influence of computation algorithm on the accuracy of rut depth measurement

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 This is a PDF file of an unedited manuscript that has been accepted for publication As a service to our customers we are providing this early version of the manuscript The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain ACCEPTED MANUSCRIPT Original Research Paper Influence of computation algorithm on the accuracy of rut depth measurement Di Wanga,b, Augusto Cannone Falchettoa,*, Matthias Goekea, Weina Wangc, Tiantian Lib, Michael P Wistubaa M AN U SC RI PT a 10 b 11 710064, China 12 c Key Laboratory for Special Area Highway Engineering of Ministry of Education, Chang’an University, Xi’an TE D 13 14 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 16 ·The difference value between straight-edge method and wire line method was calculated 17 ·The effect of rutting shape and rut depth magnitude on the accuracy of rut depth measurement was 19 20 AC C 18 EP 15 analyzed 21 Abstract 22 Rutting is one of the dominant pavement distresses, hence, the accuracy of rut depth measurements 23 can have substantially impact on the maintenance and rehabilitation (M & R) strategies and funding 24 allocation Different computation algorithms such as straight-edge method and wire line method, which ACCEPTED MANUSCRIPT are based on the same raw data, may lead to rut depth estimation which are not always consistent 26 Therefore, there is an urgent need to assess the impact of algorithm types on the accuracy of rut depth 27 computation In this paper, a 13-point-based laser sensor detection technology, commonly accepted in 28 China for rut depth measurements, was used to obtain a database of 85,000 field transverse profiles 29 having three representative rutting shapes with small, medium and high severity rut levels Based on 30 the reconstruction of real transverse profiles, the consequences from two different algorithms were 31 compared Results showed that there is a combined effect of rut depth and profile shape on the rut 32 depth computation accuracy As expected, the difference between the results obtained with the two 33 computation methods increases with deeper rutting sections: when the distress is above 15 mm 34 (severe level) the average difference between the two computation methods is above 1.5 mm, normally, 35 the wire line method provides larger results The computation suggests that the rutting shapes have a 36 minimal influence on the results An in-depth analysis showed that the upheaval outside of the wheel 37 path is a dominant shape factor which results in higher computation differences M AN U SC RI PT 25 38 Keywords: 40 Pavement distress; Multipoint laser detection; Straight-edge rut depth; Wire line rut depth; 41 Rutting shape; Rut depth magnitude EP 43 *Corresponding author Tel.: +49 531 391 62064; fax: +49 531 391 62063 E-mail addresses: di.wang@tu-braunschweig.de (D Wang), a.cannone-falchetto@tu-bs.de (A AC C 42 TE D 39 Cannone Falchetto), m.goeke@tu-braunschweig.de (M Goeke), wwn0816@yeah.net (W Wang), litiantian_2009_9@126.com (T Li), m.wistuba@tu-bs.de (M P Wistuba) ACCEPTED MANUSCRIPT 44 45 Rutting is one of the most significant distresses of asphalt pavement It consists of a permanent surface 46 deformation in the wheel path occurring when pavement materials are under high loading and shear 47 (Haas and Norman, 2001; McGhee, 2004; Sousa et al., 1991) This phenomenon can significantly 48 impact roadway safety since rainwater may fill ruts, eventually leading to loss of traction and friction due 49 to hydroplaning Therefore, timely decisions and correct solutions for maintenance and rehabilitation (M 50 & R) need to be identified for minimizing the detrimental effects of this distress SC RI PT Introduction The accuracy of rut depth measurement can substantially impact the reliability of performance 52 evaluation, on the selection of M & R strategies, as well as, on the allocation of funding In some 53 countries such as China, the pavement industry is still in the construction climax and the time for 54 massive scale pavement M & R has not come yet Hence, in the next ten years, the transportation 55 department will face the challenge of implementing a consistent M & R program with an efficient use of 56 resources M AN U 51 The original definition of the rut depth is based on the manual straight-edge measurement, however, 58 the straight-edge length varies depending on region and countries Hence, the rut depth estimation is 59 not always consistent In the US, American Association of State Highway and Transportation Officials 60 (AASHTO) and the Highway Design and Maintenance Standards Model (HDM-III) relies on a 1.2 m 61 straight-edge bar to measure rut depth (Lous, 1995) The American Society for Testing and Materials 62 (ASTM, 2010) suggested a flexible straight-edge between 1.83 m (6 feet) and 3.66 m (12 feet); the 63 straight-edge length should stretch across the highest points between adjacent upheavals However, 64 common straight-edge length was established