Earthquake-induced Soil Liquefaction Research Articles

Induced Partial Saturation: From Mechanical Principles to Engineering Design of an Innovative and Eco-Friendly Countermeasure against Earthquake-Induced Soil Liquefaction

Earthquake-induced soil liquefaction is a catastrophic phenomenon that can damage existing building foundations and other structures, resulting in significant economic losses. Traditional mitigation techniques against liquefaction present critical aspects, such as high construction costs, impact on surrounding infrastructure and effects on the surrounding environment. Therefore, research is ongoing in order to develop new approaches and technologies suitable to mitigate liquefaction risk. Among the innovative countermeasures against liquefaction, Induced Partial Saturation (IPS) is considered one of the most promising technologies. It consists of introducing gas/air bubbles into the pore water of sandy soils in order to increase the compressibility of the fluid phase and then enhance liquefaction resistance. IPS is economical, eco-friendly and suitable for urbanised areas, where the need to reduce the risk of liquefaction must be addressed, taking into account the integrity of existing buildings. However, IPS is still far from being a routine technology since more aspects should be better understood. The main aim of this review is to raise some important questions and encourage further research and discussions on this topic. The review first analyses and discusses the effects of air/gas bubbles on the cyclic behaviour of sandy soils, focusing on the soil volume element scale and then extending the considerations to the real scale. The use of useful design charts is also described. Moreover, a section will be devoted to the effect of IPS under shallow foundations. The readers will fully understand the research trend of IPS liquefaction mitigation and will be encouraged to further explore new practical aspects to overcome the application difficulties and contribute to spreading the use of this technology.

Assessing the impact of inertial load on the buckling behavior of piles with large slenderness ratios in liquefiable deposits

Pile foundations used in liquefiable deposits often have large slenderness ratios, and these piles are particularly vulnerable to earthquake-induced soil liquefaction and occur buckling instability under axial load alone as soils around the pile liquefy. However, insight into the precise impact of inertial load on the buckling behavior of piles in liquefiable deposits is still lacking, and the current methods used to calculate the critical buckling load of the pile in liquefiable ground usually ignore inertial load. In light of the above, two shaking table tests were carried out to clarify the impact of inertial load on the buckling behavior of piles partly embedded in saturated sands. Moreover, a novel calculation method considering inertial load was presented to accurately estimate the critical buckling load of the pile in liquefiable deposits. The feasibility of the presented method had been verified using the results from a centrifuge experiment and an analytical method along with a finite element method. Furthermore, parametric analysis was conducted to study the effect of soil type, peak ground acceleration (PGA), pile diameter, and unsupported length ratio on the critical buckling load of the pile in liquefiable soils. Finally, comparing the calculated results of critical buckling load between the method presented in this paper and the method in the design code confirmed the limitation of the current design code. These combined analyses demonstrate that the critical buckling load of the pile decreases due to the action of inertial load, resulting in the pile being more susceptible to buckling failure. Therefore, designers have to consider the impact of inertial load during the seismic design of pile foundations in liquefiable soils.

Semi-Supervised Learning Method for the Augmentation of an Incomplete Image-Based Inventory of Earthquake-Induced Soil Liquefaction Surface Effects

