Model Uncertainties in Foundation Design
Model Uncertainties in Foundation Design

Author: Chong Tang

Publisher: CRC Press

Published: 2021-03-16

Total Pages: 589

ISBN-13: 0429658397

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Model Uncertainties in Foundation Design is unique in the compilation of the largest and the most diverse load test databases to date, covering many foundation types (shallow foundations, spudcans, driven piles, drilled shafts, rock sockets and helical piles) and a wide range of ground conditions (soil to soft rock). All databases with names prefixed by NUS are available upon request. This book presents a comprehensive evaluation of the model factor mean (bias) and coefficient of variation (COV) for ultimate and serviceability limit state based on these databases. These statistics can be used directly for AASHTO LRFD calibration. Besides load test databases, performance databases for other geo-structures and their model factor statistics are provided. Based on this extensive literature survey, a practical three-tier scheme for classifying the model uncertainty of geo-structures according to the model factor mean and COV is proposed. This empirically grounded scheme can underpin the calibration of resistance factors as a function of the degree of understanding – a concept already adopted in the Canadian Highway Bridge Design Code and being considered for the new draft for Eurocode 7 Part 1 (EN 1997-1:202x). The helical pile research in Chapter 7 was recognised by the 2020 ASCE Norman Medal.

Amount of Uncertainty in the Methods Utilized to Design Drilled Shaft Foundations
Amount of Uncertainty in the Methods Utilized to Design Drilled Shaft Foundations

Author: Morgan Lindsey Race

Publisher:

Published: 2015

Total Pages: 606

ISBN-13: 9781321707892

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In 2001, load and resistance factor design (LRFD) for deep foundations was required by the American Association of State Highway and Transportation Officials (AASHTO). Following implementation of LRFD, localized calibration of resistance factors using data from the states of Colorado, Florida, Kansas, Louisiana/Mississippi, Missouri allowed these states to utilize higher resistance factors during design. However, characterizing the uncertainty in the design of DSF, regarding the geotechnical investigation methods and the utilized software programs, higher values of resistance factors may be calibrated to more efficiently design DSF with the same level of reliability.

Risk and Reliability in Geotechnical Engineering
Risk and Reliability in Geotechnical Engineering

Author: Kok-Kwang Phoon

Publisher: CRC Press

Published: 2018-10-09

Total Pages: 624

ISBN-13: 1482227223

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Establishes Geotechnical Reliability as Fundamentally Distinct from Structural Reliability Reliability-based design is relatively well established in structural design. Its use is less mature in geotechnical design, but there is a steady progression towards reliability-based design as seen in the inclusion of a new Annex D on "Reliability of Geotechnical Structures" in the third edition of ISO 2394. Reliability-based design can be viewed as a simplified form of risk-based design where different consequences of failure are implicitly covered by the adoption of different target reliability indices. Explicit risk management methodologies are required for large geotechnical systems where soil and loading conditions are too varied to be conveniently slotted into a few reliability classes (typically three) and an associated simple discrete tier of target reliability indices. Provides Realistic Practical Guidance Risk and Reliability in Geotechnical Engineering makes these reliability and risk methodologies more accessible to practitioners and researchers by presenting soil statistics which are necessary inputs, by explaining how calculations can be carried out using simple tools, and by presenting illustrative or actual examples showcasing the benefits and limitations of these methodologies. With contributions from a broad international group of authors, this text: Presents probabilistic models suited for soil parameters Provides easy-to-use Excel-based methods for reliability analysis Connects reliability analysis to design codes (including LRFD and Eurocode 7) Maximizes value of information using Bayesian updating Contains efficient reliability analysis methods Accessible To a Wide Audience Risk and Reliability in Geotechnical Engineering presents all the "need-to-know" information for a non-specialist to calculate and interpret the reliability index and risk of geotechnical structures in a realistic and robust way. It suits engineers, researchers, and students who are interested in the practical outcomes of reliability and risk analyses without going into the intricacies of the underlying mathematical theories.

Geotechnical Related Development and Implementation of Load and Resistance Factor Design (LRFD) Methods
Geotechnical Related Development and Implementation of Load and Resistance Factor Design (LRFD) Methods

Author: George G. Goble

Publisher: Transportation Research Board

Published: 1999

Total Pages: 80

ISBN-13: 9780309068543

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This synthesis report will be of interest to geotechnical, structural, and bridge engineers, especially those involved in the development and implementation of the geotechnical aspects of the AASHTO Bridge Code. The synthesis documents a review of geotechnical related LRFD specifications and their development worldwide to compare them with the current AASHTO LRFD Bridge Code. Design procedures for foundations, earth retaining structures, and culverts are summarized and compared with the methods specified by the AASHTO code. This TRB report provides information designed to assist engineers in implementing the geotechnical features of LRFD methods. Information for the synthesis was collected by surveying U.S. and Canadian transportation agencies and by conducting a literature search using domestic and international sources. Interviews were also conducted with selected international experts. The limited available experience in the United States and information from international practice are discussed to understand the problems that have arisen in order that solutions may be found. Based on the studies reported here, suggestions for improving the code are identified.