Author: sshao45

POSTER 19-06: LRFD PROCEDURE FOR PILES WITH PILOT HOLE IN ROCK

LRFD PROCEDURE FOR PILES WITH PILOT HOLE IN ROCK

PI: Soonkie Nam

Co-PI(s): Xiaoming Yang

Institution(s): Georgia Southern University

Abstract

In Georgia, when a hard/dense layer exists in the pile length or the vibration/noise during the driving causes secondary issues, a pilot hole is often adopted as a pile-driving assistance method to aid driving displacement piles through, especially if a competent hard rock layer exists in a reasonable depth. The use of a pilot hole reduces construction time and uncertainties related to driving through the problematic layers. However, the pilot hole is considered different from a pre-drilled hole in terms of construction method and design assumption. This process also complicates the prediction of long-term pile capacity with a predrilled hole. An objective of this study was to identify and document the current guidelines available and adopted by different states, and investigate the relationship between the load capacity of piles installed in rock and their design parameters with respect to the pilot hole, rock conditions, and installation method. Another objective was to identify a reliable design procedure that incorporates proper LRFD resistance factors, and a field verification method for quality assurance of rock. It is found that pile driving analyzer (PDA) can be applied to the piles with a pilot hole on rock and verify the structural capacity of the pile if not the geotechnical capacity due to the higher bearing capacity on rock. It also can check the internal stress to avoid the damage during striking. Thus, the study recommends the use of PDA tests and the AASHTO resistance factor for driven piles with dynamic testing, while collecting the strength properties of the rock mass. The driving refusal criterion can be used when the rock condition is evident. However, it is still recommended that the correlations between the refusal guidelines and rock properties are verified with PDA.

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POSTER 19-10: FLASH TRACKING IMPLEMENTATION GUIDELINES COMPLEMENTING EXISTING DESIGN-BUILD MANUAL

FLASH TRACKING IMPLEMENTATION GUIDELINES COMPLEMENTING EXISTING DESIGN-BUILD MANUAL

PI: Pardis Pishdad-Bozorgi

Co-PI(s): Jesus M. de la Garza

Institution(s): Georgia Institute of Technology

Abstract

The overarching objective of this research is to develop Flash Tracking implementation guidelines that would complement the existing Design-Build Manual. These standardized implementation guidelines are captured in an appendix to the DesignBuild Manual. The research methodology comprised three phases. In the first phase, the research team studied and analyzed the effectiveness of flash track best practices implementation on three GDOT projects—namely, improvements to the I-16/I-95 interchange, the I-85 Widening, and SR 400 EL. In the second phase, the team reviewed and analyzed the GDOT DesignBuild Manual to identify its strengths, weaknesses, opportunities, and threats (i.e., a SWOT analysis) in terms of its treatment of flash track best practices. This involved cross-referencing the D-B manual against the 83 flash track best practices, to determine the presence or absence of each flash track best practice in the manual. In the third phase, an appendix to the D-B manual was developed to serve as an official source on implementing flash track best practices on D-B projects. Furthermore, modified RFQ and RFP templates were developed to incorporate flash track practices on projects and specific recommendations were made for the RFQ and RFP for the Houlihan Bridge P.I. No. 0013741/0013742 – SR 25 at Savannah & Middle River Bridges.

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POSTER 20-04: WEIGHT-IN-MOTION (WIM) DATA SYNTHESIS & VALIDATION – DEEP LEARNING MODELS FOR VEHICLE CLASSIFICATIONPOSTER 20-04:

WEIGHT-IN-MOTION (WIM) DATA SYNTHESIS & VALIDATION – DEEP LEARNING MODELS FOR VEHICLE CLASSIFICATION

PI: Mi Geum Chorzepa

Co-PI(s): Jidong J. Yang, Stephan Durham, and Sonny Kim

Institution(s): University of Georgia

Abstract

Sensor-based nonintrusive systems were able to successfully report up to 8 classes of vehicle types while the traditional video-based systems could only distinguish up to 4 classes of vehicle types. Recently, machine-learning methods (Naïve Bayes, K-nearest neighbor classification, random forest, and support vector machine) and deep-learning vision models (CNNS, Faster RCNN, and YOLO models) have been applied for vehicle classification with improved accuracies. However, vehicle classification with the increased number of categories, such as 13 FHWA classes, is still a challenging task. This study investigates a composite model architecture by leveraging the state-of-the-art vision transformer models coupled with a wheel-detection model for complementary feature learning.

