The accuracy of stability evaluation of a natural slope consisting of multiple soil and rock layers, regardless the adopted analysis methods, can be highly dependent upon a precise description of the ...subsurface soil/rock stratigraphy. However, in practice, due to the limitation of site investigation techniques and project budget, stratigraphy of the slope cannot be observed completely and directly; therefore, there remains a considerable degree of uncertainty in the interpreted subsurface soil/rock stratification. Therefore, estimating and minimizing the uncertainty of the computed factor of safety (FS) due to the uncertain site stratigraphy is an important issue in gaining confidence on the stability evaluation outcome. Presented in this paper is a practical analysis approach for evaluating the stability of slopes considering uncertain stratigraphic profiles by incorporating a recently developed stochastic stratigraphic modeling technique into a conventional finite element simulation approach. The stochastic modeling techniques employed for simulating the stratigraphic uncertainty will be briefly described. The main efforts are focused on elucidating the additional benefits from the proposed analysis approach, including a more reasonable probabilistic estimation of FS with consideration of stratigraphic uncertainty, as well as an effective approach for finding the optimum location of additional borehole logs to reduce the uncertainty of FS due to uncertain subsurface stratigraphy.
Stratigraphic (or lithological) uncertainty refers to the uncertainty of boundaries between different soil layers and lithological units, which has received increasing attention in geotechnical ...engineering. In this paper, an effective stochastic geological modeling framework is proposed based on Markov random field theory, which is conditional on site investigation data, such as observations of soil types from ground surface, borehole logs, and strata orientation from geophysical tests. The proposed modeling method is capable of accounting for the inherent heterogeneous and anisotropic characteristics of geological structure. In this method, two modeling approaches are introduced to simulate subsurface geological structures to accommodate different confidence levels on geological structure type (i.e., layered vs. others). The sensitivity analysis for two modeling approaches is conducted to reveal the influence of mesh density and the model parameter on the simulation results. Illustrative examples using borehole data are presented to elucidate the ability to quantify the geological structure uncertainty. Furthermore, the applicability of two modeling approaches and the behavior of the proposed model under different model parameters are discussed in detail. Finally, Bayesian inferential framework is introduced to allow for the estimation of the posterior distribution of model parameter, when additional or subsequent borehole information becomes available. Practical guidance of using the proposed stochastic geological modeling technique for engineering practice is given.
•A stochastic geological modeling method is proposed based on Markov random field•Two modeling approaches are developed with accommodating geological structure type•Stochastic subsurface realizations are generated to quantify stratigraphic uncertainty•Bayesian inferential framework is introduced to estimate the model parameter
This paper presents a novel perspective to understanding the spatial and statistical patterns of a cone penetration dataset and identifying soil stratification using these patterns. Both local ...consistency in physical space (i.e., along depth) and statistical similarity in feature space (i.e., logQ
t
–logF
r
space, where Q
t
is the normalized tip resistance and F
r
is the normalized friction ratio, or the Robertson chart) between data points are considered simultaneously. The proposed approach, in essence, consists of two parts: (i) a pattern detection approach using the Bayesian inferential framework and (ii) a pattern interpretation protocol using the Robertson chart. The first part is the mathematical core of the proposed approach, which infers both spatial pattern in physical space and statistical pattern in feature space from the input dataset; the second part converts the abstract patterns into intuitive spatial configurations of multiple soil layers having different soil behavior types. The advantages of the proposed approach include probabilistic soil classification and identification of soil stratification in an automatic and fully unsupervised manner. The proposed approach has been implemented in MATLAB R2015b and Python 3.6, and tested using various datasets including both synthetic and real-world cone penetration test soundings. The results show that the proposed approach can accurately and automatically detect soil layers with quantified uncertainty and reasonable computational cost.
•Clustering analysis and Bayesian theory are combined in pipeline integrity assessment.•Clustering analysis can aid in determining the similarity of corrosion defects.•Measurement noises are ...considered and systematic errors are studied and estimated.•Probability of detection and probability of false alarm are incorporated into the model.
