Accepted Manuscript
Influence of computation algorithm on the accuracy of rut depth measurement
Di Wang, Augusto Cannone Falchetto, Matthias Goeke, Weina Wang, Tiantian Li,
Michael P. Wistuba
PII:

S2095-7564(17)30077-6

DOI:

10.1016/j.jtte.2017.03.001

Reference:

JTTE 117

To appear in:

Journal of Traffic and Transportation Engineering (English
Edition)

Please cite this article as: Wang, D., Cannone Falchetto, A., Goeke, M., Wang, W., Li, T., Wistuba, M.P.,
Influence of computation algorithm on the accuracy of rut depth measurement, Journal of Traffic and
Transportation Engineering (English Edition) (2017), doi: 10.1016/j.jtte.2017.03.001.
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ACCEPTED MANUSCRIPT

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Original Research Paper

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Influence of computation algorithm on the

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accuracy of rut depth measurement

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Di Wanga,b, Augusto Cannone Falchettoa,*, Matthias Goekea, Weina Wangc,

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Tiantian Lib, Michael P. Wistubaa

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710064, China

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Key Laboratory for Special Area Highway Engineering of Ministry of Education, Chang’an University, Xi’an

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Department of Civil Engineering, Technische Universität Braunschweig, Braunschweig 38106, Germany

School of Civil Engineering, Chongqing Jiaotong University, Chongqing 400074, China

Highlights

·The multipoint laser detection technology for rut depth measurement was applied.

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·The difference value between straight-edge method and wire line method was calculated.

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·The effect of rutting shape and rut depth magnitude on the accuracy of rut depth measurement was

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analyzed.


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Abstract

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Rutting is one of the dominant pavement distresses, hence, the accuracy of rut depth measurements

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can have substantially impact on the maintenance and rehabilitation (M & R) strategies and funding

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allocation. Different computation algorithms such as straight-edge method and wire line method, which

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are based on the same raw data, may lead to rut depth estimation which are not always consistent.

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Therefore, there is an urgent need to assess the impact of algorithm types on the accuracy of rut depth

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computation. In this paper, a 13-point-based laser sensor detection technology, commonly accepted in


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China for rut depth measurements, was used to obtain a database of 85,000 field transverse profiles

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having three representative rutting shapes with small, medium and high severity rut levels. Based on

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the reconstruction of real transverse profiles, the consequences from two different algorithms were

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compared. Results showed that there is a combined effect of rut depth and profile shape on the rut

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depth computation accuracy. As expected, the difference between the results obtained with the two

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computation methods increases with deeper rutting sections: when the distress is above 15 mm

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(severe level) the average difference between the two computation methods is above 1.5 mm, normally,

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the wire line method provides larger results. The computation suggests that the rutting shapes have a

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minimal influence on the results. An in-depth analysis showed that the upheaval outside of the wheel

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path is a dominant shape factor which results in higher computation differences.

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Keywords:

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Pavement distress; Multipoint laser detection; Straight-edge rut depth; Wire line rut depth;

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Rutting shape; Rut depth magnitude.

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*Corresponding author. Tel.: +49 531 391 62064; fax: +49 531 391 62063.
E-mail addresses: (D. Wang), (A.

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Cannone Falchetto), (M. Goeke), (W. Wang),
(T. Li), (M. P. Wistuba).

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Rutting is one of the most significant distresses of asphalt pavement. It consists of a permanent surface

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deformation in the wheel path occurring when pavement materials are under high loading and shear

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(Haas and Norman, 2001; McGhee, 2004; Sousa et al., 1991). This phenomenon can significantly

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impact roadway safety since rainwater may fill ruts, eventually leading to loss of traction and friction due

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to hydroplaning. Therefore, timely decisions and correct solutions for maintenance and rehabilitation (M

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& R) need to be identified for minimizing the detrimental effects of this distress.

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Introduction

The accuracy of rut depth measurement can substantially impact the reliability of performance

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evaluation, on the selection of M & R strategies, as well as, on the allocation of funding. In some

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countries such as China, the pavement industry is still in the construction climax and the time for

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massive scale pavement M & R has not come yet. Hence, in the next ten years, the transportation

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department will face the challenge of implementing a consistent M & R program with an efficient use of

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resources.

