GDOT/GTI - Project Spotlight March 2023

Thursday March 9th, 2023 the Georgia Transportation Institute will hosted a 1-hour webinar that spotlighted three recently completed GDOT Research projects. Each project had maximum of a 15-minute presentation, with a short time afterward for questions.

You can join the event by using the Microsoft Teams link below:

Microsoft Teams Webinar Link

Presenter: George Yuzhu Fu, Ph.D., P.E., Professor
Georgia Southern University

Dr. George Yuzhu Fu is Professor in Water and Environmental Engineering in the Department of Civil Engineering and Construction at Georgia Southern University. He has been studying, working and researching in Environmental Engineering with an emphasis on Water and Environment for more than 30 years. He has completed/is working over $700K in externally funded research mainly as PI, and has published more than 35 scholarly works in developing innovative and cost-effective Water and Wastewater Treatment Processes. He has developed and established Water and Environmental Research Lab (1,000 ft2) with a variety of state-of-the-art instruments with a total worth of more than $200K. He also has over 10 years of professional engineering experience in well-known national/international consulting companies including AECOM, SNC-Lavalin, and North China Municipal Engineering Design & Research Institute as Environmental and Project Engineer.

Title: GDOT RP18-09 Investigation on Water Quality Impacts of Bridge Stormwater Runoff from Scupper Drains on Receiving Waters

Abstract:

The objectives of this research project were to investigate water quality impacts of bridge stormwater runoff from scupper drains on receiving waters, and to develop an efficient simulation tool so that the Georgia Department of Transportation (GDOT) and the resource agencies can accurately anticipate potential effects and better evaluate whether scupper drains would adversely affect waters and the protected species. This 29-month project is concerned with the investigation of potential impacts and accumulation of water quality parameters including heavy metals (lead, zinc and copper), polycyclic aromatic hydrocarbons (PAHs), chemical oxygen demand (COD), nutrients, oil and grease, solids, pH, dissolved oxygen, and conductivity in bridge deck scupper drain runoff of six (6) bridge sites and the potential impact on sensitive major rivers in the State of Georgia. Four (4) of the sites are Southeast the State of Georgia, which are SR24, Rocky Ford (RF), US80 and SR297, crossing two (2) major rivers, which are Ogeechee River and Ohoopee River. The other two (2) sites are located North the State of Georgia, which are US255 and SR197, crossing two (2) other major rivers, which are Chattahoochee River and Soque River, respectively. The impact of the elevated parameters due to introduction from bridge deck scupper drains was observable on downstream water quality of all sites, except for lead, PAHs, phosphorous, and oil and grease that were only detected at the four (4) Southeast sites. It was observed that upon discharge of bridge deck scupper drain runoff, instant impairment of downstream water quality took place. Seasonal analyses showed variation in the parameters concentrations that was highly dependant on many factors, such as dry periods, rain intensity, traffic activity and stream discharge. Time intervals analyses showed that highest concentrations of contaminants were in the first 30-60 minutes’ samples, and that parameters concentration decrease with continuous rainfall due to dilution. The Stochastic Empirical Loading and Dilution Model (SELDM) was used for modeling potential projected impacts of some water quality parameters of concern on the downstream water quality at each Site location effectively.

Presenter: Jon Calabria, Ph.D., Associate Professor
The University of Georgia

Jon Calabria, a licensed landscape architect, educates students at The University of Georgia's College of Environment & Design to conserve and restore enduring landscapes that improve environmental quality within the human context. Dr. Calabria’s research includes landscape performance and the amelioration of land use impacts on freshwater and coastal systems.

Title: GDOT RP19-05 Phase 2: Enhancement and Restoration Interventions for Bird-Long Island Shoreline Alternatives: Design and Modeling for Stewardship

Abstract:

This study evaluated a variety of design interventions using nature-based solutions to preserve a culture resource on Bird-Long Island and promote indigenous vegetative communities by maintaining or increasing vegetative biodiversity. Several alternatives were proposed that utilized a combination of green and gray infrastructure, such as living shorelines, thin layer placement, and beneficial dredge. Strengths and weaknesses accompany each design alternative with relative construction costs. Environmental site data was collected during study and demonstrated that naturally occurring, reef forming organisms were naturally present and could likely populate proposed nature-based solutions.

Presenter: Baabak Ashuri, Ph.D., Professor
Georgia Institute of Technology

Baabak Ashuri is Professor in Schools of Building Construction, and Civil & Environmental Engineering, and Fellow of Brook Byers Institute for Sustainable Systems at Georgia Tech. His research/teaching occupy a distinctive position, bridging the fields of building construction, civil and environmental engineering, economics, and operations research. His work has focused on Quantitative Methods for Construction Engineering and Management with important contributions in the areas of construction analytics, innovative project delivery, and valuation of green-energy investments. He has 159 publications, including 44 refereed-journal papers, and secured $6.77M funding from NSF, FHWA, DOE, CII, GDOT, and Perkins+Will, to name a few. The impact of Dr. Ashuri’s research was recognized by several awards (CII/FIATECH Outstanding Early Career Researcher, ASCE Thomas Fitch Rowland, ASC National Research Faculty, DBIA Distinguished Leadership, and AASHTO High-Value Research “Sweet Sixteen”). Dr. Ashuri has chaired the ASCE Construction Research Council (CRC) and currently serves on the ASCE Construction Institute (CI) Board of Governors.

Title: GDOT RP 20-17 Enhancing the Accuracy of Construction Cost Estimates for Major Lump Sum (LS) Pay Items and Generating a More-Accurate List of Pay Items Throughout the Design Development Process

Abstract:

State departments of transportation (DOTs) encounter a critical challenge in estimating accurate cost estimates for major lump sum (LS) pay items, such as Traffic Control and Grading Complete, due to incomplete project information during the early stages of project development. To estimate prices for LS pay items, cost estimators and designers in state DOTs apply engineering judgment using knowledge from similar projects from the past and reach out to subject matter experts for providing additional resources. However, researching similar projects for finding appropriate estimates for the LS pay item is not a simple endeavor and leads to significant inaccuracy of cost estimates. A need exists to develop new methods that are capable of capturing key information from project documents and incorporating the complex nonlinear relationships between input and output variables in developing prediction models for LS pay item prices for highway projects. Thus, the overarching objective of this research is to develop forecasting models to estimate the prices of the Traffic Control and Grading Complete LS pay items using advanced text mining and machine learning algorithms that detect key patterns of information generated during project development and provide higher accuracy in cost estimates. In this research, a forecasting model for the prices of the Traffic Control and Grading Complete LS pay items was developed using machine learning algorithms (i.e., random forest, bagging, k-nearest neighbors, and stacking regressor). Furthermore, a web-based application tool was developed in a Python environment to help designers developing cost estimates with a data-driven tool for estimating the prices of the Traffic Control and Grading Complete LS pay items.