Toward a numerical-simulation-based liquefaction hazard assessment for urban regions using high-performance computing

Chen, Jian; O-Tani, Hideyuki; Takeyama, Tomohide; Oishi, Satoru; Hori, Muneo

doi:10.1016/j.enggeo.2019.105153

Cited by 10 publications

(2 citation statements)

References 16 publications

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“…As an example, after the Wenchuan earthquake of M 8.0 which struck China in 2008, both surface buildings and subsurface utilities were damaged by liquefaction [1,2,4]. Consequently, estimating the soil liquefaction potential is a significant issue, and must be considered when building civil engineering structures [5][6][7][8][9][10]. Soil liquefaction potential may be measured in a variety of ways, as described in the scientific literature (e.g., [11][12][13]).…”

Section: Introductionmentioning

confidence: 99%

Soil Liquefaction Prediction Based on Bayesian Optimization and Support Vector Machines

Sabri

Al-Bahrani

et al. 2022

Sustainability

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Liquefaction has been responsible for several earthquake-related hazards in the past. An earthquake may cause liquefaction in saturated granular soils, which might lead to massive consequences. The ability to accurately anticipate soil liquefaction potential is thus critical, particularly in the context of civil engineering project planning. Support vector machines (SVMs) and Bayesian optimization (BO), a well-known optimization method, were used in this work to accurately forecast soil liquefaction potential. Before the development of the BOSVM model, an evolutionary random forest (ERF) model was used for input selection. From among the nine candidate inputs, the ERF selected six, including water table, effective vertical stress, peak acceleration at the ground surface, measured CPT tip resistance, cyclic stress ratio (CSR), and mean grain size, as the most important ones to predict the soil liquefaction. After the BOSVM model was developed using the six selected inputs, the performance of this model was evaluated using renowned performance criteria, including accuracy (%), receiver operating characteristic (ROC) curve, and area under the ROC curve (AUC). In addition, the performance of this model was compared with a standard SVM model and other machine learning models. The results of the BOSVM model showed that this model outperformed other models. The BOSVM model achieved an accuracy of 96.4% and 95.8% and an AUC of 0.93 and 0.98 for the training and testing phases, respectively. Our research suggests that BOSVM is a viable alternative to conventional soil liquefaction prediction methods. In addition, the findings of this research show that the BO method is successful in training the SVM model.

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Section: Introductionmentioning

confidence: 99%

Soil Liquefaction Prediction Based on Bayesian Optimization and Support Vector Machines

Sabri

Al-Bahrani

et al. 2022

Sustainability

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“…Liquefaction represents an important indirect phenomenon capable to cause damage to buildings and infrastructure during an earthquake (e.g., [1]). Liquefaction hazard assessment is one of the relevant components of seismic risk analysis as well as risk mitigation planning [2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Regional-Scale Seismic Liquefaction Susceptibility Mapping via an Empirical Approach Validated by Site-Specific Analyses

Zumpano¹,

Pisano²,

Filice³

et al. 2022

Geosciences

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Regional-scale analyses of susceptibility to liquefaction are seldom performed in data-scarce areas. However, in spite of data limitations, such efforts can still provide useful information in case of populated, seismically active regions. The present work focuses on susceptibility zonation for soil liquefaction that might occur due to ground shaking in the Foggia Province, a data-scarce, seismically active area of about 7000 km2 located in southern Italy. The Analytical Hierarchy Process (AHP) approach is used to obtain the susceptibility to liquefaction map of the whole area, while a geological and geotechnical database including 531 boreholes from 84 localities is used for cross-validation. The data are processed by means of a simplified quantitative method to determine liquefaction potential and assess whether a specific area is prone to liquefaction or not. Our results, along with an AUC − ROC = 0.89 test value, indicate that there are widespread areas of medium to high and very high susceptibility, and that the most susceptible zones are localized along the Adriatic Sea coastline and watercourses. The final susceptibility to liquefaction map represents a step forward towards the assessment of secondary seismic hazard in the study area, thus supporting the regional and local administrations responsible for land-use planning and risk mitigation.

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Integrating the LSSVM and RBFNN models with three optimization algorithms to predict the soil liquefaction potential

Cai

Ouaer

Mohammed

et al. 2021

Engineering with Computers

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Toward a numerical-simulation-based liquefaction hazard assessment for urban regions using high-performance computing

Cited by 10 publications

References 16 publications

Soil Liquefaction Prediction Based on Bayesian Optimization and Support Vector Machines

Soil Liquefaction Prediction Based on Bayesian Optimization and Support Vector Machines

Regional-Scale Seismic Liquefaction Susceptibility Mapping via an Empirical Approach Validated by Site-Specific Analyses

Integrating the LSSVM and RBFNN models with three optimization algorithms to predict the soil liquefaction potential

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