for a 1.83 m (6 feet) lane in the long-term pavement 65 performance (LTPP) (Miller and Bellinger, 2014) Among the countries using international units (UI) 66 Denmark uses 1.8 m straight-edge, other institutions in countries such as the United Kingdom 67 (Transport Research Laboratory, TRL), Australia, New Zealand, South Africa and some of the British 68 Commonwealth of Nations define the rut depth under a 2.0 m straight-edge (Lous, 1995), A different 69 approach is used by Strategic Highway Research Program (SHRP, US), Sweden (Stroup et al., 2004) 70 and China (RIOH, 2007), for which the minimum length of a straight-edge is as wide as the driving lane AC C EP TE D 57 ACCEPTED MANUSCRIPT 71 of interest Japan uses the pulling line method, which is based on a flexible wire rather than on a 72 straight-edge (Joseph, 2001) In order to obtain rut depth in a more time-efficient manner, the automatic laser detection technology 74 was introduced The very first measurement devices consisted only three or five laser sensors, so that 75 the transverse section could only be approximated as a discrete profile The corresponding computation 76 method was named pseudo rutting (NCHRP, 2004) or AASHTO method (Cole and Shippen, 2005) The 77 assumption of the two methods are similar: the instrumented vehicle runs along the centerline of the 78 driving lane The wheels are located within the wheel paths, and the laser sensors are just right above 79 the maximum rut section The rut depth is defined as the relative height difference between the central 80 sensor and the sensors on both sides These two methods were mainly used in the US (Vedula et al., 81 2002); however, due to the limitations of the assumption and the measurements inaccuracy, it was later 82 superseded M AN U SC RI PT 73 As the technology developed to more refined laser systems, more and more sensors have been 84 installed in the detection devices Simpson (2001a) suggested that a measured transverse section can 85 be considered as a continuous profile when the number of laser sensors is more than nine and such a 86 configuration is commonly available in most of the current detection systems Based on these types of 87 measurement device, two new computation methods were implemented: straight-edge rut depth and 88 wire line rut depth (AASHTO, 2001) Cenek et al (1994) and Lous (1995) evaluated the difference 89 between the two computation methods showing that the wire line method leads to a larger result when 90 the wheel paths are relative wide Simpson (2001b) and Li (2006) performed a qualitative comparative 91 analysis demonstrating that the shape of the transverse section affects the rut depth results AC C EP TE D 83 92 A different study from the Federal Highway Administration (Joseph, 2001) found that the rut depth 93 obtained from a wire line method is larger than that measured with a 1.8 m or 1.2 m straight-edge 94 Bennett and Wang (2003) and Wu (2007) explored the possibility of differentiating the transverse rut 95 profile into W-shape and U-shape sections While no significant differences were observed for W-shape 96 profile, for U-shape sections the wire line method was associated to larger rut depths compared to the 97 straight-edge method Although the straight-edge and wire line methods were the most commonly used 98 algorithms in rut depth computation, the existing studies provided only qualitative evidence that the ACCEPTED MANUSCRIPT rutting shape affected the rut depth accuracy In addition, the separation of rut profile into two classes, 100 W-shape and U-shape, appeared to be too simplistic for thoroughly addressing the impact of the 101 computation algorithm on the actual accuracy of the rut depth 102 103 In this paper, the effect of computation algorithms on the accuracy of rut depth measurements is 104 evaluated with the aim of identifying the effective influence of rut depth and rutting shapes to provide 105 estimation on the reliability of the decision for timely M & R actions For this purpose, two typical rutting 106 shapes obtained from field observations and one virtual rutting shape were used to analyze the impact 107 of rut measurements accuracy for different rut depth magnitude In the present research, the 108 straight-edge rut depth method refers to a 2.0 m straight-edge RI PT 99 M AN U SC Objective and research approach This paper is organized as follows First the 13-point laser bar device and the experimental 110 measurements are introduced Then, straight-edge rut depth and wire line rut depth algorithms are 111 described Rutting severity level and the seven profile shapes which are conventionally observed in 112 China are presented Within this set of profiles, the three shapes which showed different rut results 113 between the two computation methods (straight-edge and wire line) are further analyzed to understand 114 the influence of the rut depth accuracy Finally, the profile characteristics affecting the measurement 115 accuracy are identified and the one-dimension rutting shape indexes are proposed for further research 116 117 