Soil Shear Wave Velocity Research Articles

Seismic Mitigation Effect and Mechanism Analysis of Split Columns in Underground Structures in Sites with Weak Interlayers

The seismic damage of underground structures has been extensively investigated, and it has been demonstrated that underground structures located at weak interlayer sites are more prone to damage. In this study, a two-story two-span rectangular frame subway station structure is analyzed. A two-dimensional soil-underground structure model is developed using the large-scale finite element analysis software ABAQUS. The equivalent linear soil-underground structure dynamic time-history analysis method is employed to examine the seismic response of underground structures at weak interlayer sites. Variations in the thickness and shear wave velocity of the weak interlayer soil are analyzed. The seismic mitigation effects of split columns and prototype columns in underground structures at weak interlayer sites are systematically compared. The findings indicate that the relative displacement and internal force of key structural components significantly increase when the weak interlayer intersects the underground structure. Furthermore, as the thickness of the interlayer increases, the displacement and internal force also escalate. When the thickness of the weak interlayer remains constant and the shear wave velocity decreases, the relative displacement and internal force of the key structural components gradually intensify. Replacing ordinary columns with split columns substantially reduces the internal force of the middle column, providing an effective seismic mitigation measure for underground structures.

Predictive model for shear wave velocity of gravelly soil and its application to liquefaction triggering assessment

Several studies have established empirical correlations between shear wave velocity (Vs) and standard penetration test blow count (SPT-N) for engineering use. However, these empirical correlations cannot be applied to gravel-rich soils since the measured SPT-N is often inflated in gravel. Therefore, an empirical correlation of Vs for gravel is developed in this study using the Engineering Geological Database for the Taiwan Strong Motion Instrumentation Program. The Vs predictive model considers gravel content (GC) and coefficient of uniformity (Cu) in addition to effective vertical stress, void ratio, fines content, plasticity index, and overconsolidation ratio, which have been considered previously. The proposed model indicates that Vs increases with GC and Cu. Moreover, the Vs adjusted for GC can be used with existing Vs -based liquefaction triggering relationships to rationally define the boundary between liquefaction and non-liquefaction case histories with different GCs.

Effect of the transition piece on the natural frequencies of monopile-supported offshore wind turbines

The lateral natural frequency of an offshore wind turbine (OWT) is a crucial factor to consider in the design of OWT foundations. Eighty percent of currently installed OWTs are mounted on monopile foundations, which are connected to the superstructure via a transition piece (TP). To explore the differences in natural frequencies between grouted connection (GC) and TP-less designs, this study incorporates a transition piece into the calculation model of the OWT system. Based on the Timoshenko beam theory and transfer matrix method, the characteristic equations of the lateral natural frequency of the OWT are derived. The study found that the effect of the transition piece on the natural frequency is strongly influenced by the OWT type and the soil stiffness, and the influence of transition piece on different order natural frequencies is different for the same OWT. For the NREL offshore 5-MW baseline wind turbine, the TP-less model reduces the first natural frequency of the OWT by 1.31 % and elevates the second and third natural frequencies by 11.88 % and 2.72 %, respectively, compared to the GC model. The increase in soil shear wave velocity amplifies the influence of the transition piece on the first and third natural frequencies, while weakening its influence on the second natural frequency.

Interaction analysis between single pile and multilayered saturated soils under horizontal transient loading

This study employs the coupled finite element-boundary element method to investigate the dynamic response of single pile subjected to horizontal transient loading, which is a common scenario in high-rise building, transportation, and ocean engineering. Firstly, the single pile is modeled as a Timoshenko beam and then discretized with the finite element method (FEM). The transient solution for multilayered saturated soils due to a horizontal load, serving as the kernel function for the boundary element method (BEM), aids in the derivation of the flexibility matrix of the soils. Considering the pile-soil displacement coordination conditions, the coupled FEM-BEM equation is constructed and solved to characterize the pile-soil interaction. Since the pile discretization pattern is applied consistently to the soils, it is more effective than the discretization of infinite domain in the finite element analysis. The validity of the presented method is confirmed through comparison with the full FEM numerical simulations, demonstrating the correctness and enhanced computational efficiency. Finally, parametric studies are carried out to discuss the effects of pile's length-diameter ratio, soil's shear wave velocity and stratification.

