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Frontiers of Structural and Civil Engineering

ISSN 2095-2430

ISSN 2095-2449(Online)

CN 10-1023/X

邮发代号 80-968

2019 Impact Factor: 1.68

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Frontiers of Structural and Civil Engineering  2020, Vol. 14 Issue (6): 1476-1491   https://doi.org/10.1007/s11709-020-0670-z
  本期目录
A step forward towards a comprehensive framework for assessing liquefaction land damage vulnerability: Exploration from historical data
Mahmood AHMAD1,2, Xiao-Wei TANG1, Jiang-Nan QIU3(), Feezan AHMAD4, Wen-Jing GU3
1. State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116024, China
2. Department of Civil Engineering, University of Engineering and Technology Peshawar (Bannu Campus), Bannu 28100, Pakistan
3. School of Economics & Management, Dalian University of Technology, Dalian 116024, China
4. Department of Civil Engineering, Abasyn University, Peshawar 25000, Pakistan
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Abstract：

The unprecedented liquefaction-related land damage during earthquakes has highlighted the need to develop a model that better interprets the liquefaction land damage vulnerability (LLDV) when determining whether liquefaction is likely to cause damage at the ground’s surface. This paper presents the development of a novel comprehensive framework based on select case history records of cone penetration tests using a Bayesian belief network (BBN) methodology to assess seismic soil liquefaction and liquefaction land damage potentials in one model. The BBN-based LLDV model is developed by integrating multi-related factors of seismic soil liquefaction and its induced hazards using a machine learning (ML) algorithm-K2 and domain knowledge (DK) data fusion methodology. Compared with the C4.5 decision tree-J48 model, naive Bayesian (NB) classifier, and BBN-K2 ML prediction methods in terms of overall accuracy and the Cohen’s kappa coefficient, the proposed BBN K2 and DK model has a better performance and provides a substitutive novel LLDV framework for characterizing the vulnerability of land to liquefaction-induced damage. The proposed model not only predicts quantitatively the seismic soil liquefaction potential and its ground damage potential probability but can also identify the main reasons and fault-finding state combinations, and the results are likely to assist in decisions on seismic risk mitigation measures for sustainable development. The proposed model is simple to perform in practice and provides a step toward a more sophisticated liquefaction risk assessment modeling. This study also interprets the BBN model sensitivity analysis and most probable explanation of seismic soil liquefied sites based on an engineering point of view.
Key words： Bayesian belief network    liquefaction-induced damage potential    cone penetration test    soil liquefaction    structural learning and domain knowledge
收稿日期: 2019-06-26      出版日期: 2021-01-12
Corresponding Author(s): Jiang-Nan QIU   
 引用本文:   
. [J]. Frontiers of Structural and Civil Engineering, 2020, 14(6): 1476-1491.
Mahmood AHMAD, Xiao-Wei TANG, Jiang-Nan QIU, Feezan AHMAD, Wen-Jing GU. A step forward towards a comprehensive framework for assessing liquefaction land damage vulnerability: Exploration from historical data. Front. Struct. Civ. Eng., 2020, 14(6): 1476-1491.
