Study regarding typical liquefaction damage during the 2021 Maduo M_(s)7.4 earthquake in China

The most important method of understanding liquefaction-induced engineering failures comes from the investigation and analysis of earthquake damage.In May 2021,the Maduo M_(s)7.4 earthquake occurred on the Tibetan Plateau of China.The most representative engineering disaster caused by this earthquake was bridge damage on liquefied sites.In this study,the mutual relationships between the anti-liquefaction pre-design situation,the ground motion intensity,the site liquefaction severity,and the bridge damage state for this earthquake were systematically analyzed for typical bridge damage on the liquefied sites.Using field survey data and the current Chinese industry code,simulations of the liquefaction scenarios at typical bridge sites were performed for the pre-design seismic ground motion before the earthquake and the seismic ground motion during the earthquake.By combining these results with post-earthquake investigation results,the reason for the serious bridge damage resulting from this earthquake is revealed,and the necessary conditions for avoiding serious seismic damage to bridges built in liquefiable sites is presented.

Seismic Sequence in Carbonate Rocks By Vibrational Liquefaction

On the basis of the records of strong seismic events taking place in soft carbonate sediments, a new seismic sequence system of vibrational liquefaction is established, which consists of a series of units, such as escaped structure of micrite veins and liquefied deformation formed in the period of seismic liquefaction, land subsidence structure after liquefaction, tsunamic hummocky and turbidite produced by seismic events, This sequence is a generalization and summation of field observation in vast areas, which shows the whole process of a strong seismic event and provides an unified theoretical explanation. The pattern of the seismic sequence by vibrational liquefaction provides one of correlation standards for geologists in the field to discriminate events in carbonate sequences. Based on the pattern of seismic sequence, the authors first advance a new conception of the Palaeo-Tanlu (Tancheng-Lujiang) Zone and discuss primarily its geological significations.

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.

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.

Seismic performances of dyke on liquefiable soils

Various field investigations of earthquake disaster cases have confirmed that earthquake-induced liquefaction is a main factor causing significant damage to dyke,research on seismic performances of dyke is thus of great importance.In this paper,seismic responses of dyke on liquefiable soils were investigated by means of dynamic centrifuge model tests and three-dimensional（3D） effective stress analysis method which is based on a multiple shear mechanism model and a liquefaction front.For the prototype scale centrifuge tests,sine wave input motions with peak accelerations 0.806 m/s2,1.790 m/s2 and 3.133 m/s2 of varied amplitudes were adopted to study the seismic performances of dyke on the saturated soil layer foundation with relative density of approximately 30%.Then,corresponding numerical simulations were conducted to investigate the distribution and variations of deformation,acceleration,excess pore-water pressure（EPWP）,and behaviors of shear dilatancy in the dyke and the liquefiable soil foundation.Moreover,detailed discussions and comparisons between numerical simulations and centrifuge tests were also presented.It is concluded that the computed results have a good agreement with the measured results by centrifuge tests.The physical and numerical models both indicate that the dyke hosted on liquefiable soils subjected to earthquake motions has exhibited larger settlement and lateral spread：the stronger the motion is,the larger the dyke deformation is.Compared to soils in the deep ground under the dyke and the free field,the EPWP ratio is much smaller in the shallow liquefiable soil beneath the dyke in spite of large deformation produced.For the same overburden depth soil from free site and the liquefiable foundation beneath dyke,the characteristics of effective stress path and stress-strain relations are different.All these results may be of theoretical and practical significance for seismic design of the dyke on liquefiable soils.

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.

Model test of stone columns as liquefaction countermeasure in sandy soils

The shaking table model test was conducted to investigate earthquake resistant behavior of stone columns under the intensity of an earthquake resistance of buildings is VIII. The test results show that when acceleration is less than 0.20 g, composite foundation is not liquefied, settlement is also small and pile dislocation is not observed; when acceleration is 0.3g, ground outside embankment＇s slope toe is liquefied and ground within stone column composite foundation is not. It is suggesting that reinforcement scale of stone column foundation should be widened properly. The designed stone column composite foundation meets the requirements for seismic resistance.

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.