{"id":1355,"date":"2023-09-28T21:56:17","date_gmt":"2023-09-28T21:56:17","guid":{"rendered":"https:\/\/kims.engr.uga.edu\/?page_id=1355"},"modified":"2023-10-04T06:03:24","modified_gmt":"2023-10-04T06:03:24","slug":"technical-content","status":"publish","type":"page","link":"https:\/\/kims.engr.uga.edu\/index.php\/technical-content\/","title":{"rendered":"Technical Content"},"content":{"rendered":"\n

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Ground Penetrating Radar – Page 2<\/a><\/mark><\/strong><\/span><\/p>\n\n\n\n

Satellite Image and Remote Sensing Analysis For Highway Asset Management<\/strong> – Page 3<\/a><\/mark><\/strong><\/span><\/p>\n\n\n\n

Investigation of Heavier-Than-Expected Vehicle Weights at Non-Interstate WIM Site in Georgia – Page 4<\/a><\/span><\/mark><\/strong><\/p>\n\n\n\n

Satellite Image and Remote Sensing Analysis for Rural Road Condition Monitoring – Page 5<\/a><\/mark><\/span><\/strong><\/p>\n\n\n\n

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“Revolutionizing Subgrade Assessment with GPR”<\/strong><\/h2>\n\n\n\n
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In our first stage, we focused on developing a state-of-the-art subgrade density prediction model utilizing cutting-edge Ground Penetrating Radar (GPR) technology. Through meticulous laboratory tests and field validation tests. particularly with high plastic silt soil, we successfully fine-tuned this model. Our research unveiled a powerful correlation between GPR scan results and subgrade soil properties, demonstrating the potential for non-destructive and highly accurate evaluations in civil engineering projects. The subsequent field tests further validated our model’s precision and reliability, establishing it as a game-changer for efficient subgrade assessment.<\/mark><\/p>\n<\/div><\/div>\n\n\n\n

“Deep Learning Redefining Pavement Integrity”<\/strong><\/h2>\n\n\n\n
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In the second phase of our groundbreaking research journey, we embarked on extensive Ground Penetrating Radar (GPR) scans of road surfaces. Our mission? To harness the incredible capabilities of deep learning to revolutionize subsurface crack detection with unparalleled efficiency. Leveraging cutting-edge synthetic data augmentation techniques, our study achieved an astonishing test Average Precision (AP) score of 0.769 for crack detection. This remarkable feat underscores the transformative potential of this technology. While our primary focus centered on subsurface crack identification, our research unveiled an exciting opportunity: the seamless integration of this advanced deep learning model with subgrade density estimation from our pioneering first stage. This innovative approach promises to redefine pavement condition assessments, empowering decision-makers with invaluable insights to optimize infrastructure maintenance and rehabilitation strategies. Ultimately, our research sets the stage for elevated infrastructure durability and safety.<\/mark><\/p>\n<\/div>

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“Validation: The Future of Infrastructure<\/strong>“<\/h2>\n\n\n\n
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In the final chapter of our research program, we embarked on a mission to validate our cutting-edge deep learning model developed in Stage 2. This model, designed for subsurface crack detection using Ground Penetrating Radar (GPR) scans of road surfaces, has already demonstrated exceptional accuracy with an impressive test Average Precision (AP) score of 0.769. Our validation results solidify its potential to revolutionize pavement condition assessments by providing timely and precise information about subsurface cracks. Moreover, our research underscores the thrilling prospect of integrating this deep learning model with subgrade density estimation from Stage 1, creating an innovative approach that promises to optimize infrastructure maintenance and rehabilitation strategies. This multifaceted research effort sets the stage for the future of infrastructure excellence, enhancing durability, and safety in the dynamic realm of civil engineering.<\/mark><\/p>\n<\/div><\/div>\n\n\n\n\n\n\n\n