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POSTER 20-07: LEVERAGING MULTISOURCE DATA FOR TRAFFIC DATA QUALITY CONTROL

LEVERAGING MULTISOURCE DATA FOR TRAFFIC DATA QUALITY CONTROL

PI: Jidong J. Yang

Co-PI(s): Stephan Durham, Sonny Kim, and Mi Geum Chorzepa

Institution(s): University of Georgia

Abstract

Maintaining High-quality traffic data serves as the foundation for core transportation planning/engineering activities and decisionmaking. The current state of the practice in traffic data quality control features rule-based data checking and validation processes, where the rules are subjective and insensitive to variation inherited with traffic data. This research study aims to leverage the multisource data available at GDOT to improve the quality control process for traffic data. Cross-validating inductive loop-based traffic count data with independent sources (e.g., from the exiting video detection data as part of the 511 system) provides a practical and robust approach for quality control of traffic data in support of various planning/engineering practices and decision-making at GDOT.

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POSTER 20-20: TACIT KNOWLEDGE MODEL TO SUPPORT KNOWLEDGE CAPTURE AND TRANSFER IN GDOT

TACIT KNOWLEDGE MODEL TO SUPPORT KNOWLEDGE CAPTURE AND TRANSFER IN GDOT

PI: David A. Guerra-Zubiaga

Co-PI(s): 

Institution(s): Kennesaw State University

Abstract

Experiences from contractors, vendors, and internal GDOT staffs are often time lost because there is little mechanism for capturing experiences-tacit knowledge. This was how this project provide value to GDOT organization. The research bases on the idea that knowledge is an important intangible asset of any organization. Let’s take a step back and introduce two types of knowledge: Explicit Knowledge and Tacit Knowledge. Explicit can be described with words, instructions, and guidelines while tacit is more complex not only to identify but also to organize, capture and reuse for supporting key decision making. Tacit knowledge is based on the idea that people know more than what they can verbally express. This research concentrates on Tacit Knowledge Capture and Store using the experimental software. Moreover, GDOT and Kennesaw Research team are well aware that capturing, transferring, and maintaining skilled employees and contractors’ experiences is beneficial in terms of cost, efficiency, time, and efforts spend on special projects (i.e.: I-85 Bridge Collapse). The research team met with different GDOT offices, such as Office of Traffic Operations, Office of Bridge Maintenance, Bridge Construction, and Bridge Design to invent the instances (sketches, videos, patterns, and storytelling) of tacit knowledge and store them in the experimental software base on the Tacit Knowledge Model in which the KSU research team had defined previously. Tacit Knowledge Model is created based on Object Oriented Design and Programming using Unified Modeling Language (UML) notation. The team can reach its goal by (1) understanding previous GDOT knowledge management projects, (2) studying the I-85 bridge collapse case study (3) evaluating the findings and utilizing them at a specific GDOT department or area to consolidate specific benefits within that department or area via defined scenarios, (4) defining Tacit Knowledge Model and finally (4) developing a new tool with the potential of being implemented throughout the entire organization. •Keywords: Knowledge management, Tacit knowledge management, Explicit knowledge, Implicit knowledge, Tacit knowledge lifecycle, Tacit knowledge model.

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POSTER 20-21: A FORECAST OF STATE MOTOR FUEL REVENUES: THE EFFECT OF NEW TECHNOLOGIES AND THE STATE VEHICLE FLEET MIX ON GEORGIA MOTOR FUEL RECEIP

A FORECAST OF STATE MOTOR FUEL REVENUES: THE EFFECT OF NEW TECHNOLOGIES AND THE STATE VEHICLE FLEET MIX ON GEORGIA MOTOR FUEL RECEIPTS

PI: Laura Wheeler

Co-PI(s): 

Institution(s): Georgia State University

Abstract

This display outlines the research on the impact of alternative fuel vehicles on the state motor fuel receipts and the development of a model to forecast motor fuel receipts.

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POSTER 20-24: AERMOD, RLINE, AND RLINEXT CASE STUDY ANALYSES IN ATLANTA, GEORGIA

AERMOD, RLINE, AND RLINEXT CASE STUDY ANALYSES IN ATLANTA, GEORGIA

PI: Randall Guensler

Co-PI(s): Hongyu Lu, William Reichard, Ziyi Dai, Tian Xia, Angshuman Guin, Ph.D., and Michael O. Rodgers, Ph.D.