In the petroleum industry, oil and gas pipeline operators routinely employ non-destructive in-line inspection (ILI) tools to perform integrity assessment, in which corrosion defects are detected, located and sized. However, the inspection technology is not perfect, and its accuracy is influenced by intrinsic measurement error of the ILI device as well as measurement noise. The quality of the inspection result should be assessed, and the inspection device should be calibrated before further assessment is performed. In the present work, a calibration model for an ultrasonic ILI device is proposed based on physical principles, and both systematic error and random error are able to be estimated. In addition to the errors from inspection devices, the soil environment introduces various uncertainties into the corrosion propagation. This paper presents a methodology to infer the actual corrosion defect depth based on detection theory and to account for the effect of soil property variation by combining cluster analysis with a Bayesian inferential framework. A numerical study on calibration uncertainty shows the influence of the number of field verifications. The proposed model framework is applied to a 110-km pipeline system to illustrate its application.
Stochastic modeling methods and uncertainty quantification are important tools for gaining insight into the geological variability of subsurface structures. Previous attempts at geologic inversion ...and interpretation can be broadly categorized into geostatistics and process-based modeling. The choice of a suitable modeling technique directly depends on the modeling applications and the available input data. Modern geophysical techniques provide us with regional data sets in two- or three-dimensional spaces with high resolution either directly from sensors or indirectly from geophysical inversion. Existing methods suffer certain drawbacks in producing accurate and precise (with quantified uncertainty) geological models using these data sets. In this work, a stochastic modeling framework is proposed to extract the subsurface heterogeneity from multiple and complementary types of data. Subsurface heterogeneity is considered as the “hidden link” between multiple spatial data sets. Hidden Markov random field models are employed to perform three-dimensional segmentation, which is the representation of the “hidden link”. Finite Gaussian mixture models are adopted to characterize the statistical parameters of multiple data sets. The uncertainties are simulated via a Gibbs sampling process within a Bayesian inference framework. The proposed modeling method is validated and is demonstrated using numerical examples. It is shown that the proposed stochastic modeling framework is a promising tool for three-dimensional segmentation in the field of geological modeling and geophysics.
Borehole drilling and cone penetration test (CPT) are frequently employed site investigation methods for identifying subsurface stratification. However, these two methods have their respective pros ...and cons, and their corresponding soil type classification protocols are different. Therefore, an approach that can jointly interpret raw data from both investigation methods and provide unified soil classification results is in great demand. Motivated by the aforementioned point, this paper presents a novel semi-supervised clustering based stratification identification approach using information from both boreholes and CPT logs. The proposed approach is established on a hidden Markov random field (HMRF) framework so that the supervision constraints could be introduced by using borehole data during the clustering of CPT sounding samples. Further, the presented approach employs a Monte Carlo Expectation Maximization (MCEM) algorithm to perform the clustering process, which enables estimating the subsurface stratification in a probabilistic manner. The performances of the proposed approach are evaluated using real-world site investigation data. The test results indicate that the proposed approach is effective and robust for identifying subsurface stratification.
•A stratification interpretation approach considering both CPT and borehole data is presented.•Considering constraints derived from borehole data can improve the accuracy of obtained stratification.•The presented method can interpret stratification using borehole and CPT data under a unified soil classification system.•The presented method can interpret stratification at multiple boreholes and CPTs locations at a practical site.
Despite the rapid development of risk analysis, there is a relative absence of risk criteria for dams in developing countries recently, which restrained the practical application of the research ...results. This paper proposes guidelines for establishing risk criteria of dams in developing countries in considering the coordination of social, economic and engineering factors, then establishes a method of targeted analysis and demonstrates relevant parameters selected according to the ALARP principle and F-N curves, using China as an example. Different individual life risk criteria are established based on different safety levels for existing dams and newly built dams. Social life risk criteria and economic risk criteria are established on the basis of the different safety levels of all size reservoirs, the seriousness of the consequences of accidents, and the acceptability of social risks. The process of establishing risk criteria of dams and the analysis of the parameters demonstrated in this paper are meaningful to provide a reference and promote the required level of management for developing countries.
•A probabilistic site characterization approach using CPT sounding data is proposed.•Spatial constraints of the stratigraphic sequence along the path of CPTs are considered.•The proposed method can ...simultaneously interpret multiple CPT sounding records.•The proposed method is adaptive for probability/reliability-based analysis and design.
