ASCE GSP 195-2009 PERFORMANCE MODELING AND EVALUATION OF PAVEMENT SYSTEMS AND MATERIALS.pdf

1、 GEOTECHNICAL SPECIAL PUBLICATION NO. 195 PERFORMANCE MODELING AND EVALUATION OF PAVEMENT SYSTEMS AND MATERIALS SELECTED PAPERS FROM THE 2009 GEOHUNAN INTERNATIONAL CONFERENCEAugust 36, 2009 Changsha, Hunan, China HOSTED BY Changsha University of Science and Technology, China CO-SPONSORED BY ASCE Ge

2、o-Institute, USA Asphalt Institute, USA Central South University, China Chinese Society of Pavement Engineering, Taiwan Chongqing Jiaotong University, China Deep Foundation Institute, USA Federal Highway Administration, USA Hunan University, China International Society for Asphalt Pavements, USA Jia

3、ngsu Transportation Research Institute, China Korea Institute of Construction Technology, Korea Korean Society of Road Engineers, Korea Texas Department of Transportation, USA Texas Transportation Institute, USA Transportation Research Board (TRB), USA EDITED BY Halil Ceylan, Ph.D. Xueyan Liu, Ph.D.

4、 Kasthurirangan Gopalakrishnan, Ph.D. Likui Huang Published by the American Society of Civil Engineers Library of Congress Cataloging-in-Publication Data Performance modeling and evaluation of pavement systems and materials : selected papers from the 2009 GeoHunan International Conference, August 3-

5、6, 2009, Changsha, Hunan, China / hosted by Changsha University of Science and Technology, China ; co-sponsored by ASCE Geo-Institute, USA et al. ; edited by Halil Ceylan et al. p. cm. - (Geotechnical special publication ; no. 195) Includes bibliographical references and indexes. ISBN 978-0-7844-104

6、7-9 1. Pavements, Asphalt concrete-Design and construction-Evaluation-Congresses. 2. Pavements, Concrete-Design and construction-Evaluation-Congresses. I. Ceylan, Halil. II. Changsha li gong da xue. III. American Society of Civil Engineers. Geo-Institute. IV. GeoHunan International Conference on Cha

7、llenges and Recent Advancements in Pavement Technologies and Transportation Geotechnics (2009 : Changsha, Hunan Sheng, China) TE278.P447 2009 625.8-dc22 2009022665 American Society of Civil Engineers 1801 Alexander Bell Drive Reston, Virginia, 20191-4400 www.pubs.asce.org Any statements expressed in

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13、ss above); email: permissionsasce.org. A reprint order form can be found at http:/pubs.asce.org/support/reprints/. Copyright 2009 by the American Society of Civil Engineers. All Rights Reserved. ISBN 978-0-7844-1047-9 Manufactured in the United States of America. Geotechnical Special Publications 1

14、Terzaghi Lectures 2 Geotechnical Aspects of Stiff and Hard Clays 3 Landslide Dams: Processes, Risk, and Mitigation 7 Timber Bulkheads 9 Foundations 2Director, Pengfang Paving Engineering Research Co. Ltd., 10 Qiaonan Village, Banan District, Chongqing 400054, China; 3Pengfang Paving Engineering Re

15、search Co. Ltd., 10 Qiaonan Village, Banan District, Chongqing 400054, China; 4Xinjiang University, 21 Youhao Road, Urumqi 830008, China; ABSTRACT: The conventional methods of pavement performance assessment indices were established by statistical analyses based on single item and multiple linear

16、regression techniques. These regression models have many deficiencies and are not able to truly reflect the inherent complex nonlinear relationships among the performance indices. However, the Back-Propagation (BP) neural network method with ca comprehensive nonlinear dynamic system is able to addre

17、ss some of these weaknesses. In this paper, the International Roughness Index (IRI), Damage Rate (DR), Structure Strength Index (SSI), Sideway Force Coefficient (SFC), and Rutting Depth (RD) were selected as the five index variables. These variables are considered as some of the most significant fac

18、tors that affect pavement performance. Additionally, these indices were easily classified as non-dimensional quantities and became input data units in the application of the BP neural network. In the study, Pavement Management Index (PMI) was accordingly sub-divided into five groups representing fiv

