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.

Seismic Soil Characterization to Estimate Site Effects Induced by Near-Fault Earthquakes: The Case Study of Pizzoli (Central Italy) during the Mw 6.7 2 February 1703, Earthquake

Many of the urban settlements in Central Italy are placed nearby active faults and, consequently, the ground motion evaluation and seismic site effects under near-fault earthquakes are noteworthy issues to be investigated. This paper presents the results of site investigations, the seismic site characterization, and the local seismic response for assessing the effects induced by the Mw 6.7 2 February 1703, near-fault earthquake at the Madonna delle Fornaci site (Pizzoli, Central Italy) in which notable ground failure phenomena were observed, as witnessed by several coeval sources. Even though recent papers described these phenomena, the geological characteristics of the site and the failure mechanism have never been assessed through in-situ investigations and numerical modeling. Within a project concerning the assessment of soil liquefaction potential and co-seismic ground failure, deep and shallow continuous core drilling, geophysical investigations and in-hole tests have been carried out. Subsequently, the geotechnical model has been defined and the numerical quantification of the different hypotheses of failure mechanisms has been evaluated. Analyses showed that liquefaction did not occur, and the excess pore water pressure induced by the shaking was not the source of the ground failure. Therefore, it was hypothesized that the sinkhole was likely caused by earthquake-induced gas eruption.

Site Amplification during Strong Earthquakes Investigated by Vertical Array Records

A number of vertical array records during eight destructive earthquakes in Japan are utilized, after discussing criteria for desirable requirements of vertical arrays, to formulate seismic amplification between ground surface and outcrop base for seismic zonation. A correlation between peak spectrum amplification and Vs (S-wave velocity) ratio (base Vs/surface Vs) was found to clearly improve by using Vs in an equivalent surface layer wherein predominant frequency or first peak is exerted, though the currently used average Vs in top 30 m is also meaningful, correlating positively with the amplification. We also found that soil nonlinearity during strong earthquakes has only a marginal effect even in soft soil sites on the amplification between surface and outcrop base except for ultimate soil liquefaction failure, while strong nonlinearity clearly appears in the vertical array amplification between surface and downhole base. Its theoretical basis has been explained by a simple study on a two-layered system in terms of radiation damping and strain-dependent equivalent nonlinearity.

Sandy Soil Liquefaction Prediction Based on Clustering-Binary Tree Neural Network Algorithm Model

The neural network algorithm is a small sample machine learning method built on the statistical learning theory and the lowest structural risk principle. Classical neural network algorithms mainly aim at solving two-classification problems, making it infeasible for multiclassification problems encountered in engineering practice. According to the main factors affecting sand liquefaction, a sand liquefaction discriminant model based on a clustering-binary tree multiclass neural network algorithm is established using the class distance idea in cluster analysis. The model can establish the nonlinear relationship between sand liquefaction and various influencing factors by learning limited samples. The research results show that the hierarchical structure based on the clustering-binary tree neural network algorithm is reasonable, and the sand liquefaction level can be categorized accurately.

Liquefaction Vulnerability Analysis using N-SPT Value and Grain Size Analysis on Gumbasa Irrigation Canal in the Post-Disaster Petobo Area, Sulawesi

Strong earthquakes occurred in Central Sulawesi, Indonesia, in late 2018, causing an inheritance disaster, soil liquefaction on Gumbasa Irrigation Canal at Petobo, Sulawesi. Soil liquefaction is a phenomenon of a decreasing soil bearing capacity triggered by strong vibrations in certain soil conditions. It immediately changes the soil characteristic from solid to liquid. Liquefaction vulnerability analysis was done using Idriss-Boulanger’s simplified procedure based on SPT value in several spots. The Petobo liquefaction zone has seven boreholes, five of which are located near the Gumbasa Irrigation Canal. The soil sample at those boreholes was taken to the laboratory for further soil testing using grain size analysis. The simplified procedure is intended to calculate the safety factor using Cyclic Resistance Ratio, Cyclic Stress Ratio, and Magnitude Scaling Factor. The liquefaction vulnerability analysis resulted in the AB 1 – AB 3 area near Gumbasa Irrigation Canal, which liquefied. Meanwhile, LP 1 and LP 4 are contrary. LP 1 is located upstream of the canal, whereas LP 4 the downstream. Grain size analysis yields a consistent result that AB 1 – AB 3 soil is quite scattered inside the liquefiable constraint.