Author: sshao45

ENHANCING THE GDOT’S MAINTENANCE DECISION TREES CONSIDERING THE EFFECTIVENESS OF VARIOUS TREATMENT OPTIONS IN DIFFERENT GEOGRAPHICAL LOCATIONS AND OVER TIME

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PI: Brian Moore

Co-PI(s): Baabak Ashuri

Institution(s): Kennesaw State University & Georgia Institute of Technology

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Abstract

This research aims to help the Georgia Department of Transportation (GDOT) Office of Maintenance better allocate funding and improve maintenance strategies at the network level through updating the decision tree considering temporal and spatial variation in cost, deterioration rate, and maintenance effectiveness. The GDOT has adopted a comprehensive pavement management measure, Overall Condition Index (OCI). The OCI serves as a basis to trigger maintenance and rehabilitation. It is derived by averaging six distress indices, load cracking, edge cracking, block cracking, reflective cracking, rutting, raveling, and an additional adjustment index score for asphalt pavements. This new measure takes advantage of the automated data collection method, however, the corresponding maintenance and rehabilitation criteria for pavements need to be studied and updated considering regional effects, cost variation, the effectiveness of different treatment options, and decision timing. The major objective of this research is to enhance the GDOT’s maintenance decision trees through: (a) empirically analyzing the effectiveness of various treatment options in different geographical locations and climate conditions across the state; (b) improving lifecycle cost estimates for the treatment options; and (c) conducting tradeoff analysis among various maintenance strategies (e.g., 20% pavement improvement vs. wait and replace the entire pavement), in order to determine the optimal strategy that can be implemented over time. The updated framework for maintenance and rehabilitation projects can help the GDOT better plan investments to improve the conditions of its strategic assets, and thus, allocate its limited resources more efficiently and effectively to various project types in different districts. Substantial amounts of cost-saving are anticipated as the result of updating the decision tree and utilizing optimal strategies for intervention.

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UPDATING LANE DISTRIBUTION FACTORS FOR PAVEMENT DESIGN

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PI: Jidong J. Yang

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

Institution(s): University of Georgia

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Abstract

The Lane Distribution Factor (LDF) is a critical input in pavement design. The LDF values can be outdated due to many reasons, such as population and economic growth, demographic and travel behavior changes, technology adoption, among others. Using the outdated LDFs could potentially result in over or under-design of pavement structure. Thus, the LDF values require timely update to reflect true truck traffic distribution across lanes. In this study, a hierarchical model is developed using the latest statewide traffic data to estimate the LDF values and update LDF design tables.

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UAS-ASSISTED INSPECTION OF BRIDGES FOR CORROSION EFFECTS

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PI: Iris Tien

Co-PI(s): 

Institution(s): Georgia Institute of Technology

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Abstract

New technologies, such as UAVs and machine learning, have been used to conduct imagery-based bridge inspections and efficiently evaluate damage on the bridge. However, corrosion detection is still an open problem, and corrosion detection algorithms have only proven adequate in certain environments and conditions. The main goal of this project is to explore the use of UAVs as a proof-of-concept to detect and characterize corrosion on bridges in Georgia. The first objective of this research is to investigate the use of UAVs for bridge inspections. The second objective of this research is to develop and evaluate automatic corrosion detection and evaluation algorithms. As part of this project, imagery data from two bridges, selected in collaboration with GDOT personnel, was collected using the Skydio 2+ drone. After the images were cleaned and labeled, they were used in varying computer vision and machine learning algorithms for corrosion detection. First, texture thresholding and color thresholding methods were implemented. Then, K-Means was investigated as an unsupervised machine learning algorithm using texture and color features. Finally, deep learning methods were investigated with automated featured extraction. It was found that the Skydio 2+ drone can be used to reach places that are hard for inspectors to reach, but the trees in Georgia limit where the drone can fly and how much of the bridge can be assessed. Therefore, pre-flight planning is recommended. Additionally, it was found that no corrosion segmentation technique as of yet works perfectly. K-Means segmentation, coupled with background removal, has the highest recall, but the mIoU is low. Texture thresholding performed with the second highest recall and an mIoU close to 90%. The deep learning techniques, SpotRust and FCN, have the lowest performance. This research shows that these methods must be developed further to be useful in supporting bridge inspections in Georgia.

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FIELD EVALUATION OF WIRELESS ULTRASONIC THICKNESS MEASUREMENT WITH STEEL BRIDGE MEMBERS

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PI: Yang Wang

Co-PI(s): 

Institution(s): Georgia Institute of Technology

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Abstract

The objective of this project is to develop and implement a long-term ultrasonic thickness measurement system on steel members of in-service bridges using the Martlet wireless ultrasonic devices. Each Martlet wireless ultrasonic sensing device consists of an ultrasonic transducer, a Martlet motherboard with wireless communication, a pulser daughterboard for pulse excitation generation, and an ultrasonic daughterboard for signal filtering and amplification. The developed system is composed of the Martlet wireless sensing devices and a gateway server connecting to a 4G LTE network. It has the ability to collect ultrasonic thickness measurement data at predefined intervals and automatically upload the data into the cloud. The system has been validated at two bridges in Georgia.

