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Front. Struct. Civ. Eng.    2021, Vol. 15 Issue (2) : 506-519    https://doi.org/10.1007/s11709-021-0677-0
RESEARCH ARTICLE
Predicting lateral displacement caused by seismic liquefaction and performing parametric sensitivity analysis: Considering cumulative absolute velocity and fine content
Nima PIRHADI1(), Xiaowei TANG2, Qing YANG2, Afshin ASADI3, Hazem Samih MOHAMED1
1. School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu 610500, China
2. State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116024, China
3. Civil Engineering Discipline, Department of Engineering, International College of Auckland, Auckland 1010, New Zealand
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Abstract

Lateral displacement due to liquefaction (DH) is the most destructive effect of earthquakes in saturated loose or semi-loose sandy soil. Among all earthquake parameters, the standardized cumulative absolute velocity (CAV5) exhibits the largest correlation with increasing pore water pressure and liquefaction. Furthermore, the complex effect of fine content (FC) at different values has been studied and demonstrated. Nevertheless, these two contexts have not been entered into empirical and semi-empirical models to predict DH. This study bridges this gap by adding CAV5 to the data set and developing two artificial neural network (ANN) models. The first model is based on the entire range of the parameters, whereas the second model is based on the samples with FC values that are less than the 28% critical value. The results demonstrate the higher accuracy of the second model that is developed even with less data. Additionally, according to the uncertainties in the geotechnical and earthquake parameters, sensitivity analysis was performed via Monte Carlo simulation (MCS) using the second developed ANN model that exhibited higher accuracy. The results demonstrated the significant influence of the uncertainties of earthquake parameters on predicting DH.
Keywords lateral spreading displacement      cumulative absolute velocity      fine content      artificial neural network      sensitivity analysis      Monte Carlo simulation     
Corresponding Author(s): Nima PIRHADI   
Just Accepted Date: 26 March 2021   Online First Date: 30 April 2021    Issue Date: 27 May 2021
 Cite this article:   
Nima PIRHADI,Xiaowei TANG,Qing YANG, et al. Predicting lateral displacement caused by seismic liquefaction and performing parametric sensitivity analysis: Considering cumulative absolute velocity and fine content[J]. Front. Struct. Civ. Eng., 2021, 15(2): 506-519.
 URL:  
https://academic.hep.com.cn/fsce/EN/10.1007/s11709-021-0677-0
https://academic.hep.com.cn/fsce/EN/Y2021/V15/I2/506
Fig.1  Flowchart of the process for performing MCS basis on ANN model.
parameter min value mean value max value std. deviation
Mw 6.4 7.18 7.9 0.45
W (%) 1.64 10.25 56.8 8.78
T15 (m) 0.2 8.78 16.7 4.81
F15 (%) 0 16.57 70 13.11
D5015 (mm) 0.036 0.35 1.98 0.4
CAV5 (m/s) 3.7 14.58 27.85 3.14
Tab.1  Features of main case history data set that was used to develop ANN1 model
parameter min value mean value max value std. deviation
Mw 6.5 7.27 7.9 0.39
W (%) 1.64 9.82 56.8 9.01
T15 (m) 0.5 9.83 16.7 4.46
F15 (%) 0 11.89 27 6.67
D5015 (mm) 0.086 0.4 1.98 0.42
CAV5 (m/s) 3.7 15.09 24.9 2.58
Tab.2  Features of second data set with F15<28% that was used to develop ANN2 model
parameter min value mean value max value std. deviation
Mw 6.4 7.07 7.5 0.48
W (%) 1.85 10.8 41.38 9.02
T15 (m) 0.5 8.43 16 4.94
F15 (%) 2 18.71 48 13.8
D5015 (mm) 0.071 0.43 1.98 0.56
CAV5 (m/s) 3.7 14.3 24.9 3.95
Tab.3  Features of testing subset of the main case history data set that was used to develop ANN1 model
parameter min value mean value max value std. deviation
Mw 6.4 7.19 7.9 0.47
W (%) 2.03 11.36 56.8 9.28
T15 (m) 0.2 8.61 16 5.04
F15 (%) 2 18.7 70 15.13
D5015 (mm) 0.036 0.34 1.98 0.39
CAV5 (m/s) 3.7 14.87 27.85 3.47
Tab.4  Features of validating subset of the main case history data set that was used to develop ANN1 model
parameter min value mean value max value std. deviation
Mw 6.4 7.2 7.9 0.45
W (%) 1.64 9.88 41.38 7.49
T15 (m) 0.5 8.85 16.7 4.78
F15 (%) 0 15.71 54 12.2
D5015 (mm) 0.078 0.33 1.98 0.36
CAV5 (m/s) 3.7 14.62 16.28 2.79
Tab.5  Features of training subset of the main case history data set that was used to develop ANN1 model
parameter min value mean value max value std. deviation
Mw 6.5 7.25 7.5 0.38
W (%) 1.85 10.9 41.38 9.51
T15 (m) 0.5 10.31 16 4.64
F15 (%) 2 12.16 25 6.62
D5015 (mm) 0.09 0.39 1.98 0.46
CAV5 (m/s) 3.7 14.9 16.28 2.86
Tab.6  Features of testing subset of the main case history data set that was used to develop ANN2 model
parameter min value mean value max value std. deviation
Mw 6.5 7.27 7.5 0.41
W (%) 2.05 9.42 41.38 9.43
T15 (m) 0.5 9.27 16 4.57
F15 (%) 2 12.36 26 7.06
D5015 (mm) 0.036 0.35 1.98 0.39
CAV5 (m/s) 3.7 14.87 27.85 3.77
Tab.7  Features of validating subset of the main case history data set that was used to develop ANN2 model
parameter min value mean value max value std. deviation
Mw 6.5 7.27 7.9 0.39
W (%) 1.64 9.67 56.8 8.87
T15 (m) 0.5 9.84 16.7 4.42
F15 (%) 0 11.73 27 6.66
D5015 (mm) 0.086 0.39 1.98 0.4
CAV5 (m/s) 3.7 15.19 24.9 2.47
Tab.8  Features of training subset of the main case history data set that was used to develop ANN2 model
Fig.2  Structure of three-layered MLPs.