Soil liquefaction often occurs as a secondary hazard during earthquakes and can lead to significant structural and infrastructure damage. Liquefaction is most often documented through field reconnaissance and recorded as point locations. Complete liquefaction inventories across the impacted area are rare but valuable for developing empirical liquefaction prediction models. Remote sensing analysis can be used to rapidly produce the full spatial extent of liquefaction ejecta after an event to inform and supplement field investigations. Visually labeling liquefaction ejecta from remotely sensed imagery is time-consuming and prone to human error and inconsistency. This study uses a partially labeled liquefaction inventory created from visual annotations by experts and proposes a pixel-based approach to detecting unlabeled liquefaction using advanced machine learning and image processing techniques, and to generating an augmented inventory of liquefaction ejecta with high spatial completeness. The proposed methodology is applied to aerial imagery taken from the 2011 Christchurch earthquake and considers the available partial liquefaction labels as high-certainty liquefaction features. This study consists of two specific comparative analyses. (1) To tackle the limited availability of labeled data and their spatial incompleteness, a semi-supervised self-training classification via Linear Discriminant Analysis is presented, and the performance of the semi-supervised learning approach is compared with supervised learning classification. (2) A post-event aerial image with RGB (red-green-blue) channels is used to extract color transformation bands, statistical indices, texture components, and dimensionality reduction outputs, and performances of the classification model with different combinations of selected features from these four groups are compared. Building footprints are also used as the only non-imagery geospatial information to improve classification accuracy by masking out building roofs from the classification process. To prepare the multi-class labeled data, regions of interest (ROIs) were drawn to collect samples of seven land cover and land use classes. The labeled samples of liquefaction were also clustered into two groups (dark and light) using the Fuzzy C-Means clustering algorithm to split the liquefaction pixels into two classes. A comparison of the generated maps with fully and manually labeled liquefaction data showed that the proposed semi-supervised method performs best when selected high-ranked features of the two groups of statistical indices (gradient weight and sum of the band squares) and dimensionality reduction outputs (first and second principal components) are used. It also outperforms supervised learning and can better augment the liquefaction labels across the image in terms of spatial completeness.

Drainage explains soil liquefaction beyond the earthquake near-field

Earthquake-induced soil-liquefaction is a devastating phenomenon associated with loss of soil rigidity due to seismic shaking, resulting in catastrophic liquid-like soil deformation. Traditionally, liquefaction is viewed as an effectively undrained process. However, since undrained liquefaction only initiates under high energy density, most earthquake liquefaction events remain unexplained, since they initiate far from the earthquake epicenter, under low energy density. Here we show that liquefaction can occur under drained conditions at remarkably low seismic-energy density, offering a general explanation for earthquake far-field liquefaction. Drained conditions promote interstitial fluid flow across the soil during earthquakes, leading to excess pore pressure gradients and loss of soil strength. Drained liquefaction is triggered rapidly and controlled by a propagating compaction front, whose velocity depends on the seismic-energy injection rate. Our findings highlight the importance of considering soil liquefaction under a spectrum of drainage conditions, with critical implications for liquefaction potential assessments and hazards.

Probabilistic-based seismic fragility assessment of earthquake-induced site liquefaction

As a very complex and uncertain problem, seismic liquefaction is affected by many factors, such as earthquake intensity, site conditions, soil properties, etc. During the past half a century, the state-of-the-art in analysis and evaluation of earthquake-induced soil liquefaction and its consequences as well as the state-of-the-practice in improving seismic stability to resist liquefaction have been made much progress. However, the fragility analysis and assessment for seismic liquefaction of sites have not been paid enough attention to. In this paper, the concept of seismic liquefaction fragility for building sites is proposed. The soil resistance to liquefaction and liquefaction possibility are expressed as a fragility function of ground motion intensity, which can be used to describe minor, moderate and severe liquefaction potential under different ground motion intensity. Combined with the existing research results of seismic liquefaction evaluation, the analysis method of liquefaction fragility is given. Taking a specific site as an example for liquefaction fragility analysis, it is found that taking the calculated exceedance probability of different liquefaction levels as the observation value, the liquefaction fragility function follows both lognormal distribution and log-logistic distribution. With the increase of liquefaction level, the median value of liquefaction fragility function increases, while the site liquefaction fragility curve moves to the right, which shows that the ability of the site to resist more severe liquefaction is increasing. When convolved with the analysis results of liquefaction triggering and hazard, the liquefaction fragility function can be a much efficient tool for seismic liquefaction risk analysis of specific sites.

