Logistic Regression Model for Evaluating Soil Liquefaction Probability Using CPT Data

A simple model for evaluating liquefaction probability using cone penetration test (CPT) data is developed based on logistic regression analyses of 396 case histories. The proposed model uses the normalized cone penetration resistance and soil behavior type index as input parameters; therefore, only CPT testing is necessary for evaluating the liquefaction probability of a site. The selection of the model parameters and the expression of equations are based on results of probability examinations and rigorous statistical analyses. Moreover, the derivation of the logistic regression model is presented in a system of equations. The incorporation of these procedures in developing the model not only fully satisfies the statistic requirements but also highlights the physical meanings of the model parameters. Comparisons of the proposed probability model with previously proposed deterministic and probabilistic approaches are performed to demonstrate the improvements. For practical purposes, the developed model is implemented to establish the relationship between the factor of safety against liquefaction and the probability of liquefaction.     

Simplified Cone Penetration Test-based Method for Evaluating Liquefaction Resistance of Soils

This paper presents a new simplified method for assessing the liquefaction resistance of soils based on the cone penetration test (CPT). A relatively large database consisting of CPT measurements and field liquefaction performance observations of historical earthquakes is analyzed. This database is first used to train an artificial neural network for predicting the occurrence and nonoccurrence of liquefaction based on soil and seismic load parameters. The successfully trained and tested neural network is then used to generate a set of artificial data points that collectively define the liquefaction boundary surface, the limit state function. An empirical equation is further obtained by regression analysis to approximate the unknown limit state function. The empirical equation developed represents a deterministic method for assessing liquefaction resistance using the CPT. Based on this newly developed deterministic method, probabilistic analyses of the cases in the database are conducted using the Bayesian mapping function approach. The results of the probabilistic analyses, expressed as a mapping function, provide a simple means for probability-based evaluation of the liquefaction potential. The newly developed simplified method compares favorably to a widely used existing method.     

Estimating Liquefaction-Induced Lateral Displacements Using the Standard Penetration Test or Cone Penetration Test

A semiempirical approach to estimate liquefaction-induced lateral displacements using standard penetration test (SPT) or cone penetration test (CPT) data is presented. The approach combines available SPT- and CPT-based methods to evaluate liquefaction potential with laboratory test results for clean sands to estimate the potential maximum cyclic shear strains for saturated sandy soils under seismic loading. A lateral displacement index is then introduced, which is obtained by integrating the maximum cyclic shear strains with depth. Empirical correlations from case history data are proposed between actual lateral displacement, the lateral displacement index, and geometric parameters characterizing ground geometry for gently sloping ground without a free face, level ground with a free face, and gently sloping ground with a free face. The proposed approach can be applied to obtain preliminary estimates of the magnitude of lateral displacements associated with a liquefaction-induced lateral spread.     

Standard Penetration Test-Based Probabilistic and Deterministic Assessment of Seismic Soil Liquefaction Potential

This paper presents new correlations for assessment of the likelihood of initiation (or “triggering”) of soil liquefaction. These new correlations eliminate several sources of bias intrinsic to previous, similar correlations, and provide greatly reduced overall uncertainty and variance. Key elements in the development of these new correlations are (1) accumulation of a significantly expanded database of field performance case histories; (2) use of improved knowledge and understanding of factors affecting interpretation of standard penetration test data; (3) incorporation of improved understanding of factors affecting site-specific earthquake ground motions (including directivity effects, site-specific response, etc.); (4) use of improved methods for assessment of in situ cyclic shear stress ratio; (5) screening of field data case histories on a quality/uncertainty basis; and (6) use of high-order probabilistic tools (Bayesian updating). The resulting relationships not only provide greatly reduced uncertainty, they also help to resolve a number of corollary issues that have long been difficult and controversial including: (1) magnitude-correlated duration weighting factors, (2) adjustments for fines content, and (3) corrections for overburden stress.     

