Mechanisms to explain soil liquefaction triggering,development,and persistence during an earthquake

Mechanisms have been proposed to explain the triggering,development,and persistence of soil liquefaction.The mechanism explaining the horizontal failure plane(triggering)and its depth below the phreatic surface is governed by the flux properties and effective stress at that plane.At the failure plane,the pore water pressure was higher than the effective stress,and the volume change was the highest.The pore water pressure is a function of the soil profile features(particularly the phreatic zone width)and bedrock motion(horizontal acceleration).The volume change at the failure plane is a function of the intrinsic permeability of the soil and bedrock displacement.The failure plane was predicted to occur during the oscillation with the highest amplitude,disregarding further bedrock motion,which was consistent with low seismic energy densities.Two mechanisms were proposed to explain the persistence of soil liquefaction.The first is the existence of low-permeability layers in the depth range in which the failure planes are predicted to occur.The other allows for the persistence and development of soil liquefaction;it is consistent with homogeneous soils and requires water inflow from bedrock water springs.The latter explains many of the features of soil liquefaction observed during earthquakes,namely,surficial effects,“instant”liquefaction,and the occurrence of short-and long-term changes in the level of the phreatic surfaces.This model(hypothesis),the relationship between the flux characteristics and loss of soil shear strength,provides self-consistent constraints on the depth below the phreatic surfaces where the failure planes are observed(expected to occur).It requires further experimental and observational evidence.Similar reasoning can be used to explain other saturated soil phenomena.

Application of machine learning to the Vs-based soil liquefaction potential assessment

Earthquakes can cause violent liquefaction of the soil, resulting in unstable foundations that can cause serious damage to facilities such as buildings, roads, and dikes. This is a primary cause of major earthquake disasters. Therefore, the discrimination and prediction of earthquake-induced soil liquefaction has been a hot issue in geohazard research. The soil liquefaction assessment is an integral part of engineering practice. This paper evaluated a dataset of 435 seismic sand liquefaction events using machine learning algorithms. The dataset was analyzed using seven potential assessment parameters. Ten machine learning algorithms are evaluated for their ability to assess seismic sand liquefaction potential, including Linear Discriminant Analysis(LDA), Quadratic Discriminant Analysis(QDA), Naive Bayes(NB), KNearest Neighbor(KNN), Artificial Neural Network(ANN), Classification Tree(CT), Support Vector Machine(SVM), Random Forest(RF), e Xtreme Gradient Boosting(XGBoost), Light Gradient Boosting Machine(Light GBM). A 10-fold cross-validation(CV) method was used in the modeling process to verify the predictive performance of the machine learning models. The final percentages of significant parameters that influenced the prediction results were obtained as Cyclic Stress Ratio(CSR) and Shear-Wave Velocity( VS1) with 56% and 38%, respectively. The final machine learning algorithms identified as suitable for seismic sand liquefaction assessment were the CT, RF, XGBoost algorithms, with the RF algorithm performing best.

Comparison of Vs and SPT Soil Liquefaction Assessments of NCEER: Including Hypothesis Testing

Soil liquefaction is one of the complex research topics in geotechnical engineering and engineering geology. Especially after the 1964 Niigata earthquake (Japan) induced many soil liquefaction incidents, a variety of soil liquefaction studies were conducted and reported, including the liquefaction potential assessment methods utilizing the shear wave velocity (Vs) or SPT-N profiles (SPT: standard penetration test). This study used the Vs and SPT methods recommended by the National Center for Earthquake Engineering Research (NCEER) to examine which is more conservative according to the assessment results on 41 liquefiable soil layers at sites in two major cities in Taiwan. Statistical hypothesis testing was used to make the analysis more quantitative and objective. Based on three sets of hypothesis tests, it shows that the hypothesis—the SPT method is more conservative than the Vs method—was not rejected on a 5% level of significance.

Energy-based numerical models for assessment of soil liquefaction

This study presents promising variants of genetic programming （GP）, namely linear genetic programming （LGP） and multi expression programming （MEP） to evaluate the liquefaction resistance of san- dy soils. Generalized LGP and MEP-based relationships were developed between the strain energy density required to trigger liquefaction （capacity energy） and the factors affecting the liquefaction characteristics of sands. The correlations were established based on well established and widely dispersed experimental results obtained from the literature. To verify the applicability of the derived models, they were employed to estimate the capacity energy values of parts of the test results that were not included in the analysis. The external validation of the models was verified using statistical criteria recommended by researchers. Sensitivity and parametric analyses were performed for further verification of the correlations. The results indicate that the proposed correlations are effectively capable of capturing the liquefaction resistance of a number of sandy soils. The developed correlations provide a significantly better prediction performance than the models found in the literature. Furthermore, the best LGP and MEP models perform superior than the optimal traditional GP model. The verification phases confirm the efficiency of the derived correlations for their general application to the assessment of the strain energy at the onset of liquefaction.

