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
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.
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
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
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
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
Fig.3
Fig.4
Fig.5
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
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
Fig.6
Fig.7
Fig.8
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
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
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 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
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