This session presents applications of pavement evaluation to advance the state of the practice.
Statistical Model for Detecting Voids for Curled or Warped Concrete Pavements
Kevin Alland, University of PittsburghShow Abstract
Julie Marie Vandenbossche, University of Pittsburgh
John Brigham, University of Pittsburgh
A statistical classifier is developed to interpret Falling Weight Deflectometer (FWD) data for the detection of voids under jointed concrete pavement slabs. The classifier is trained using the Seasonal Monitoring Program (SMP) sections in the Long Term Pavement Performance (LTPP) database and data from the Minnesota Road Research Facility (MnROAD). A two level cross validation process is used to assess the performance of existing void detection methods, based on a threshold of a single variable, and the LASSO classifier, which is based on several variables. Simple void detection methods based on the normalized 9,000 lb deflection were found to perform better than void detection methods based on variable deflection analysis. The LASSO classifier outperformed any of the existing void detection techniques. The LASSO classifier was validated using two field trials in Pennsylvania, and an LTPP GPS section where significant faulting had developed.
Toward a Swarm of Inexpensive Multimodal Sensor Systems for Autonomous and Quantitative Condition Assessment of Roads
Yulu Chen, University of Southern CaliforniaShow Abstract
Mohammad Jahanshahi, Purdue University
Preetham Manjunatha, University of Southern California
Sami Masri, University of Southern California
Burcin Becerik-Gerber, University of Southern California
Current manual road condition assessment procedures are time consuming and laborious. On the other hand, state-of-the-art commercial data collection approaches are expensive although the data analysis tasks are not fully automated. Due to these limitations, a section of a road is assessed once a year or once every two years. Since insufficient inspection is an important contributor to the poor condition of roads, this study presents the development, evaluation, and field application of a novel, relatively inexpensive, vision-based sensor system employing commercially available off-the-shelf devices that can be mounted on several vehicles and hence collect data from a section of the road more often. In addition, an approach is proposed to interpret the data, and detect, quantify and localize defects autonomously. The proposed hardware-software package system is ideal to be used for crowdsourcing as a complement to the existing commercial road assessment vehicles and reduce the operation cost.
Fast and Accurate Pothole Detection Algorithm Based on Saliency Maps
Youngtae Jo, Korea Institute of Civil Engineering and Building Technology (KICT)Show Abstract
Potholes cause diverse problems such as car accidents and damaged wheels. A pothole detection algorithm is an essential part of an automatic pothole maintenance systems. Typically, potholes have been detected by manual counting using humans, which is expensive and slow. Recently, pothole detection systems based on video cameras have been studied for fast and inexpensive pothole detection. In previous work, the authors developed an algorithm using video data that accurately detects potholes using motion and intensity features. The detection algorithm was highly accurate, but it could not detect potholes online because it was computationally heavy. Thus, the authors also developed a lightweight pothole detection algorithm for online operation, but it obtained incorrect detection results in certain situations such as potholes overlapped by shadow and those surrounded by similar intensity values. Thus, this paper proposes a pothole detection algorithm based on saliency map information in order to improve the previously developed lightweight algorithm with minimum additional computation time. Experimental results show that the proposed algorithm outperforms the previous lightweight algorithm with only a small amount of additional computation that is suitable for online pothole detection.
Agency Experience Using 3-D Ground-Penetrating Radar for Pavement Evaluation
Derek Tompkins, American Engineering Testing, Inc.Show Abstract
Kyle Hoegh, Minnesota Department of Transportation
Dean Mikulik, Minnesota Department of Transportation
Shongtao Dai, Minnesota Department of Transportation
Hyunhwa Yu, Federal Highway Administration (FHWA)
Lev Khazanovich, University of Pittsburgh
This paper describes a research effort conducted by the University of Minnesota and the Minnesota Department of Transportation to explore applications of 3D GPR technology for assessing its road network and to develop analysis methods for time-history 3D GPR data, with the goal of being able to collect and interpret 3D GPR data to provide internal and subsurface characteristics of the pavement in a timely, direct manner. The research also addressed additional issues, including 1) the modification of the 3D GPR equipment to improve the reliability, repeatability, and coverage of measurements and 2) the creation of supporting tools to access the developed 3D GPR analysis so that the agency does not rely on third-party software for each specific pavement evaluation type. For the benefit of interested agencies, the paper also describes applications and case studies to illustrate the potential of this technology.