Distribution and characteristics of gravelly soil liquefaction in the Wenchuan M_s 8.0 earthquake

In this paper,a distribution map of gravelly soil liquefaction that was caused by the Wenchuan M8.0 earthquake in China is proposed based on a detailed field investigation and an analysis of geological soil profiles. The geological background of the earthquake disaster region is summarized by compiling geological cross sections and borehole logs. Meanwhile,four typical liquefied sites were selected to conduct sample drillings,dynamic penetration tests (DPT),and shear wave velocity tests,to understand the features of liquefied gravelly soil. One hundred and eighteen (118) liquefied sites were investigated shortly after the earthquake. The field investigation showed:(1) sandboils and waterspouts occurred extensively,involving thousands of miles of farmland,120 villages,eight schools and five factories,which caused damage to some rural houses,schools,manufacturing facilities and wells,etc.; (2) the Chengdu plain is covered by a gravelly soil layer with a thickness of 0 m to 541 m according to the geological cross sections; (3) there were 80 gravelly soil liquefied sites in the Chengdu plain,shaped as five belt areas that varied from 20 km to 40 km in length,and about ten gravelly soil liquefied sites distributed within Mianyang area; and (4) the grain sizes of the sampled soil were relative larger than the ejected soil on the ground,thus the type of liquefied soil cannot be determined by the ejected soil. The gravelly soil liquefied sites are helpful in enriching the global database of gravelly soil liquefaction and developing a corresponding evaluation method in further research efforts.

A hybrid approach for evaluating CPT-based seismic soil liquefaction potential using Bayesian belief networks

Discernment of seismic soil liquefaction is a complex and non-linear procedure that is affected by diversified factors of uncertainties and complexity.The Bayesian belief network(BBN)is an effective tool to present a suitable framework to handle insights into such uncertainties and cause–effect relationships.The intention of this study is to use a hybrid approach methodology for the development of BBN model based on cone penetration test(CPT)case history records to evaluate seismic soil liquefaction potential.In this hybrid approach,naive model is developed initially only by an interpretive structural modeling(ISM)technique using domain knowledge(DK).Subsequently,some useful information about the naive model are embedded as DK in the K2 algorithm to develop a BBN-K2 and DK model.The results of the BBN models are compared and validated with the available artificial neural network(ANN)and C4.5 decision tree(DT)models and found that the BBN model developed by hybrid approach showed compatible and promising results for liquefaction potential assessment.The BBN model developed by hybrid approach provides a viable tool for geotechnical engineers to assess sites conditions susceptible to seismic soil liquefaction.This study also presents sensitivity analysis of the BBN model based on hybrid approach and the most probable explanation of liquefied sites,owing to know the most likely scenario of the liquefaction phenomenon.

Simplified Analysis of the Effect of Soil Liquefaction on the Earthquake Pile Response

The time-history response of a structure-pile system during soil liquefaction is highly complicated and several analytical methods have been proposed through the accuracy verification based on the comparison with the experimental works. However, the analytical methods with higher accuracy often require large computational loads and are not necessarily preferred in the actual design practice. On the other hand, while the response spectrum method is not accurate compared to the aforementioned methods, it can provide useful design guidelines in the preliminary stage for structure-pile systems under soil liquefaction with acceptable accuracy. In this paper, the previously proposed response spectrum method for a structure-pile-soil system is used where the effect of soil liquefaction is taken into account by introducing the so-called p-multiplier method. It is shown that, while in the case of inner partial liquefaction with a non-liquefied layer at the top, the demand on the pile moment is large due to the inertial effect of that non-liquefied layer at the top, in the case of overall liquefaction near the ground surface, the demand is smaller than the case of inner partial liquefaction.

A constrained neural network model for soil liquefaction assessment with global applicability

A constrained back propagation neural network(C-BPNN)model for standard penetration test based soil liquefaction assessment with global applicability is developed,incorporating existing knowledge for liquefaction triggering mechanism and empirical relationships.For its development and validation,a comprehensive liquefaction data set is compiled,covering more than 600 liquefaction sites from 36 earthquakes in 10 countries over 50 years with 13 complete information entries.The C-BPNN model design procedure for liquefaction assessment is established by considering appropriate constraints,input data selection,and computation and calibration procedures.Existing empirical relationships for overburden correction and fines content adjustment are shown to be able to improve the prediction success rate of the neural network model,and are thus adopted as constraints for the C-BPNN model.The effectiveness of the C-BPNN method is validated using the liquefaction data set and compared with that of several liquefaction assessment methods currently adopted in engineering practice.The C-BPNN liquefaction model is shown to have improved prediction accuracy and high global adaptability.