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The original definition of the rut depth is based on the manual straight-edge measurement, however,

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the straight-edge length varies depending on region and countries. Hence, the rut depth estimation is

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not always consistent. In the US, American Association of State Highway and Transportation Officials

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(AASHTO) and the Highway Design and Maintenance Standards Model (HDM-III) relies on a 1.2 m

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straight-edge bar to measure rut depth (Lous, 1995). The American Society for Testing and Materials

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(ASTM, 2010) suggested a flexible straight-edge between 1.83 m (6 feet) and 3.66 m (12 feet); the

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straight-edge length should stretch across the highest points between adjacent upheavals. However,

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common straight-edge length was established for a 1.83 m (6 feet) lane in the long-term pavement


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performance (LTPP) (Miller and Bellinger, 2014). Among the countries using international units (UI)

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Denmark uses 1.8 m straight-edge, other institutions in countries such as the United Kingdom

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(Transport Research Laboratory, TRL), Australia, New Zealand, South Africa and some of the British

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Commonwealth of Nations define the rut depth under a 2.0 m straight-edge (Lous, 1995),. A different

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approach is used by Strategic Highway Research Program (SHRP, US), Sweden (Stroup et al., 2004)

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and China (RIOH, 2007), for which the minimum length of a straight-edge is as wide as the driving lane

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of interest. Japan uses the pulling line method, which is based on a flexible wire rather than on a

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straight-edge (Joseph, 2001).
In order to obtain rut depth in a more time-efficient manner, the automatic laser detection technology

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was introduced. The very first measurement devices consisted only three or five laser sensors, so that

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the transverse section could only be approximated as a discrete profile. The corresponding computation

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method was named pseudo rutting (NCHRP, 2004) or AASHTO method (Cole and Shippen, 2005). The


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assumption of the two methods are similar: the instrumented vehicle runs along the centerline of the

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driving lane. The wheels are located within the wheel paths, and the laser sensors are just right above

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the maximum rut section. The rut depth is defined as the relative height difference between the central

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sensor and the sensors on both sides. These two methods were mainly used in the US (Vedula et al.,

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2002); however, due to the limitations of the assumption and the measurements inaccuracy, it was later

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superseded.

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As the technology developed to more refined laser systems, more and more sensors have been

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installed in the detection devices. Simpson (2001a) suggested that a measured transverse section can

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be considered as a continuous profile when the number of laser sensors is more than nine and such a

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configuration is commonly available in most of the current detection systems. Based on these types of

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measurement device, two new computation methods were implemented: straight-edge rut depth and

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wire line rut depth (AASHTO, 2001). Cenek et al. (1994) and Lous (1995) evaluated the difference

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between the two computation methods showing that the wire line method leads to a larger result when

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the wheel paths are relative wide. Simpson (2001b) and Li (2006) performed a qualitative comparative

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analysis demonstrating that the shape of the transverse section affects the rut depth results.

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A different study from the Federal Highway Administration (Joseph, 2001) found that the rut depth

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obtained from a wire line method is larger than that measured with a 1.8 m or 1.2 m straight-edge.

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Bennett and Wang (2003) and Wu (2007) explored the possibility of differentiating the transverse rut

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profile into W-shape and U-shape sections. While no significant differences were observed for W-shape

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profile, for U-shape sections the wire line method was associated to larger rut depths compared to the

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straight-edge method. Although the straight-edge and wire line methods were the most commonly used

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algorithms in rut depth computation, the existing studies provided only qualitative evidence that the

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rutting shape affected the rut depth accuracy. In addition, the separation of rut profile into two classes,

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W-shape and U-shape, appeared to be too simplistic for thoroughly addressing the impact of the

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computation algorithm on the actual accuracy of the rut depth.