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NONDESTRUCTIVE/NONCONTACT INSPECTION PROTOCOLS AND TECHNOLOGIES FOR AGING MECHANICALLY STABILIZED EARTH AND MODULAR BLOCK RETAINING WALLS

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PI: Marcel Maghiar

Co-PI(s): Gustavo Maldonado, and Soonkie Nam

Institution(s): Georgia Southern University

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Abstract

The MSE walls have been built for several decades in the U.S. and became one of the most common types of retaining structures. As the MSE walls are aging, proper inspections and monitoring are necessary to prevent critical failures and to determine appropriate maintenance schedule and priority. Also, non-destructive/non-contact methods are getting more attention, especially in structure inspections, due to their convenience and improved accessibility of the technology. However, there are currently no established inspection guidelines which have been approved by either the FHWA or GDOT. This project provides GDOT with a better decision-making benefit as methods to be developed through this endeavor will offer a more thorough and systematic inspection leading to appropriate actions, and the redeployment of digital data collected in-situ can support other investigations for connected projects. As existing MSEWs and MBWs age, a trackable history of the condition and stability of each individual retaining wall will be an asset for those making decisions on preventive maintenance and emergency care of these critical structures. The team?s results of the proposed research study and the new technologies/applications developed will be readily applicable to other types of retaining walls and ancillary structures.

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STRUCTURAL MONITORING OF STEEL-MEMBER BRIDGES WITH FATIGUE LIFE PROGNOSIS DUE TO DYNAMIC VEHICULAR LOADS

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PI: Yang Wang

Co-PI(s): Ryan Sherman

Institution(s): Georgia Institute of Technology

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Abstract

Standard procedures exist to calculate the remaining fatigue life of steel bridges. However, such procedures rely on conservative estimates of the live load stress range that do not account for system-level behavior. Data acquisition (DAQ) systems exist that are capable of measuring the true structural response of in-service bridge systems. Leveraging such systems with fatigue analysis procedures, allows the remaining fatigue life to be more accurately estimated, thereby often extending the service life of critical bridge systems. The outcome of this project is to provide a long-lasting impact on the GDOT bridge asset management program by aiding in decisions regarding the repair, retrofit, and replacement of fatigue-sensitive bridges. A structural monitoring system will be developed to collect live data. The data will be used to create a finite element model for dynamic behavior and an algorithm to estimate the remaining fatigue life.

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QUALITY MANUAL FOR STEEL BRIDGE FABRICATION

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PI: Ryan J. Sherman

Co-PI(s): Lauren Stewart

Institution(s): Georgia Institute of Technology

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Abstract

In any fabrication process, an undesired and often unavoidable obstacle that fabricators and owners deal with is the detection and correction of nonconformances, which is defined as an alteration in the fabrication process that results in the materials or fabricated component not meeting the requirements agreed upon in the project specifications. The objective of this research project is to investigate academic literature, unpublished research, industry knowledge, and the results from proposed experimental investigations to determine the appropriate methods to systematically detect and correct nonconformances encountered during steel bridge fabrication. To accomplish this objective, extensive literature reviews of existing data and research will be conducted, and both small- and large-scale experimental testing will be conducted. The small-scale testing will focus primarily on material testing, and the large-scale testing will focus primarily on fatigue testing. This research will result in the first draft of a standardized GDOT Steel Fabrication Quality Manual (SFQM) and new experimental data that will benefit both GDOT and the structural steel industry.

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USE OF GROUND PENETRATING RADAR TO DETECT CEMENT CONTENT IN CEMENT-STABILIZED SUBGRADE

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PI: S. Sonny Kim

Co-PI(s): Bjorn Birgisson

Institution(s): University of Georgia

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Abstract

Cement stabilization has been successfully used to improve poor-quality subgrade soils by increasing the soil support to remedy these soils useful for pavement construction. Cement stabilization has the potential to reduce initial construction costs through improved subgrade stability in the pavement structure. Cement stabilization also provides greater long-term stability of the pavement structure and lower pavement life-cycle costs through reduced pavement maintenance. Unfortunately, flexible pavements over cement-stabilized subgrade are experiencing reflective cracking originating from the shrinkage cracks on top of cement-stabilized subgrade due to poor construction. In this study, ground-penetrating radar (GPR) was used to capture the inconsistent layer thickness of cement-stabilized subgrade and its cement content. The results show that GPR is capable of capturing different dielectric constants along with different percent cement contents in subgrade soils.

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