Application of machine learning algorithms for the evaluation of seismic soil liquefaction potential

This study investigates the performance of four machine learning(ML)algorithms to evaluate the earthquake-induced liquefaction potential of soil based on the cone penetration test field case history records using the Bayesian belief network(BBN)learning software Netica.The BBN structures that were developed by ML algorithms-K2,hill climbing(HC),tree augmented naive(TAN)Bayes,and Tabu search were adopted to perform parameter learning in Netica,thereby fixing the BBN models.The performance measure indexes,namely,overall accuracy(OA),precision,recall,F-measure,and area under the receiver operating characteristic curve,were used to evaluate the training and testing BBN models’performance and highlight the capability of the K2 and TAN Bayes models over the Tabu search and HC models.The sensitivity analysis results showed that the cone tip resistance and vertical effective stress are the most sensitive factors,whereas the mean grain size is the least sensitive factor in the prediction of seismic soil liquefaction potential.The results of this study can provide theoretical support for researchers in selecting appropriate ML algorithms and improving the predictive performance of seismic soil liquefaction potential models.

Liquefaction proneness of stratified sand-silt layers based on cyclic triaxial tests

Most studies on liquefaction have addressed homogeneous soil strata using sand or sand with fine content without considering soil stratification.In this study,cyclic triaxial tests were conducted on the stratified sand specimens embedded with the silt layers to investigate the liquefaction failures and void-redistribution at confining stress of 100 kPa under stress-controlled mode.The loosening of underlying sand mass and hindrance to pore-water flow caused localized bulging at the sand-silt interface.It is observed that at a silt thickness of 0.2H(H is the height of the specimen),nearly 187 load cycles were required to attain liquefaction,which was the highest among all the silt thicknesses with a single silt layer.Therefore,0.2H is assumed as the optimum silt thickness(t_(opt)).The silt was placed at the top,middle and bottom of the specimen to understand the effect of silt layer location.Due to the increase in depth of the silt layer from the top position(capped soil state)to the bottom,the cycles to reach liquefaction(N_(cyc,L))increased 2.18 times.Also,when the number of silt layers increased from single to triple,there was an increase of about 880%in N_(cyc,L).The micro-characterization analysis of the soil specimens indicated silty materials transported in upper sections of the specimen due to the dissipated pore pressure.The main parameters,including thickness(t),location(z),cyclic stress ratio(CSR),number of silt layers(n)and modified relative density(D_(r,m)),performed significantly in governing the lique-faction resistance.For this,a multilinear regression model is developed based on critical parameters for prediction of N_(cyc,L).Furthermore,the developed constitutive model has been validated using the data from the present study and earlier findings.

Effects of non-liquefiable crust layer and superstructure mass on the response of 2×2 pile groups to liquefaction-induced lateral spreading

In this research,two shake table experiments were conducted to study the effects of non-liquefiable crust layer and superstructure mass on the responses of two sets of 22 pile groups to liquefactioninduced lateral spreading.In this regard,an inclined base layer overlain by a very loose liquefiable layer was constructed in both models;while only in one model,a non-liquefiable crust layer was built.A lumped mass,being representative of a superstructure,was attached to the cap of one pile group in both models.The models were fully instrumented with various sensors,including acceleration,displacement,and pore water pressure transducers.Also,the piles were instrumented with pair strain gauges to measure pure bending moments induced by cyclic and monotonic loadings associated with ground shaking and lateral spreading,respectively.The results showed that the existence of the non-liquefiable crust layer increases both the maximum and residual soil displacements at the free field and also the maximum bending moments in the piles.The results of the experiments indicated that the crust layer induces a high kinematic lateral soil pressure and force on the piles which are not present in the crustless case.The crust layer increases the pile cap displacement before liquefaction,albeit decreases it after liquefaction,due to the elastic rebound of the piles in the liquefiable layer.The crust layer postpones both liquefaction triggering and dissipation of excess pore water pressure.The existence of the superstructure mass on the pile caps decreases the acceleration amplitude of the pile caps,while increases their maximum displacement.