 链接本文:  
https://academic.hep.com.cn/fsce/CN/10.1007/s11709-020-0670-z
https://academic.hep.com.cn/fsce/CN/Y2020/V14/I6/1476
Fig.1  
Fig.2  
case earthquake sounding site Ds (m) Dw (m) Fc (%) M amax (g) Ts (m) s'v (kPa) Ic qc1ncs liquefaction LPI LLDV references
1 1989 M= 7.0 Loma Prieta-Oct 18 Leonardini 39 (LEN-39) 2.9 1.9 11 7 0.17 2.6 43 2.00 48.97 yes 9 hhigh Bennet & Tinsley [30], Toprak & Holzer [6], Moss et al. [2]
2 1989 M= 7.0 Loma Prieta-Oct 18 Marinovich 67 (MRR-67) 6.5 6.2 15 7 0.28 1.2 115 1.75 143.89 no 0.1 little to none Bennet & Tinsley [30], Toprak & Holzer [6], Moss et al. [2]
3 1989 M= 7.0 Loma Prieta-Oct 18 McGowan Farm 136 (MCG-136) 4.0 2.4 15 7 0.26 3.1 58 2.09 75.47 no 5.6 moderate Bennet & Tinsley [30], Toprak & Holzer [6], Moss et al. [2]
4 1989 M= 7.0 Loma Prieta-Oct 18 Model Airport 18 (AIR-18) 2.8 2.4 23 7 0.29 1.6 47 2.40 60.71 yes 13.4 high Bennet & Tinsley [30], Toprak & Holzer [6], Moss et al. [2]
5 1989 M= 7.0 Loma Prieta-Oct 18 Model Airport 21 (AIR-21) 2.6 2.4 5 7 0.29 1.2 45 2.15 48.61 yes 10.9 high Bennet & Tinsley [30], Toprak & Holzer [6], Moss et al. [2]
6 1999 M= 7.6 Chi-Chi-Sep 20 Nantou Site C3 (CPT-3) 2.5 1.0 38 7.6 0.38 1 31 2.28 67.75 yes 7.3 high PEER 2000 [31], Moss et al. [2,32]
7 1999 M= 7.6 Chi-Chi-Sep 20 Nantou Site C7 (CPT-7) 3.5 1.0 60 7.6 0.38 2 40 2.46 44.77 yes 36.2 very high PEER 2000 [31], Moss et al. [2,32]
8 1999 M= 7.6 Chi-Chi-Sep 20 Nantou Site C8 (CPT-8) 7.0 1.0 65 7.6 0.38 4 72 2.52 92.05 yes 18 high PEER 2000 [31], Moss et al. [2,32]
9 1999 M= 7.6 Chi-Chi-Sep 20 WuFeng Site C (WBC-6) 4.5 1.2 14 7.6 0.6 1 51 2.35 103.88 yes 27.7 very high PEER 2000 [31], Moss et al. [2,32]
10 1999 M= 7.6 Chi-Chi-Sep 20 Yanlin Site C22 (CPT-22) 3.5 1.1 27 7.6 0.25 1.4 41 2.05 57.81 yes 28 very high PEER 2000 [31], Moss et al. [2,32]
11 1999 M= 7.6 Chi-Chi-Sep 20 Yanlin Site C25 (CPT-25) 5.5 3.5 61 7.6 0.25 3 79 2.47 58.98 yes 17.9 high PEER 2000 [31], Moss et al. [2,32]
12 1999 M= 7.6 Chi-Chi-Sep 20 Yanlin Site C32 (CPT-32) 6.0 0.7 32 7.6 0.25 3 61 2.11 64.13 yes 24.7 very high PEER 2000 [31], Moss et al. [2,32]
13 1999 M= 7.6 Chi-Chi-Sep 20 Yanlin Site C4 (CPT-4) 4.5 0.7 45 7.6 0.25 3 46 2.27 82.10 yes 23.1 very high PEER 2000 [31], Moss et al. [2,32]
14 1999 M= 7.4 Kocaeli-Aug 17 Adapazari Site B (CPT-B1) 3.8 3.3 35 7.4 0.4 1 63 1.84 88.82 yes 5.2 moderate PEER 2000 [33], Sancio [34], Moss et al. [2,32], Bray et al. [35]
15 1999 M= 7.4 Kocaeli-Aug 17 Adapazari Site C2 (CPT-C4) 3.7 0.4 35 7.4 0.4 0.7 37 2.13 72.07 yes 22 high PEER 2000 [33], Sancio [34], Moss et al. [2,32], Bray et al. [35]
16 1999 M= 7.4 Kocaeli-Aug 17 Adapazari Site D (CPT-D1) 2.2 1.5 65 7.4 0.4 0.7 32 2.36 47.65 yes 15.8 very high PEER 2000 [33], Sancio [34], Moss et al. [2,32], Bray et al. [35]
17 1999 M= 7.4 Kocaeli-Aug 17 Adapazari Site E (CPT-E1) 2.3 0.5 2 7.4 0.4 1.5 25 2.05 54.84 yes 24.5 very high PEER 2000 [33], Sancio [34], Moss et al. [2,32], Bray et al. [35]
18 1999 M= 7.4 Kocaeli-Aug 17 Adapazari Site G (CPT-G1) 2.1 0.5 65 7.4 0.4 1.2 23 1.90 61.72 yes 38.8 very high PEER 2000 [33], Sancio [34], Moss et al. [2,32], Bray et al. [35]
19 1999 M= 7.4 Kocaeli-Aug 17 Adapazari Site J (CPT-J2) 2.5 0.6 82 7.4 0.4 2 28 2.34 59.07 yes 24.6 very high PEER 2000 [33], Sancio [34], Moss et al. [2,32], Bray et al. [35]