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In this paper, the effect of computation algorithms on the accuracy of rut depth measurements is

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evaluated with the aim of identifying the effective influence of rut depth and rutting shapes to provide

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estimation on the reliability of the decision for timely M & R actions. For this purpose, two typical rutting

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shapes obtained from field observations and one virtual rutting shape were used to analyze the impact

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of rut measurements accuracy for different rut depth magnitude. In the present research, the

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straight-edge rut depth method refers to a 2.0 m straight-edge.


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Objective and research approach

This paper is organized as follows. First the 13-point laser bar device and the experimental

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measurements are introduced. Then, straight-edge rut depth and wire line rut depth algorithms are

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described. Rutting severity level and the seven profile shapes which are conventionally observed in

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China are presented. Within this set of profiles, the three shapes which showed different rut results

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between the two computation methods (straight-edge and wire line) are further analyzed to understand

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the influence of the rut depth accuracy. Finally, the profile characteristics affecting the measurement

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accuracy are identified and the one-dimension rutting shape indexes are proposed for further research.

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In this section, the 13-point-based laser bar and an extensive dataset of road profiles from the Jiangsu

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province in China are introduced together with the two most common rut depth computation methods

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(RIOH, 2007).

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3.1


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In the recent past, the Research Institute of Highway (RIOH), which is part of the Chinese Ministry of

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Transport (MOT) has developed an automatic road detection system, named “multifunctional

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high-speed highway condition monitor system” (CiCS, 2010), equipped with a 13-point laser sensor

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Rut depth measurement technology and computation algorithms

13-point-based laser bar and experimental measurements

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(Fig. 1). This device is based on the relative height measurement at discrete points, where the laser bar

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is 2300 mm wide and is installed on the vehicle at 300 mm above the pavement. Nine vertical laser

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sensors are unevenly positioned along the laser bar, with more sensors in the wheel path area and less

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in the non-wheel path zone. Two additional, oblique laser sensors are installed on both left and right end

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of the laser bar so that an overall detection width of 3600 mm can be achieved. The detailed layout and

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laser spacing are shown in Fig. 2.

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Fig. 1 Instrumented vehicle with 13-point laser bar in the road measuring.

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Fig. 2 Layout of laser sensors.

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Table 1 shows the original data of a representative rut transverse profile collected by the

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13-point-based laser bar. Each profile data consists of three lines. In the first row, "G40" indicates the

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identification number of the roadway; "A" represents the upper line, while "104" identifies the stake mark

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which means the profile is located in K1+120.82 m. In the second row, each point shows the vertical

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height of the pavement measured by the laser sensors with an accuracy of 0.1 mm. The measurement

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system calculates the relative height of the laser sensors; therefore, the reference point of the profile is

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the lowest sensor which is marked by zero. The third row is the horizontal position of the detecting laser

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points in units of 1 mm. The sensor D7 in the middle (Fig. 2) is represented by 2500 mm so that the width


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of the measurement is the difference between the first and the last coordinate.

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Row

Raw data

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Table 1 Original data of 13-point-based laser bar

Unit

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G40A, 112, 082

cm

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258, 267, 157, 118, 102, 145, 224, 195, 114, 47, 0, 26, 122

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699, 1073, 1343, 1625, 1875, 2125, 2500, 2875, 3125, 3375, 3659, 3944, 4322


0.1 mm
mm

3.2

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The 13 raw discrete points are used to reconstruct an approximate continuous rut’s cross section relying

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on MATLAB (MathWorks, 2015) as follows: each discrete elevation point is connected by a straight line

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one-by-one to the cross-sectional shape and both endpoints are connected and extended to determine

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the raw baseline. Then, the corresponding rut depth can be computed with the straight-edge method

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(Hadley and Mayers, 1991) or wire line method (RIOH, 2007, 2008).

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Rut depth calculation algorithms

However, there exists a road camber in the highway, in China. Normally, the slope angle, α , is

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approximately 3‰ in the non-super elevation section, hence, the original baseline is not horizontal. In

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China, seven typical rutting shapes were identified in "field test methods of subgrade and pavement for

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highway engineering" (RIOH, 2008), but their base lines are a horizontal. As shown in Fig. 3, there are

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two definitions for rut depth: perpendicular to the datum of the elevation measurements, which is the one

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related to the horizontal line, and the other one is perpendicular to the measurement bar (straight-edge

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or wire) associated to the raw baseline. It is obviously that the different definitions lead the differences of

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related rut depths.