This paper presents a new probabilistic site characterization approach for both soil classification and property estimation using sounding data from multiple cone penetration tests (CPTs) at a project site. A hidden Markov random field (HMRF) model based Bayesian clustering approach is developed, which can describe not only the heterogeneity of properties in statistically homogeneous soil layers, but also the correlation between spatial distributions of different soil layers. The latter has not been well considered in the existing CPT interpretation methods. A Monte Carlo Markov chain based expectation maximization (MCMC-EM) algorithm is adopted to calibrate the established HMRF model, so that both the subsurface soil/rock stratification and the pertinent soil properties can be estimated in a probabilistic manner. The proposed CPT interpretation approach is validated and demonstrated using a series of numerical examples, including using real CPT data. It is shown that the proposed method is able to accurately identify soil layers, pinpoint their boundaries, and provide reasonable estimates of the associated soil properties. In addition, comparative studies show that combining analysis of CPT data from multiple soundings, rather than interpreting them separately, can significantly enhance the accuracy of interpretation and simplify the subsequent task of interpreting stratigraphic profiles.
Toppling failure of rock slopes is a complicated mode due to a combination of both continuous and discontinuous deformation, especially in dealing with anti-dip rock slopes. In this paper, a novel ...continuum-based discrete element method (CDEM), which is useful in modeling the entire progressive process from continuous to discontinuous deformation, is proposed to analyze the deformation characteristics, the failure mechanism and the evolution process of a large-scale open pit slope with a typical anti-dip structure. To simulate the slope deformation, the shear strength reduction method (SSR) is adopted to represent the strength degradation of rock mass in the deterioration process. The simulated results are validated using data obtained from a field investigation and continuous monitoring by employing an advanced remote sensing technique called ground-based interferometric synthetic aperture radar (GB-InSAR). To analyze the evolution trend of the anti-dip slope, the subsequent toppling failure mode is predicted using the validated CDEM models. Based on a case study of a slope at the Fushun open pit mine (in Fushun, China), the unique geological structure with various joints and discontinuities, groundwater, intense rainfall, and mining activities are identified as the main triggers for different failure stages. The comparison between the field data and the simulation shows that CDEM accurately depicts the rock deformation and the failure pattern of the studied slope. The proposed numerical modeling techniques can be used for predicting failures of other similar excavated rock slopes. The simulated evolution process and the recorded deformation patterns help engineers to gain a better understanding of rock mass movement of anti-dip slopes, and the presented methodology could be utilized for similar studies and engineering designs.
•A novel CDEM is proposed to analyze flexural toppling of an open-pit anti-dip slope.•An advanced technique called GB-InSAR is employed to monitor the slope deformation.•The numerical results are validated by monitoring results and field investigation.•The subsequent toppling failure is predicted to analyze the slope evolution trend.
•A stochastic geological modeling framework is proposed based on Markov random field.•Stochastic stratigraphic realizations are generated with uncertainty quantification.•A probabilistic approach is ...built to analysis tunnel performance in multiple strata.•The key design parameters along the entire tunnel alignment can be obtained.
Subsurface formations with multiple soil/rock strata are a common geological condition for shield-driven tunnel (i.e., tunnel constructed using shield-driven machines) construction. The excavation face, under such conditions, often encounters a frequently changing stratigraphic configuration that consists of various lithological units. Furthermore, due to a lack of direct and continuous observations of the subsurface region, it is difficult to predict the stratigraphic profile along the entire excavation path with a high degree of certainty. Such a widely changing and uncertain excavation environment may lead to wide variation in the state of stress and deformation of the tunnel structure along the longitudinal direction. This poses a challenge for design engineers in obtaining accurate performance evaluations or reasonable design outcomes for tunnel construction in subsurface ground with multiple strata. This paper aims to address this challenge by presenting a stochastic geological modeling framework for uncertainty quantification of stratigraphic profiles using sparsely located observation information from geotechnical site investigations. In the proposed modeling framework, the underground soil stratigraphic profile is regarded as a Markov random field with specific energy functions, which is able to describe the inherent anisotropic and non-stationary spatial correlation of lithological units in the subsurface stratigraphic structure. By incorporating the developed stochastic geological modeling framework with a finite element simulation of the tunnel excavation, a probabilistic analysis approach is established to evaluate the effects of stratigraphic uncertainty on the structural performance of a shield-driven tunnel.
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