Evaluation of Flow Liquefaction and Liquefied Strength Using the Cone Penetration Test

Flow liquefaction is a major design issue for large soil structures such as mine tailings impoundments and earth dams. If a soil is strain softening in undrained shear and, hence, susceptible to flow liquefaction, an estimate of the resulting liquefied shear strength is required for stability analyses. Many procedures have been published for estimating the residual or liquefied shear strength of cohesionless soils. This paper presents cone penetration test-based relationships to evaluate the susceptibility to strength loss and liquefied shear strength for a wide range of soils. Case-history analyses by a number of investigators are reviewed and used with some additional case histories. Extrapolations beyond the case-history data are guided by laboratory studies and theory.     

Coupled Use of Cone Tip Resistance and Small Strain Shear Modulus to Assess Liquefaction Potential

Resistance against earthquake-related liquefaction is usually assessed using relationships between an index of soil strength such as normalized cone tip resistance and the cyclic resistance ratio (CRR) developed from observed field performance. The alternative approach based on laboratory testing is rarely used, mainly because of the apprehension that laboratory results may not reflect field behavior since the quality of laboratory data is often compromised by sampling disturbance. In this study, a database of laboratory data obtained mainly from cyclic testing of frozen (undisturbed) samples and in situ index measurements from near sampling locations comprised of cone tip resistance, qc, and shear wave velocity, Vs, have been assembled. These data indicate that neither normalized cone tip resistance nor normalized shear wave velocity individually correlate well with laboratory-measured CRR. However, the ratio of qc to the small strain shear modulus, G0, relates reasonably with CRR via separate correlations depending on geologic age. The derived qc/G0-CRR relationships were also found to be consistent with earthquake field-performance case histories.     

Accounting for Soil Aging When Assessing Liquefaction Potential

It has been recognized that liquefaction resistance of sand increases with age due to processes such as cementation at particle contacts and increasing frictional resistance resulting from particle rearrangement and interlocking. As such, the currently available empirical correlations derived from liquefaction of young Holocene sand deposits, and used to determine liquefaction resistance of sand deposits from in situ soil indices [standard penetration test (SPT), cone penetration test (CPT), shear wave velocity test (Vs)], are not applicable for old sand deposits. To overcome this limitation, a methodology was developed to account for the effect of aging on the liquefaction resistance of old sand deposits. The methodology is based upon the currently existing empirical boundary curves for Holocene age soils and utilizes correction factors presented in the literature that comprise the effect of aging on the in situ soil indices as well as on the field cyclic strength (CRR). This paper describes how to combine currently recorded SPT, CPT, and Vs values with corresponding CRR values derived for aged soil deposits to generate new empirical boundary curves for aged soils. The method is illustrated using existing geotechnical data from four sites in the South Carolina Coastal Plain (SCCP) where sand boils associated with prehistoric earthquakes have been found. These sites involve sand deposits that are 200,000?to?450,000?years in age. This work shows that accounting for aging of soils in the SCCP yields less conservative results regarding the current liquefaction potential than when age is not considered. The modified boundary curves indicate that old sand deposits are more resistant to liquefaction than indicated by the existing empirical curves and can be used to evaluate the liquefaction potential at a specific site directly from the current in situ properties of the soil.     

Compaction Grouting Test Program for Liquefaction Control

Following the 1989 Loma Prieta earthquake a detailed analysis of liquefaction risk was carried out for an industrial site near the Pajaro River at Watsonville, Calif. A ground improvement project, by grouting, was proposed to prevent lateral spreading and a compaction grout test program was undertaken to validate and refine the project design. In phase 1 of the test program the grout hole spacing was 2.5 and 1.8 m (8 and 6 ft). In phase 2, a closer hole spacing was used, 1.5 and 1.2 m (5 and 4 ft.) The test program employed cone penetration tests and standard penetration tests measurements for evaluation before and after each phase. The results showed the relationship of ground improvement versus grout hole spacing, grout take, and grout pressure. The test program showed that a zone of susceptible soils at the site could be effectively improved to increase their strength and resistance to liquefaction and thus prevent lateral spreading during severe earthquakes.     