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Fig. 3

Implication of the baseline.


Bennett and Wang (2003) suggested that there is a cosine relation between the two rut depths, since

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α is a very small value, then the cos( α ) 1. Further calculation showed that the difference value

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between the two results does not exceed 0.01 mm while the rut depth is less than 50 mm. Therefore, the

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different baselines have a minimal influence on the results. In this paper, we correct the original baseline

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to horizontal as the national standard suggested, therefore, in this paper the baseline is a horizontal line.

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3.2.1

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The straight-edge rut depth algorithm is based on the Strategic Highway Research Program (SHRP)

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algorithm in Hadley and Myers (1991). The analysis starts at sensor one (D1) (Fig. 2) which is the


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closest to the pavement kerb. It progresses until the rutting in one wheelpath is established. It is then

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repeated for the second wheelpath starting at the right most sensor and moving downwards. Once a

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viable placement point had been established, the vertical distances of all intermediate placement points

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were established.

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Straight-edge method

In this paper, according to the SHRP project (Simpson, 2003), the straight-edge is defined as an


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imaginary straight ruler which stretches across the road profile, however, the length of the straight-edge

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is limited. In this study, the straight-edge is defined as 2.0 m long. As shown in Fig. 4, the straight-edge

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touches the highest points/peaks of the cross section, while the wheel path is beyond the ruler’s length,

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and the straight-edge only goes across the highest points between adjacent upheavals.

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Fig. 4 Illustration of straight-edge method in different rutting shapes. (a) First rutting shape. (b) Second rutting shape.

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3.2.2 Wire line method

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According to the Highway Performance Assessment Standards (RIOH, 2007), the wire line is defined as

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an imaginary line which stretches across the entire road profile, while both ends of the line overlap with

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the endpoints of the cross section. According to Fig. 5, the wire line touches the highest points/peaks of

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the cross section, while the rut depth is given by the maximum vertical distance between the road profile

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and the wire line.

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Fig. 5 Illustration of wire line method in different rutting shapes. (a) First rutting shape. (b) Second rutting shape.

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In this section, the standard category of rutting magnitude used in China and the typical rutting shapes

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identified by the Chinese national standard are presented. Then, the four shapes which led to different

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results in two computation methods are analyzed and illustrated.

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4.1

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The rutting severity magnitude is commonly defined in terms of different ranges in rut depth (Fwa and


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Ong, 2008; Li, 2012). According to the Chinese national standards (RIOH, 2007), a pavement is

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affected by rutting phenomena when it presents a permanent deformation of 10 mm or larger. And when

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the depths are lower than 10 mm, the pavement won’t be affected by rutting phenomena. Rutting

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between 6 and 10 mm can be associated to the small level, and rutting between 10 and 15 mm is

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Rut depth magnitude and typical rutting shapes in China

Rutting severity level definition

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defined as medium level rutting. For these two cases, M & R judgement is allocated based on the

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Pavement Condition Index (PCI) and special maintenance treatment for rutting is not required.

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Deformations over 15 mm are considered in the category of high severity, then maintenance actions

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have to be planned immediately.

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In this study, an expressway road, 10.7 km long, affected by rutting distress, was selected and

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investigated. The road was designed and built in 2008, in Jiangsu province of China. The pavement is

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21 m wide and no maintenance or re-vamping was performed previously. The layers’ structural package

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is as follows: 5 cm of SMA-16 wearing course, 7 cm AC-25 binder layer and 10 cm AC-30 base layer.

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This road was tested with the 13-point-based laser bar and the cross section profile was measured and

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recorded every 20 cm.

