|Table of Contents|

Prediction method for asphalt pavement crack based on GRNN neural network(PDF)

《深圳大学学报理工版》[ISSN:1000-2618/CN:44-1401/N]

Issue:
2017年No.4(331-440)
Page:
378-384
Research Field:
土木建筑工程
Publishing date:

Info

Title:
Prediction method for asphalt pavement crack based on GRNN neural network
Author(s):
Ke Wenhao1 Chen Huaxin1 Lei Yu2 and Zhang Tao2
1) School of Materials Science and Engineering, Chang’an University, Xi’an 710064, Shaanxi Province, P.R.China
2) China Communications Construction Company First Highway Consultants Company Limited, Xi’an 710065, Shaanxi Province, P.R.China
Keywords:
road engineering prediction method crack expressway asphalt concrete pavement general regression neural network
PACS:
U 416.2
DOI:
10.3724/SP.J.1249.2017.04378
Abstract:
The relationship between crack and influencing factors is analyzed by using correlation analysis method. The prediction model for crack of asphalt pavement is established by using of general regression neural network (GRNN). In order to establish the model, 50 sets of measured data of expressway pavement are selected for determining model parameters, and 6 sets of measured data are selected for model validation. The service life and the cumulative number of standard axle loads are highly positive correlated with crack. The asphalt concrete layer thickness, the semi-rigid structural layer thickness and the surface layer asphalt content are moderately negative correlated with crack. The bottom layer asphalt content is low positively correlated with crack. The correlation between annual minimum temperature and crack is weak. The deviation of predicted values and measured ones is small. The maximum deviation is 12.71%, which shows that the model is feasible.

References:

[1] Hofko B. Addressing the permanent deformation behavior of hot mix asphalt by triaxial cyclic compression testing with cyclic confining pressure[J]. Journal of Traffic and Transportation Engineering English Edition,2015,2(1):17-29.
[2] Durango P L. Adaptive optimization models for infrastructure management[D]. Berkeley, USA: University of California Berkeley,2002.
[3] Mensching D J,McCarthy L M,Mehta Y,et al. Modeling flexible pavement overlay performance for use with quality related specifications[J]. Construction and Building Materials,2013,48(6): 1072-1080.
[4] Mandapaka V. Mechanistic-empirical and life-cycle cost analysis for optimizing flexible pavement maintenance and rehabilitation[J]. Journal of Transportation Engineering,2012,138(5): 625-633.
[5] Yared H D,Ibrahim O,Denis J,et al. Mechanics-based top-down fatigue cracking initiation prediction framework for asphalt pavements[J]. Road Materials and Pavement Design,2015,16(4): 907-927.
[6] Shahab F,Ali K. Reinforcing overlay to reduce reflection cracking: an experimental investigation[J]. Geotextiles and Geomembranes,2015,43(3):216-227.
[7] Gedafa D S,Hossain M,Romanoschi S A. Perpetual pavement temperature prediction model[J]. Road Materials & Pavement Design, 2014,15(1):55-65.
[8] 武建民,刘大彬,李福聪,等. 基于时间序列分析法的沥青路面使用性能预测[J]. 长安大学学报自然科学版,2015,35(3):1-7.
Wu Jianmin,Liu Dabin,Li Fucong,et al. Performance prediction of asphalt pavement maintenance based on time series analysis[J]. Journal of Chang’an University Natural Science Edition,2015,35(3): 1-7.(in Chinese)
[9] 周鹏飞,温胜强,康海贵. 基于马尔可夫链与神经网络组合的路面使用性能预测[J]. 重庆交通大学学报自然科学版,2012,31(5):997-1001.
Zhou Pengfei,Wen Shengqiang,Kang Haigui. Pavement performance combining forecasting based on BP neural network and markov model[J]. Journal of Chongqing Jiaotong University Natural Science Edition,2012,31(5): 997-1001.(in Chinese)
[10] 马士宾,王丽洁,王清洲,等. 基于信息扩散理论的沥青路面使用性能预测[J]. 河北工业大学学报自然科学版,2012,41(1):103-108.
Ma Shibin,Wang Lijie,Wang Qingzhou,et al. Asphalt pavement performance prediction based on the information diffusion theory[J]. Journal of Hebei University of Technology Natural Science Edition,2012,41(1):103-108.(in Chinese)
[11] 孔祥杰. 沥青路面性能衰变预测及养护维修决策方法研究[D]. 北京:北京工业大学,2015.
Kong Xiangjie. Study on prediction method of performance decay and maintenance decision method of asphalt pavement[D]. Beijing: Beijing University of Technology,2015.(in Chinese)
[12] 谢峰. 基于BP神经网络的高速公路路面性能预测[J]. 公路交通科技应用技术版,2015,129(9): 73-75.
Xie Feng. Prediction of expressway pavement performance based on BP neural network[J]. Journal of Highway and Transportation Research and Development Application Technology Edition,2015,129(9): 73-75.(in Chinese)
[13] 韦金城,余四新. 青临高速试验路沥青路面结构应变分析和永久变形预估[J]. 公路交通科技,2015,32(8): 1-5.
Wei Jincheng,Yu Sixin. Analysis of strain and prediction of permanent deformation for asphalt pavement of Qingzhou-Linshu expressway test road[J]. Journal of Highway and Transportation Research and Development,2015,32(8): 1-5.(in Chinese)
[14] 肖金平,韦慧,赵健,等. 湖南省高速公路路面使用性能衰变模型[J]. 中南大学学报自然科学版,2015,46(7):2686-2692.
Xiao Jinping,Wei Hui,Zhao Jian,et al. Decay model of Hunan expressway pavement performance[J]. Journal of Central South University Science and Technology,2015,46(7):2686-2692.(in Chinese)
[15] 白志军. 路面预防性养护中超薄磨耗层的性能评估与寿命预测[J]. 公路与汽运,2015,169(4): 163-166.
Bai Zhijun. Performance evaluation and life prediction of ultra-thin wear layer in pavement preventive maintenance[J]. Highways & Automotive Applications,2015,169(4): 163-166.(in Chinese)
[16] 魏建国,龚文剑,南秋彩,等. G6高速公路巴新麻段沥青路面使用性能预测研究[J]. 公路与汽运,2015,168(3): 92-95.
Wei Jianguo,Gong Wenjian,Nan Qiucai,et al. Study on performance prediction of asphalt pavement of Paxing Ma section of G6 expressway[J]. Highways & Automotive Applications,2015,168(3): 92-95.(in Chinese)
[17] 程培峰,郑婉. 基于改进残差灰色模型预测路面使用性能的研究[J]. 中外公路,2014,34(3): 60-63.
Chen Peifeng,Zheng Wan. Research on pavement performance prediction based on improved residual gray model[J]. Journal of China & Foreign Highway,2014,34(3): 60-63.(in Chinese)
[18] 陈涛,郭卫卫,孟令智,等. 基于广义回归神经网络的路面摩擦系数预测模型[J]. 公路,2014,59(6): 1-6.
Chen Tao,Guo Weiwei,Meng Lingzhi,et al. Prediction model of pavement friction coefficient based on generalized regression neural network[J]. Highway,2014,59(6): 1-6.(in Chinese)
[19] 刘亚敏,韩森,徐鸥明. 基于遗传算法的SMA路面抗滑性能预测模型[J]. 应用基础与工程科学学报,2013,21(5): 890-898.
Liu Yamin,Han Sen,Xu Ouiming. Prediction Model for Skid-resistance of Stone Mastic Asphalt Pavement Based on Genetic Algorithm[J]. Journal of Basic Science and Engineering,2013,21(5): 890-898.(in Chinese)
[20] 柯文豪,雷宇,陈团结. 基于路用性能的沥青路面全寿命周期设计方法[J]. 长安大学学报自然科学版,2013,33(3): 7-13.
Ke Wenhao,Lei Yu,Chen Tuanjie. Performance based life-cycle design method for asphalt pavement[J]. Journal of Chang’an University Natural Science Edition,2013,33(3): 7-13.(in Chinese)
[21] 王斌,黄卫,杨军,等. 连续配筋混凝土路面路用性能预测与评价方法[J]. 中国公路学报,2012,25(5): 24-30.
Wang Bin,Huang Wei,Yang Jun,et al. Prediction and evaluation methods for pavement performance of continuous reinforced concrete pavement[J]. China Journal of Highway and Transport,2012,25(5): 24-30.(in Chinese)
[22] 孙志林,黄晓明. 沥青路面线性疲劳损伤特性及形变规律[J]. 东南大学学报自然科学版,2012,42(3): 521-525.
Sun Zhilin,Huang Xiaoming. Linear fatigue damage characteristics and deformation law of asphalt pavement[J]. Journal of Southeast University Natural Science Edition,2012,42(3): 521-525.(in Chinese)
[23] Sun Lu,Ge Minli,Gu Wenjun,et al. Characterizing uncertainty in pavement performance prediction[J]. Journal of Southeast University English Edition,2012,28(1): 85-93.
[24] 马士宾,王丽洁,王清洲,等. 基于信息扩散理论的沥青路面使用性能预测[J]. 河北工业大学学报,2012,41(1): 103-108.
Ma Shibin,Wang Lijie,Wang Qingzhou,et al. Asphalt pavement performance prediction based on the information diffusion theory[J]. Journal of Hebei University of Technology, 2012,41(1): 103-108.(in Chinese)
[25] 曾胜,黄雄立. SMA路面长期性能的调查分析及预测[J]. 长沙理工大学学报自然科学版,2010,7(1):12-17.
Zeng Sheng,Huang Xiongli. Investigation and prediction research on long-term performance of SMA pavement[J]. Journal of Changsha University of Science and Technology Natural Science,2010,7(1):12-17.(in Chinese)

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Last Update: 2017-06-26