20 1989 M= 7.0 Loma Prieta-Oct 18 Miller Farm (CMF-3) 5.8 4.9 27 7 0.36 3 97 2.10 52.73 yes 11.8 high Bennet & Tinsley [30], Toprak & Holzer [6], Moss et al. [2]
21 1989 M= 7.0 Loma Prieta-Oct 18 Miller Farm (CMF-5) 6.7 4.9 13 7 0.36 2.5 100 1.83 88.08 yes 17.9 very high Bennet & Tinsley [30], Toprak & Holzer [6], Moss et al. [2]
22 1989 M= 7.0 Loma Prieta-Oct 18 Miller Farm (CMF-10) 8.2 3 20 7 0.36 2.7 102 2.06 93.37 no 5.1 moderate Bennet & Tinsley [30], Toprak & Holzer [6], Moss et al. [2]
23 1989 M= 7.0 Loma Prieta-Oct 18 Farris Farm (FAR-58) 7.4 4.8 4 7 0.36 4.5 111 1.72 110.46 yes 7.8 moderate Bennet & Tinsley [30], Toprak & Holzer [6], Moss et al. [2]
24 1989 M= 7.0 Loma Prieta-Oct 18 Farris Farm (FAR-59) 8.0 4.8 7 7 0.36 5 116 1.79 95.96 yes 10 high Bennet & Tinsley [30], Toprak & Holzer [6], Moss et al. [2]
25 1999 M= 7.4 Kocaeli-Aug 17 Adapazari Site F (CPT-F1) 2.4 0.5 42 7.4 0.4 1.2 26 1.80 72.06 yes 34.5 very high PEER 2000 [33], Sancio [34], Moss et al. [2,32], Bray et al. [35]
26 1989 M= 7.0 Loma Prieta-Oct 18 Radovich 98 (RAD-98) 5.3 3.5 9 7 0.28 4 79 1.86 100.31 no 7.1 moderate Bennet & Tinsley [30], Toprak & Holzer [6], Moss et al. [2]
27 1989 M= 7.0 Loma Prieta-Oct 18 Miller Farm (CMF-8) 6.0 4.9 25 7 0.36 3.7 99 2.03 63.76 yes 16.1 high Bennet & Tinsley [30], Toprak & Holzer [6], Moss et al. [2]
28 1989 M= 7.0 Loma Prieta-Oct 18 Leonardini 37 (LEN-37) 4.9 2.5 12 7 0.17 5 67 2.08 59.84 no 8.2 little to none Bennet & Tinsley [30], Toprak & Holzer [6], Moss et al. [2]
29 1994 M= 6.7 Northridge-Jan 17 Balboa Blvd Unit C (BAL-10) 8.9 7.2 50 6.7 0.84 1.5 147 2.33 128.42 yes 1.7 little to none Bennet et al. [36], Holzer et al. [37], Moss et al. [32], Cetin [38]
summary of the testing data set case history records
30 1989 M= 7.0 Loma Prieta-Oct 18 Salinas R. Bridge 117 (SRB-117) 7.2 6.4 9 7 0.12 1.6 123 2.28 95.24 no 0 little to none Bennet & Tinsley [30], Moss et al. [2]
31 1999 M= 7.6 Chi-Chi-Sep 20 WuFeng Site B (WBC-1) 3.0 1.1 35 7.6 0.6 2 36 2.16 78.71 yes 26.1 very high PEER 2000 [31], Moss et al. [2,32],
32 1999 M= 7.6 Chi-Chi-Sep 20 Yanlin Site C19 (CPT-19) 5.0 0.6 54 7.6 0.25 1 50 2.39 64.26 yes 19.3 very high PEER 2000 [31], Moss et al. [2,32]
33 1999 M= 7.6 Chi-Chi-Sep 20 Yanlin Site C24 (CPT-24) 6.5 1.2 30 7.6 0.25 2.6 69 2.09 65.73 yes 32.9 very high PEER 2000 [31], Moss et al. [2,32]
34 1989 M= 7.0 Loma Prieta-Oct 18 Farris Farm (FAR-61) 7.4 4.2 11 7 0.36 5 105 1.90 89.91 yes 13.1 high Bennet & Tinsley [30], Toprak & Holzer [6], Moss et al. [2]
35 1999 M= 7.4 Kocaeli-Aug 17 Adapazari Site H (CPT-H1) 2.5 1.7 15 7.4 0.4 1 37 2.10 62.18 yes 14.1 high PEER 2000 [33], Sancio [34], Moss et al. [2,32], Bray et al. [35]
Tab.1  
category factors of seismic soil liquefaction number of grade explanation range
seismic parameter earthquake magnitude, M 4 super
big
strong
medium		 8≤M
7≤M<8
6≤M<7
4.5≤M<6
peak ground acceleration, amax (g) 4 super
high
medium
low		 0.40≤amax
0.30 ≤amax<0.40
0.15≤amax<0.30
0 ≤amax<0.15
soil parameter fines content, Fc (%) 3 many
medium
less		 50<Fc
30<Fc≤ 50
0≤ Fc≤30
equivalent clean sand penetration resistance, qc1Ncs 4 super
big
medium
small		 135 ≤qc1Ncs
90 ≤qc1Ncs<135
45 ≤qc1Ncs<90
0 ≤qc1Ncs<45
soil behavior type index, Ic 4 gravelly sand to dense sand
clean sand
silty sand or sand with silt
sandy silt		 Ic<1.31
1.31≤Ic<1.61
1.61≤Ic<2.40
2.40 ≤Ic<2.60