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4.2

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In order to analyze the sensitivity of computation methods, the rutting shapes which results in different

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rut depth computations need to be first identified. Due to the computation approach, the differences are

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observed only for lane without upheaval in center. In this case, the wire line method provides larger rut

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values than the straight-edge method. Seven typical rutting shapes in China are identified in the current


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national standard (RIOH, 2008). According to the computation approaches, four profile shapes show

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differences in rut depth: Types 4, 5, 6 and 7 (Fig. 6).

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Typical profile shapes in China

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Fig. 6

Rutting profile shapes in China.

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The rutting shapes depend on a variety of factors which are linked to a series of dominant

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phenomena. According to previous studies (N.D. Lea International Ltd, 1995; Sha, 2001), the dominant

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phenomena can be divided into four types: structure deformation (SD), plastic deformation (PD),

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surface abrasion (SA) and densifications deformation (DD). Since a pavement structure consisting of a

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thin asphalt surface with thick semi-rigid base is widely used for highway pavements in China, and due

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to the combined interaction of climate and construction with traffic load, the major rutting phenomena in


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China can be restricted to PD and DD. In this study, only three typical shapes were collected from the

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database, which could be considered as PD (Types 3, 5) and DD (Type 6) from Fig. 6. As shown in Fig.

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7, the rutting shapes associated to this specific configuration and conditions are illustrated. And in the

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figure, the value of deformation (D) refers to the rut depth.

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Fig. 7

Rutting shape collected from database. (a) Plastic deformation rutting shape-Type 5. (b) Densifications
deformation rutting shape-Type 6.

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In order to analyze the rutting shapes effect, one extra rutting shape needs be simulated in this


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research. Zhu (2007) studied the common rutting shape in heavy traffic load in China and the author

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pointed out that Type 7 rutting shape is very uncommon, since it could be observed only for rural road

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when a traffic overload occured. Hence, in this study, only Type 4 rutting shape is necessary to be

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virtually reconstructed. The simulation process is illustrated in Fig. 8.

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Simulation process of Type 4 rutting shape.

The simulation process is as follows.

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Type 4 rutting transverse sections with different rut depths were drawn according to the


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definition of Chinese national standard (RIOH, 2008).
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The 13-point-based measurement process was simulated with the actual sensors spacing
distribution of the 13-point-based laser bar configuration.

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The rutting section was reconstructed by connecting the discrete elevation points and the

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maximum rut depth was then calculated using both straight-edge and wire line methods.

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Fig. 8

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5

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In order to study the effect of the rut depth magnitude and rutting shape on the accuracy of rutting

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measurements, Types 5 and 6 rutting shapes were used. Type 4 rutting shape was also included in the

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analysis based on reconstructed profile with a simulated rut depth between 6 mm and 19 mm with 1 mm

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interval. The two computational methods, straight-edge and wire line, were implemented into a MATLAB

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(Mathworks, 2015) code and applied to different profiles to compare the potential differences in the

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estimations of rut depth.

Assessment of rut depth magnitude and rutting shape effect

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5.1

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Fig. 9 presents the differences of rut depth for Types 4, 5 and 6 rutting shapes. In the horizontal axis,

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each number represents the rut depths magnitude, for example, “8” represents the rutting sections

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which show rutting larger than 8 mm but smaller than 9 mm.

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Rut depth magnitude effect

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Fig. 9

Differences in rut depth between straight-edge and wire line methods.

The progressive increasing differences for all the three rutting shapes can be observed. For small and

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medium rut depth levels, the average difference is below 1.5 mm, hence, both computation methods are


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acceptable. However, much higher differences (up to 2.7 mm) can be found for larger rut depth (19 mm).

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Such a severe discrepancy is expected to significantly affect the accuracy of the measurements and,

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therefore, the specific computation method needs to be carefully selected as a satisfactory balance

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between road users’ safety and maintenance costs. According to previous researches (Fwa and Ong,

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2008; Guo et al., 2013), the rutting can easily induce the occurrence of hydroplaning risk, and the safety

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critical rut depth will decrease compared with the dry condition, hence, the authors suggest engineers to

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use the wire line method in M & R decision.