Institution(s): Georgia Institute of Technology

Abstract

This research assessed the impact of USEPA?s AERMOD dispersion model (version of 19191) source types on predicted pollutant concentrations via a case study for the I-75/I-575 Northwest Corridor (NWC) in Atlanta, GA. Using MOVES-Matrix for MOVES 2014b, carbon monoxide (CO) emissions rates for every hour of a one-year study period were generated using traffic volumes and speed from Atlanta Regional Commission’s Activity-Based Model (ABM) 2020 and AERMET meteorological profiles provided by the state environmental agency (EPD). To develop concentration profiles to assess prediction differences across source types, consistent input datasets for hourly emissions by ABM link, hourly AERMET data, and gridded receptor placement (standard 20-meter grids and variable grids after link-screening) were run with the AERMOD source types: AREAPOLY (manually created and automatically generated), LINE, VOLUME, RLINE, and RLINEXT (with and without noise barriers). The team processed more than one trillion source-receptor pairs the study area using the cyberinfrastructure resources provided by the Partnership for an Advanced Computing Environment (PACE) at Georgia Tech. The results indicate that predictions from AREAPOLY and LINE are essentially identical. Predictions from RLINE and RLINEXT are almost the same, but these predictions are higher in most cases than any other source type. The VOLUME source type always yields the lowest concentrations and is less sensitive to wind directions and speed, due to the embedded wind meander dispersion parameters implemented only for VOLUME sources. Machine learning results indicate that wind speed, receptor ID (which accounts for adjacent roads and their and their mass flux emission rates in grams/meter2/second), and wind direction influence the results much more than source type selection. Introducing noise barriers to RLINEXT lowered concentration as expected, but modeling barrier effects was challenging due to the restrictive assumptions in AERMOD. Sensitivity analysis for RLINEXT suggests that barrier height, distance to the roadway, wind speed, and wind direction all affect morel predictions. Modelers need to exercise care in matching barriers to roadway link segments (i.e., barrier edge effects were observed).

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POSTER 20-25: VIRTUAL PUBLIC INVOLVEMENT (VPI): GUIDANCE FOR ENCOURAGING PUBLIC PARTICIPATION AND SOLICITING FEEDBACK DURING THE TRANSPORTATION PLANNING PROCESS

VIRTUAL PUBLIC INVOLVEMENT (VPI): GUIDANCE FOR ENCOURAGING PUBLIC PARTICIPATION AND SOLICITING FEEDBACK DURING THE TRANSPORTATION PLANNING PROCESS

PI: Baabak Ashuri

Co-PI(s): Gordon Kingsley

Institution(s): Georgia Institute of Technology

Abstract

In transportation planning and decision-making processes, public involvement (PI) is critical as the daily users of transportation share useful insights, opinions, and observations on the performance and needs of the transportation systems. Virtual Public Involvement (VPI) is the use of digital technology to engage individuals in project planning and decision making. It is intended to supplement face-to-face information sharing with virtual tools, and thus broaden existing approaches to public involvement to include more voices. The overarching objectives of this research were to gather and evaluate information on existing approaches to VPI and to provide recommendations for the development of a single-platform VPI environment on ArcGIS Hub that encourages widespread participation, facilitates two-way communication, and integrates VPI requirements into an inter- office, cradle-to-grave VPI process. The literature review provides examples of public participation design and key factors that contribute to the successful acceptance of VPI by the public. A review of GDOT practice identified the tasks, challenges, and goals for VPI by project phase and department and identified gaps in existing practice. A review of the community of practice was compiled to provide approaches to key tasks necessary for the design, implementation, and institutionalization of VPI across the country. Finally, 11 recommendations for institutionalizing VPI at GDOT were provided.

