Georgia concrete pavement performance and longevity

Issued as final reportGeorgia. Dept. of Transportatio

Towards the Implementation of a Geotechnical Asset Management Program in the State of Georgia

PI# 0000240717Experiences at U.S. departments of transportation (DOTs) have demonstrated the value of geotechnical asset management (GAM) to enable a framework for informed decisions that align the DOT\u2019s objectives with investment and performance targets. However, because Georgia currently lacks a such a program, this study was performed to set the stage for developing a GAM program in the state with a primary focus on retaining walls. While walls were identified as the asset of the highest importance in Georgia, other critical infrastructure assets (i.e., slopes, embankments, and bridge foundations) were also considered. The proposed GAM system consisted of three phases: (1) inventory during design, (2) as-built inventory, and (3) maintenance inspection. Towards the development of a state-wide GAM program, a computational platform that accommodated the different proposed phases was developed and tested in metro Atlanta areas. The study also reviewed image-based and remote-sensing technologies for GAM. In particular, proof-of-concept studies that combined image-based and machine learning technologies for optimizing GAM processes for retaining walls in the metro Atlanta area were conducted, showing promising results. The study concluded by providing a road map for establishing a GAM program in the state of Georgia, considering short-term and long-term recommendations

Development of optimization modules for GDOT pavement management system

Issued as final reportThis item was temporarily removed from SMARTech at the request of the Georgia Tech Research Institute on May 8, 2009.Georgia Department of Transportatio

Quantifying Raveling Using 3D Technology with Loss of Aggregates as a New Performance Indicator

Pavement raveling is one of the predominant distresses in the United States that impacts roadway safety and driver comfort on open-graded friction course (OGFC) pavements. Raveling specific treatments, such as fog seal and micro-milling the OGFC layer, can prolong pavement life and reduce resurfacing costs and environmental impact. However, with the current qualitative condition assessment methods (which rate pavements at Severity Levels 1–3 or as light, moderate, or severe), it is difficult to determine the optimal timing for these raveling treatments to be most effective. Therefore, there is an urgent need to develop a method to quantitatively evaluate the raveling condition. While 3D pavement technology provides opportunities for quantifying pavement raveling conditions using 3D pavement surface data, there are two main challenges for quantifying pavement raveling: (1) estimating a reference surface that represents the pavement without any raveling so that the actual pavement can be compared to the reference surface to quantify the raveling, and (2) obtaining pavement images with quantified raveling conditions (aggregate loss volume) for validation. This paper proposes a method with the loss of aggregate as a new performance indicator to automatically quantify raveling using 3D pavement surface data already collected by transportation agencies for pavement evaluation. The proposed method is validated using pavement images (with known aggregate loss) from simulated pavement mats fabricated in the lab and synthetic pavement images obtained by procedural generation. The proposed method consists of (1) 3D data acquisition; (2) pre-processing with (a) outlier removal and image smoothing, (b) two-sensor image stitching, and (c) range image rectification; (3) raveling detection using (a) region of interest selection, (b) reference surface estimation, (c) potential aggregate loss identification, and (d) noise removal; and (4) aggregate loss quantification. The validation results show a strong correlation (R = 0.99) between the computed aggregate loss and the expected aggregate loss. Better performance was observed with the proposed method than with other methods (such as the watershed method and the model fitting method). The proposed method provides a cost-effective means to quantify the loss of aggregates in support of quantitative raveling condition forecasting by leveraging 3D pavement data already collected by transportation agencies

Detection of curves with unknown endpoints using minimal path techniques

©2010. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.Presented at BMVC 2010, the British Machine Vision Conference, Aberystwyth, 31st August - 3rd September 2010.DOI: 10.5244/C.24.62We present a novel method to detect curves with unknown endpoints using minimal path techniques. Our work builds on the state of the art minimal path techniques currently used to detect curves. Existing algorithms in the literature require the user to specify both endpoints of the curve or one endpoint plus the total length of the curve. However, in our approach, the user may specify any arbitrary initial point on the curve and the algorithm can detect the complete curve (even with multiple branches) automatically without the need for any additional information. We apply this algorithm to the problem of crack detection in civil structures where cracks are modeled as 2D curves. The results demonstrate that the algorithm is robust to variations in background and texture and is able to detect curves accurately

Quantifying Raveling Using 3D Technology with Loss of Aggregates as a New Performance Indicator