A Machine Learning-Based Framework for Estimating Fragility Parameters in RC/MR Frames Considering Seismic, Structural, and Site Attributes

ABSTRACT This study investigates applying machine learning (ML) algorithms to estimate seismic fragility curves of reinforced concrete moment-resisting frames. The goal is to achieve computationally efficient estimations while maintaining accuracy compared to traditional methods. Various multi-classification algorithms are explored for this purpose. A greedy search method identifies key fragility features (structure period, soil shear wave velocity, ground motion intensity). Analysis shows that the decision tree method strikes an optimal balance between accuracy and speed. ML-based fragility curves closely align with analytical results, with area ratios under 10%, mean absolute error (MAE) below 0.06, and root mean square error (RMSE) below 0.082.

Evaluation of empirical and machine learning models for predicting shear wave velocity of granular soils based on laboratory element tests

Shear wave velocity (Vs) is crucial for designing geotechnical systems subjected to dynamic loads, especially in seismically active regions. The shear wave velocity of geomaterials can be determined using in situ and laboratory tests. However, due to time and cost limitations, the Vs is not easily available in most projects. Various empirical models have been developed by researchers for predicting the shear wave velocity of geomaterials. However, most of these models have been developed for specific soil types and loading characteristics. In this work, for predicting the shear wave velocity of granular soils using various combinations of input parameters, various empirical models were proposed. Furthermore, machine learning (ML) methods were utilized to predict the Vs. The suggested models consider the impact of grading characteristics such as fine content (FC), gravel content (GC), median particle size (D50), uniformity coefficient (Cu), and coefficient of curvature (Cc), as well as void ratio (e), mean effective confining pressure (σm′), consolidation stress ratio (KC), and specimen preparation techniques for reconstitution of specimens. To achieve this, a series of bender element tests were performed on various sand and gravel mixtures. Furthermore, data from previous studies were also used. So, the study utilized 513 data points from laboratory element experiments conducted on granular soils. For predicting the Vs of granular soils, four empirical models and three ML models, namely Artificial Neural Network (ANN), Support Vector Regression (SVR), and Gradient Boosting Regression (GBR), were developed in this study. The findings showed that the ANN model outperforms the other comparative models in terms of accuracy and error. While the empirical models may serve as useful tools for initial Vs estimation in construction projects, the study primarily highlights the significance of using ML methods to enhance the prediction accuracy of Vs based on the available soil properties.

Machine Learning Models for Predicting Shear Wave Velocity of Soils

As regards regions prone to seismic activity, shear wave velocity (Vs) is a design parameter for geotechnical systems exposed to dynamic loads. Evaluating Vs for geomaterials involves on-site and laboratory assessments; however, its availability is often limited in projects owing to resource and time constraints. Various mathematical and empirical models have been proposed to predict Vs for cohesive or granular soils; however, a majority of these models are specific to certain soil types and loading conditions. In this study, machine learning techniques were used for Vs prediction. These models encompass factors such as grading attributes, void ratio (e), mean effective confining pressure (σ’m), consolidation stress ratio (KC), and specimen preparation methods. To achieve this, a series of bender element tests was performed on various sand and gravel mixtures supplemented with culled data from earlier investigations. This study facilitated the development of three machine learning models aimed at predicting the Vs for granular soils: artificial neural networks (ANN), support vector regression (SVR), and gradient boosting regression (GBR), aimed at predicting Vs for granular soils. The findings of the study demonstrated that the ANN model exhibited enhanced precision and reduced error compared with the other models.

Influence of spatial variability of soil shear-wave velocity considering intralayer correlation on seismic response of engineering site

Shear-wave velocity uncertainty significantly impacts seismic response analysis at engineering sites. Previous studies focused on the correlations of shear-wave velocities within soil layers. However, the influence of the vertical spatial variability of shear-wave velocity within individual soil layers was not considered. This study selected a typical Site Class II engineering site to investigate this influence based on the equivalent linear seismic response analysis method of layered soil deposits. Existing field test data on shear-wave velocity were used to discretize the shear-wave velocity within soil layers into a one-dimensional (1D) random field through covariance matrix decomposition and local average process theory. Monte Carlo simulations generated random shear-wave velocity profiles with different vertical correlation distances. Three artificial records with different earthquake return periods were used as input motions at the engineering bedrock for the 1D free-field model. Considering soil shear-wave velocity uncertainty, the peak shear strain increased by 19–27% with the input ground motion amplitude. Moreover, the site peak shear strain variability increased by 25–34% with the vertical correlation distance. The proposed 1D random field model effectively simulated the interlayer correlation and spatial variability of shear-wave velocity within soil layers, demonstrating its significance in seismic response analysis.