Remote Sensing Applications for Road Condition Monitoring by Means of Multispectral Satellite Imagery<\/strong><\/h2>\n\n\n\n
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Road networks play a crucial role in socio-economical status of nations. Road conditions such as pavement aging and pavement damages affect rideability, safety and user costs. Maintaining high quality for road pavements is a crucial task for highway agencies. To that end, conditions of roads should be regularly assessed to grasp useful information about roads through time. Conventionally, road condition assessment has been done by visiting and inspecting roads. Inspectors use different indexes such as pavement condition index (PCI), International roughness index (IRI), and structural index to determine condition of roads. However, due to hazards related to these labor and time intensive in-situ visits, this method has made many problems. To overcome this issue, remote sensing that had been utilized in various fields, showed potentials to be used in pavement engineering by the mean of monitoring techniques.\u00a0<\/mark><\/p>\n<\/div>

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To monitor road quality, either as a supplement or as an alternative to the ground-based methods, the application of remote sensing has been investigated by several researchers in last two decades. Remote sensing methods use electromagnetics radiations in different regions of electromagnetic spectrum to obtain information about the target object. In pavement engineering, one of the most common means of remote sensing is multispectral imagery. Multispectral images comprise a limited set of electromagnetic channels or bands, possessing wide bandwidth that facilitates the capture of signals from very far distances. This characteristic permits the deployment of multiple multispectral sensors into the orbit for the acquisition of multispectral images. When compared to other land categories, monitoring of roads via multispectral satellite images demands a significantly high spatial resolution. With recent enhanced spatial resolution of the multispectral sensors, these platforms have attracted significant attention among pavement researchers interested in multispectral imagery. However, despite the use of very high-resolution images, models were unable to achieve very high accuracy and the mixed pixel issue remains a as the main challenge in using multispectral satellite imagery in road pavement monitoring. The integration of machine learning techniques with remote sensing has emerged as a promising approach for monitoring pavement conditions<\/mark><\/p>\n<\/div><\/div>\n\n\n\n

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By leveraging machine learning algorithms to analyze this data, it becomes possible to automate the detection, classification, and tracking of pavement conditions over time, as well as predict future deterioration. In the first phase of the project, the preliminary study was done on both concrete and asphalt pavement sections using both multispectral and hyperspectral images to track changes of spectral profiles of concrete and asphalt pavements over time. It was seen that overall, concrete pavements have higher reflectance values in all the spectrum than asphalt pavements. Figure 2 shows the reflectance values (Red band) of the studied asphalt pavement section obtained from a satellite platform from 2010-2022. As seen in this figure, maintenance work can be detected from satellite imagery over time. Also, the result of hyperspectral scanning showed that there will be changes in the spectral properties of asphalt pavements because of aging and degradation by traffic passes.<\/mark><\/p>\n<\/div><\/div>\n\n\n\n

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In the second phase, our group has developed a model to utilize multispectral satellite images and ML algorithms to detect the type of pavements, and the conditions of asphalt pavements in a very large highway facility. Using historical satellite data, the dataset was generated with over 5.8 million datapoints and the result of classification showed an accuracy 74% in detecting road type and conditions.\u00a0<\/mark><\/p>\n<\/div><\/div>\n\n\n

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Investigation of Heavier-Than-Expected Vehicle Weights at Non-Interstate WIM Site in Georgia <\/strong><\/h2>\n\n\n\n
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The Port of Savannah, among the busiest maritime hubs in the U.S., has witnessed an escalating container capacity, significantly straining local infrastructure. This stress manifests as potential damage to pavements and bridges.\u00a0This\u00a0research focuses on quantitatively measuring the impact of heavy vehicle traffic on these structures and gauging the extent of damage. Additionally, there’s growing concern over vehicles in Georgia exceeding weight limitations beyond the Port’s traffic. This study seeks to quantify the frequency and weight of such over-limit vehicles. To achieve this, Weigh-in-Motion (WIM) technology will be employed at select Georgia sites.<\/mark><\/p>\n\n\n\n

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Primary Objectives:<\/mark><\/strong><\/p>\n\n\n\n