Pavement raveling is one of the predominant distresses in the United States that impacts roadway safety and driver comfort on open-graded friction course (OGFC) pavements. Raveling specific treatments, such as fog seal and micro-milling the OGFC layer, can prolong pavement life and reduce resurfacing costs and environmental impact. However, with the current qualitative condition assessment methods (which rate pavements at Severity Levels 1–3 or as light, moderate, or severe), it is difficult to determine the optimal timing for these raveling treatments to be most effective. Therefore, there is an urgent need to develop a method to quantitatively evaluate the raveling condition. While 3D pavement technology provides opportunities for quantifying pavement raveling conditions using 3D pavement surface data, there are two main challenges for quantifying pavement raveling: (1) estimating a reference surface that represents the pavement without any raveling so that the actual pavement can be compared to the reference surface to quantify the raveling, and (2) obtaining pavement images with quantified raveling conditions (aggregate loss volume) for validation. This paper proposes a method with the loss of aggregate as a new performance indicator to automatically quantify raveling using 3D pavement surface data already collected by transportation agencies for pavement evaluation. The proposed method is validated using pavement images (with known aggregate loss) from simulated pavement mats fabricated in the lab and synthetic pavement images obtained by procedural generation. The proposed method consists of (1) 3D data acquisition; (2) pre-processing with (a) outlier removal and image smoothing, (b) two-sensor image stitching, and (c) range image rectification; (3) raveling detection using (a) region of interest selection, (b) reference surface estimation, (c) potential aggregate loss identification, and (d) noise removal; and (4) aggregate loss quantification. The validation results show a strong correlation (R = 0.99) between the computed aggregate loss and the expected aggregate loss. Better performance was observed with the proposed method than with other methods (such as the watershed method and the model fitting method). The proposed method provides a cost-effective means to quantify the loss of aggregates in support of quantitative raveling condition forecasting by leveraging 3D pavement data already collected by transportation agencies

Technology Review and Roadmap for Inventorying Complete Streets for Integration into Pavement Asset Management Systems

Complete Streets provide mobility for all modes of transportation including active transportation. Complete Streets are being implemented in the US and transportation agencies must maintain these assets, which requires bringing them into asset management systems. Many gaps exist to include Complete Streets in asset management, and there is no comprehensive plan for filling those gaps. This project developed a road map to fill those gaps. To create this roadmap, the study completed the following tasks: 1) develop and refine a survey 2) perform national and in-depth surveys, 3) synthesize survey outcomes, 4) identify current statuses, challenges, and needs, and 5) develop a roadmap for Complete Streets asset management. All 50 state Department of Transportations participated in the national survey while Caltrans, Georgia DOT, and the Atlanta and Washoe County Metropolitan Planning Organizations contributed to the in-depth survey. This repot synthesizes the outcomes of the surveys and literature review. The survey results showed that many agencies have some Complete Streets guidance (39/50), but far less have a dedicated liaison or office (15/40), and only seven agencies have Complete Streets performance measures. The three primary challenges are: 1) inadequate funding related to organizational structure 2) the need for a rating system, and 3) the need for improved data accessibility, collection methods, and management techniques. The proposed roadmap includes asset management development and improved data collection and analysis pathways. The roadmap is intended to be used as a starting point for the incorporation of Complete Streets into asset management.View the NCST Project Webpag

A Roadmap for Integrating Complete Streets Infrastructure into Pavement Asset Management Systems

Transportation agencies nationwide use pavement management systems (PMS) to maintain roads and highways. Pavement management has typically been used for auto-oriented infrastructure. However, state and local agencies are increasingly adopting complete streets policies to promote roadway designs focused on the needs of all transportation users. Complete streets designs include new assets such as pedestrian and bicycling infrastructure that are not typically incorporated into mainstream pavement management systems and do not have asset management systems of their own. Including pedestrian and bicycling features into asset management systems (directly in PMS or via other approaches) would help ensure that sidewalks and bike lanes are properly maintained over time and continue to provide the safety, environmental, and public health benefits attributed to complete streets design. Researchers at the Georgia Institute of Technology and the University of California, Davis surveyed all 50 state departments of transportation and conducted in-depth interviews with agency experts to understand the implementation status of complete streets asset management, identify what state transportation agencies need to improve their asset management plans, and develop a road map for implementing complete streets asset management. This policy brief summarizes the findings from that research and provides policy implications. View the NCST Project Webpag