Soft Computing to Predict Earthquake-Induced Soil Liquefaction via CPT Results

Earthquake-induced soil liquefaction (EISL) can cause significant damage to structures, facilities, and vital urban arteries. Thus, the accurate prediction of EISL is a challenge for geotechnical engineers in mitigating irreparable loss to buildings and human lives. This research aims to propose a binary classification model based on the hybrid method of a wavelet neural network (WNN) and particle swarm optimization (PSO) to predict EISL based on cone penetration test (CPT) results. To this end, a well-known dataset consisting of 109 datapoints has been used. The developed WNN-PSO model can predict liquefaction with an overall accuracy of 99.09% based on seven input variables, including total vertical stress (σv), effective vertical stress (σv′), mean grain size (D50), normalized peak horizontal acceleration at ground surface (αmax), cone resistance (qc), cyclic stress ratio (CSR), and earthquake magnitude (Mw). The results show that the proposed WNN-PSO model has superior performance against other computational intelligence models. The results of sensitivity analysis using the neighborhood component analysis (NCA) method reveal that among the seven input variables, qc has the highest degree of importance and Mw has the lowest degree of importance in predicting EISL.

Shallow foundations subjected to earthquake-induced soil liquefaction resting on deep soil mixing columns

Shallow foundations subjected to earthquake-induced soil liquefaction resting on deep soil mixing columns

Enhancing the seismic performance of piles in liquefiable soils by slag powder

Case studies show that earthquake-induced soil liquefaction can cause large lateral deformation of piles, leading to bridge damage. In this study, an iron and steel manufacturing by-product, slag powder, was utilized for the first time to mitigate soil liquefaction, thereby reducing the lateral deformation of piles during earthquakes. First, the liquefaction resistance of soil improved by slag powder was analyzed by cyclic triaxial tests. The results indicate that as the slag powder content increased, the soil liquefaction resistance increased. When the cyclic stress ratio is 0.2, the number of liquefaction cycles of the reinforced soil increases by 30 compared with that of the unreinforced soil. Second, a numerical modeling method of slag powder reinforced sand was proposed and validated by the cyclic triaxial test results. Third, the seismic response of piles in liquefiable soils enhanced by slag powder was evaluated using finite element analyses, and slag powder was determined to be effective in decreasing the lateral deformation of piles in liquefiable soils, the maximum displacement of the pile was reduced by 45 %. Last, an empirical formula that can easily evaluate the mitigation effect of slag powder on the lateral deformation of piles is proposed and verified by the results of finite element analysis.

Numerical Modeling of Liquefaction-Induced Downdrag: Validation against Centrifuge Model Tests

Earthquake-induced soil liquefaction can cause soil settlement around piles, resulting in drag load and pile settlement after shaking stops. Estimating the axial load distribution and pile settlement is important for designing and evaluating the performance of axially loaded piles in liquefiable soils. Commonly used neutral plane solution methods model the liquefiable layer as an equivalent consolidating clay layer without considering the sequencing and pattern of excess pore pressure dissipation and soil settlement. Moreover, changes in the pile shaft and the tip resistance due to excess pore pressures are ignored. A TzQzLiq numerical model was developed using the existing TzLiq material and the new QzLiq material for modeling liquefaction-induced downdrag on piles. The model accounts for the change in the pile’s shaft and tip capacity as free-field excess pore pressures develop or dissipate in soil. The developed numerical model was validated against data from a series of large centrifuge model tests, and the procedure for obtaining the necessary information and data from those is described. Additionally, a sensitivity study on TzLiq and QzLiq material properties was performed to study their effect on the developed drag load and pile settlement. Analysis results show that the proposed numerical model can reasonably predict the time histories of axial load distribution and settlement of axially loaded piles in liquefiable soils both during and postshaking.

Probabilistic assessment of the earthquake-induced soil liquefaction hazard at national scale: macrozonation of the Italian territory

Probabilistic assessment of the earthquake-induced soil liquefaction hazard at national scale: macrozonation of the Italian territory

Verification of a System for Sustainable Research on Earthquake-Induced Soil Liquefaction in 1-g Environments

Within the presented research, model tests were performed in 1-g conditions to investigate the liquefaction potential of Skopje sand as a representative soil from the Vardar River’s terraces in N. Macedonia. A series of shaking table tests were performed on a fully saturated, homogeneous model of Skopje sand in the newly designed and constructed laminar container in the Institute of Earthquake Engineering and Engineering Seismology (IZIIS), Skopje, N. Macedonia. The liquefaction depth in each shaking test was estimated based on the measured acceleration and pore water pressure as well as the frame movements of the laminar container. The surface settlement measurements indicated that the relative density increased by ~12% after each test. The observations from the tests confirmed that liquefaction was initiated along the depth at approximately the same time. The number of cycles required for liquefaction increased as the relative density increased. As the pore water pressure rose and reached the value of the effective stresses, the acceleration decreased, thus the period of the soil started to elongate. The results showed that the investigated Skopje sand was highly sensitive to void parameters and, under specific stress conditions, the liquefaction that occurred could be associated with large deformations. The presented experimental setup and soil material represent a well-proven example of a facility for continuous and sustainable research in earthquake geotechnical engineering.