Optimizing Terrain Classification Methods for the Determination of Bedrock Depth and the Average Shear Wave Velocity of Soil

The advancement of remote sensing has enabled the creation of high-resolution Digital Elevation Models (DEMs). Topographic features such as slope gradient (SG), local convexity (LC), and surface texture (ST), derived from DEMs, are related to subsurface geological conditions. In South Korea, bedrock depth (Dbedrock) and the average shear wave velocity of soil (VSsoil) serve as metrics for determining the site class, which represents the degree of site amplification in seismic design criteria. These metrics, typically measured through geotechnical and geophysical investigations, require predictive methods for preliminary estimation over large areas. Previous studies developed an automatic terrain classification (AC) scheme using SG, LC, and ST, and subsequent research revealed that terrain classification effectively represents subsurface conditions such as Dbedrcok and average shear wave velocity down to 30 m depth. However, AC intrinsically depends on the regional features of DEMs, dividing regions based on nested means of topographic features (SG, LC, and ST). In this study, we developed two terrain classification methods to determine the thresholds of class divisions, aiming to optimize Dbedrock and VSsoil predictions: Sequentially Optimized Classification (SOC) and Non-Sequentially Optimized Classification (NOC). Through the study of the sensitivity of terrain classification methods, smoothing levels, and threshold levels for terrain class generation, we identified the best classification method by comparing it with the geological and mountainous region distribution. Subsequently, we developed DEM-dependent regression models for each class to enhance the accuracy of predicting Dbedrock and VSsoil. The main findings of this study are: (1) the terrain class map suggested in this study represents the distribution of alluvial plane and mountainous regions well, and (2) the DEM calibration for each class provides increased accuracy of Dbedrock and VSsoil predictions in South Korea. We anticipate that the terrain class map, along with Dbedrock and VSsoil maps, will be effectively utilized in geological interpretations and land-use planning for seismic design.

Confidence Interval for the Population Mean of Rayleigh Distribution with Application to Shear wave Velocity of Soils in Thailand

Confidence Interval for the Population Mean of Rayleigh Distribution with Application to Shear wave Velocity of Soils in Thailand

Fuzzy inference system model for the spectrum characteristic periods of the Türkiye Building Earthquake Code (2007)

Seismic design codes define response spectra with crisp numerical classifications of seismic parameters, which mainly affect the spectrum's shape and determination of seismic design loads. The efficiency of structural safety and construction costs depends on the optimum design and accurately determined seismic forces. As presented in the seismic design codes, several parameters are utilized to calculate the seismic design forces with response spectra. This study proposes a rule-based fuzzy inference system (FIS) model with fuzzy set numbers to determine the relevant parameters. By defining the soil profile thickness and shear wave velocity as inputs, the model generates the spectrum characteristic periods specified in the Türkiye Building Earthquake Code (TBEC 2007). The response spectra of twenty different samples with the FIS and crisp models were generated and compared to assess the model's superiority. Unlike crisp seismic code classifications, the proposed FIS model accounts for imperfections in soil group selection and topmost soil layer thickness, offering a more realistic representation of uncertainties and proving to be an effective tool for addressing linguistic vagueness in seismic response spectra analysis. The comparison between fuzzy and crisp output seismic parameters revealed significant differences in response spectra shape and spectrum intensity values. The FIS model-generated spectra were more conservative in certain building locations, while in others, they provided similar or lower values, suggesting potential cost savings in design. The FIS model demonstrates its efficacy in producing more accurate and robust designs by considering the uncertainties inherent in the problem. Furthermore, this approach has the potential to be extended to study seismic parameters of other design codes, although further research is required to comprehensively explore its capabilities and limitations.

Convolutional neural network-based seismic fragility analysis of subway station structure considering spatial variation of site shear-wave velocity

Prior research demonstrated that the uncertainty of soil parameter can significantly impact the seismic response analysis and seismic performance evaluation of underground structures. To this end, the seismic response of a three-story, three-span subway station embedded in a typical layered engineering site was investigated in this study and the uncertainty of soil parameters was explicitly considered using the shear-wave velocity interlayer correlation model. In addition, an incremental dynamic analysis considering the uncertainty of the shear-wave velocity was performed using the one-dimensional convolutional neural network (1D-CNN) model as a surrogate model for the traditional finite element method. The results indicate that the uncertainty of soil shear-wave velocity increases the average PGA values for minor, moderate, and severe failures with a probability of exceeding 50% by about 10%, and increases the logarithmic standard deviation by about 40%. When considering the uncertainty of shear-wave velocity, the discreteness of the fragility curve will increase. Moreover, the rightward shift of the fragility curve midpoint leads to a comparatively more safety seismic performance evaluation of the structure under significant seismic input.