19、e grades; namely (1) excellent, (2) good, (3) medium, (4) subordinated, and (5) inferior. In this paper, pavement performance assessment based on the BP neural network method and PMI is presented along with a practical application example; followed by a summary of findings and recommendations 1. INT

20、RODUCTION The assessment of asphalt pavement performance is the basis of a series of 1pavement management works (such as prediction of performance, making a plan of maintenance or rebuilding, rational allocation of maintenance funds, etc.). Consequently, the authenticity of the assessment results co

21、nstitutes a key factor in determining the success or failure of the whole pavement management decision. Additionally, it also influences the carryover effect of pavement management system directly. Therefore, it appears particularly important to research the assessment methodology for asphalt paveme

22、nt performance management. Currently, combining subject and object matters is a common way to establish a comprehensive assessment index system for pavement workability, which is achieved through statistics of single item and multiple linear regression techniques. This subsequently allows for the es

23、tablishment of a connection between subjective scoring and objective measured data such as Present Serviceability Index (PSI) in AASHTO, Riding Comfort Index (RCI) in Canada, Maintenance Control Index in Japan, MCI, tc. (Li N. et al., 1997). This methodology has a certain application value. But the

24、regression technique has its own deficiency; so it is difficult to truly reflect the internal complex nonlinear relationships through the specific regression relation established by this methodology. Furthermore, its adaptability is subject to certain restrictions. Based on the foregoing, this paper

25、 explores the design of Back-Propagation (BP) neural network and the selection of pavement performance assessment indices as basis of assessing pavement performance. The paper also provides an example of applying the BP neural network for pavement performance assessment. 2. BACK-PROPAGATION NEURAL N

26、ETWORK In the 80s of 20th century, headed by Rumelhart and McClelland, experts put forward the BP algorithm of Multilayer Feed forward Neural Networks (MFNN). It is a study process with supervision and also an application of Gradient Descent in MFNN (XU Li-na, 2003). BP networks excel at data modeli

27、ng because of their superior function approximation capabilities (Meier and Tutumluer, 1998). Artificial neural network (ANN), as a highly complex nonlinear dynamical system, has high-dimensionality. In recent successful applications, the use of ANNs was introduced for the analysis of jointed concre

28、te pavement responses under dual-wheel and tri-tandem type aircraft gear loadings (Ceylan et al., 1998 and 2000). An ANN model was verified and validated with the results of the ILLI-SLAB finite element solutions, which were intended to enable pavement engineers to easily incorporate current sophist

29、icated finite element methodology into routine practical design. As a result of this verification analysis, a simplified HMA |E*| prediction model was accordingly developed based on the ANN methodology (Gopalakrishnan et al., 2008). GEOTECHNICAL SPECIAL PUBLICATION NO. 19523. BP NEURAL NETWORK DESIG

30、N FOR PAVEMENT PERFORMANCE ASSESSMENT 3.1. The Selection of Pavement Performance Assessment Index Pavement Performance is a synthesis concept, which characterizes the changing trends of pavement behavior and service function under traffic loading, environmental changes (i.e., moisture variations, te

31、mperature fluctuations, etc), and other influencing factors. In essence performance reflects the different degrees of pavement behavior meeting or adapting to the driving requirements including functional performance and structural performance. Based on the current and ever-growing heavy traffic spe

32、ctrum in China including the traffic channelization and overloaded trucks, this paper will analytical determine the subentry indices that are considered more influential on the asphalt pavement assessment. These indices include International Roughness Index (IRI), Damage Rate (DR), Structure Strengt

33、h Index (SSI), Sideway Force Coefficient (SFC), and Rutting Depth (RD). 3.2. Dimensionless Processing of Pavement Performance Assessment Indices Based on the theoretical discussions above, it is clearly evident that comprehensive evaluation of asphalt pavement performance is affected by many factors

34、. Due to the different dimensions of the subentry indices and different types of dimensions, there is no general or common characteristic of these indices and it is practically difficult to compare them directly when analyzing them. For this reason, it is often recommended to normalize these subentr

35、y indices to some similar dimensionless interval with a common utility function so as to enable comprehensive analysis and obtain accurate results. In this paper, the linear dimensionless method-extremum method was selected and utilized to calculate the pavement performance indices. Comparing to PMI

36、, SSI and SFC belong to the direct index grouping, which means that a larger index in terms of magnitude is better. The IRI, CR, and RD on the other hand belong to the inverse group of indices, which means that the smaller index in magnitude is the better the result in terms of pavement performance.