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It is, therefore, undeniable that rut depth plays an important role for the selection of the most

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appropriate computation method. To further evaluate this effect, a sensitivity coefficient analysis based,

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analysis of variance analysis (ANOVA) was used (Shi, 2012). Statistical significance level was set to

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α = 0.05. This parameter is associated to the output of statistical analysis, p-value, which represents

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the parameter discriminating the actual significance of the specific test. When p-value is smaller than

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the significance level ( α = 0.05), then it may be concluded that there is a statistical significant difference

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among the groups; otherwise, the groups compared are statistically equivalent.

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Table 2

ANOVA on rut depth.

Analysis output

Sum of squares

df.

Mean square

F

Sig.

Between groups


7.913

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0.659

6.819

0

Within groups

3.771

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0.097

Total

11.684

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As shown in Table 2, F-test statistics is 6.819, and the corresponding probability p-value is 0 (bold


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section in Table 2). This suggests that the rut depth has a statistical significance impact on the

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computation algorithm.

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5.2

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As previously mentioned, the peculiar rutting shape represented a notable factor (Bennett and Wang,

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2003; Wu, 2007) affecting the rut depth measurement accuracy. Hence, ANOVA was used to analysis

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its significance.

Rutting shape effect

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ANOVA on rutting shape.


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Analysis output

Sum of squares

df.

Mean square

F

Sig.

Between groups

1.362


2

0.454

2.112

0.111

Within groups

10.322

36

0.215

Total

11.684

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As shown in Table 3, F-test statistics value is 2.112 and the corresponding probability p-value is 0.111

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(bold section in Table 3), which means that rutting shape is not affecting the measured accuracy.

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Therefore, it is necessary to study the difference between each pair of rutting shapes. A multiple

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comparison statistical test based on the least significant difference (LSD) method was used (Shi, 2012).

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Table 4 shows the comparison results between each type of three different rutting shapes.

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Table 4 LSD-based multiple comparisons of three rutting shape types.

Rutting shape type

Mean difference

Std. error

Sig.

4 and 5

0.02308

0.18189

4 and 6

0.38077

5 and 6

0.40385

95% confidence interval
Upper bound


0.900

-0.3426

0.3888

0.18189

0.042

0.0151

0.7465

0.18189

0.031

0.0381

0.7696

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Lower bound

As shown in Table 4, p-value is 0.900 for group of Types 4 and 5, which means that there is no

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statistical significance within this group. However, p-value are 0.042 and 0.031 (bold section in Table 4)

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for groups of Types 4 and 6, 5 and 6, respectively. Hence, there is a significant influence of the rutting

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shape for these two groups confirming a substantial similarity between Types 4 and 5.

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As mentioned before, one of the dominant rutting causes is plastic deformation, which leads to

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upheaval on the right side along the driving direction. As illustrated in Fig. 6, the upheaval outside of the

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right wheel path is present in both Types 4 and 5, while there is no upheaval in Type 6. Hence, the

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results are most likely associated to the presence of the upheaval outside of the right wheel path, which


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will lead to smaller differences between the two computation algorithms.

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In this paper, the effect of computation algorithms on the accuracy of rut depth is evaluated with the aim

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of identifying the influence of rutting shapes, and rut depth on the reliability of the decision for timely

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maintenance and rehabilitation decision and activities. For this purpose, two field rutting shapes and

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one simulated rutting profile typically observed in China were selected to analyze the impact on rut

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measurements accuracy. Based on the analysis performed, the following conclusions can be drawn:

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Conclusions

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(1) According to the current Chinese national standard (RIOH, 2008), four profile shapes with
upheaval show different rut depth estimation between the two computation algorithms.

(2) The rut depth estimation obtained with the wire line method results are, in most cases, larger
than those derived from the straight-edge method.

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(3) The rut depth leads to significant differences between the two computation methods. The


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different values show a progressively increasing trend for all the three rutting shapes, it is up to

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2.7 mm when rut depth magnitude is 19 mm.

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(4) The selection of the computation methods should be carefully selected as a satisfactory balance

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between road users’ safety and maintenance costs. Due to the safety consideration, the findings

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of this study suggest engineers to use the wire line method in M & R decisions.
(5) The overall rutting shape does not significantly affect the rut depth measurement accuracy.