1. Provide insights into the expected rate of transportation infrastructure degradation in Georgia, enabling GDOT to comprehend both the immediate and anticipated implications of heavy vehicle weights.<\/mark><\/p>\n\n\n\n

2. Bolster the reliability of infrastructure supporting the transport of goods near port facilities, thereby enhancing statewide economic development. This will be achieved through on-site examinations of pavements and bridge structures and an in-depth analysis of the assets informed by dynamic WIM data.<\/mark><\/p>\n\n\n\n

Detailed Objectives:<\/mark><\/strong><\/p>\n\n\n\n

1. Examine the distinct characteristics of various non-interstate WIM sites in Georgia, leveraging data from GDOT\u2019s Traffic Analysis & Data Application (GDOT-TADA).<\/mark><\/p>\n\n\n\n

2. Gather data concerning gross vehicle weight and its frequency of occurrence.<\/mark><\/p>\n\n\n\n

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Weigh-in-Motion (WIM) system is specialized technologies designed to capture axle and gross vehicle weights as vehicles pass over sensors. This system can provide valuable data sets that include gross vehicle weight, axle loads, vehicle speed, and type.<\/mark><\/p>\n\n\n\n

In this research, WIM data from non-interstate sites in Georgia will be analyzed to assess the impact of heavy vehicle traffic on local infrastructure. Using data from the GDOT-TADA, the research aims to classify vehicles, calculate frequency based on gross vehicle weight, and determine\u00a0ESALs(Equivalent Single Axle Load). This will help\u00a0quantifying\u00a0the stress on pavements and bridges, informing infrastructure planning and policy enforcement.<\/mark><\/p>\n<\/div><\/div>\n\n\n\n

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In Georgia, there are 30 active Weigh-in-Motion (WIM) sites. Of these, seven are situated on non-interstate roads, as illustrated in Figure 2. For this study, publicly available monthly WIM statistical data from 2021 to 2023 were collected from the GDOT-TADA website. This dataset comprises both directional and weight-based traffic volumes. Additionally, raw data for these seven non-interstate WIM sites were directly obtained from GDOT.<\/mark><\/p>\n<\/div>

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Using data collected from GDOT-TADA, the traffic characteristics of each WIM station were analyzed. Figure 3 represents one of the seven selected non-interstate WIM stations, specifically 115-0052. The graph is color-coded to differentiate between the years 2021, 2022, and 2023. The x-axis denotes the months, while the y-axis represents the average daily traffic volume. Given that the existing weight limit for vehicles in Georgia is 80kips and the weight limit for vehicles to and from the Port is 100kips, separate graphs were created to represent traffic volumes exceeding 80kips, between 80-100kips, and above 100kips. Additionally, both bidirectional and unidirectional (eastbound and westbound) traffic volumes were graphed. The key findings from the analysis are as follows:<\/mark><\/p>\n\n\n\n

\u2022 Consistent heavy vehicle traffic trends from 2021 to 2023.<\/mark><\/p>\n\n\n\n

\u2022 Eastbound traffic was more than twice as high as westbound. <\/mark><\/p>\n\n\n\n

\u2022 A higher proportion of vehicles exceeded 100kips.<\/mark><\/p>\n\n\n\n

The same graphical format was applied to analyze the traffic characteristics of the other six non-interstate WIM stations.<\/mark><\/p>\n<\/div><\/div>\n\n\n

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Using GDOT’s WIM raw data, we analyzed heavy vehicle traffic, focusing on vehicle classes 4 to 13 as categorized in Figure 4. An example analysis for WIM station 003-0132, based on 2021 data, is displayed in Figure 5. The left set of graphs shows the distribution of vehicle weight, single axle weight, and tandem axle weight for all target vehicle classes. For single-unit vehicles (Classes 4-7), the middle set of graphs displays similar distributions, while the right set of graphs covers multi-unit vehicles (Classes 8-13).<\/mark><\/p>\n\n\n\n

