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• River bed elevation changes can contribute significantly to flood damage. • To model episodic river bed changes, we propose a new stochastic model. • We test the model using data from a highly active montane river. • Over long and short times, the modeled distributions fit the data well. • The model can forecast future geomorphic risk to riverine infrastructure. In some steep valleys, flood-induced changes in river bed elevation pose significantly greater risks to infrastructure than floodwaters alone. Over the short term, the river may aggrade or degrade by several meters during a single flood. Whereas floodwaters recede after each event, moreover, riverbed changes add up over successive floods. To quantify the resulting geomorphic risk and its evolution over time, we propose in this paper a new stochastic model of river bed elevation change. The bed is assumed to rise and drop according to a random walk, driven by the composition of two gamma processes that respectively pace the hydrologic forcing and the geomorphic response. The model can therefore incorporate various sources of uncertainty, associated with precipitation and debris flow activity within the contributing watershed. To test the model, we apply it to a highly active montane river, the Laonong River in southwestern Taiwan. Model calibration is achieved from a combination of long and short term data, including radiocarbon-dated deposits and modern river records. The modelled distributions fit the data well, including the likelihood of extreme changes. The model also produces a reasonable hindcast of the geomorphic damage suffered over the last ten years by Highway 20, a vulnerable road link sited along the river, and can be used to forecast future geomorphic risk. [ABSTRACT FROM AUTHOR]

The Gangweigou floodway, across the Quiren and Rende districts in Tainan, Taiwan, was designed to bypass partial discharge in the Gangweigou River during heavy rainfall events for the purpose of flood control. This floodway was an excavated open channel supported by single or double rows of precast RC cantilever pile walls on each side of its banks; these piles were installed by the jetting-assisted method. Twenty days after the completion of the floodway, it was put to work for the first time during a heavy rainfall. Unexpectedly, the cantilever pile walls in several sections failed and caused the collapse of the slope behind the walls. This paper explores the failure mechanism and the real causes of failure in this case. Based on the conditions of hydrology and hydraulics, failure processes and the results of stability analyses, it was concluded that ignoring a possible bed incision or bank erosion was likely the primary problem with the improper design. The most critical condition was when the water level in the floodway quickly dropped, which resulted in rapid drawdown. The failure of the case could have been avoided if both the rapid drawdown condition and the potential of the channel bed incision were considered. [ABSTRACT FROM AUTHOR]

Earthquakes can generate seismic disturbances that propagate vast distances and trigger landslides that can achieve high-speeds. It remains difficult to identify the co-seismic ground motion of these landslides and their triggering earthquakes. In this paper, we report on the analysis of co-seismic ground motions generated by the initiating fracture and deposition impact of the Tsaoling landslide. The landslide, with a source volume of 125 × 10 6 m 3 , was triggered by the 1999 Chi-Chi earthquake in Taiwan. The ground motion was recorded by a strong ground motion station, CHY080, near the scar area. The polarization of the seismic waves indicates that the peak acceleration was parallel to the dip direction. Modified ensemble empirical mode decomposition (EEMD), with additional clustering analysis, was applied to decompose the seismic signals. Two instances were found in the seismic records of a series of peculiar wave packets, with the first being associated with the landslide initiation and the second the landslide impact on the deposit valley. To confirm the first landslide breakage, the decomposed signals were compared with the predictions of the analytic elastic wave model and Newmark analysis. The landslide impact was verified with a computational fluid dynamic simulation. Comparison between the EEMD decomposed signals, elastic wave theory, Newmark rigid body analysis, and numerical simulation demonstrates the claimed landslide motion. [ABSTRACT FROM AUTHOR]

Site characterization, which aims to characterize the subsurface stratigraphic configuration and the associated geo-properties, has long been a significant challenge in geological and geotechnical practice. Due to the complexity and inherent spatial variability of the geological bodies and the limited availability of borehole data, uncertainty is unavoidable in the characterized subsurface stratigraphic configuration and the associated geo-properties. In previous studies, the stratigraphic uncertainty and the geo-properties uncertainty were characterized separately. This paper proposes a conditional random field approach for a coupled characterization of stratigraphic and geo-properties uncertainties. The spatial correlation of the stratum existence between different subsurface elements and the spatial correlation of geo-properties are characterized by two autocorrelation functions, determined with the maximum likelihood principle. With the knowledge of the spatial correlation of the stratum existence, the stratigraphic configuration can be sampled using a modified random field approach. Then, the spatial correlation of the geo-properties is updated based on the sampled stratigraphic configuration. With the updated spatial correlation of the geo-properties, the spatial distribution of the geo-properties can readily be simulated with the conditional random field theory. The effectiveness of the proposed approach is demonstrated through a case study of probabilistic site characterization of an offshore wind farm site in Taiwan. To extend the applicability of the proposed approach, a probabilistic evaluation of liquefaction potential at this site under a given seismic shaking level is performed. • A probabilistic method for characterizing the geological model is proposed. • Characterizations of stratigraphic and geo-properties uncertainties are coupled. • Stratigraphic configuration is simulated with modified random field approach. • Spatial variability of geo-properties is simulated with conditional random field. • The spatial correlation structure is estimated with maximum likelihood principle. [ABSTRACT FROM AUTHOR]

This paper presents a new application of the Bayesian approach to objectively determine logic-tree weights for ground motion prediction equations (GMPEs) used in seismic hazard analysis. This is a considerable improvement over the current practice based on subjective judgment to determine the logic-tree weights for GMPEs. To demonstrate the feasibility of this Bayesian application, case studies at three sites in Taiwan were conducted. The results demonstrate that the logic-tree weights for three Next Generation Attenuation GMPEs can be determined in an objective and repeatable manner using this Bayesian calculation, which is based on the difference between predicted and observed peak ground accelerations. • A Bayesian application to determine logic-tree weights for PSHA • The determination based on observed earthquake ground motions • Case studies using the method also presented [ABSTRACT FROM AUTHOR]

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]

Liquefaction potential analysis is a required task in the foundation design process of offshore wind turbine in Taiwan. The standard penetration test (SPT) is mostly used in the preliminary soil investigation of the pilot offshore wind farm in Chang-Bin, Taiwan. Due to the different experimental conditions and operating conditions, the N value (SPT-N) of SPT varies greatly. This study applies statistical methods in conjunction with the New Japan Road Association simplified-empirical method (NJRA method) to incorporate the uncertainty of SPT-N values into the offshore liquefaction potential assessment to quantify the risk of seabed liquefaction. The study statistics the field experimental geotechnical parameters SPT-N and determines the probability density function of the SPT-N distribution of each layer of soil in the offshore wind farm. In order to quantify the risk of seabed soil liquefaction potential, the Monte Carlo random sampling method is used, and by the NJRA method to carry out the seabed liquefaction potential in Changhua, Taiwan. After comparing, the results obtained by the current SPT-based soil liquefaction potential assessment by deterministic approach are conservative. In the ground investigation of offshore wind farm development, the method proposed in this paper can access the thickness of liquefiable soil layers under any given probability for optimizing offshore wind turbine foundation design. • We propose a new method to quantify the risk of the seabed soil liquefaction potential. • This method can assess the thickness of liquefiable soil layers under any given probability. • We present probability distribution of soil SPT-N obtained from offshore wind farms in Taiwan. • The effect of uncertainty of SPT-N on the soil liquefaction potential assessment is presented. [ABSTRACT FROM AUTHOR]