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However, the upheaval on the right wheel path represents a dominant factor which impact on the


334

results, and may lead to smaller differences between the two computation algorithms.

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According the analysis above, the difference estimation between two algorithms is a combined effect

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of rutting shape and rut depth. For rutting shape, the findings of this study suggest the upheaval outside

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of the right wheel path is the dominant factor, however, it is not an accurate parameter and only

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qualitative study is conducted in this paper. Therefore, the geometry of rutting shape characteristic

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should be established and quantized analysis in further research. For rutting depth, the difference value

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between two algorithms is gradually changed in this paper, serious cases should be studied to find out


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the critical magnitude. And additional transverse profiles derived from 13-point laser bars with more rut

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types should be further analyzed, using the proposed method to quantify the potential errors and further

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understand the impact of rut type on rut depth measurement error.

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Acknowledgments

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The authors would like to thank Prof. Yichang Tsai from Georgia Institute of Technology for his technical

346

support. The research was sponsored by China Postdoctoral Science Foundation (2014M562287), and

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National Natural Science Foundation of China (51508034, 51408083, 51508064).

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References

349

AASHTO, 2001. Standard Practice for Determining Maximum Rut Depth in Asphalt Pavements.

350

AASHTOPP38. American Association of State Highway and Transportation Officials, Washington

351

DC.

AC
C

EP

TE
D

M
AN
U

SC

335


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ACCEPTED MANUSCRIPT

ASTM, 2010. Standard Test Method for Measuring Rut-depth of Pavement Surfaces Using a

353

Straightedge.

354

Conshohocken.

355
356

E1703/E1703M-10.

American

Society

for

Testing

and


Materials,

West

Bennett, C.R., Wang, H., 2003. Harmonising Automated Rut Depth Measurements. California Transit

RI
PT

352

Association, Sacramento.

Cenek, P.D., Patrick, J.E., McGuire, J.F., et al., 1994. New Zealand experience in comparing manual

358

and automatic pavement condition rating systems. In: The 3rd International Conference on

359

Managing Pavements, San Antonio, 1994.

364
365
366
367
368
369

370
371
372
373
374
375
376
377

M
AN
U

363

Cole, M., Shippen, N., 2005. Automated Data Collection Equipment for Monitoring Highway Condition.
FHWA-OR-RD-05-10. Oregon Department of Transportation, Salem.
Fwa, T.F., Ong, G.P., 2008. Wet-pavement hydroplaning risk and skid resistance: analysis. Journal of
Transportation Engineering 134(5), 182–190.

Guo, X., Zhang, C., Cui, B., et al., 2013. Analysis of impact of transverse slope on hydroplaning risk

TE
D

362

(Accessed 26 February 2016).

level. Procedia-Social and Behavioral Sciences 96, 2310-2319.

Haas, R., Norman, W., 2001. Reinventing the pavement management wheel. In: 5th International
Conference on Managing Pavements, Seattle, 2001.

EP

361

CiCS, 2010. Equipment Introduction. Available at: />
Hadley, W.O., Myers, M.G., 1991. Rut Depth Estimates Developed from Cross Profile Data. AU-179.
Texas Research and Development Foundation, Austin.

AC
C

360

SC

357

Joseph, B., 2001. A Synopsis on the Current Equipment Used for Measuring Pavement Smoothness.
Federal Highway Administration, Washington DC.
Li, F., 2012. A Methodology for Characterizing Pavement Rutting Condition Using Emerging 3D Line
Laser Imaging Technology (PhD thesis). Georgia Institute of Technology, Atlanta.
Li, Q., 2006. Highway Pavement Rutting Detection, Evaluation and Prediction Technology (PhD thesis).
Southeast University, Nanjing.

17



ACCEPTED MANUSCRIPT

378

Lous,

K.,

1995.

Modeling

Rutting

in

Flexible

Pavements

in

HDM-4.

Available

at:

379


/>
380

ut-depths-in-flexible-pavement/file (Accessed 1 August 2016).

