Rank Correlation Method for Evaluating Manual Pavement Distress Data Variability

Bogus, Susan M.; Song, Jong-Hwa; Waggerman, Raymond; Lenke, Lary R

doi:10.1061/(asce)1076-0342(2010)16:1(66)

Cited by 23 publications

(10 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It should be noted that current manual evaluation methods operationally used by transportation management agencies rely on only visual observation to estimate distress rates (e.g., estimate the length of the cracks), which is highly subjective [21]. However, inspectors of this research physically measured the distress rates to collect ground reference data, which is objective.…”

Section: Discussionmentioning

confidence: 99%

Characterizing Pavement Surface Distress Conditions with Hyper-Spatial Resolution Natural Color Aerial Photography

et al. 2016

View full text Add to dashboard Cite

Roadway pavement surface distress information is critical for effective pavement asset management, and subsequently, transportation management agencies at all levels (i.e., federal, state, and local) dedicate a large amount of time and money to routinely evaluate pavement surface distress conditions as the core of their asset management programs. However, currently adopted ground-based evaluation methods for pavement surface conditions have many disadvantages, like being time-consuming and expensive. Aircraft-based evaluation methods, although getting more attention, have not been used for any operational evaluation programs yet because the acquired images lack the spatial resolution to resolve finer scale pavement surface distresses. Hyper-spatial resolution natural color aerial photography (HSR-AP) provides a potential method for collecting pavement surface distress information that can supplement or substitute for currently adopted evaluation methods. Using roadway pavement sections located in the State of New Mexico as an example, this research explored the utility of aerial triangulation (AT) technique and HSR-AP acquired from a low-altitude and low-cost small-unmanned aircraft system (S-UAS), in this case a tethered helium weather balloon, to permit characterization of detailed pavement surface distress conditions. The Wilcoxon Signed Rank test, Mann-Whitney U test, and visual comparison were used to compare detailed pavement surface distress rates measured from HSR-AP derived products (orthophotos and digital surface models generated from AT) with reference distress rates manually collected on the ground using standard protocols. The results reveal that S-UAS based hyper-spatial resolution imaging and AT techniques can provide detailed and reliable primary observations suitable for characterizing detailed pavement surface distress conditions comparable to the ground-based manual measurement, which lays the foundation for the future application of HSR-AP for automated detection and assessment of detailed pavement surface distress conditions.

show abstract

Section: Discussionmentioning

confidence: 99%

Characterizing Pavement Surface Distress Conditions with Hyper-Spatial Resolution Natural Color Aerial Photography

et al. 2016

View full text Add to dashboard Cite

show abstract

“…Manual data collection used in many countries is slow and it provides poor data [1]. The most common method of measurement is still the geodetic method, with the use of a total station in combination with Global Navigation Satellite System (GNSS).…”

Section: Introductionmentioning

confidence: 99%

Detecting Forest Road Wearing Course Damage Using Different Methods of Remote Sensing

et al. 2018

View full text Add to dashboard Cite

Currently, a large part of forest roads with a bituminous surface course constructed in the Czech Republic in the second half of the last century has been worn out. The aim of the study is to verify the possibility and the accuracy of the road wearing course damage detected by four different remote sensing methods: close range photogrammetry, terrestrial laser scanning, mobile laser scanning and airborne laser scanning. At the beginning of verification, cross sections of the road surface were surveyed geodetically and then compared with the cross sections created in the DTMs which were acquired using the four methods mentioned above. The differences calculated between particular models and geodetic measurements show that close range photogrammetry achieved an RMSE of 0.0110 m and the RMSE of terrestrial laser scanning was 0.0243 m. Based on these results, we can conclude that these two methods are sufficient for the monitoring of the asphalt wearing course of forest roads. These methods allow precise and objective localization, size and quantification of the road damage. By contrast, mobile laser scanning with an RMSE of 0.3167 m does not reach the required precision for the damage detection of forest roads due to the vegetation that affects the precision of the measurements. Similar results are achieved by airborne laser scanning, with an RMSE of 0.1392 m. As regards the time needed, close range photogrammetry appears to be the most appropriate method for damage detection of forest roads.

show abstract

“…Given the horizontal and vertical accuracy of the DSMs, the discrepancy between the manual depth measurement and the DAT-based depth measurement could be from errors in either method. However, rutting depth measured by different inspectors can exhibit a high degree of variability (Bogus et al 2010) because this method relies on subjective visual observation. These results can be interpreted to indicate that the DAT-based rutting measurement method is more accurate than manual method.…”

Section: Resultsmentioning

confidence: 99%

“…Despite safety precautions (safety training and high-visibility garments), this can still be dangerous work, especially in high traffic volume sections. In addition, data collected by different inspectors can exhibit a high degree of variability (Bogus et al 2010) More recently, many sophisticated and automated devices integrated with high-performance sensors (e.g. 3D laser scanners) have gradually been developed for rutting assessment.…”

Section: Introductionmentioning

confidence: 99%

Pavement Surface Permanent Deformation Detection and Assessment Based on Digital Aerial Triangulation

Zhang¹,

Bogus²,

Lippitt³

2015

Computing in Civil Engineering 2015

Self Cite

View full text Add to dashboard Cite

Pavement surface distress information is essential to pavement management. Assessment of surface permanent deformation is particularly important given pavement's ubiquitous use and that permanent deformation significantly influences its integrity and performance. However, current assessment methods are expensive, labor-intensive, time-consuming, and potentially dangerous to inspectors. Digital aerial triangulation (DAT) and unmanned airborne systems (UAS) provide a new method for detecting and assessing permanent deformation that hold potential to overcome these limitations. Using hyperspatial resolution multispectral digital aerial photography (HRM-DAP) acquired from an UAS as input, DAT was employed to generate digital surface models (DSMs) of the pavement surface. Permanent deformation distress estimates extracted from these DSMs were compared to ground reference data collected using standard protocols via orthogonal regression analysis. The results show that the aerial triangulation of HRM-DAP provides consistent and reliable data for permanent deformation detection and assessment.

show abstract

Rank Correlation Method for Evaluating Manual Pavement Distress Data Variability

Cited by 23 publications

References 4 publications

Characterizing Pavement Surface Distress Conditions with Hyper-Spatial Resolution Natural Color Aerial Photography

Characterizing Pavement Surface Distress Conditions with Hyper-Spatial Resolution Natural Color Aerial Photography

Detecting Forest Road Wearing Course Damage Using Different Methods of Remote Sensing

Pavement Surface Permanent Deformation Detection and Assessment Based on Digital Aerial Triangulation

Contact Info

Product

Resources

About