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1.
ABSTRACTRecent hurricanes between 2004 and 2008 in the Gulf of Mexico provide valuable new information about design model biases and uncertainties because multiple offshore platforms were loaded beyond the predicted capacity of their pile systems and because there was a failure of a pile system. A Bayesian calibration of model bias factors based on predicted versus observed performance of pile systems in hurricanes indicates that the conventional American Petroleum Institute design method for pile capacity is slightly conservative by about 10% for base shear (i.e. lateral) failures of pile systems in clay, unbiased for overturning (i.e. axial) failures of pile systems in clay, and conservative by more than 50% for overturning failures of piles systems in sand. The epistemic uncertainty in the updated bias factors is represented by coefficient of variation values of about 0.25 for base shear and overturning failures of pile systems in clay and 0.35 for overturning failures of pile systems in sand. A reliability assessment with the calibrated model bias factors shows that the current design practice produces lower reliability for a pile system with three piles versus one with eight piles and lower reliability for a pile system failing in overturning versus one failing in base shear. Therefore, the current design practice could potentially be improved by taking into account the mode of failure and the redundancy in the pile system to provide for a more uniform level of reliability. 相似文献
2.
The cross-site variability (i.e., variability from site to site) makes the statistics of the bias factor of a design model vary from site to site. How to characterize the cross-site variability of the model bias factor is important for design of pile foundations based on site-specific load test data. In this study, a probabilistic model that allows for explicit modeling of the cross-site variability is suggested. An equation is derived based on Bayes’ theorem to calibrate the suggested model with load test data from different sites, which is applicable even when the number of load tests at each site is small. A procedure based on hybrid Markov Chain Monte Carlo simulation is employed to solve the Bayesian equation. How to update the statistics of the model bias factor, when applied to a future site, with site-specific load test data is also described. As an illustration, the probabilistic model is applied to the design of bored piles in Shanghai, China. It is found that, given a certain number of site-specific pile load tests, the effect of updating depends on the mean and the COV of the measured model bias factor. With the assistance of regional experience, a small number of load tests can significantly reduce the uncertainty associated with the design model, and further increase in the number of load tests may not change the site-specific statistics of the bias factor and hence the resistance factor substantially. 相似文献
3.
Geotechnical models are usually associated with considerable amounts of model uncertainty. In this study, the model uncertainty of a geotechnical model is characterised through a systematic comparison between model predictions and past performance data. During such a comparison, model input parameters (such as soil properties) may also be uncertain, and the observed performance may be subjected to measurement errors. To consider these uncertainties, the model uncertainty parameters, uncertain model input parameters and actual performance variables are modelled as random variables, and their distributions are updated simultaneously using Bayes’ theorem. When the number of variables to update is large, solving the Bayesian updating problem is computationally challenging. A hybrid Markov Chain Monte Carlo simulation is employed in this paper to decompose the high-dimensional Bayesian updating problem into a series of updating problems in lower dimensions. To increase the efficiency of the Markov chain, the model uncertainty is first characterised with a first order second moment method approximately, and the knowledge learned from the approximate solution is then used to design key parameters in the Markov chain. Two examples are used to illustrate the proposed methodology for model uncertainty characterisation, with insights, discussions, and comparison with previous methods. 相似文献
4.
Uncertainties in parameters of landslide susceptibility models often hinder them from providing accurate spatial and temporal predictions of landslide occurrences. Substantial contribution to the uncertainties in landslide assessment originates from spatially variable geotechnical and hydrological parameters. These input parameters may often vary significantly through space, even within the same geological deposit, and there is a need to quantify the effects of the uncertainties in these parameters. This study addresses this issue with a new three-dimensional probabilistic landslide susceptibility model. The spatial variability of the model parameters is modeled with the random field approach and coupled with the Monte Carlo method to propagate uncertainties from the model parameters to landslide predictions (i.e., factor of safety). The resulting uncertainties in landslide predictions allow the effects of spatial variability in the input parameters to be quantified. The performance of the proposed model in capturing the effect of spatial variability and predicting landslide occurrence has been compared with a conventional physical-based landslide susceptibility model that does not account for three-dimensional effects on slope stability. The results indicate that the proposed model has better performance in landslide prediction with higher accuracy and precision than the conventional model. The novelty of this study is illustrating the effects of the soil heterogeneity on the susceptibility of shallow landslides, which was made possible by the development of a three-dimensional slope stability model that was coupled with random field model and the Monte Carlo method. 相似文献
5.