data R
training 0.92
testing 0.89
validating 0.90
all 0.90
Tab.9  Certificates of ANN1 model for the whole data set
data R
training 0.95
testing 0.92
validating 0.89
all 0.91
Tab.10  Certificate of ANN2 model for data set with F15≤28%
sample no Mw R (km) W% S (%) T15 (m) F15 (%) D5015(mm) PGA (g) CAV5 (m/s) DH (cm)
1 7.6 5 7.4 0 0.5 20.8 0.11 0.67 45.226 0
2 7.6 5 13.7 0 0.8 20.8 0.11 0.67 45.226 0.45
3 7.6 5 18.4 0 0.8 20.8 0.11 0.67 45.226 0.55
4 7.6 5 25.2 0 0.8 20.8 0.11 0.67 45.226 0.8
5 7.6 5 37.3 0 0.8 20.8 0.11 0.67 45.226 1.05
6 7.6 5 49.9 0 0.8 20.8 0.11 0.67 45.226 2.05
7 7.6 5 5.7 0 0.5 13 0.18 0.67 45.226 0
8 7.6 5 6.6 0 0.75 13 0.18 0.67 45.226 0.1
9 7.6 5 7.9 0 0.75 13 0.18 0.67 45.226 0.17
10 7.6 5 9 0 0.75 13 0.18 0.67 45.226 0.23
11 7.6 5 15 0 0.75 13 0.18 0.67 45.226 0.29
12 7.6 5 21.2 0 0.75 13 0.18 0.67 45.226 0.49
13 7.6 5 11.9 0 1.1 20.8 0.11 0.67 45.226 0
14 7.6 5 26.3 0 1.1 20.8 0.11 0.67 45.226 0
15 7.6 13 5.9 3.8 1.7 22.3 0.12 0.39 24.816 0.05
16 7.6 13 16.2 3.8 1.7 22.3 0.12 0.39 24.816
17 7.6 5 12.2 0 0.45 30 0.13 0.67 45.226
18 7.6 5 14.3 0 0.45 30 0.13 0.67 45.226
19 7.6 5 24.6 0 0.45 30 0.13 0.67 45.226
20 7.6 5 57.7 0 0.45 30 0.13 0.67 45.226
21 7.6 5 8 0 1 31.4 0.1 0.67 45.226
22 7.6 5 10.5 0 1 31.4 0.1 0.67 45.226
23 7.6 5 19 0 1 31.4 0.1 0.67 45.226
24 7.6 5 31.3 0 1 31.4 0.1 0.67 45.226
25 7.6 5 9.6 0 1.8 48.5 0.1 0.67 45.226
26 7.6 5 11.7 0 1.8 48.5 0.1 0.67 45.226
27 7.6 5 13.3 0 1.8 48.5 0.1 0.67 45.226
28 7.6 5 23.7 0 1.8 48.5 0.1 0.67 45.226
Tab.11  Model parameters and measured DH at sites affected by the 1999 Chi-Chi earthquake
performance criteria models used to predict DH
Youd et al. [31] Javadi et al. [27] Rezania et al. [9] first ANN
R 0.514 -0.74 0.433 0.665
MAE 3.77 1.04 0.49 0.35
RSME 4.37 1.19 0.7 0.53
Tab.12  Performance of ANN1 in comparison with additional available models
performance criteria models used to predict DH
Youd et al. [31] Javadi et al. [27] Rezania et al. [9] first ANN second ANN
R 0.934 -0.813 -0.233 0.892 0.9
MAE 4.84 1.2 0.42 0.88 0.28
RSME 5.34 1.3 0.57 0.93 0.37
Tab.13  Performance of ANN2 on the basis of samples with F15≤28%, in comparison with additional available models
Fig.3  Comparison between ANN1 Predicted value for DH (m) and other three models, considering 28 independent samples from Chi-Chi earthquake with whole range of the parameters. (a) Youd et al. [31]; (b) Javadi et al. [27]; (c) Rezania et al. [9]; (d) ANN1 presented in this study.
Fig.4  Comparison between ANN1 and ANN2 Predicted value for DH(m) with other three models considering 16 independent samples from Chi-Chi earthquake with limited range of F15 less than 28%. (a) Youd et al. [31]; (b) Javadi et al. [27]; (c) Rezania et al. [9]; (d) ANN1 presented in this study; (e) ANN2 presented in this study.
input variable statistical parameters distribution function Ref. no.
mean std dev. min max mean COV*
Mw 7.05 0.51 6.4 7.7 0.05 normal distribution [61]
W (%) 20.69 0 1 41.38 0.2 normal distribution –
T15 (m) 6.9 0 0.2 13.6 0.2 normal distribution –
F15 (%) 35 0 0 70 0.2 normal distribution –
D5015 (mm) 0.3125 17.55 0.036 0.589 0.2 normal distribution –
CAV5 (m/s) 9.904 8.5 1.977 17.831 0.1 normal distribution [32]
Tab.14  Statistical aspects of variables used for ANN developed model with DH≤2 m and sensitivity analysis through MCS
Fig.5  The parameters vs. the probability of the logarithm of DH being greater than 1 m. (a) Mw; (b) W%; (c) T15 (m); (d) F15 (%); (e) D5015 (mm); (d) CAV5 (m/s).
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