37、 On this basis, different models were adapted to conduct the dimensionless processing in this paper (JIN Cong, 2001). 3.3. Design of BP Neural Network The three-layer BP neural network designed in this paper is as follow: Input layer is the input variable of the BP network. For comprehensive assessm

38、ent of freeway asphalt pavement performance, the designed number of input cells was five, GEOTECHNICAL SPECIAL PUBLICATION NO. 195 3which corresponded to the five factors affecting comprehensive pavement performance assessment (i.e., IRI, CR, RD, SSI, and SFC). These are shown in Figure 1. For accur

39、ate results, each neural network input index should be processed with a dimensionless method. In this paper, the neural network contains only one hidden layer. The number of hidden layer cells is determined by Experiential Formula (Wang Ai-min et al., 2006). The number of hidden layer cells in BP ne

40、ural network designed in this paper is shown in Table 1. In this table, the corresponding suggested values are put forward. The output layer is the output variable of the neural network. The index for pavement performance comprehensive assessment referred in this paper is the Pavement Management Ind

41、ex (PMI). In accordance with the degrees of pavement conditions, five grades representing five pavement comprehensive performance conditions were utilized (i.e., excellent, good, medium, subordinated, and inferior). Therefore, in the design of the BP neural network, the desired output of output laye

42、r is indicated by five output variables, namely excellent, good, medium, subordinated, or inferior, which correspond to the orthogonal vectors: =1 0 0 0 0=0 1 0 0 0=0 0 1 0 0=0 0 0 1 0 =0 0 0 0 1. The BP neural network structure for asphalt pavement performance comprehensive evaluation is accordingl

43、y shown in Figure 1. FIG. 1. BP neural network structure diagram for pavement performance assessment Table 1. The Construction of Neural Network GEOTECHNICAL SPECIAL PUBLICATION NO. 1954BP neural network number of input layer cells “m” number of hidden layer cells “p” suggested values “p” Number of

44、output layer cells “n” asphalt pavement 5 3-12 6 5 Learning rate is a variable indicator of the quantitative power value produced in every circuit of the BP network in terms of training time. Larger learning rate may cause unsteadiness of the system; smaller learning rate may cause longer training t

45、ime and slower the convergence pace. However, a smaller learning rate tends to minimize network errors due to trough confinement. As a result, a smaller learning rate would generally be desired to ensure system steadiness and error minimization. Thus, a learning rate of 0.01-0.8 was recommended for

46、this study. 4. THE REALIZATION OF PAVEMENT PERFORMANCE ASSESSMENT BASED ON BP NEURAL NETWORK Here in this paper, “Microsoft Visual Basic” Visual was adapted as the programming language for the BP neural network modes for pavement performance comprehensive assessment. Figures 2 and 3 show specific le

47、arning training parameters interface and assessment application interface, respectively. FIG. 2. Learning training interface FIG. 3. Assessment and application of BP neural network interface of BP neural network GEOTECHNICAL SPECIAL PUBLICATION NO. 195 55. THE APPLICATION OF PAVEMENT PERFORMANCE COM

48、PREHENSIVE ASSESSMENT METHODOLOGY BASED ON BP NEURAL NETWORK Pavement performance comprehensive index-Pavement Management Index is mathematically obtained by assigning scores based on the assessed pavement performance. The specific method applied in this study uses a 5-point approach and assigns exc

49、ellent, good, medium, subordinated, and inferior grades to characterize the different pavement performances; with 1 point for each grade. Specific assessment grades and interrelated maintenance management measures are shown in Table 2. Table 2. Maintenance Criteria of PMI Index Assessment Grade inferior subordinated medium good excellent PMI (0,1 (1,2 (2,3 (3,4 (4,5 Maintenanc