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Short Courses

Sunday, July 23

Full Day Course

8:00 a.m. – 5:00 p.m.

Quantitative Risk Assessment in Geotechnical Engineering

Instructor: Vaughan Griffiths

The morning section of this full day short course starts with an Introduction to Risk Assessment in Geotechnical Engineering (1.5 hours) followed by an Introduction to Random Variables (1.5 hour). The afternoon section covers Simple Tools for Probabilistic Analysis (1.5 hours) followed by More Advanced Tools for Probabilistic Analysis (1.5 hours) and ending with a Question and Answer session. The morning section covers the fundamentals of loads and resistances, basic probability theory, set theory and Venn diagrams, in addition to conditional probability, total probability theorem, and Bayes’ theorem. It also covers random variables (RVs), common probabilistic density functions, identities relating to expectation and variances, covariance and correlation, and example calculations for linear functions. The afternoon session is focused on simple tools for probabilistic analysis such as the First Order Second Moment (FOSM) and First Order Reliability Method (FORM) methods. Examples of earth pressure and bearing capacity are used to illustrate FOSM and FORM, respectively. This is followed by more advanced probabilistic analysis tools that include Monte-Carlo (M-C) methods and the Random Finite Element Method (RFEM). For M-C methods, the same bearing capacity example will be repeated. For the RFEM method, Software demos of settlement and slope stability analysis will be given.

Half Day Course

8:00 a.m. – 12:00 p.m.

Risk-Informed Decisions in Geotechnical Engineering

Instructors: Bob Gilbert and Gregory Baecher

Geotechnical engineers make decisions every day. So do engineers who collaborate with geotechnical engineers, constructors who work with them, owners who use geotechnical services, insurers who provide professional liability insurance, and regulators. Engineering decisions share two characteristics. First, outcomes from these decisions have consequences, both bad and good. Second, the outcomes are uncertain. Risk-informed decision making is a formal approach to organize, structure, communicate, and support decisions that have uncertain consequences. The goal for this short course is to describe methods available to practitioners to produce engineering solutions that are effective and innovative in managing risk. The methods are demonstrated with case histories and aim at answering the following questions (1) what are the risks associated with a project and how are they best managed? (2) how do we demonstrate that a new method or technology will not add risk? (3) is more site characterization warranted? (4) can the quality assurance/quality control plan be improved? (5) what is the value of the observational method during and after construction? and (6) how can risk be reasonably shared among stakeholders?

Half Day Courses

1:00 – 5:00 p.m.

Bayesian Analysis for Learning Geotechnical Parameters and Models with Measurements

Instructor: Iason Papaioannou

Geotechnical planning and construction is typically associated with large uncertainties and limited data on site conditions. To describe the geotechnical performance as accurately as possible, it is necessary to combine information from different sources (site measurements, expert knowledge, and data from literature). This process can be formalized through Bayesian updating as part of a probabilistic reliability and risk assessment. This short course will introduce the basic concepts and theory of Bayesian updating, and will demonstrate its application to geotechnical engineering by means of practical examples. In the first part of the course, the basic steps of Bayesian analysis will be introduced: choosing the prior distribution model, definition of the likelihood function, updating the model, updating reliability and risk. The second part will present tools for the numerical implementation of Bayesian updating with a focus on Monte Carlo simulation methods.

Big and small Data for Geotechnical Design

Instructors: Kok-Kwang Phoon and Jianye Ching

This short course takes the participants on a whirlwind tour from data to data-informed decision making. Topics that are covered include (1) from reliability to machine learning, (2) statistical analysis of soil databases, (3) statistical analysis of load test databases, and (4) Bayesian machine learning for site characterization. This short course is suitable for students and engineers. Lecture 1 (20 minutes) focuses on big and small data for geotechnical design. Lecture 2 (1 hour) covers univariate and multivariate soil statistics. Lecture 3 (1 hour) covers model statistics for foundations. Finally, Lecture 4 (1.5 hours) focuses on data-driven site characterization. Only basic knowledge contained in a standard undergraduate text on statistics and probability is needed for Lectures 1 to 4.

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