In this section, the 13-point-based laser bar and an extensive dataset of road profiles from the Jiangsu 118 province in China are introduced together with the two most common rut depth computation methods 119 (RIOH, 2007) 120 3.1 121 In the recent past, the Research Institute of Highway (RIOH), which is part of the Chinese Ministry of 122 Transport (MOT) has developed an automatic road detection system, named “multifunctional 123 high-speed highway condition monitor system” (CiCS, 2010), equipped with a 13-point laser sensor EP TE D 109 AC C Rut depth measurement technology and computation algorithms 13-point-based laser bar and experimental measurements ACCEPTED MANUSCRIPT (Fig 1) This device is based on the relative height measurement at discrete points, where the laser bar 125 is 2300 mm wide and is installed on the vehicle at 300 mm above the pavement Nine vertical laser 126 sensors are unevenly positioned along the laser bar, with more sensors in the wheel path area and less 127 in the non-wheel path zone Two additional, oblique laser sensors are installed on both left and right end 128 of the laser bar so that an overall detection width of 3600 mm can be achieved The detailed layout and 129 laser spacing are shown in Fig M AN U SC RI PT 124 130 131 Fig Instrumented vehicle with 13-point laser bar in the road measuring 133 134 AC C EP TE D 132 Fig Layout of laser sensors 135 Table shows the original data of a representative rut transverse profile collected by the 136 13-point-based laser bar Each profile data consists of three lines In the first row, "G40" indicates the 137 identification number of the roadway; "A" represents the upper line, while "104" identifies the stake mark 138 which means the profile is located in K1+120.82 m In the second row, each point shows the vertical 139 height of the pavement measured by the laser sensors with an accuracy of 0.1 mm The measurement 140 system calculates the relative height of the laser sensors; therefore, the reference point of the profile is ACCEPTED MANUSCRIPT 141 the lowest sensor which is marked by zero The third row is the horizontal position of the detecting laser 142 points in units of mm The sensor D7 in the middle (Fig 2) is represented by 2500 mm so that the width 143 of the measurement is the difference between the first and the last coordinate 144 Row Raw data RI PT Table Original data of 13-point-based laser bar Unit G40A, 112, 082 cm 258, 267, 157, 118, 102, 145, 224, 195, 114, 47, 0, 26, 122 699, 1073, 1343, 1625, 1875, 2125, 2500, 2875, 3125, 3375, 3659, 3944, 4322 0.1 mm mm 3.2 147 The 13 raw discrete points are used to reconstruct an approximate continuous rut’s cross section relying 148 on MATLAB (MathWorks, 2015) as follows: each discrete elevation point is connected by a straight line 149 one-by-one to the cross-sectional shape and both endpoints are connected and extended to determine 150 the raw baseline Then, the corresponding rut depth can be computed with the straight-edge method 151 (Hadley and Mayers, 1991) or wire line method (RIOH, 2007, 2008) SC 145 146 M AN U Rut depth calculation algorithms However, there exists a road camber in the highway, in China Normally, the slope angle, α , is 153 approximately 3‰ in the non-super elevation section, hence, the original baseline is not horizontal In 154 China, seven typical rutting shapes were identified in "field test methods of subgrade and pavement for 155 highway engineering" (RIOH, 2008), but their base lines are a horizontal As shown in Fig 3, there are 156 two definitions for rut depth: perpendicular to the datum of the elevation measurements, which is the one 157 related to the horizontal line, and the other one is perpendicular to the measurement bar (straight-edge 158 or wire) associated to the raw baseline It is obviously that the different definitions lead the differences of 159 related rut depths AC C EP TE D 152 160 ACCEPTED MANUSCRIPT 161 Fig Implication of the baseline Bennett and Wang (2003) suggested that there is a cosine relation between the two rut depths, since 163 α is a very small value, then the cos( α ) Further calculation showed that the difference value 164 between the two results does not exceed 0.01 mm while the rut depth is less than 50 mm Therefore, the 165 different baselines have a minimal influence on the results In this paper, we correct the original baseline 166 to horizontal as the national standard suggested, therefore, in this paper the baseline is a horizontal line 167 3.2.1 168 The straight-edge rut depth algorithm is based on the Strategic Highway Research Program (SHRP) 169 algorithm in Hadley and Myers (1991) The analysis starts at sensor one (D1) (Fig 2) which is the 170 closest to the pavement kerb It progresses until the rutting in one wheelpath is established It is then 171 repeated for the second wheelpath starting at the right most sensor and moving downwards Once a 172 viable placement point had been established, the vertical distances of all intermediate placement points 173 were established RI PT 162 M AN U SC Straight-edge