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The importance of the geological model in a geotechnical engineering project has long been recognized. However, the uncertainty associated with the geological model has rarely been quantified and explicitly considered in the geotechnical design. This paper explores the role of the geological model uncertainty and the benefit of reducing such uncertainty in a geotechnical design. To this end, the landslide that occurred on 25 April 2010 at 3.3 K of Freeway No. 3 in Northern Taiwan, referred to herein as NH-3 Slope, is re-analyzed with an assumed geological model (a dip-slope model in this case) under various uncertainty scenarios. Traditionally, the design (i.e., a choice of design parameters such as slope height, slope angle, and supporting anchors) of a rock slope seeks to satisfy a target factor of safety (FS). In the case of NH-3 Slope, the apparent dip angle of the slip surface along the dip direction of slope constitutes the geological model of concern. Different survey techniques produce data with varying degrees of error (or uncertainty) in the measured apparent dip angle. For example, data from a Regional Geological Map (RGM) typically yields an estimate of the bedding plane attitude with a significant level of uncertainty due primarily to low data density. In contrast, LiDAR can significantly reduce the uncertainty of the derived bedding plane attitudes. By lowering this uncertainty, the variation (or uncertainty) in the computed FS may be reduced, which tends to yield a lower failure probability (P f), given other conditions being the same. The benefit of lowering the geological model uncertainty in a geotechnical design is demonstrated through a retrospective analysis of the NH-3 Slope. • We explored the role of geological uncertainty on a dip slope failure in Taiwan. • We treated bedding attitudes as the main geological uncertainty in the stability analysis. • We found the use of LiDAR can lower the uncertainty of derived bedding attitudes. • We demonstrated the engineering benefit of reducing geological uncertainty. [ABSTRACT FROM AUTHOR]

This paper presents an update of the Hyperbolic Function (HBF) method to evaluate the liquefaction potential of sandy soils. The HBF method was created after the 1999 Chi-Chi, Taiwan Earthquake (M w = 7.6) using the database of case histories developed based on field observations and in situ and laboratory tests in areas affected by the 1999 event. The updated HBF method reported herein reflects the 20-year efforts of research and practice in Taiwan after the 1999 event. Here, the original database created by Hwang and his co-workers was first re-examined and updated. The updated Chi-Chi database was supplemented with the cases derived from the 2016 Meinong, Taiwan Earthquake, and some selected data from other parts of the world. This new database was then used to update the parameters of the HBF model through rigorous statistical methods. The HBF model is unbiased (as it corresponds to a probability of 50%). The updated HBF method, a deterministic model, is further extended into a probabilistic version through rigorous statistical calibration. This probabilistic version is expressed as a mapping function that relates the factor of safety (F S) against liquefaction to the probability of liquefaction (P L). The design recommendation for engineering practice in Taiwan is provided based on the calibrated P L - FS mapping function. The updated HBF method is demonstrated through examples to be an easy-to-use procedure for liquefaction potential evaluation. Use of Bayes' theorem for updating the liquefaction probability and the new concept of liquefaction likelihood ratio are also discussed. • An updated database of liquefaction and non-liquefaction cases from Taiwan is created. • An easy-to-use HBF model for liquefaction evaluation is presented, which emerged from two decades of research in Taiwan. • Procedures for the calibration of empirical models for liquefaction evaluation are introduced and illustrated. • The concept of the liquefaction likelihood ratio (LR) is presented with a graph of LR contour curves for Bayesian updating. [ABSTRACT FROM AUTHOR]