CPT-Based Probabilistic and Deterministic Assessment of In Situ Seismic Soil Liquefaction Potential

This paper presents a complete methodology for both probabilistic and deterministic assessment of seismic soil liquefaction triggering potential based on the cone penetration test (CPT). A comprehensive worldwide set of CPT-based liquefaction field case histories were compiled and back analyzed, and the data then used to develop probabilistic triggering correlations. Issues investigated in this study include improved normalization of CPT resistance measurements for the influence of effective overburden stress, and adjustment to CPT tip resistance for the potential influence of “thin” liquefiable layers. The effects of soil type and soil character (i.e., “fines” adjustment) for the new correlations are based on a combination of CPT tip and sleeve resistance. To quantify probability for performance-based engineering applications, Bayesian “regression” methods were used, and the uncertainties of all variables comprising both the seismic demand and the liquefaction resistance were estimated and included in the analysis. The resulting correlations were developed using a Bayesian framework and are presented in both probabilistic and deterministic formats. The results are compared to previous probabilistic and deterministic correlations.     

First-Order Reliability Method for Probabilistic Liquefaction Triggering Analysis Using CPT

The potential for liquefaction triggering of a soil under a given seismic loading is measured herein by probability of liquefaction. The first order reliability method (FORM) is used to calculate reliability index, from which the probability of liquefaction is obtained. This approach requires the knowledge of parameter and model uncertainties; the latter is the focus of this paper. An empirical model for determining liquefaction resistance based on cone penetration test (CPT) is established through “neural network learning” of case histories. This resistance model along with a reference seismic loading model forms a performance function or limit state for liquefaction triggering analysis. Within the framework of the FORM, the uncertainty of this limit state model is characterized through an extensive series of sensitivity studies using Bayesian mapping functions that have been calibrated with a set of quality case histories. In addition, a deterministic model for assessing liquefaction potential in terms of factor of safety is presented, and the probability-safety factor mapping functions for estimating the probability of liquefaction for a given factor of safety in the absence of the knowledge of parameter uncertainty are also established. Examples are presented to illustrate the proposed methods.     

Identification of the Soil–Structure Interaction System Using Earthquake Response Data

This paper demonstrates how system identification techniques can be successfully applied to a soil–structure interaction system using the earthquake response data. The parameters identified are the shear moduli of several near-field soil regions and Young’s moduli of the shell sections of the structure. The soil–structure interaction system is modeled by the finite element method combined with the infinite element formulation for the unbounded layered soil medium. The simulated earthquake responses using the identified parameters are shown to be in excellent agreement with the observed response data. Prediction of the responses is also carried out for a larger earthquake event using the identified parameters as the initial properties in the equivalent linearization procedure. It has been found that the predicted responses are also compared very well with the measured responses.     

DSC Model for Soil and Interface Including Liquefaction and Prediction of Centrifuge Test

Realistic predictions of dynamic soil–structure interaction problems require appropriate constitutive models for the characterization of soils and interfaces. This paper presents a unified model based on the disturbed state concept (DSC). The parameters for the models for the Nevada sand, and sand–metal interface are obtained based on available triaxial test data on the sand and interfaces. The predicted stress–strain–pore water pressure behavior for the sand using the DSC model is compared with the test data. In addition, a finite element procedure with the DSC model, based on the generalized Biot’s theory, is used to predict the measured responses for a pile (aluminum) sand foundation problem obtained by using the centrifuge test. The predictions compared very well with measured pore water pressures. The DSC model is used to identify microstructural instability leading to liquefaction. A procedure is proposed to apply the proposed method for analysis and design for dynamic response and liquefaction.     

Assessment of Direct Cone Penetration Test Methods for Predicting the Ultimate Capacity of Friction Driven Piles

This paper evaluates the applicability of eight direct cone penetration test (CPT) methods to predict the ultimate load capacity of square precast prestressed concrete (PPC) driven friction piles. Analyses and evaluation were conducted on 35 driven friction piles of different sizes and lengths that were failed during pile load testing. The CPT methods, as well as the static α and β methods, were used to estimate the load carrying capacities of the investigated piles (QP). The Butler–Hoy method was used to determine the measured load carrying capacities from pile load tests (Qm). The pile capacities determined using the different methods were compared with the measured pile capacities obtained from the pile load tests. Four criteria were selected as bases of evaluation: the best fit line for Qp versus Qm, the arithmetic mean and standard deviation for the ratio Qp/Qm, the cumulative probability for Qp/Qm, and the histogram and log normal distribution for Qp/Qm. Results of the analyses showed that the best performing CPT methods are the LCPC method by Bustamante and Gianeselli as well as the De Ruiter and Beringen method. These methods were ranked number one according to the mentioned criteria.     