method In this paper, according to the SHRP project (Simpson, 2003), the straight-edge is defined as an 175 imaginary straight ruler which stretches across the road profile, however, the length of the straight-edge 176 is limited In this study, the straight-edge is defined as 2.0 m long As shown in Fig 4, the straight-edge 177 touches the highest points/peaks of the cross section, while the wheel path is beyond the ruler’s length, 178 and the straight-edge only goes across the highest points between adjacent upheavals 180 181 EP (a) AC C 179 TE D 174 (b) 182 183 Fig Illustration of straight-edge method in different rutting shapes (a) First rutting shape (b) Second rutting shape ACCEPTED MANUSCRIPT 3.2.2 Wire line method 185 According to the Highway Performance Assessment Standards (RIOH, 2007), the wire line is defined as 186 an imaginary line which stretches across the entire road profile, while both ends of the line overlap with 187 the endpoints of the cross section According to Fig 5, the wire line touches the highest points/peaks of 188 the cross section, while the rut depth is given by the maximum vertical distance between the road profile 189 and the wire line 190 RI PT 184 M AN U SC (a) 191 192 (b) 194 TE D 193 Fig Illustration of wire line method in different rutting shapes (a) First rutting shape (b) Second rutting shape 195 196 In this section, the standard category of rutting magnitude used in China and the typical rutting shapes 197 identified by the Chinese national standard are presented Then, the four shapes which led to different 198 results in two computation methods are analyzed and illustrated 199 4.1 200 The rutting severity magnitude is commonly defined in terms of different ranges in rut depth (Fwa and 201 Ong, 2008; Li, 2012) According to the Chinese national standards (RIOH, 2007), a pavement is 202 affected by rutting phenomena when it presents a permanent deformation of 10 mm or larger And when 203 the depths are lower than 10 mm, the pavement won’t be affected by rutting phenomena Rutting 204 between and 10 mm can be associated to the small level, and rutting between 10 and 15 mm is AC C EP Rut depth magnitude and typical rutting shapes in China Rutting severity level definition ACCEPTED MANUSCRIPT defined as medium level rutting For these two cases, M & R judgement is allocated based on the 206 Pavement Condition Index (PCI) and special maintenance treatment for rutting is not required 207 Deformations over 15 mm are considered in the category of high severity, then maintenance actions 208 have to be planned immediately RI PT 205 In this study, an expressway road, 10.7 km long, affected by rutting distress, was selected and 210 investigated The road was designed and built in 2008, in Jiangsu province of China The pavement is 211 21 m wide and no maintenance or re-vamping was performed previously The layers’ structural package 212 is as follows: cm of SMA-16 wearing course, cm AC-25 binder layer and 10 cm AC-30 base layer 213 This road was tested with the 13-point-based laser bar and the cross section profile was measured and 214 recorded every 20 cm 215 4.2 216 In order to analyze the sensitivity of computation methods, the rutting shapes which results in different 217 rut depth computations need to be first identified Due to the computation approach, the differences are 218 observed only for lane without upheaval in center In this case, the wire line method provides larger rut 219 values than the straight-edge method Seven typical rutting shapes in China are identified in the current 220 national standard (RIOH, 2008) According to the computation approaches, four profile shapes show 221 differences in rut depth: Types 4, 5, and (Fig 6) AC C EP TE D Typical profile shapes in China M AN U SC 209 222 223 Fig Rutting profile shapes in China 224 The rutting shapes depend on a variety of factors which are linked to a series of dominant 225 phenomena According to previous studies (N.D Lea International Ltd, 1995; Sha, 2001), the dominant 226 phenomena can be divided into four types: structure deformation (SD), plastic deformation (PD), 10 ACCEPTED MANUSCRIPT surface abrasion (SA) and densifications deformation (DD) Since a pavement structure consisting of a 228 thin asphalt surface with thick semi-rigid base is widely used for highway pavements in China, and due 229 to the combined interaction of climate and construction with traffic load, the major rutting phenomena in 230 China can be restricted to PD and DD In this study, only three typical shapes were collected from the 231 database, which could be considered as PD (Types 3, 5) and DD (Type 6) from Fig As shown in Fig 232 7, the rutting shapes associated to this specific configuration and conditions are illustrated And in the 233 figure, the value of deformation (D) refers to the rut depth 234 M AN U SC (a) 235 236 237 240 EP TE D (b) AC C 238 239 RI PT 227 Fig Rutting shape collected from database (a) Plastic deformation rutting shape-Type (b) Densifications deformation rutting shape-Type 241 In order to analyze the rutting