The vehicle weight distribution graph is color-coded based on load limits: the traditional 80 kips, 10% above standard (88 kips), and the new 100 kips limit. Single axle weight is color-coded according to reference points set at 18 kips for pavement and 20 kips for bridges. Tandem axle weight uses 34 kips as the standard reference point for both pavement and bridge, with additional markers at 25% above standard (42.5 kips) and 50% above standard (51 kips) also indicated.<\/mark><\/p>\n<\/div><\/div>\n\n\n\n

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Utilizing the dataset spanning from 2021 to 2023, the analysis incorporates all seven non-interstate WIM stations. The findings aim to illuminate the traffic characteristics unique to each station. An upcoming focus will be to quantify the degradation impact on both pavement and bridge structures due to heavy vehicles that exceed standard weight limits. Furthermore, plans are in place to correlate the weight violation frequencies with maintenance records to establish if higher rates of infractions lead to quicker degradation of infrastructure. Ultimately, this research intends to provide foundational data that can be used to refine transportation infrastructure policies, potentially influencing future road maintenance scheduling and budget allocation strategies.<\/mark><\/p>\n\n\n

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Satellite Image and Remote Sensing Analysis for Rural Road Condition Monitoring<\/strong><\/h2>\n\n\n\n
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Introduction: To support local industries in Georgia, including agriculture and manufacturing, the state updated the permissible weights of trucks on its roads. This led to a rise in heavy vehicle usage, putting added stress on Georgia’s rural road infrastructure, particularly with the growth of the agricultural sector and the new electric vehicle industry. With sparse populations in these rural areas, there’s a smaller tax base, making road maintenance challenging. This study introduces a cutting-edge approach using remote sensing and machine learning (ML) algorithms to monitor and assess the condition of these rural roads. The method promises to be cost-effective, eliminating the need for extensive field inspections and aiding governments in prioritizing maintenance projects. Literature Review: Previous studies, notably GDOT RP 20-27, have explored the use of satellite images and remote sensing for highway management. This study achieved a classification accuracy of 74% using the XGBoost classifier to predict road conditions, drawing from a massive dataset of 5.8 million data points spanning 2010-2022. The importance of local road systems to communities cannot be overstated, yet many face challenges due to budget constraints and the absence of reliable road condition data. This current study takes a data-centric approach to address these issues, emphasizing the unique characteristics of chip-sealed pavements, which are common in local roads. It also seeks to refine and enhance ML techniques for more accurate road condition monitoring. <\/mark><\/p>\n<\/div><\/div>\n\n\n\n

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Objectives: <\/mark><\/p>\n\n\n\n

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  • Develop and evaluate ML models to predict the surface condition of rural roads using remote sensing data. <\/mark><\/li>\n<\/ul>\n\n\n\n

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    • Examine the potential of multispectral satellite images to assess the quality of chip-sealed asphalt pavements.<\/mark><\/li>\n<\/ul>\n\n\n\n

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      Ground Penetrating Radar – Page 2 Satellite Image and Remote Sensing Analysis For Highway Asset Management – Page 3 Investigation of Heavier-Than-Expected Vehicle Weights at Non-Interstate WIM Site in Georgia – Page 4 Satellite Image and Remote Sensing Analysis for Rural Road Condition Monitoring – Page 5<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-1355","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/kims.engr.uga.edu\/index.php\/wp-json\/wp\/v2\/pages\/1355","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/kims.engr.uga.edu\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/kims.engr.uga.edu\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/kims.engr.uga.edu\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/kims.engr.uga.edu\/index.php\/wp-json\/wp\/v2\/comments?post=1355"}],"version-history":[{"count":66,"href":"https:\/\/kims.engr.uga.edu\/index.php\/wp-json\/wp\/v2\/pages\/1355\/revisions"}],"predecessor-version":[{"id":1584,"href":"https:\/\/kims.engr.uga.edu\/index.php\/wp-json\/wp\/v2\/pages\/1355\/revisions\/1584"}],"wp:attachment":[{"href":"https:\/\/kims.engr.uga.edu\/index.php\/wp-json\/wp\/v2\/media?parent=1355"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}