Objective Site Characterization Using Clustering of Piezocone Data

Cluster analysis is a statistical method for grouping similar mathematical data sets and is used herein for delineating geostratigraphy from piezocone penetration test data. In terms of site characterization, clustering is an improvement over other statistical methods because no preliminary estimation of the inherent groups within the analyzed data is needed, and no overlapping is permitted between identified clusters. Clustering can accommodate single or multivariables and no data filtering is required. Its application to defining stratigraphic interfaces is illustrated using five case studies with layered profiles. Clustering is able to detect major changes within the stratigraphy not apparent by visually examining the trends of piezocone data or by available cone soil classification methods.     

Mining of Existing Data for Cement-Solidified Wastes Using Neural Networks

This paper summarizes the results of an investigation into the use of neural networks to analyze data collected from the literature regarding the interaction of wastes and hydraulic binders in, and final properties of, cement-solidified wastes. Neural network models were constructed for prediction of the effects of contaminants on setting time, unconfined compressive strength, and leachate pH. It was found that construction of successful models was possible, with prediction errors approaching experimental error, and that modeling was useful for generalizing about the relative effects of the input variables on the outputs using the results from the different studies. The work has shown that the potential for practical implementation of models of this type in prediction of key properties related to long-term behavior, and/or formulation design in waste treatment facilities clearly exists, but more detailed definition of the data space by experimentation, with more complete harmonization of methods and reporting of experimental results, will be necessary to develop reliable commercial models.     

Local Information Access for Search and Rescue Using Wireless Data Storage Mediums

After a natural disaster strikes buildings, it is vital to immediately retrieve the related local information for efficient search and rescue (S&R) operations. Although it seems convenient to store the required local information (e.g., information about neighborhood, buildings) in a centralized database, S&R teams usually cannot access centralized databases because the information infrastructure is usually damaged or overloaded immediately after a disaster. This paper describes the search and rescue data access point (SR-DAP) system that was designed for storing and retrieving the required local information in/from data storage units that are deployed at buildings. In the paper, the developed approach is presented, and two key technologies (i.e., radio-frequency identification (RFID) tags and wireless sensor nodes) that are used as local storage mediums in SR-DAP are empirically evaluated. The results of the field experiments show that current technologies can be effectively utilized in the developed system. However, comparison of the technologies highlights the fact that the current wireless sensor technology is advantageous over RFID technology.     

Model Development and Validation for Intelligent Data Collection for Lateral Spread Displacements

The geotechnical earthquake engineering community often adopts empirically derived models. Unfortunately, the community has not embraced the value of model validation, leaving practitioners with little information on the uncertainties present in a given model and the model’s predictive capability. In this study, we present a machine learning technique known as support vector regression (SVR) together with rigorous validation for modeling lateral spread displacements and outline how this information can be used for identifying gaps in the data set. We demonstrate the approach using the free face lateral displacement data. The results illustrate that the SVR has relatively better predictive capability than the commonly used empirical relationship derived using multilinear regression. Moreover, the analysis of the SVR model and its support vectors helps in identifying gaps in the data and defining the scope for future data collection.     

Persistence of Skewness in Longitudinal Dispersion Data: Can the Dead Zone Model Explain It After All?

Field studies reporting coefficients of temporal skewness that do not decrease in the main flow direction have cast doubt on the transient storage (TS) or dead zone model of longitudinal dispersion in rivers and streams. In this study, the conditions under which the TS model predicts persistent or growing skewness coefficients are investigated. The findings clearly show that, though not outright impossible, an instantaneous slug release into a uniform channel reach is, indeed, extremely unlikely to result in persistent or growing skewness coefficients. In contrast, the passage of a tracer or pollutant along a sequence of (hydraulically) different subreaches may easily give rise to nondecaying skewness coefficients, the occurrence of which is governed by the parameter sets of the subreaches concerned. Thus, the TS model does show a certain potential to explain the persistence of skewness. The findings reported here are expected to be useful in guiding future field studies on the subject. An application of the newly derived criterion to stream tracer data has been successful.