Abstract
The purpose of this paper is to address the “what,” “why,” and “how” questions posed by engineers who are not familiar with geotechnical reliability and have not kept abreast of recent rapid developments in this field. Geotechnical reliability can be broadly defined as a methodology that enhances decision making at different life-cycle stages covering design, construction, operation and maintenance, retrofit, and decommission/reuse by exploiting the richer characterization of data using probabilistic models. Besides engineered systems, it also covers the risk assessment and management of geohazards such as earthquakes and landslides. Various application areas related to the design and construction stages of engineered systems can be put in context in the form of an uncertainty-informed Burland Triangle. Among these application areas, the estimation of a characteristic value, load and resistance factor design (LRFD), resistance factor calibration for simplified reliability-based design (RBD), and first-order second-moment (FOSM) reliability analysis do not need in-depth knowledge/expertise in reliability and a significant amount of information exists to support these applications in practice. This paper argues for their adoption because they will nudge a mindset shift to be more responsive to data. Data infrastructure is now considered to be as important as physical infrastructure. The concern that there are insufficient data for probabilistic analysis has also been largely and comprehensively resolved by recent advances in Bayesian machine learning methods that can deal with MUSIC-3X (Multivariate, Uncertain and Unique, Sparse, Incomplete, and potentially Corrupted with “3X” denoting 3D spatial variability) site data directly. Sparsity (insufficient data) is only one out of six attributes in a real-world MUSIC-3X data set. Geotechnical reliability is now pictured as one important step toward digital transformation and engaging complex new challenges posed by climate change and resilience engineering. This paper urges the geotechnical engineering profession to lay aside its questions on quantity, quality, and/or other “ugly” attributes and offer our data an opportunity to speak for itself. There is prima facie evidence to warrant a thorough exploration of data-centric geotechnics.
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Data Availability Statement
No data, models, or code were generated or used during the study.
Acknowledgments
The author thanked Ms. Puvaneswary Rajarathnam and the Australian Geomechanics Society (AGS) Sydney Chapter (Chair, Dr. Ali Parsa-Pajouh) for the invitation to deliver a keynote lecture at the AGS Sydney Symposium 2022. This paper is an extended version of the keynote paper (https://qrco.de/bdSqnv). The author is grateful to the anonymous reviewers for their insightful comments and to Professor Michael Beer for his encouragement to prepare this review paper for the ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems: Part A. Civil Engineering. Ms. Brigid Cami, Professor Jianye Ching, Dr. Sina Javankhoshdel, Dr. Richard Kelly, Mr. Samuel Kivell, Dr. Monica Löfman, Professor Takayuki Shuku, and Professor Chong Tang assisted in the preparation of the final draft.
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ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 9 • Issue 2 • June 2023
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© 2023 American Society of Civil Engineers.
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Published online: Apr 5, 2023
Published in print: Jun 1, 2023
Discussion open until: Sep 5, 2023
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- Construction methods
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- Disaster risk management
- Engineering fundamentals
- Geotechnical engineering
- Infrastructure
- Load and resistance factor design
- Load factors
- Mathematics
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