In this paper, a Bayesian approach for updating a semi-empirical model for predicting excavation-induced maximum ground settlement using centrifuge test data is presented. The Bayesian approach involves three steps: (1) prior estimate of the maximum ground settlement and model bias factor, (2) establishment of the likelihood function and posterior distribution of the model bias factor using the settlement measurement in the centrifuge test, and (3) development of posterior distribution of the predicted maximum settlement. This Bayesian approach is demonstrated with a case study of a well-documented braced excavation, and the results show that the accuracy of the maximum settlement prediction can be improved and the model uncertainty can be reduced with Bayesian updating. 相似文献
6.
The evaluation of variability in ultimate pile capacity from the load-settlement data is useful in the context of code calibration and reliability based design in pile foundations. This paper examines the applicability of two non-linear analytical methods to calculate the load-settlement response of piles using actual test data in terms of percentage deviation from the measured capacity. The degree of agreement associated with each method with respect to field test data is quantified using two different failure criteria (FHWA and Eurocode) for determination of the ultimate load of pile. The analytical methods are used to quantify the variability associated with the soil-pile interface parameters and ultimate capacity using Monte Carlo simulations, which is useful in load-resistance factored/reliability design of pile foundations. Study reveals that variability depends on the method of analysis, percent deviation of prediction from measured values and failure criteria. 相似文献
7.
A primary concern of the mining industry is to meet production targets, which are required and defined by customers. Deviations from these targets, in terms of quality and quantity, highly affect the economical aspect. Recently, an efficient resource model updating framework concept has been proposed aiming for the improvement of raw material quality control and process efficiency in any type of mining operation. The concept integrates online sensor measurements, obtained during production, into the resource model. In this way, due to the spatial variability, quality attributes of the blocks that will be produced in the next days or weeks are being updated based on real-time measurements. The concept has been applied in a lignite field with the aim of identifying local impurities in a lignite seam and to improve the prediction of coal quality attributes in neighbouring blocks. This paper investigates the added value of using the resource model updating framework by using the value of information analysis. The expected benefit of additional information (integration of the online sensor measurements into the resource model) is compared to a case where there is no additional information integrated into the process. These benefits are evaluated based on the economic impact determined by applying the resource model updating framework in mine planning. 相似文献
8.
由于施工技术水平、岩土工程条件等不确定性因素的影响,基桩中经常出现各种缺陷。为此,提出了基于贝叶斯理论的灌注桩多个缺陷统计特性的分析方法。在考虑钻芯法检测不确定性的基础上,采用泊松分布模型模拟基桩中多个缺陷的出现概率,推导了缺陷平均出现率后验分布的计算公式。提出了估计缺陷尺寸修正的贝叶斯抽样方法,给出了评价钻芯法检测概率的方法。算例分析表明,钻芯法的检测概率对准确地估计缺陷平均出现率有明显的影响,如果不考虑检测不确定性因素的影响,缺陷平均出现率将被低估。随着检测到缺陷数目的增加,更新的缺陷平均出现率的均值逐渐增加,更新的变异系数逐渐减小。此外,先验的信息能够有效地减小缺陷平均出现率和缺陷尺寸估计的不确定性。 相似文献
9.
Model calibration and history matching are important techniques to adapt simulation tools to real-world systems. When prediction uncertainty needs to be quantified, one has to use the respective statistical counterparts, e.g., Bayesian updating of model parameters and data assimilation. For complex and large-scale systems, however, even single forward deterministic simulations may require parallel high-performance computing. This often makes accurate brute-force and nonlinear statistical approaches infeasible. We propose an advanced framework for parameter inference or history matching based on the arbitrary polynomial chaos expansion (aPC) and strict Bayesian principles. Our framework consists of two main steps. In step 1, the original model is projected onto a mathematically optimal response surface via the aPC technique. The resulting response surface can be viewed as a reduced (surrogate) model. It captures the model’s dependence on all parameters relevant for history matching at high-order accuracy. Step 2 consists of matching the reduced model from step 1 to observation data via bootstrap filtering. Bootstrap filtering is a fully nonlinear and Bayesian statistical approach to the inverse problem in history matching. It allows to quantify post-calibration parameter and prediction uncertainty and is more accurate than ensemble Kalman filtering or linearized methods. Through this combination, we obtain a statistical method for history matching that is accurate, yet has a computational speed that is more than sufficient to be developed towards real-time application. We motivate and demonstrate our method on the problem of CO 2 storage in geological formations, using a low-parametric homogeneous 3D benchmark problem. In a synthetic case study, we update the parameters of a CO 2/brine multiphase model on monitored pressure data during CO 2 injection. 相似文献
11.