Machine Learning Techniques for Soil Characterization Using Cone Penetration Test Data

Seismic response assessment requires reliable information about subsurface conditions, including soil shear wave velocity (Vs). To properly assess seismic response, engineers need accurate information about Vs, an essential parameter for evaluating the propagation of seismic waves. However, measuring Vs is generally challenging due to the complex and time-consuming nature of field and laboratory tests. This study aims to predict Vs using machine learning (ML) algorithms from cone penetration test (CPT) data. The study utilized four ML algorithms, namely Random Forests (RFs), Support Vector Machine (SVM), Decision Trees (DT), and eXtreme Gradient Boosting (XGBoost), to predict Vs. These ML models were trained on 70% of the datasets, while their efficiency and generalization ability were assessed on the remaining 30%. The hyperparameters for each ML model were fine-tuned through Bayesian optimization with k-fold cross-validation techniques. The performance of each ML model was evaluated using eight different metrics, including root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), coefficient of determination (R2), performance index (PI), scatter index (SI), A10−I, and U95. The results demonstrated that the RF model consistently performed well across all metrics. It achieved high accuracy and the lowest level of errors, indicating superior accuracy and precision in predicting Vs. The SVM and XGBoost models also exhibited strong performance, with slightly higher error metrics compared with the RF model. However, the DT model performed poorly, with higher error rates and uncertainty in predicting Vs. Based on these results, we can conclude that the RF model is highly effective at accurately predicting Vs using CPT data with minimal input features.

Train-induced vibration mitigation based on foundation improvement

The distance between railway systems and buildings has been significantly reduced due to the rapid development of public transportation. Environmental vibration is increasingly influencing human health and comfort as well as vibration-sensitive equipment. To alleviate the issue, amplifying the coupling loss between soil and building using foundation improvement techniques, is an effective way to mitigate building vibration from external sources. In this study, a 4-story building need to be built over the railway was used to study the vibration mitigation by foundation improvement. The vibration mitigation performance of foundation design parameters, including foundation type, shear wave velocity of soil, distance from the source, pile diameter, pile length, dimension of the pile cap, and pile group, were studied by a finite element method model. A guideline for optimizing foundation design, which can effectively improve the coupling loss between soil and foundation, has been proposed and applied to the construction of a 4-story building. The train-track dynamic model and the full-scale track-ground-building finite element method model were built to predict the proposed building vibration with the designed foundation. The measurement was conducted before and after the building construction to validate the accuracy of model and the feasibility of the foundation improvement design. The soil-foundation coupling loss is significant from 10 to 80 Hz, which indicates the foundation improvement for vibration mitigation by amplifying the coupling loss is feasible.

Rheological behavior of zein biopolymer and stiffness characteristic of biopolymer treated soil

The modification of polymeric biomolecules to achieve improved cementation is important for their potential applications. This study presents the rheological behaviors of eco-friendly zein biopolymer and stiffness characteristics of biopolymer-treated soil under various solvent and curing conditions. The rheological and stiffness characteristics of zein biopolymer are evaluated in terms of shear stress, viscosity, and shear wave velocity. The time sensitivity, flow index, and microstructure of biopolymer gel are analyzed. The biopolymer gel generally exhibits a weakly non-Newtonian shear-thickening behavior, except for the specimen cured for 24 h under chamber condition. At a constant shear rate, the biopolymer gel shows a rheopexy behavior, which is characterized by an increase in viscosity over time. The shear wave velocity of biopolymer-treated soil increases with the curing period, up to 50 and 96 % under atmospheric and chamber conditions, respectively. The peak absorption values for the hydroxyl functional group decrease significantly as the curing period and ethanol content increase. Furthermore, the microparticle sizes of the zein biopolymer gel decrease with increasing ethanol content. Therefore, understanding the stiffness characteristics of biopolymer-treated soil specimens for potential soil stabilization can be improved based on the rheological properties of zein biopolymer.

Effect of soil heterogeneity on seismic tunnel lining forces

The crucial role that underground structures play today in transportation and utility networks makes their seismic vulnerability a fundamental topic.The present paper deals with the role of soil heterogeneity in tunnel seismic behaviour. Starting from a real case history regarding a cross-section of the Catania (Italy) underground network, a finite element parametric study was carried out, focusing the attention on the soil discontinuity in terms of the impedance ρ∙VS of each layer crossed by the tunnel, being ρ and VS the soil layer density and shear wave velocity, respectively. Different tunnel depths and seismic inputs were assumed. The numerical dynamic lining internal forces were compared with the analytical solutions proposed by Wang (1993) and Penzien (2000) for homogeneous soil deposits; two new approaches were proposed to use them for heterogeneous soils, highlighting the limitations of the original solutions and the range of applicability of the proposed approaches.

