Changes in soil conditions before and after earthquakes at a repetitive soil liquefaction site in Taiwan

Beishih Village of Hsinhua Township in southern Taiwan is a unique location for studying soil liquefaction. Soil liquefaction was observed at the same site after earthquakes in 1946, 2010, and 2016, each of which had a Richter magnitude greater than six. This recurrence provides an opportunity for analyzing soil condition variations resulting from soil liquefaction. Seismic data sets were collected in 2011, 2014, 2016, and 2017. We used seismic refraction tomography and the multichannel analysis of surface waves to estimate P- and S-wave velocities. In S-wave velocity profiles, low shear velocity zones were located beneath sand volcanoes shortly after two earthquakes and disappeared 4 years after a 2010 earthquake. However, the P-wave velocity is less sensitive to soil condition changes, possibly because groundwater obscures the effect of soil liquefaction on velocity profiles. In addition, we used seismic wave velocities to determine the importance of soil properties such as Poisson’s ratio, shear modulus, and porosity to identify the cause of the low shear velocity zone. Notably, although porosity decreased after soil grain rearrangement, sand and clay mixing increased the Poisson’s ratio, reducing the shear modulus of the soil. In addition, a soil layer between 2 and 7 m and a deeper layer below 10 m that resulted in sand volcanoes were both liquefied. We also considered how the evaluation of soil liquefaction potential could be affected by long-term variations in soil conditions and changes resulting from liquefaction. The factor of safety was used to evaluate the liquefaction potential of the site. The results revealed that the assessment conducted long after the earthquake underestimated risk because the soil developed shear strength after the earthquake.

Liquefaction hazard assessment and ground failure probability analysis in the Kathmandu Valley of Nepal

During the 2015 Gorkha Earthquake (Mw7.8), extensive soil liquefaction was observed across the Kathmandu Valley. As a densely populated urban settlement, the assessment of liquefaction potential of the valley is crucial especially for ensuring the safety of engineering structures. In this study, we use borehole data including SPT-N values of 410 locations in the valley to assess the susceptibility, hazard, and risk of liquefaction of the valley soil considering three likely-to-recur scenario earthquakes. Some of the existing and frequently used analysis and computation methods are employed for the assessments, and the obtained results are presented in the form of liquefaction hazard maps indicating factor of safety, liquefaction potential index, and probability of ground failure (PG). The assessment results reveal that most of the areas have medium to very high liquefaction susceptibility, and that the central and southern parts of the valley are more susceptible to liquefaction and are at greater risk of liquefaction damage than the northern parts. The assessment outcomes are validated with the field manifestations during the 2015 Gorkha Earthquake. The target SPT-N values (Nimproved) at potentially liquefiable areas are determined using back analysis to ascertain no liquefaction during the aforesaid three scenario earthquakes.

A Liquefaction Study Using ENN, CA, and Biogeography Optimized-Based ANFIS Technique

In any construction projects,assessment of liquefaction potential induced due to seismic excitation during earthquake is a critical concern.The objective of present model development is to classify and assess liquefaction potential of soil.This paper addresses Emotional Neural Network(ENN), Cultural Algorithm(CA) and biogeography optimized(BBO) based adaptive neuro-fuzzy inference system (ANFIS) for liquefaction study.The performance of neural emotional network and cultural algorithm has been also discussed. BBO-ANFIS combines the biogeography features to optimize the ANFIS parameters to achieve higher prediction accuracy.The model is trained with case history of liquefaction databases.Two parameters are used as input such as the cyclic stress ratio and standard penetration test (SPT) value.The performance of these models was assessed using different indexes i.e. sensitivity, specificity, FNR, FPR and accuracy rate.The performance of all models is compared.Among the models, the BBO-ANFIS model has been outperformed and can be adopted as new reliable technique for liquefaction study.

Liquefaction Potential Determination and Hazard Mapping Based on Standard Penetration Tests in Long Beach and Tuzla Regions of Cyprus

This study proposes an improved and precise liquefaction risk index for the evaluation and translation of outcomes into maps to establish susceptible liquefiable areas. Cyprus is the third largest and populated island in the Mediterranean Sea, which is rapidly expanding in every way. Significant infrastructures, such as hotels, educational institutions, and large residential complexes are being built. Historically, two major earthquakes with magnitudes of 6.5 Mw struck the island in 1953 and 1996. Potential liquefaction areas have been detected on the island's east coast as a result of these significant earthquakes. In this case study, the liquefaction potential of Tuzla and Long Beach in the northern part of Cyprus is estimated using the standard penetration test (SPT) data from more than 200 boreholes at different locations at the sites. The overall results are presented in a liquefaction risk index obtained from the factor of safety (FS) coefficient. It is clear that both study areas are susceptible to liquefaction. Thus, risk index maps are prepared to identify susceptible liquefiable areas. In addition, the average factor of the safety line was introduced for both sites to create a correlation between the liquefaction risk area and FS values of every borehole. It is clear that the adopted approach precisely provides the suspected depth of the liquefiable soil layer when compared with the risk index maps. Additionally, the results prove that the liquefaction potential must be considered during the design stage of new infrastructure in these areas.

Liquefaction Evaluation Based on Shear Wave Velocity Using Random Forest

Liquefaction evaluation on the sands induced by earthquake is of significance for engineers in seismic design. In this study, the random forest (RF) method is introduced and adopted to evaluate the seismic liquefaction potential of soils based on the shear wave velocity. The RF model was developed using the Andrus database as a training dataset comprising 225 sets of liquefaction performance and shear wave velocity measurements. Five training parameters are selected for RF model including seismic magnitude ( M w ), peak horizontal ground surface acceleration ( a max ), stress-corrected shear wave velocity of soil ( V s 1 ), sandy-layer buried depth (ds), and a new introduced parameter, stress ratio (k). In addition, the optimal hyperparameters for the random forest model are determined based on the minimum error rate for the out-of-bag dataset (ERROOB) such as the number of classification trees, maximum depth of trees, and maximum number of features. The established random forest model was validated using the Kayen database as testing dataset and compared with the Chinese code and the Andrus methods. The results indicated that the random forest method established based on the training dataset was credible. The random forest method gave a success rate for liquefied sites and even a total success rate for all cases higher than 80%, which is completely acceptable. By contrast, the Chinese code method and the Andrus methods gave a high success rate for liquefaction but very low for nonliquefaction which led to the increase of engineering cost. The developed RF model can provide references for engineers to evaluate liquefaction potential.