Field Evaluation of Reduced-Temperature Warm Mix Asphalt: Construction Feasibility, Thermal Behavior, and Environmental Impacts
Reduced-temperature warm mix asphalt (WMA) technologies are increasingly being explored as practical solutions for reducing energy consumption and emissions during asphalt paving operations. This webinar presents findings from a full-scale field study conducted in Oklahoma comparing reduced-temperature chemical WMA and conventional hot mix asphalt (HMA) under real-world construction conditions. The work was conducted as part of the speakers’ graduate research at Oklahoma State University prior to joining Applied Research Associates. The study evaluated temperature trends, heat loss behavior, thermal segregation, in-place compaction, fuel consumption, particulate matter emissions, and environmental impacts during plant production and paving operations.
Results from the study demonstrated that reduced-temperature WMA achieved comparable in-place density while reducing plant fuel consumption, thermal segregation, particulate matter emissions, and cradle-to-laid environmental impacts relative to conventional HMA. Practical lessons learned from field implementation, instrumentation, and life cycle assessment (LCA) will be discussed, with emphasis on how sustainability-focused asphalt technologies can be evaluated using both construction and environmental performance metrics.
Key learning objectives from the presentation include the following:
- Understand how reduced-temperature WMA compares to conventional HMA in terms of temperature trends, heat loss behavior, and in-place compaction.
- Learn how field instrumentation and thermal profiling technologies can be used to monitor asphalt production and paving operations.
- Understand the effects of reduced production temperatures on plant fuel consumption, particulate matter emissions, and environmental impacts.
- Learn how thermal segregation analyses can be incorporated into field evaluations of asphalt paving operations.
- Understand how life cycle assessment (LCA) can be integrated with field data to evaluate the sustainability impacts of asphalt paving technologies.
July 22, 2026 | 12-1 PM EDT
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Speaker – Adeoluwa Gbolade, Ph.D., E.I.T.
Dr. Adeoluwa (Ade) Gbolade is a Staff Civil Engineer at ARA, where he supports pavement and geotechnical engineering research and consulting projects for transportation infrastructure applications. He has contributed to projects involving pavement performance evaluation, mechanistic analysis, asphalt materials, geotechnical investigations and design, and transportation asset management. His experience also includes coordinating technical tasks, supporting multidisciplinary project teams, and assisting with the development of engineering recommendations for clients and agencies.
Prior to joining ARA, Ade completed his Ph.D. in Civil Engineering at Oklahoma State University, where his research focused on sustainable pavement engineering, warm mix asphalt technologies, life cycle assessment, balanced mix design, and aggregate compaction behavior. During his time at Oklahoma State University, he led and supported multiple field and laboratory studies in collaboration with contractors, industry partners, and transportation agencies. He has delivered technical presentations at national conferences and industry meetings, including the Transportation Research Board Annual Meeting and the Association of Asphalt Paving Technologists Annual Meeting, and has received multiple awards for his research contributions in pavement engineering and sustainability.
Speaker – Bibek Parajuli, M.S., E.I.T.
Bibek Parajuli is a civil engineer at ARA with experience in pavement engineering, large-scale data analysis, and applied artificial intelligence for transportation infrastructure. Bibek builds AI systems and data pipelines that take raw field measurements through signal processing, analysis, and visualization for interpreting large datasets. His AI work spans retrieval-augmented generation (RAG) systems, AI agents, and document-grounded chatbots for transportation specifications and engineering knowledge bases. At ARA, he has developed pavement condition deterioration models and optimization tools for prioritizing capital improvement budgets across agency networks. His focus is on combining pavement engineering practice with data science and AI to support performance-driven, sustainable infrastructure decisions.
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