Geologic uncertainty appears in the form of one soil layer embedded in another or the inclusion of pockets of different soil types within a more uniform soil mass. An efficient coupled Markov chain (CMC) model has been proposed to simulate geological uncertainty in the literature. This model, however, cannot be directly applied to geotechnical engineering. The primary problem lies in the estimation of horizontal transition probability matrix (HTPM), one key input of the CMC model. The HTPM is difficult to estimate due to the wide spacing between boreholes in the horizontal direction. Hence, a practical method for estimating the HTPM is verified using artificial borehole data. The effectiveness of this method is evaluated using the approach as follows. Several virtual boreholes are created using a prescribed HTPM. The HTPM estimated from the virtual boreholes is compared with the prescribed (or actual) HTPM. The evaluation results show that the estimated HTPM agrees well with the prescribed HTPM if the prescribed HTPM and VTPM are both highly diagonally dominant (diagonal element is larger than the sum of off-diagonal elements).

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The purpose of this study is to examine in more detail under what conditions would spatial averaging over some prescribed region be sufficient to reproduce the response statistics arising from a spatially varying field. The spatially variable undrained shear strength will be first simulated by a random field. The active lateral force of the spatially variable soil mass to a retaining wall is simulated using the random field finite element method. This active lateral force is the actual active lateral force exhibited by the spatially variable soil. This actual active lateral force is compared to the active lateral force of a homogeneous soil mass whose shear strength equal to the spatial average over a prescribed area/line of interest. Based on these numerical studies, it is observed that the actual active lateral force (a random variable) and the spatial average active lateral force (a second random variable) are at most equal in “distribution”, but not “almost everywhere”.

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This paper presents a probabilistic slope stability analysis approach that formulates the slope failure event as a series of mutually exclusive and collectively exhaustive events using conditional probability and utilizes Monte Carlo simulation (MCS) to determine the occurrence probability for each of the mutually exclusive and collectively exhaustive events in a progressive manner. The probabilities of each event are aggregated to represent the overall slope failure probability pf. The pf values obtained from the proposed approach are shown to agree well with those pf values that have been obtained by searching a large number of potential slip surfaces for the minimum factor of safety in each MCS sample. The computational efficiency, however, is shown to improve by, at least, an order of magnitude. In addition, the approach identifies the key failure modes (i.e., those slip surfaces that have significant effects on pf) which can be readily used in the mitigation of landslide risk.

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To mitigate the disasters of decrepit earth-fill dams, improvement work is conducted. Since there is a recent demand for low-cost improvements, the development of a design method for optimum improvement work at a low cost is the final objective of this research. The reliability-based design approach is discussed as such a design method. Heavy rains are the most serious events for aged fill-dams, and sometimes overflows leading to breaching occur. From the estimated probability of overflow, the probability of submergence in the downstream is estimated for the reliability-based design. The remarkable point in this paper is that the probability of occurrences of heavy rains and the uncertainty of the soil properties are simultaneously considered for the design. Considering the disaster loss, the risk of submergence could be assessed.

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Worldwide, many people depend on groundwater for daily drinking water. Groundwater contamination is being increasingly reported in recent years. The development of a rational remediation method is an important subject in geo-environmental engineering. This research project aims to develop a reliability-based remediation method for the pump-and-treat method, which is the most popular remediation method for contaminated groundwater. In this paper, the general concept of the proposed method and the reliability analysis method for determining the remediation target are explained, and the usefulness of the proposed method is discussed based on three-dimensional numerical simulation results.

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This paper proposes a method for optimal sampling planning, i.e., the number and placement of additional sampling for land with arbitrary shape and with existing sampling data, under the assumption of Gaussian random field with respect to a characteristic parameter. A set of optimal locations for additional sampling are evaluated as a solution of optimization problem, in which its objective function is Value of Information (VoI), and the optimization method is Particle Swarm Optimization. Optimal number of sampling is also evaluated by total cost, i.e., sum of observation cost and VoI. The balance of penalties and observation cost determines the optimal number of additional sampling.

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With an increasing demand of a renewable energy, new offshore wind turbine farms are being planned in some parts of the world. Foundation installations need a significant cost of the total budget of offshore wind turbine (OWT) projects. Hence, a cost reduction from foundation parts is a key when a cost-efficient designing of OWT budget. Mono-piles have been largely used, accounting about 78% of existing OWT foundations, because they are considered as a most economical alternative with a relatively shallow-water, less than 30m, depths. And OWT design codes such as EC, GL, DNV, API, and Eurocode are being developed in a form of reliability based limit state design method. In this paper, reliability analysis using a response surface and simulation methods for an OWT mono-pile foundation were performed to investigate the sensitivities of mono-pile design parameters, and to find practical implications of reliability analysis.

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