Adaptive element free Galerkin method applied to analysis of earthquake induced liquefaction

An automatically adaptive element free method is presented to analyze the seismic response of liquefiable soils. The method is based on the element free Galerkin method （EFGM） and the fission procedure that is part of h-refinement, indicated by error estimation. In the proposed method, a posteriori error estimate procedure that depends on the energy norm of stress and the T-Belytschko （TB） stress recovery scheme is incorporated. The effective cyclic elasto-plastic constitutive model is used to describe the nonlinear behavior of the saturated soil. The governing equations are established by u-p formulation. The proposed method can effectively avoid the volumetric locking due to large deformation that usually occurs in numerical computations using the finite element method （FEM）. The efficiency of the proposed method is demonstrated by evaluating the seismic response of an embankment and comparing it to results obtained through FEM. It is shown that the proposed method provides an accurate seismic analysis of saturated soil that includes the effects of liquefaction .

Intensity measures for seismic liquefaction hazard evaluation of sloping site

This work investigates the correlation between a large number of widely used ground motion intensity measures(IMs) and the corresponding liquefaction potential of a soil deposit during earthquake loading. In order to accomplish this purpose the seismic responses of 32 sloping liquefiable site models consisting of layered cohesionless soil were subjected to 139 earthquake ground motions. Two sets of ground motions, consisting of 80 ordinary records and 59 pulse-like near-fault records are used in the dynamic analyses. The liquefaction potential of the site is expressed in terms of the the mean pore pressure ratio, the maximum ground settlement, the maximum ground horizontal displacement and the maximum ground horizontal acceleration. For each individual accelerogram, the values of the aforementioned liquefaction potential measures are determined. Then, the correlation between the liquefaction potential measures and the IMs is evaluated. The results reveal that the velocity spectrum intensity(VSI) shows the strongest correlation with the liquefaction potential of sloping site. VSI is also proven to be a sufficient intensity measure with respect to earthquake magnitude and source-to-site distance, and has a good predictability, thus making it a prime candidate for the seismic liquefaction hazard evaluation.

Dynamic p-y curves for vertical and batter pile groups in liquefied sand

In this study,centrifuge model tests of vertical and batter pile groups in liquefied sand were conducted on a centrifuge shaking table.The dynamic p-y curves for these pile groups before and during sand liquefaction were obtained from calculations based on test data.The results confirm that liquefaction contributes to a reduction in the energy consumption of pile foundations,with the degradation effect being more pronounced for batter pile groups.At shallow depths,the difference in the backbone gradients of the p-y curves after liquefaction for vertical and batter pile groups indicates that the lateral stiffness of a batter pile group is greater than that of a vertical pile group.As shaking intensity increases,the lateral stiffness of a vertical pile group increases with depth during the late stage of sand liquefaction.However,the lateral stiffness of a batter pile group during liquefaction does not vary with depth.The results of this study provide a reference for the seismic design of vertical and batter pile groups in liquefied soil.

Numerical analysis on seismic response and failure mechanism of articulated pile−structure system in a liquefiable site from shaking-table experiments

This study investigates the seismic response and failure mode of a pile-structure system in a liquefiable site by employing a numerical simulation model combined with the shaking-table results of a soil-pile-structure dynamic system.The pile and soil responses obtained from the numerical simulations agreed well with the experimental results.The slopes of the dynamic shear-stress-shear-strain hysteretic curves at different positions also exhibited a decreasing trend,indicating that the shear strength of the soil in all parts of the foundation decreased.The peak acceleration of the soil and pile was not clearly amplified in the saturated sand layer but appeared to be amplified in the top part.The maximum bending moments appeared in the middle and lower parts of the pile shaft;however,the shear forces at the corresponding positions were not large.It can be observed from the deformation mode of the pile-group foundation that a typical bending failure is caused by an excessive bending moment in the middle of the pile shaft if the link between the pile top and cap is articulated,and sufficient attention should be paid to the bending failure in the middle of the pile shaft.

A step forward towards a comprehensive framework for assessing liquefaction land damage vulnerability:Exploration from historical data

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 learming(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 liquefed sites based on an engineering point of view.

Evaluation of liquefaction-induced lateral displacement using Bayesian belief networks

Liquefaction-induced lateral displacement is responsible for considerable damage to engineered structures during major earthquakes.Therefore,an accurate estimation of lateral displacement in liquefaction-prone regions is an essential task for geotechnical experts for sustainable development.This paper presents a novel probabilistic framework for evaluating liquefaction-induced lateral displacement using the Bayesian belief network(BBN)approach based on an interpretive structural modeling technique.The BBN models are trained and tested using a wide-range casehistory records database.The two BBN models are proposed to predict lateral displacements for free-face and sloping ground conditions.The predictive performance results of the proposed BBN models are compared with those of frequently used multiple linear regression and genetic programming models.The results reveal that the BBN models are able to learn complex relationships between lateral displacement and its influencing factors as cause-effect relationships,with reasonable precision.This study also presents a sensitivity analysis to evaluate the impacts of input factors on the lateral displacement.