Seismic performance of self-centering rocking bents considering soil-structure interaction

The rocking bridge has attracted a significant research interest, due to its excellent self-centering capacity after the severe earthquake. However, the vast majority of previous studies on the seismic behavior of rocking bents were usually conducted based on the assumption of the rigid bedrock without considering of soil-structure interaction (SSI). Hence, this paper investigates the seismic performance of the self-centering rocking bent considering the SSI effect. Focusing on the double-column self-centering rocking bent equipped with and without angle steels (SCRB-A and SCRB) and the conventional double-column reinforced concrete bent (RCB), the high-fidelity finite element models considering the SSI effect are established. The seismic responses of the bents with fixed-base (without SSI) and flexible-base (with SSI) are comparatively evaluated. The influence of the shear wave velocity of soil on the seismic behavior of the bents with the SSI is also discussed. The results show that the SSI has significantly effect on the seismic responses of the three bents. The displacement responses are generally increased when the SSI is considered, and the amplifications of the seismic responses of the SCRB-A and SCRB considering SSI are more obvious than those of the RCB. With the increase of the shear wave velocity, the influence of the SSI on the displacement responses of the RCB becomes progressively less sensitive, however, the totally different trends can be found in the SCRB-A and SCRB with SSI. Moreover, SCRB-A exhibits better seismic performance when compared with the SCRB and RCB for the cases with or without the SSI effect.

Effect of parameters associated with soil-to-structure relative stiffness on seismic fragility curves of subway station

Seismic response and vulnerability of subway station underground structures are greatly influenced by kinematic soil-structure interaction, which is a function of soil properties and stiffness. Therefore, this paper investigates the influence of parameters associated with soil-to-structure relative stiffness (F) on the seismic fragility curves of underground structures. Incremental dynamic analysis method is conducted employing finite element analysis to establish the fragility curves of the station structure considering 44 earthquake records. The soil was simulated as a two-dimensional (2D) finite element domain and its nonlinear behavior is described employing the Davidenkov constitutive model. Appropriate plasticity models simulated the nonlinearity of concrete and reinforcing rebars. It was found that the failure probability of the structure decreases significantly as soil shear wave velocity increases, and the change of probability of structural failure was quantified for changes in shear wave velocity within the range 200 m/s to 600 m/s. It was also found that the failure probability of subway station in heterogeneous soil is significantly higher than that of a structure buried in homogeneous soil. On the other hand, the cross-section type of the structure has less influence on its seismic vulnerability. The seismic performance of the structure with higher F is relatively better, while there are special cases where some structures with high F that are more susceptible to failure under earthquake than structures with lower F. The findings can provide helpful guide for the performance-based seismic design of underground structures.

Influence of pile end soil on torsional vibration of pile considering the stress diffusion

This study focuses on the influence of the pile end soil on the torsional vibration of a pile based on a fictitious soil pile model considering stress diffusion. The pile end soil is simulated as a cone-shaped fictitious soil pile whose parameters remain the same as the soil. The dynamic equilibrium equations for the pile–soil system subjected to torsional dynamic loadings are developed and solved to obtain the corresponding analytical solution. Then, comparisons with other solutions are conducted to verify the reliability of the proposed solution. Finally, parametric analysis results are presented based on the proposed solution to portray the influence of the stress diffusion angle, thickness and shear wave velocity of the pile end soil.

Generation and Evaluation of CPT Data Using Kriging Interpolation Technique

The cone penetration test (CPT) has been the de facto field exploration method in geotechnical engineering for decades. Variations of CPT can measure parameters for seismic, environmental, and hydrological applications. Analyzing response often requires properties in areas that have little or no data. Therefore, given the limited CPT data, it is critical to understand how to accurately estimate the soil properties at unsampled locations. In this paper, we generated soil shear wave velocity profiles using the kriging interpolation technique and assessed their performance using site response analysis. Four kriging interpolation-based shear wave velocity profiles and four additional CPT-based shear wave velocity profiles defined site conditions for response analysis. We performed a series of 1-D equivalent linear site response analyses using STRATA software. The site response analysis results are presented as amplification factors (AF), peak ground acceleration (PGA) profiles, surface spectral acceleration, and surface acceleration time histories. Compared to CPT-based profiles, the results of kriging interpolation-based profiles were evaluated and discussed. The results confirmed the reliability of the kriging interpolation technique in predicting soil parameters at unsampled locations.