[1] Ding Zhike, Xu Feiyun, Zhao Xun,. Roughness evaluation of three-dimensional asphalt pavementbased on two-dimensional power spectral density [J]. Journal of Southeast University (English Edition), 2022, 38 (4): 401-407. [doi:10.3969/j.issn.1003-7985.2022.04.009]
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Roughness evaluation of three-dimensional asphalt pavementbased on two-dimensional power spectral density(
)
)Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]
- Volumn:
- 38
- Issue:
- 2022 4
- Page:
- 401-407
- Research Field:
- Traffic and Transportation Engineering
- Publishing date:
- 2022-12-20
Info
- Title:
- Roughness evaluation of three-dimensional asphalt pavementbased on two-dimensional power spectral density
- Author(s):
- Ding Zhike; Xu Feiyun; Zhao Xun
- School of Mechanical Engineering, Southeast University, Nanjing 211189, China
- PACS:
- U416.2
- DOI:
- 10.3969/j.issn.1003-7985.2022.04.009
- Abstract:
- To solve the problem of the lack of comprehensive evaluation of three-dimensional(3D)asphalt pavement roughness, a method for evaluating the asphalt pavement roughness is proposed based on two-dimensional(2D)power spectral density(PSD). By calculating the 2D PSD of a 3D asphalt pavement and converting it into the longitudinal average asphalt pavement PSD, the relationship between the evaluation method of the 3D asphalt pavement roughness and the current evaluation standard of roughness is established. Combined with the road-fitting formula used in international standards, the elevation data of the A, B, C, and D grades of the 3D asphalt pavement are simulated by the harmonic superposition method. According to the proposed method, the longitudinal PSD of each level of simulated asphalt pavement is calculated and compared with the standard spectral line of each pavement level. This approach verifies the effectiveness of the proposed method in evaluating the roughness of the 3D asphalt pavement. Compared with the PSD of a certain horizontal profile elevation, it is verified that the fluctuation amplitude of the spectral line calculated by the proposed method is greatly improved. The results show that the proposed method can evaluate the roughness of asphalt pavements more comprehensively and accurately and has strong practicability.
References:
[1] Zamora Alvarez E J, Ferris J B, Scott D, et al. Development of a discrete roughness index for longitudinal road profiles[J].International Journal of Pavement Engineering, 2018, 19(12): 1043-1052. DOI:10.1080/10298436.2016.1237180.
[2] Lee J, Abdel-Aty M, Nyame-Baafi E. Investigating the effects of pavement roughness on freeway safety using data from five states[J]. Transportation Research Record: Journal of the Transportation Research Board, 2020, 2674(2): 127-134. DOI:10.1177/0361198120905834.
[3] Sime M, Bailey G, Hajj E Y, et al. Impact of pavement roughness on fuel consumption for a range of vehicle types[J].Journal of Transportation Engineering, Part B: Pavements, 2021, 147(3): 04021022. DOI:10.1061/jpeodx.0000272.
[4] Zhou X Q, Sun L J, Yan L. Development and trend of pavement evenness evaluation [J]. Highway Transportation Technology, 2005(10): 18-22.(in Chinese)
[5] Wang D W, Wang C H, Bernhard S, et al. Overview on evaluation methods of pavement evenness for pavements without speed limitations in Germany[J].China Journal of Highway and Transport, 2019(4): 105-113, 129.(in Chinese)
[6] Zhang Z, Zhang H L, Xu S, et al. Pavement roughness evaluation method based on the theoretical relationship between acceleration measured by smartphone and IRI[J].International Journal of Pavement Engineering, 2022, 23(9): 3082-3098. DOI:10.1080/10298436.2021.1881783.
[7] Yu H. Mechanical parts defect detection method based on computer vision technology[C]//2015 Seventh International Conference on Measuring Technology and Mechatronics Automation. Nanchang, China, 2015: 630-633. DOI:10.1109/ICMTMA.2015.158.
[8] Mahmoudzadeh A, Golroo A, Jahanshahi M R, et al.Estimating pavement roughness by fusing color and depth data obtained from an inexpensive RGB-D sensor[J]. Sensors(Basel, Switzerland), 2019, 19(7): 1655. DOI:10.3390/s19071655.
[9] Dong X D, Lou P, Yan J W, et al. A detection method for pavement roughness with binocular vision[C]//Proc SPIE 11373, Eleventh International Conference on Graphics and Image Processing(ICGIP 2019). Hangzhou, China, 2020, 11373: 613-619. DOI:10.1117/12.2557811.
[10] Sun L. Simulation of pavement roughness and IRI based on power spectral density[J].Mathematics and Computers in Simulation, 2003, 61(2): 77-88. DOI:10.1016/S0378-4754(01)00386-X.
[11] Papagiannakis A T, Raveendran B. International standards organization-compatible index for pavement roughness[J].Transportation Research Record: Journal of the Transportation Research Board, 1998, 1643(1): 110-115. DOI:10.3141/1643-14.
[12] Niu K, Li C. Simulation of pavement roughness based on time domain model [J]. Journal of Southeast University(English Edition), 2010, 26(3): 475-479. DOI:10.3969/j.issn.1003-7985.2010.03.021.
[13] Huang Z Q, Zheng W H. Modeling of ADAMS 3D random road with Matlab[J]. Modern Defence Technology, 2018, 46(3): 165-170.(in Chinese)
[14] Zhou J. Study on field test procedures for highway subgrade and pavement [J]. Building Materials and Decoration, 2018, (47): 274.(in Chinese)
Memo
- Memo:
-
Biographies: Ding Zhike(1998—), male, graduate; Xu Feiyun(corresponding author), male, doctor, professor, fyxu@seu.edu.cn.
Foundation item: The National Natural Science Foundation of China(No. 51975117).
Citation: Ding Zhike, Xu Feiyun, Zhao Xun. Roughness evaluation of three-dimensional asphalt pavement based on two-dimensional power spectral density[J].Journal of Southeast University(English Edition), 2022, 38(4):401-407.DOI:10.3969/j.issn.1003-7985.2022.04.009.