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Front. Struct. Civ. Eng.    2020, Vol. 14 Issue (2) : 487-500    https://doi.org/10.1007/s11709-020-0609-4
RESEARCH ARTICLE
Assessing artificial neural network performance for predicting interlayer conditions and layer modulus of multi-layered flexible pavement
Lingyun YOU1,2, Kezhen YAN1(), Nengyuan LIU1
1. College of Civil Engineering, Hunan University, Changsha 410082, China
2. Department of Civil and Environmental Engineering, Michigan Technological University, Houghton, MI 49931, USA
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Abstract

The objective of this study is to evaluate the performance of the artificial neural network (ANN) approach for predicting interlayer conditions and layer modulus of a multi-layered flexible pavement structure. To achieve this goal, two ANN based back-calculation models were proposed to predict the interlayer conditions and layer modulus of the pavement structure. The corresponding database built with ANSYS based finite element method computations for four types of a structure subjected to falling weight deflectometer load. In addition, two proposed ANN models were verified by comparing the results of ANN models with the results of PADAL and double multiple regression models. The measured pavement deflection basin data was used for the verifications. The comparing results concluded that there are no significant differences between the results estimated by ANN and double multiple regression models. PADAL modeling results were not accurate due to the inability to reflect the real pavement structure because pavement structure was not completely continuous. The prediction and verification results concluded that the proposed back-calculation model developed with ANN could be used to accurately predict layer modulus and interlayer conditions. In addition, the back-calculation model avoided the back-calculation errors by considering the interlayer condition, which was barely considered by former models reported in the published studies.
Keywords asphalt pavement      interlayer conditions      finite element method      artificial neural network      back-calculation     
Corresponding Author(s): Kezhen YAN   
Just Accepted Date: 21 February 2020   Online First Date: 09 April 2020    Issue Date: 08 May 2020
 Cite this article:   
Lingyun YOU,Kezhen YAN,Nengyuan LIU. Assessing artificial neural network performance for predicting interlayer conditions and layer modulus of multi-layered flexible pavement[J]. Front. Struct. Civ. Eng., 2020, 14(2): 487-500.
 URL:  
https://academic.hep.com.cn/fsce/EN/10.1007/s11709-020-0609-4
https://academic.hep.com.cn/fsce/EN/Y2020/V14/I2/487
type layer elastic modulus (MPa) thickness (m) Poisson’s ratio
A AC 1000 0.1 0.25
base 1000 0.2 0.25
subgrade 100 infinite 0.35
B AC 3000 0.1 0.25
base 1500 0.2 0.25
subgrade 100 infinite 0.35
C AC 5000 0.3 0.25
base 1000 0.2 0.25
subgrade 150 infinite 0.35
D AC 5000 0.3 0.25
base 1500 0.2 0.25
subgrade 150 infinite 0.35
Tab.1  Geometrical and materials properties of pavement
Fig.1  Illustration of ANSYS model and the comparison of the vertical displacement with the Ref. [66]. (a) Boundry conditions, circle loading, and element meshes; (b) comparison of the verticle displacement (r=0.0 m).
Fig.2  Asphalt pavement deflection at different interlayer conditions. (a) Type-A; (b) Type-B; (c) Type-C; (d) Type-D.
layer elastic modulus (MPa) thickness (m) Poisson’s ratio interlayer condition
AC 1000 to 21000 0.1 to 0.3 0.25 six types of interlayer condition characterized by the friction coefficient of 0.2, 0.6, 0.8, 1.0, 5.0, and 7.0.
base 500 to 4000 0.2 to 0.5 0.25
subgrade 50 to 300 infinite 0.35
Tab.2  Summary of pavement parameters used in back-calculations
item Model-1 Model-2 Model-3
training functions trainlm traingdm trainoss
adaption learning functions learngdm learngdm learngdm
number of layers 3 2 3
number of neurons 10 15 15
transfer function tansig tansig tansig
trainparam epochs 5000 2000 3000
trainparam goal 1E–5 1E–5 1E–5
trainparam IR 0.1 0.1 0.1
trainParam Max_air 5 7 5
trainParam Min_grad 1E–9 1E–9 1E–9
Tab.3  Training parameter setting for back-calculation of interlayer conditions
statistical criteria R2 Se/Sy
very poor ≤0.19 ≥0.90
poor 0.20–0.39 0.76–0.90
fair 0.40–0.69 0.56–0.75
good 0.79–0.89 0.36–0.55
excellent ≥0.90 ≤0.35
Tab.4  Statistical criteria for the correlation between measured and predicted values [77]
friction coefficient Model-1 Model-2 Model-3
R2 Se/Sy R2 Se/Sy R2 Se/Sy
training group 0.9215 0.451 0.8909 0.515 0.8813 0.521
verification group 0.9152 0.483 0.8808 0.532 0.8753 0.546
Tab.5  Model evaluation results of interlayer friction coefficients
Fig.3  Training process diagram of the friction coefficient back-calculation model. (a) Model-1; (b) Model-2; (c) Model-3.