This paper briefly reviews several calculation methods to evaluate the bearing capacity expressed in terms of undrained strength ( cu) of piles bored in clay for their entire length and of piles whose tip is embedded into weak rock. The scope of the paper is to compare the results obtained with those from full-scale pile tests. These tests were carried out within the city of Matera which is well studied from a geotechnical point of view and for which there are statistically significant data on the geomechanical properties of the Subapennine Blue clays and the underlying Gravina Calcarenites. This represents the first attempt to show, on the basis of laboratory and field data, the influence of variability of the above mentioned soils and rocks on the real behaviour of deep foundations.For piles completely bored into Matera clay, the calculation in terms of total stress are able to interpret sufficiently well the bearing capacity of the piles. For piles having their toe embedded in calcarenite, the variability of the strength of the weak rock presents greater
uncertainties in the calculation of base and soft resistance. 相似文献
12.
ABSTRACTThe paper presents methodologies for exploration planning under uncertain conditions based on virtual exploration and Bayesian updating. The process starts with site characterization using existing information to produce geologic profiles. Initial distributions of cost and time are obtained with a Bayesian network model that optimizes the construction strategy for particular geologic conditions. This is followed by the unique step to determine with a “virtual exploration” if additional exploration (e.g. borings) is warranted, and if so, where it should be best done. All this is then applied to the planned Abu Dhabi subway tunnels providing the transportation planners with necessary information for planning and design. 相似文献
13.
Physical-scaled model testing under 1 g conditions is carried out in obtaining the vertical response of fixed head floating-inclined single piles embedded in dry sand. Practical pile inclinations of 5° and 10° besides a vertical pile (0°) subjected to static and dynamic vertical pile head loadings are considered. To account for the effects of soil nonlinearity as well as the soil–pile interface nonlinearity on the response of piles, a range of low-to-high magnitude of pile head displacements is considered for the static case while a varying amplitude of harmonic accelerations for a wide range of frequencies is considered for the dynamic case. Experimental results are obtained in the form of pile head stiffnesses and strains generated in the pile under both the static and dynamic loadings. Results suggest that the nonlinear behavior of soil as well as the nonlinearity generated at the interface between the soil and the pile as the result of applied loading considerably affect the response of piles. The soil–pile interface nonlinearity that governs the slippage of pile shows a clear influence on the pile head stiffnesses by providing two distinct values of stiffnesses corresponding to the push and the pull directional movement of piles; the two values are significantly different. Axial and bending strains generated in the piles show expected dependency on the amplitude of applied loading; the pile head-level bending strain increases almost linearly with the increase in the angle of pile inclination. 相似文献
14.
Though the technology of using stabilizing piles to prevent landsliding is not new, the design of such piles with a meaningful optimization framework has been rarely reported. In this paper, a multiobjective optimization-based framework for design of stabilizing piles is presented, in which both reinforcement effectiveness and cost efficiency could be explicitly considered. The design parameters considered in the proposed design framework are the pile parameters, including pile diameter, spacing, length, and position, and the design objectives considered are the reinforcement effectiveness and cost efficiency. The design of stabilizing piles is then implemented as a multiobjective optimization problem. In that the desire to maximize the reinforcement effectiveness and that to maximize the cost efficiency are two conflicting objectives, the output of this multiobjective optimization will be a Pareto front that depicts a trade-off between these two design objectives. With the obtained Pareto front, an informed decision regarding the design of stabilizing piles is reached. The effectiveness and significance of the proposed multiobjective optimization-based design framework for stabilizing piles are demonstrated through two illustrative examples: one is the design of stabilizing piles in a one-layer earth slope and the other the design of stabilizing piles in a two-layer earth slope. Further, parametric analyses are conducted to investigate the influences of the pile design parameters on the stability of reinforced slopes. 相似文献
15.
