency and risk mitigation, demonstrating the value of risk-integrated planning compared to cost-driven approaches. The approach offers a practical, data-driven methodology for allocating resources while ensuring long-term resilience. By equipping stakeholders with advanced optimization capabilities, this research supports the development of an improved transportation workforce prepared to address future challenges and contributing to resilient infrastructure management strategies.

Dr. Kaan Ozbay joined Civil and Urban Engineering at NYU Tandon School of Engineering as a tenured full Professor in 2013. He is the founding Director of the C2SMART Center at NYU Tandon School of Engineering which was established in 2017. Prior to that, Professor Ozbay was a tenured full Professor at Rutgers University’s Department of Civil and Environmental Engineering where he joined as an Assistant Professor in July 1996. In 2008, he was a visiting scholar at the Operations Research and Financial Engineering (ORFE) Department at, Princeton University.  Dr. Ozbay is the recipient of several awards including the prestigious National Science Foundation (NSF) CAREER award, IBM faculty award, INFORMS Franz Edelman Finalist Award, in addition to several best paper and excellence in research awards. His research interests in transportation cover a wide range of topics including data-driven AI/ML applications in smart cities, development and calibration of large-scale complex transportation simulation models. He has co-authored 4 books and published approximately 500 refereed papers in scholarly journals and conference proceedings. Prof. Ozbay is also an Associate Editor of the ITS journal and serves as the Associate Editor of Networks and Spatial Economic journal and Transportmetrica B: Transportation Dynamics journal. Since 1994, Dr. Ozbay, has been the Principal Investigator and Co-Principal Investigator of 125 research projects funded at a level of more than $35M by USDOT, National Science Foundation, NCHRP, NJDOT, NY State DOT, NYC DOT, New Jersey Highway Authority, FHWA, VDOT, Dept. of Homeland Security, among others.  

Presentation Slides:

To be added.

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:

To be added.