Probabilistic Assessment of Stress Normalization for CPT Data

Currently available cone penetration test (CPT) stress normalization schemes exhibit no consensus on the estimation of the stress normalization component. Depending on which power law stress normalization exponent is used, very different interpretations may result in the analyses where normalized CPT data are used (e.g., CPT-based soil classification and seismic soil liquefaction initiation assessment). Within the confines of this paper, it is intended to clarify and resolve some of these differences, and to propose improved recommendations for CPT stress normalization. For this purpose, available stress normalization databases from theoretical, numerical, and field data analyses approaches were compiled. For the soil types, and stress conditions where compiled database is not conclusive, additional finite element simulations have been performed. The resulting relationship not only eliminates several sources of bias intrinsic to previous, similar correlations, and provides greatly reduced overall uncertainty and variance, it also helps to establish a consensus to the stress normalization issue that have long been difficult and controversial. Key elements in the development of these new correlations are: (1) accumulation of a significantly expanded database of analytical/numerical CPT simulation results, as well as field and chamber test data from homogeneous soil layers; (2) use of improved knowledge and understanding of factors affecting CPT and stress normalization; and (3) use of high-order probabilistic tools (Bayesian updating).     

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.     

Probabilistic Models for Cyclic Straining of Saturated Clean Sands

A maximum likelihood framework for the probabilistic assessment of postcyclic straining of saturated clean sands is described. Databases consisting of cyclic laboratory test results including maximum shear and postcyclic volumetric strains in conjunction with relative density, number of stress (strain) cycles, and “index” test results were used for the development of probabilistically based postcyclic strain correlations. For this purpose, in addition to the compilation of existing data from literature, a series of stress-controlled cyclic triaxial and simple shear tests were performed on laboratory-constituted saturated clean sand specimens. The variabilities in testing conditions (i.e., type of test, consolidation procedure, confining pressure, rate of loading, etc.) were corrected through a series of correction schemes, the effectiveness of which were later confirmed by the discriminant analyses results. Volumetric and shear strain boundary curves were developed in the cyclic stress ratio versus N1,60,CS or qc,1 domain. In addition to being based on significantly extended and higher quality databases, contrary to the existing judgmentally derived deterministic ones, proposed correlations have formal probabilistic bases, and so provide insight regarding uncertainty of strain predictions or probability of exceeding a target strain value. Probabilistic uses of the proposed correlations were illustrated by three sets of examples. A companion paper applied and calibrated the proposed volumetric strain correlation to semiempirically evaluate postearthquake settlement of level, free-field sites. For the calibration, case history soil profiles, composed of a broad range of sand types and depositional characteristics, shaken by a number of earthquakes, were used. Superior predictions of field settlements by this laboratory data-based cyclic strain assessment approach were concluded to be strongly mutually supportive.     

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.     

Reliability-Based Load and Resistance Factor Rating Using In-Service Data

Traditional bridge evaluation techniques are based on design-based deterministic equations that use limited site-specific data. They do not necessarily conform to a quantifiable standard of safety and are often quite conservative. The newly emerging load and resistance factor rating (LRFR) method addresses some of these shortcomings and allows bridge rating in a manner consistent with load and resistance factor design (LRFD) but is not based on site-specific information. This paper presents a probability-based methodology for load-rating bridges by using site-specific in-service structural response data in an LRFR format. The use of a site-specific structural response allows the elimination of a substantial portion of modeling uncertainty in live load characterization (involving dynamic impact and girder distribution), which leads to more accurate bridge ratings. Rating at two different limit states, yield and plastic collapse, is proposed for specified service lives and target reliabilities. We consider a conditional Poisson occurrence of identically distributed and statistically independent (i.i.d.) loads, uncertainties in field measurement, modeling uncertainties, and Bayesian updating of the empirical distribution function to obtain an extreme-value distribution of the time-dependent maximum live load. An illustrative example uses in-service peak-strain data from ambient traffic collected on a high-volume bridge. Serial independence of the collected peak strains and of the counting process, as well as the asymptotic behavior of the extreme peak-strain values, are investigated. A set of in-service load and resistance factor rating (ISLRFR) equations optimized for a suite of bridges is developed. Results from the proposed methodology are compared with ratings derived from more traditional methods.     