shapes effect, one extra rutting shape needs be simulated in this 242 research Zhu (2007) studied the common rutting shape in heavy traffic load in China and the author 243 pointed out that Type rutting shape is very uncommon, since it could be observed only for rural road 244 when a traffic overload occured Hence, in this study, only Type rutting shape is necessary to be 245 virtually reconstructed The simulation process is illustrated in Fig 11 246 247 Simulation process of Type rutting shape The simulation process is as follows 250 Type rutting transverse sections with different rut depths were drawn according to the TE D • 249 definition of Chinese national standard (RIOH, 2008) • 251 252 The 13-point-based measurement process was simulated with the actual sensors spacing distribution of the 13-point-based laser bar configuration 254 The rutting section was reconstructed by connecting the discrete elevation points and the EP • 253 maximum rut depth was then calculated using both straight-edge and wire line methods AC C 248 Fig M AN U SC RI PT ACCEPTED MANUSCRIPT 255 256 In order to study the effect of the rut depth magnitude and rutting shape on the accuracy of rutting 257 measurements, Types and rutting shapes were used Type rutting shape was also included in the 258 analysis based on reconstructed profile with a simulated rut depth between mm and 19 mm with mm 259 interval The two computational methods, straight-edge and wire line, were implemented into a MATLAB 260 (Mathworks, 2015) code and applied to different profiles to compare the potential differences in the 261 estimations of rut depth Assessment of rut depth magnitude and rutting shape effect 12 ACCEPTED MANUSCRIPT 262 5.1 263 Fig presents the differences of rut depth for Types 4, and rutting shapes In the horizontal axis, 264 each number represents the rut depths magnitude, for example, “8” represents the rutting sections 265 which show rutting larger than mm but smaller than mm M AN U SC RI PT Rut depth magnitude effect 266 267 Fig 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 269 medium rut depth levels, the average difference is below 1.5 mm, hence, both computation methods are 270 acceptable However, much higher differences (up to 2.7 mm) can be found for larger rut depth (19 mm) 271 Such a severe discrepancy is expected to significantly affect the accuracy of the measurements and, 272 therefore, the specific computation method needs to be carefully selected as a satisfactory balance 273 between road users’ safety and maintenance costs According to previous researches (Fwa and Ong, 274 2008; Guo et al., 2013), the rutting can easily induce the occurrence of hydroplaning risk, and the safety 275 critical rut depth will decrease compared with the dry condition, hence, the authors suggest engineers to 276 use the wire line method in M & R decision AC C EP TE D 268 277 It is, therefore, undeniable that rut depth plays an important role for the selection of the most 278 appropriate computation method To further evaluate this effect, a sensitivity coefficient analysis based, 279 analysis of variance analysis (ANOVA) was used (Shi, 2012) Statistical significance level was set to 280 α = 0.05 This parameter is associated to the output of statistical analysis, p-value, which represents 281 the parameter discriminating the actual significance of the specific test When p-value is smaller than 282 the significance level ( α = 0.05), then it may be concluded that there is a statistical significant difference 13 ACCEPTED MANUSCRIPT among the groups; otherwise, the groups compared are statistically equivalent 284 Table ANOVA on rut depth Analysis output Sum of squares df Mean square F Sig Between groups 7.913 12 0.659 6.819 Within groups 3.771 39 0.097 Total 11.684 51 RI PT 283 As shown in Table 2, F-test statistics is 6.819, and the corresponding probability p-value is (bold 287 section in Table 2) This suggests that the rut depth has a statistical significance impact on the 288 computation algorithm 289 5.2 290 As previously mentioned, the peculiar rutting shape represented a notable factor (Bennett and Wang, 291 2003; Wu, 2007) affecting the rut depth measurement accuracy Hence, ANOVA was used to analysis 292 its significance Rutting shape effect 293 ANOVA on rutting shape TE D Table M AN U SC 285 286 Analysis output Sum of squares df Mean square F Sig Between groups 1.362 0.454 2.112 0.111 Within groups 10.322 36 0.215 Total 11.684 38 As shown in Table 3, F-test statistics value is 2.112 and the corresponding probability p-value is 0.111 296 (bold section in Table 3), which means that rutting shape is not affecting the measured accuracy 297 Therefore, it is necessary to study the difference between each pair of rutting shapes A multiple 298 comparison statistical test based on the least significant difference (LSD) method was used (Shi, 2012) 299 Table shows the comparison results between each type of three different rutting shapes AC C 300 EP 294 295 301 302 303 304 14 ACCEPTED MANUSCRIPT 305 Table LSD-based multiple comparisons