site condition vertical effective stress, s'v (kPa) 4 super
big
medium
small		 150 ≤s'v
100≤s'v< 150
50 ≤s'v<100
0 ≤s'v<50
groundwater table depth, Dw (m) 3 deep
medium
shallow		 4≤Dw
2 <Dw<4
Dw≤2
depth of soil deposit, Ds (m) 3 deep
medium
shallow		 10 ≤Ds< 20
5≤Ds<10
0 ≤Ds<5
thickness of soil layer, Ts (m) 3 thick
medium
thin		 10≤Ts
5≤Ts<10
0 <Ts<5
liquefaction state and its land damage vulnerability liquefaction potential (LP) 2 no
yes		 0
1
liquefaction potential index (LPI) 4 very low
low
high
very high		 0
0<LPI≤5
5 <LPI≤15
15 <LPI
liquefaction land damage vulnerability (LLDV) 5 little to none
low
moderate
high
very high		 –
–
–
–
–
Tab.2  
grade explanation of land damage status
little to non No expression of liquefaction. There is no ground failure and sand boils phenomenon.
low Few sand boils with volume lesser than 0.2 m3, but there is no ground failure.
moderate Medium sand boils with volume lesser than 0.5 m3 and few ground cracks occur, but there is no lateral spreading.
high Frequent sand boils phenomenon with volume lesser than 1.0 m3, undulations and moderate ground cracks and lateral spreading occur.
very high Serious sand boils phenomenon with volume may larger than 1.0 m3, widespread surface largely ground cracks, lateral spreading and severe settlements of structures and damage to services.
Tab.3  
Fig.3  
Fig.4  
method description
C4.5 decision tree (DT)-J48 A C4.5 decision tree (DT)-J48 [39] recursively partitions the training data by means of attribute splits and generates a pruned or unpruned tree using the information-theoretical concept of entropy.
Naive Bayesian (NB) classifier Naive Bayesian classifier [40] is designed to predict accurately the class of test cases and in which the training cases contain class information using kernal estimation.
K2 machine learning K2 [17] adds arcs with a fixed topological ordering of variables. In this method, the ordering is initially set as a naïve Bayes network where the target class variable is fixed the first in the ordering.
Tab.4  
kappa statistic interpretation
0.81–1.00 almost perfect
0.61–0.80 substantial
0.41–0.60 moderate
0.21–0.40 fair
0.00–0.20 slight
-1.00–0.00 poor
Tab.5  
method data set correctly classified classes incorrectly classified classes overall accuracy (%) kappa statistic
BBN-K2 and DK training 26 3 89.667 0.853
testing 5 1 83.333 0.739
C4.5 (DT)-48 training 25 4 86.207 0.804
testing 5 1 83.333 0.739
BBN-K2 training 24 5 82.759 0.751
testing 4 2 66.667 0.500
Naive Bayesian (NB) classifier training 25 4 86.207 0.804
testing 5 1 83.333 0.714
Tab.6  
Fig.5  
Fig.6  
Fig.7  
Fig.8  
Fig.9  
node mutual info percent variance of beliefs
liquefaction land damage vulnerability 2.32133 100.000000 0.6397658
liquefaction potential index 0.00139 0.059900 0.0000866
groundwater table 0.00072 0.030900 0.0000429
liquefaction potential 0.00070 0.030100 0.0000408
peak ground acceleration 0.00049 0.020900 0.0000300
soil behavior type index 0.00047 0.020300 0.0000277
depth of soil deposit 0.00028 0.012200 0.0000168
vertical effective stress 0.00024 0.010400 0.0000146
equivalent clean sand penetration resistance 0.00015 0.006330 0.0000088
thickness of soil layer 0.00009 0.003820 0.0000050
earthquake magnitude 0.00002 0.000764 0.0000010
fines content 0.00001 0.000272 0.0000004
Tab.7  
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