Correlation of Elastic Constants with Penetration Resistance in Sandy Soils

In this note, we investigate some existing correlations between soil modulii and penetration resistance using a direct approach. We make use of settlement data from case histories for both shallow and deep foundations over sandy soils. Knowledge of foundation dimensions, loads, and measured settlements permits us, using elasticity theory, to calculate the theoretical soil modulus. We can then directly compare the calculated modulus with measured penetration resistance. A total of 276 case histories are considered. The resulting data set is used to assess the efficacy of two well-known correlations between soil modulii and penetration resistance. We find that both correlations provide reasonably good representations of the data, but have a conservative bias in the sense that the predicted modulus values are smaller than the best-fit results. From an engineering standpoint, both correlations represent reasonable solutions and our results should lend comfort to future users of either correlation.     

Practical Issues for the Application of High-Performance Concrete to Highway Structures

In recent years, the advantages of high-performance concrete (HPC) have been well documented. Among others, these advantages include enhanced design flexibility and improved durability performance that results in reduced maintenance costs and an increased service life. Despite these obvious benefits, the implementation of HPC has been very slow. This can be attributed to several factors including the uncertainty related to current design codes and a lack of familiarity of designers and contractors with practices and requirements for proper design and construction of high-performance concrete structures. This paper introduces and discusses several fundamental issues that affect the implementation of HPC and impact the practitioner. These include issues related to quality control∕quality assurance, specifications, material performance, and structural behavior. Within the scope of this discussion, the fundamental differences and similarities between HPC and conventional concrete are discussed. The objective of this discussion is to provide the practicing engineer with a conceptual understanding of the practical issues that affect the design and use of HPC for highway structures with the desire to further stimulate the implementation of HPC.     

Laboratory Investigation on Assessing Liquefaction Resistance of Sandy Soils by Shear Wave Velocity

Shear wave velocity (Vs) offers engineers a promising alternative tool to evaluate liquefaction resistance of sandy soils, and the lack of sufficient in-situ databases makes controlled laboratory study very important. In this study, semitheoretical considerations were first given based on review of previous liquefaction studies, which predicted a possible relationship between laboratory cyclic resistance ratio (CRRtx) and Vs normalized with respect to the minimum void ratio, confining stress and exponent n of Hardin equation. Undrained cyclic triaxial tests were then performed on three reconstituted sands with Vs measured by bender elements, which verified this soil-type-dependent relationship. Further investigation on similar laboratory studies resulted in a database of 291 sets of data from 34 types of sandy soils, based on which the correlation between liquefaction resistance and Vs was established statistically and further converted to equivalent field conditions with well-defined parameters, revealing that CRR will vary proportionally with (Vs1)4. Detailed comparisons with Vs-based site-specific investigations show that the present lower-bound CRR–Vs1 curve is a reliable prediction especially for sites with higher CSR or Vs1. The framework of liquefaction assessment based on the present laboratory study is proposed for engineering practice.     

Assessing Probability-based Methods for Liquefaction Potential Evaluation

This paper presents an assessment of existing and new probabilistic methods for liquefaction potential evaluation. Emphasis is placed on comparison of probabilities of liquefaction calculated with two different approaches, logistic regression and Bayesian mapping. Logistic regression is a well-established statistical procedure, whereas Bayesian mapping is a relatively new application of the Bayes’ theorem to the evaluation of soil liquefaction. In the present study, simplified procedures for soil liquefaction evaluation, including the Seed–Idriss, Robertson–Wride, and Andrus–Stokoe methods, based on the standard penetration test, cone penetration test, and shear wave velocity measurement, respectively, are used as the basis for developing Bayesian mapping functions. The present study shows that the Bayesian mapping approach is preferred over the logistic regression approach for estimating the site-specific probability of liquefaction, although both methods yield comparable probabilities. The paper also compares the three simplified methods in the context of probability of liquefaction, and argues for the use of probability-based procedures for evaluating liquefaction potential.     

Host differences in neoplastic risk: Behavioral and social contributors to disease.

Examines the behavioral and social variables that contribute to the development and course of cancer. Lifestyle factors that influence cancer initiation are discussed, including tobacco and alcohol use, occupation, dietary fat consumption, and stress effects on the immune system. Social factors that delay secondary prevention (i.e., seeking diagnosis, self-examination) are identified. The effect of behavioral-emotional factors on hormonally dependent tumors and patient noncompliance are discussed as factors affecting cancer progression. The relationship between socioeconomic status (SES) and treatment outcome is addressed. It is concluded that an understanding of modifiable differences in host risk for cancer is necessary for more effective treatment. (PsycINFO Database Record (c) 2010 APA, all rights reserved)