of three rutting shape types Rutting shape type Mean difference Std error Sig and 0.02308 0.18189 and 0.38077 and 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 RI PT Lower bound As shown in Table 4, p-value is 0.900 for group of Types and 5, which means that there is no 308 statistical significance within this group However, p-value are 0.042 and 0.031 (bold section in Table 4) 309 for groups of Types and 6, and 6, respectively Hence, there is a significant influence of the rutting 310 shape for these two groups confirming a substantial similarity between Types and SC 306 307 As mentioned before, one of the dominant rutting causes is plastic deformation, which leads to 312 upheaval on the right side along the driving direction As illustrated in Fig 6, the upheaval outside of the 313 right wheel path is present in both Types and 5, while there is no upheaval in Type Hence, the 314 results are most likely associated to the presence of the upheaval outside of the right wheel path, which 315 will lead to smaller differences between the two computation algorithms 316 317 In this paper, the effect of computation algorithms on the accuracy of rut depth is evaluated with the aim 318 of identifying the influence of rutting shapes, and rut depth on the reliability of the decision for timely 319 maintenance and rehabilitation decision and activities For this purpose, two field rutting shapes and 320 one simulated rutting profile typically observed in China were selected to analyze the impact on rut 321 measurements accuracy Based on the analysis performed, the following conclusions can be drawn: 323 324 325 TE D EP Conclusions AC C 322 M AN U 311 (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 326 (3) The rut depth leads to significant differences between the two computation methods The 327 different values show a progressively increasing trend for all the three rutting shapes, it is up to 328 2.7 mm when rut depth magnitude is 19 mm 15 ACCEPTED MANUSCRIPT 329 (4) The selection of the computation methods should be carefully selected as a satisfactory balance 330 between road users’ safety and maintenance costs Due to the safety consideration, the findings 331 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 333 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 RI PT 332 According the analysis above, the difference estimation between two algorithms is a combined effect 336 of rutting shape and rut depth For rutting shape, the findings of this study suggest the upheaval outside 337 of the right wheel path is the dominant factor, however, it is not an accurate parameter and only 338 qualitative study is conducted in this paper Therefore, the geometry of rutting shape characteristic 339 should be established and quantized analysis in further research For rutting depth, the difference value 340 between two algorithms is gradually changed in this paper, serious cases should be studied to find out 341 the critical magnitude And additional transverse profiles derived from 13-point laser bars with more rut 342 types should be further analyzed, using the proposed method to quantify the potential errors and further 343 understand the impact of rut type on rut depth measurement error 344 Acknowledgments 345 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 347 National Natural Science Foundation of China (51508034, 51408083, 51508064) 348 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 16 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: http://www.xssj.cc/news/2010429/n785955.html 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 http://www.lpcb.org/index.php/documents/pavement-deterioration1459-1995-rut-depth-modelling-r 380 ut-depths-in-flexible-pavement/file (Accessed 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: http://de.mathworks.com/ (Accessed 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 406 414 AC C EP TE D M AN U 415 19 RI PT ACCEPTED MANUSCRIPT 416 Di Wang has been a PhD student at Institut für Straßenwesen der TU Braunschweig (ISBS) of 418 Technische Universität Braunschweig since May 2015 He was a research associate in the School of 419 Highway at Chang’an University, Xi’an, China He received a Master’s degree and a Bachelor’s degree 420 from Chang’an University His research interests are characterization and modeling of asphalt materials 421 at low temperatures, asphalt materials recycling and diffusion process of rejuvenators and fresh binder 422 in the aged binder contained in reclaimed asphalt pavement materials M AN U SC 417 423 425 TE D 424 Augusto Cannone Falchetto has been a research associate at ISBS since 2013 He had been a 427 research associate in the Department of Civil Engineering at the University of Minnesota, USA from 428 2008 to 2013.He received his PhD degree in Civil Engineering (minor Statistics) in University of 429 Minnesota, USA His research interests are characterization and modeling of asphalt materials at low 430 temperature, asphalt materials recycling, and size effect and scaling of quasi-brittle material AC C 431 EP 426 20