A Stacked Generalization Model to Enhance Prediction of Earthquake-Induced Soil Liquefaction.

Earthquakes cause liquefaction, which disturbs the design phase during the building construction process. The potential of earthquake-induced liquefaction was estimated initially based on analytical and numerical methods. The conventional methods face problems in providing empirical formulations in the presence of uncertainties. Accordingly, machine learning (ML) algorithms were implemented to predict the liquefaction potential. Although the ML models perform well with the specific liquefaction dataset, they fail to produce accurate results when used on other datasets. This study proposes a stacked generalization model (SGM), constructed by aggregating algorithms with the best performances, such as the multilayer perceptron regressor (MLPR), support vector regression (SVR), and linear regressor, to build an efficient prediction model to estimate the potential of earthquake-induced liquefaction on settlements. The dataset from the Korean Geotechnical Information database system and the standard penetration test conducted on the 2016 Pohang earthquake in South Korea were used. The model performance was evaluated by using the R2 score, mean-square error (MSE), standard deviation, covariance, and root-MSE. Model validation was performed to compare the performance of the proposed SGM with SVR and MLPR models. The proposed SGM yielded the best performance compared with those of the other base models.

Soil-pile-quay wall interaction in liquefaction-induced lateral spreading ground

Earthquake-induced soil liquefaction and lateral spreading often causes damage to pile foundations of quays and offshore structures. In this study, the soil-pile-quay wall interaction in liquefaction-induced lateral spreading ground was analyzed from three perspectives—experimental investigation, numerical simulation, and parametric analysis. A large-scale (1 g acceleration) shake-table test was conducted on a 2 × 2 pile group behind a quay wall, and described in terms of sensor arrangement, model configuration, and test results. The test resulted in phenomena such as the quay wall overturning to the water side, and ground settlement. A 2D (two-dimensional) nonlinear FE (finite element) model of a coupled dynamic soil-water system, which considered soil-pile-quay wall interactions, was validated using the experimental data. Based on the validated model, a parametric study was conducted to illuminate the effects of the pile head fixing conditions, pile stiffness, superstructure mass, permeability coefficient, and acceleration. The results indicated that these parameters had a significant influence on the soil-pile-quay wall interaction in the liquefaction-induced lateral spreading ground. Investigating the effects of each of the five parameters on the dynamic performance of pile foundations in this study has guiding implications for tests and numerical simulations.

Earthquake-induced liquefaction hazard mapping at national-scale in Australia using deep learning techniques

Australia is a relatively stable continental region but not tectonically inert, having geological conditions that are susceptible to liquefaction when subjected to earthquake ground motion. Liquefaction hazard assessment for Australia was conducted because no Australian liquefaction maps that are based on modern AI techniques are currently available. In this study, several conditioning factors including Shear wave velocity (Vs30), clay content, soil water content, soil bulk density, soil thickness, soil pH, distance from river, slope and elevation were considered to estimate the liquefaction potential index (LPI). By considering the Probabilistic Seismic Hazard Assessment (PSHA) technique, peak ground acceleration (PGA) was derived for 50 yrs period (500 and 2500 yrs return period) in Australia. Firstly, liquefaction hazard index (LHI) (effects based on the size and depth of the liquefiable areas) was estimated by considering the LPI along with the 2% and 10% exceedance probability of earthquake hazard. Secondly, ground acceleration data from the Geoscience Australia projecting 2% and 10% exceedance rate of PGA for 50 yrs were used in this study to produce earthquake induced soil liquefaction hazard maps. Thirdly, deep neural networks (DNNs) were also exerted to estimate liquefaction hazard that can be reported as liquefaction hazard base maps for Australia with an accuracy of 94% and 93%, respectively. As per the results, very-high liquefaction hazard can be observed in Western and Southern Australia including some parts of Victoria. This research is the first ever country-scale study to be considered for soil liquefaction hazard in Australia using geospatial information in association with PSHA and deep learning techniques. This study used an earthquake design magnitude threshold of Mw 6 using the source model characterization. The resulting maps present the earthquake-triggered liquefaction hazard and are intending to establish a conceptual structure to guide more detailed investigations as may be required in the future. The limitations of deep learning models are complex and require huge data, knowledge on topology, parameters, and training method whereas PSHA follows few assumptions. The advantages deal with the reusability of model codes and its transferability to other similar study areas. This research aims to support stakeholders’ on decision making for infrastructure investment, emergency planning and prioritisation of post-earthquake reconstruction projects.