385
386
387
388
389
390
391
392

RI
PT

384

McGhee, K.H., 2004. Automated Pavement Distress Collection Techniques: a Synthesis of Highway
Practice. NCHRP Synthesis 334. TRB, Washington DC.

Miller, J.S., Bellinger, W.Y., 2014. Distress Identification Manual for the Long-term Pavement

SC

383

2016).


Performance Program. FHWA-HRT-13-092. U.S. Department of Transportation, Washington DC.
NCHRP, 2004. Guide for Mechanistic-Empirical Design of New and Rehabilitated Pavement Structures.

M
AN
U

382

MathWorks, 2015. MATLAB R2015a. Available at: (Accessed 1 August

Project 1-37A. National Research Council, Washington DC.

N.D. Lea International Ltd, 1995. Modeling Road Deterioration and Maintenance Effects in HDM-4.
Asian Development Bank, Vancouver.

Research Institute of Highway (RIOH), 2007. Highway Performance Assessment Standards. JTG
H20-2007. Ministry of Transport of the People’s Republic of China, Beijing.

TE
D

381

Research Institute of Highway (RIOH), 2008. Field Test Methods of Subgrade and Pavement for

394

Highway Engineering. JTG E60-2008. Ministry of Transport of the People’s Republic of China,


395

Beijing.

397

Sha, Q.L., 2001. Premature Damage and Preservative Measure of Bituminous Pavement on
Expressway. China Communications Press, Beijing.

AC
C

396

EP

393

398

Shi, L.W., 2012. From Beginning to Professorial. Tsinghua University Press, Beijing.

399

Simpson, A.L., 2001a. Characterization of Transverse Profiles. FHWA/RD 01-024. Federal Highway

400
401
402


Administration, Washington DC.
Simpson, A.L., 2001b. Measurement of Rutting in Asphalt Pavements (PhD thesis). The University of
Texas at Austin, Austin.

403

Simpson, A.L., 2003. Measurement of a rut. In: TRB 82nd Annual Meeting, Washington DC, 2003.

404

Sousa, J.B., Craus, J., Monismith, C.L., 1991. Summary Report on Permanent Deformation in Asphalt

405

Concrete. SHRP-A-318. U.S. National Research Council, Washington DC.

18


ACCEPTED MANUSCRIPT

408
409
410
411
412
413

ARRB walking profiler. In: TRB 83rd Annual Meeting, Washington DC, 2004.
Wu, H.Y., 2007. Research on the Method of Extracting and Calculating the Characteristics of Pavement

Rut (Master thesis). Harbin Institute of Technology, Harbin.

RI
PT

407

Stroup, G., Gudamettla J., Hays J., 2004. Profile, rut depth and cross slope measurements using the

Zhu, Y.S., 2007. Asphalt Pavement Rutting Prediction Model in Heavy Traffic (PhD thesis). Tongji
University, Shanghai.

Vedula, K., Hossain, M., Reigle, J., et al., 2002. Comparison of 3-point and 5-point rut depth data
analysis. In: The Pavement Evaluation Conference, Roanoke, 2002.

SC

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Di Wang has been a PhD student at Institut für Straßenwesen der TU Braunschweig (ISBS) of

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Technische Universität Braunschweig since May 2015. He was a research associate in the School of

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Highway at Chang’an University, Xi’an, China. He received a Master’s degree and a Bachelor’s degree

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from Chang’an University. His research interests are characterization and modeling of asphalt materials

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at low temperatures, asphalt materials recycling and diffusion process of rejuvenators and fresh binder

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in the aged binder contained in reclaimed asphalt pavement materials.

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Augusto Cannone Falchetto has been a research associate at ISBS since 2013. He had been a

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research associate in the Department of Civil Engineering at the University of Minnesota, USA from


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2008 to 2013.He received his PhD degree in Civil Engineering (minor Statistics) in University of

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Minnesota, USA. His research interests are characterization and modeling of asphalt materials at low

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temperature, asphalt materials recycling, and size effect and scaling of quasi-brittle material.

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