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POSTER OT-06: FOSTERING SMART AND SUSTAINABLE TRAVEL THROUGH ENGAGED COMMUNITIES USING INTEGRATED MULTIDIMENSIONAL INFORMATION-BASED SOLUTIONS

FOSTERING SMART AND SUSTAINABLE TRAVEL THROUGH ENGAGED COMMUNITIES USING INTEGRATED MULTIDIMENSIONAL INFORMATION-BASED SOLUTIONS

PI: Srinivas Peeta

Co-PI(s): OT-06

Institution(s): Georgia Institute of Technology

Abstract

This project will develop systematic deployment tools that smart and connected communities (SCCs) can use to achieve their sustainable travel goals in a quantifiable manner by leveraging advances in information, communication, and sensor technologies. While the deployment of advanced technological solutions offers great promise for communities to improve residents’ quality of life, they encounter challenges in realizing these aspirations due to the diversity in technological and travel needs and barriers faced by the residents. Solutions to achieve sustainability objectives related to enhancing travel mobility, safety, equity, access, active lifestyle, and health will be developed using an immersive living lab (City of Peachtree Corners, GA). They include building novel partnerships involving emerging micromobility services in the private sector and the well-established public transit modes, personalized behavioral interventions to nudge and incentivize personal auto users to consider sustainable alternatives, and community level public policy interventions to enable flexible and novel travel alternatives. For underserved residents, the solutions include strategies to overcome information deserts in lower-income neighborhoods, age-related technology savviness issues for senior residents, and reduced access to smartphones and transportation options. These solutions will be developed using data collected from residents and other sources, and will be deployed using an information design system that provides targeted information to the various community stakeholders using multiple delivery mechanisms. This project seeks to foster deployment paradigms associated with holistic, community-level decision-making to achieve sustainable travel goals while meeting the needs of different stakeholders. It draws on methods from multi-objective optimization, multi-agent simulation, machine learning, behavioral economics, and data and policy analytics, to generate relevant multidimensional solutions. Further, it will lead to novel paradigms and algorithms for the solution options themselves, and for the development of generalizable principles related to practical deployment frameworks in the inherently complex SCCs.

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POSTER OT-05: ENHANCING URBAN FREIGHT EFFICIENCY THROUGH SIMULATION-GUIDED INITIATIVES

ENHANCING URBAN FREIGHT EFFICIENCY THROUGH SIMULATION-GUIDED INITIATIVES

PI: Sofia Perez-Guzman

Co-PI(s): OT-05

Institution(s): Georgia Institute of Technology

Abstract

Current freight demand patterns pose challenges to energy efficiency and counteract supply-side progress. Urbanization and logistic sprawl exacerbate the complexities of supplying urban cores, giving rise to issues like pollution, congestion, and noise. Understanding the impact of urban freight and land-use decisions on energy efficiency, congestion, and emissions is paramount. A Behavioral Microsimulation Software (BMS) was devised to investigate this. The BMS simulates freight vehicle tours in a study area, offering insights into freight activity and policy impacts. At the general level, the BMS provides aggregate metrics; at the detailed level, it outputs tour details. It leverages freight trip generation estimates, and the US Bureau of Economic Analysis Input-Output Accounts for trade analysis. This versatile BMS evaluates a spectrum of supply chain activities?transportation supply to land use interventions. The study scrutinized three policies: (1) logistical development, (2) off-hour deliveries (OHD), and (3) demand management on e-commerce, all applied to a metropolitan and a specific region in New York State. The logistical development analysis showed that new facility development leads to smaller increases in freight (business-to-business) Vehicles-Miles Traveled (VMT) when located closer to the city?s center than toward the area’s outskirts. Moving closer to the city?s center reduces VMT when relocating an existing facility. These results underscore supply-side initiatives’ role in efficiency. The OHD investigation highlighted how higher penetration of OHD leads to lower freight (business-to-business) VMT, emissions, and fuel consumption, enhancing efficiency and reducing costs due to more compact and efficient tours. These findings emphasize the potency of demand-side strategies in fostering efficient urban freight systems. The demand management on e-commerce case showed that various forms of consumer adoption of household delivery consolidation could lead to notable reductions in the total number of deliveries, tours, and stops, resulting in cost savings, reduced fuel consumption, and decreased environmental emissions from business-to-customers freight activity. The findings serve as a practical illustration of the potential benefits that demand management strategies can bring to e-commerce deliveries. Reduced VMT yields cost savings, lower energy consumption, and decreased emissions, yielding substantial societal benefits. For policymakers and practitioners, these insights are invaluable for shaping effective urban freight policies. Efficient urban freight systems harmonize with sustainability goals, cost savings, and reduced environmental impact, driving positive outcomes for communities and economies.

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