Cyclic behaviour of stratified soil under liquefied states

A substantial amount of work on the cyclic behaviour of cohesionless soil has been performed after neglecting the reality of stratification existing nearby marine structures or alluvial plains. This complicated mechanism of stratified soil failure on earthquake-induced soil liquefaction is affected significantly by the presence of a less permeable soil layer. Several past studies investigated that under the action of dynamic loads, the presence of a silt layer in homogeneous sandy soil crucially modifies the pore pressure dissipation characteristics, axial strain rate and stress–strain behaviour of the whole stratified specimen. In the present study, cyclic behaviour of sand specimen interlayered with silt layer has been presented in respect of its pre-and post-liquefaction behaviour. Undrained stress-controlled cyclic triaxial tests have been performed on homogeneous sand specimens and on specimens interlayered with a silt layer, placed at H/4, H/2 and 3H/4 (where H is the height of the specimen from the top) to signify the role of placement depth. The instability and failure of such specimens have been compared using the failure line and phase transformation line. Few key observations suggested that the number of liquefaction failure cycles enhanced two times and secant modulus (under post liquefied state) improved by 4.8 times when the placement depth of the silt layer increased from H/4 to 3H/4 under similar loading conditions. Additionally, the role of initial static shear stress has been investigated at three different confining pressures in a few stratified specimens. It has been observed that failure of the stratified specimen is due to the obstruction of pore water movement caused by the presence of the silt layer. A comparison of the present work has been made with significant studies using liquefaction resistant curves to ensure the efficacy of this study.

Mapping soil liquefaction susceptibility across Europe using the analytic hierarchy process

Mapping the susceptibility of earthquake-induced soil liquefaction at the continental scale is a challenge. Susceptibility of soils to liquefaction is the tendency of certain geomaterials to undergo a severe stiffness degradation and loss of shear strength. The latter could be induced by cyclic loading induced by seismic events. The liquefaction surface evidence is a local phenomenon, and detailed geotechnical field investigations are not available for regional studies, not to mention at continental scales. The literature review shows earthquake-induced soil liquefaction evidence in several European countries, yet, a comprehensive picture of the susceptibility at the European scale is not available. This work aims to develop a methodology to assess the earthquake-induced soil liquefaction susceptibility in Europe using geospatial parameters weighted via the Analytic Hierarchy Process (AHP). The major outcome of the study is a novel Liquefaction Susceptibility map of Europe (LSE), based on the building of ten different European macro-units for earthquake-induced soil liquefaction. These European macro-units have been delineated in this work in order to be homogenous areas from the geological, physiographical, and geomorphological points of view. The adopted input parameters are the depositional environment of the sediments, the distance from water bodies (coast and rivers), and the compound topographic index (as a proxy of the soil saturation). The resolution of the LSE map is 900 × 900 m. The results have been cross-compared with soil liquefaction susceptibility maps available for a region of Greece (i.e. Thrace), Portugal, Bulgaria, and sites where liquefaction manifestations occurred across Europe. The LSE can be adopted to identify at large scale the areas susceptible to liquefaction including also the territories across Europe characterized by low seismicity and potentially affected by anthropogenic seismicity.