Fig.4  Back-calculation results of the interlayer friction coefficient (training group). (a) Model-1; (b) Model-2; (c) Model-3.
Fig.5  Back-calculation results of the interlayer friction coefficient (verification group). (a) Model-1; (b) Model-2; (c) Model-3.
item Model-1 Model-2 Model-3
training function trainlm traingdm trainoss
adaption learning function learngdm learngdm learngdm
number of layers 3 2 3
number of neurons 10 15 15
transfer function tansig tansig tansig
trainparam epochs 5000 2000 3000
trainparam goal 1E–7 1E–7 1E–7
trainparam IR 0.01 0.01 0.01
trainparam Max_air 6 8 6
trainparam Min_grad 1E–10 1E–10 1E–10
Tab.6  Training parameter setting for the back-calculation of AC layer modulus
AC layer modulus Model-1 Model-2 Model-3
R2 Se/Sy R2 Se/Sy R2 Se/Sy
training group 0.9828 0.167 0.9716 0.204 0.9605 0.305
verification group 0.9706 0.195 0.9669 0.245 0.9548 0.345
Tab.7  Model evaluation results for the back-calculation of AC layer modulus
Fig.6  Training process diagram of AC layer modulus back-calculation model. (a) Model-1; (b) Model-2; (c) Model-3.
Fig.7  Back-calculation results of AC layer modulus (training group). (a) Model-1; (b) Model-2; (c) Model-3.
Fig.8  Back-calculation results of AC layer modulus (verification group). (a) Model-1; (b) Model-2; (c) Model-3.
pavement
structure		 the distance between sensor position and loading center
0.0 m 0.3 m 0.6 m 0.9 m 1.2 m 1.5 m 2.1 m
A 298 244 175 122 84 59 38
B 338 277 194 134 93 65 40
C 151 132 105 82 64 48 30
D 193 162 126 98 74 56 36
E 193 159 115 82 57 42 28
Tab.8  First survey of the deflection basin data (mm)
pavement
structure		 the distance between sensor position and loading center
0.0 m 0.3 m 0.6 m 0.9 m 1.2 m 1.5 m 2.1 m
A 295 242 177 117 80 59 39
B 332 276 197 128 85 63 41
C 173 147 119 87 65 49 31
D 201 165 134 97 73 56 35
E 188 157 117 81 56 41 28
Tab.9  Second survey of the deflection basin data (mm)
pavement structure PADAL multiple regression ANN model-1 ANN model-2
layer modulus (MPa) K (MN/m3) layer modulus (MPa) friction coefficient layer modulus (MPa) friction coefficient layer modulus (MPa)
A 3900 20 12100 0.23 12850 0.25 12685
B 4500 10 18900 0.2 19447 0.21 19630
C 12000 200 18000 0.68 18568 0.76 18855
D 4600 10 11000 0.2 11430 0.26 11359
E 5100 10 18600 0.23 19057 0.2 18925
Tab.10  Pavement parameters back-calculation results of the first survey
pavement structure PADAL multiple regression ANN model-1 ANN model-2
layer modulus (MPa) K (MN/m3) layer modulus (MPa) friction coefficient layer modulus (MPa) friction coefficient layer modulus (MPa)
A 3900 190 11300 0.73 11550 0.65 11585
B 3500 50 12100 0.28 12700 0.26 12830
C 11000 10000 12900 2.8 13490 2.6 13555
D 4800 190 12700 0.67 12900 0.72 12859
E 5900 100 14500 0.45 15250 0.51 15325
Tab.11  Pavement parameters back-calculation results of the second survey
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