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Learning about a Site Using Sparse Site-Specific Data - Recent Advancements (Keynote Lecture - APSSRA2020)


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About This Lecture

Site-specificity is a unique feature in geotechnical engineering. Site investigation data obtained from one site cannot be directly used for another site. The same principle of site-specificity applies to load-test and monitoring data. However, it is not uncommon that non-site-specific data are used to support site-specific decision-making. For instance, engineers routinely adopt transformation models to estimate design soil/rock parameters, and most transformation models are calibrated by non-site-specific data. It is quite extreme and unrealistic to ban such models. In contrast, the success of such transformation models indicates that non-site-specific data may have certain values for site-specific decision-making. As we enter the era of BIG DATA, it is timely for geotechnical engineering people to ponder the value of non-site-specific databases. Computer science people have been very successful in exploiting the value in non-person-specific or non-case-specific databases (BIG DATA). It is natural for geotechnical engineering people to ask whether we can also exploit some value from non-site-specific databases. This is the main focus of this talk. The answer is YES. The talk will first introduce some exiting BIG DATA in geotechnical engineering. Some are soil/rock property databases, and some are load-test databases. Then, the talk will introduce some advanced methods developed by the author that can extract useful knowledge from BIG DATA to facilitate site-specific decision-making. Without BIG DATA and the advanced methods, such site-specific decision-making was very challenging or even infeasible, but sensible decision-making is now possible with the aid of BIG DATA and the advanced methods. Two types of decision-making will be illustrated: the determination of design soil/rock parameters for foundation design and the prediction of geotechnical structure performances.

Instructor

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Jianye Ching, PhD, Distinguished Professor, Department of Civil Engineering, National Taiwan University

Dr. Ching is Distinguished Professor in the Dept. of Civil Engineering at National Taiwan University. He obtained his PhD degree in 2002 in University of California at Berkeley. His main research interests are geotechnical risk & reliability, random fields & spatial variability, probabilistic site characterization & geotechnical data analytics. He is Chair of TC304 (risk) in ISSMGE and Chair of Geotechnical Safety Network (GEOSNet). He is Editor-in-Chief of Journal of GeoEngineering, Managing Editor of Georisk, Associate Editor of Canadian Geotechnical Journal, and Editorial Board Member of Structural Safety. Dr. Ching is the recipient of the Outstanding Research Award (2011, 2014) and the Wu-Da-Yu Award (2009) from the Ministry of Science and Technology of Taiwan.

  1. Subjects

    Machine Learning and Big Data
  2. Course Number

    TC304-APSSRA-02
  3. Course Launched

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