Predicting Earthquake-Induced Soil Liquefaction Based on Machine Learning Classifiers: A Comparative Multi-Dataset Study

The study and prediction of soil liquefaction is an important and complex issue in geotechnical earthquake engineering. This paper attempts to compare the predictability of soil liquefaction potential between several machine learning classification models, which includes some tree-based classifiers, multilayer perceptron (MLP) neural networks, Support Vector Machine (SVM), some state-of-the-art ensemble methods, K nearest neighbors method, classical Naive Bayesian classifier and logistic regression. Three data sets covering shear-wave velocity measurements, cone penetration testing (CPT), and real historic earthquakes cases are employed to train and evaluate the machine learning classifiers. In order to make the best use of large varieties of statistical and machine learning classification algorithms, it is necessary to give a comparative evaluation of the model performance before model selection and offer advice on a unified stable model for all sorts of collected datasets. In the comparative study, data preprocessing is first performed to ensure the dataset into all machine models is of good quality. Then all three datasets with different input features are passed into the machine learning algorithms to obtain its confusion matrix and some evaluation indices. Reliable assessment of model performance is done through a repeated sub-sampling process. Experimental results are also supported by ROC curves. The results of this study indicated that although most machine learning methods are able to represent the complex relationship between seismic proper seismic properties of soils and corresponding liquefaction potential, ensemble learning has achieved more successful results in all three datasets test and can be a fairly promising approach on prediction of earthquake-induced soil liquefaction.

A Review of Failure Mechanism of Pile-supported Structures due to Earthquake Induced Soil Liquefaction

A Review of Failure Mechanism of Pile-supported Structures due to Earthquake Induced Soil Liquefaction

Field Insights and Analysis of the 2018 Mw 7.5 Palu, Indonesia Earthquake, Tsunami and Landslides

A devastating Mw 7.5 earthquake and tsunami struck northwestern Sulawesi, Indonesia on 28 September 2018, causing over 4000 fatalities and severe damage to several areas in and around Palu City. Severe earthquake-induced soil liquefaction and landslides claimed hundreds of lives in three villages within Palu. The mainshock occurred at 18:03 local time at a depth of 10 km on a left-lateral strike-slip fault. The hypocenter was located 70 km north of Palu City and the rupture propagated south, under Palu Bay, passing on land on the west side of Palu City. The surface rupture of the earthquake has been mapped onshore along a 30 km stretch of the Palu-Koro fault. We present results of field surveys on the effects of the earthquake, tsunami and liquefaction conducted between 1–3 and 12–19 of October 2018. Seismic intensities on the Modified Mercalli Intensity (MMI) scale are reported for 375 sites and reach a maximum value of 10. We consolidate published tsunami runup heights from several field studies and discuss three possible interrelated tsunami sources to explain the variation in observed tsunami runup heights. Due to limited instrumentation, PGA and PGV values were recorded at only one of our field sites. To compensate, we use our seismic intensities and Ground Motion to Intensity Conversion Equations (GMICEs) and Ground Motion Prediction Equations (GMPEs) developed for similar tectonic regions. Our results indicate that the maximum predicted PGAs for Palu range from 1.1 g for GMICEs to 0.6 g for GMPEs.

A potential microbiological approach to the evaluation of earthquake-induced soil liquefaction

SummaryEarthquakes occur thousands of times every day around the world. They are naturally destructive seismic events and often result in soil liquefaction. Soil microbiota plays a vital role in soil environments and may serve as an effective indicator to assess soil liquefaction after earthquakes. This study aimed to detect the microbial community abundance and composition in soil samples of different depths. Soil samples were collected in Southern Taiwan immediately after the 2010 earthquake. Their physical characteristics were determined, and their microbial communities were analyzed through 16S amplicon sequencing. The results revealed that Nitrospirae phylum dominated in the liquefied layer. In particular, the genus HB118, dominant in the liquefied layer, was not detected at other soil depths or in the expelled liquefied soil. This finding not only provides valuable insights into changes in microbial community composition at different soil depths after earthquakes but also suggests a useful indicator for monitoring liquefied soil.