In spite of extensive studies on laterally loaded piles carried out over years, none of them offers an expedite approach as to gaining the nonlinear response and its associated depth of mobilization of limiting force along each pile in a group. To serve such a need, elastic–plastic solutions for free‐head, laterally loaded piles were developed recently by the author. They allow the response to be readily computed from elastic state right up to failure, by assigning a series of slip depths, and a limiting force profile. In this paper, equivalent solutions for fixed‐head (FixH) single piles were developed. They are subsequently extended to cater for response of pile groups by incorporating p‐multipliers. The newly established solutions were substantiated by existing numerical solutions for piles and pile groups. They offer satisfactory prediction of the nonlinear response of all the 6 single piles and 24 pile groups investigated so far after properly considering the impact of semi‐FixH restraints. They also offer the extent to ultimate state of pile groups via the evaluated slip depths. The study allows ad hoc guidelines to be established for determining input parameters for the solutions. The solutions are tailored for routine prediction of the nonlinear interaction of laterally loaded FixH piles and capped pile groups. Copyright © 2008 John Wiley & Sons, Ltd. 相似文献
16.
In this paper research was presented on the development of a growth-rate-dependent model for pile set-up prediction using the restrike and static/statnamic load testing data collected from different projects. The data included: a) restrike records from ninety-five production piles and restrike and load test results of nine instrumented piles driven in soft clays from the relocation project of Highway No. 1 in Louisiana (LA-1); and b) restrike and static load testing data of five fully instrumented square PPC piles driven at four different bridge sites in various soil layers from sands to clays in Florida. Research effort was focused on the prediction of the ultimate shaft resistances with pile set-up formulated using the pile resistance growth rate-dependent model. The timeframe of interest was studied for a practical set-up magnitude such as 90% of the ultimate shaft resistance (Q 90). As an application of the rate-dependent model, it was found that piles at the LA-1 relocation project, in general, reached about 95% of the ultimate shaft resistances at the time of 2 weeks after pile installation. The strategy of incorporation of pile set-up in adjusting pile driving criteria or/and design during pile construction, such as the experience-based plan of a two-week waiting period adopted by Louisiana DOTD, was investigated and justified. 相似文献
17.
This paper introduces a simplified method to investigate the influence of thermal loads on the shaft friction and tip resistance of energy piles. The method is based on the influence factors (λ and η) which are back-calculated drawing on a large number of field and model tests. Values for λ and η during heating and cooling are suggested. Moreover, a new equation is proposed to calculate total shaft friction. The equations concerning the relationship between η and temperature difference are recommended to investigate the impacts of the thermal load on the pile tip resistance. The slope of the linear equation of an end-bearing pile is 2.14 times that of a floating pile indicating that the pile tip resistance of an end-bearing pile is much more affected by the same thermal load. 相似文献
18.
The uncertain settlement response of pile groups is determined using a ‘hybrid’ formulation and a first-order perturbation technique. The spatially varying soil modulus, which gives rise to the uncertainties in the pile group settlement, is modeled as a homogeneous random field. The random field is assumed to be one-dimensional since the ‘hybrid’ formulation does not account for horizontal variation in the soil properties. Using the proposed method, the coefficient of variation of the pile group settlement is computed. The single-pile solutions obtained compare favorably with the solutions from a conventional stochastic finite element analysis. Pile groups of sizes ranging from two to twenty-five piles are studied. It is observed that the coefficient of variation is not significantly affected by the pile spacing as well as the group size. By defining an appropriate performance function, the reliability index of a pile group system is also found to be approximately the same as that of a single-pile system. These observations suggest that the solutions for a single pile may be used to estimate the uncertainties in the settlement response of pile groups. 相似文献
19.
This paper examines the potential of relevance vector machine (RVM) in prediction of ultimate capacity of driven piles in cohesionless soils. RVM is a Bayesian framework for regression and classification with analogous sparsity properties to the support vector machine (SVM). In this study, RVM has been used as a regression tool. It can be seen as a probabilistic version of SVM. In this study, RVM model outperforms the artificial neural network (ANN) model based on root-mean-square-error (RMSE) and mean-absolute-error (MAE) performance criteria. It also estimates the prediction variance. An equation has been developed for the prediction of ultimate capacity of driven piles in cohesionless soils based on the RVM model. The results show that the RVM model has the potential to be a practical tool for the prediction of ultimate capacity of driven piles in cohesionless soils. 相似文献
20.
In this paper, a framework based on Bayesian theory and proof pile load test results was used to update resistance factors of axially loaded driven piles. Prior to implementation of the framework, resistance factors were calibrated based on the distribution of the measured-to-predicted pile ultimate bearing capacity using the results of static pile load tests conducted to failure. These resistance factors and the distribution were considered to be “prior.” The prior distribution of the measured-to-predicted ultimate bearing capacity was updated based on Bayesian theory to incorporate additional proof pile load test results. Using the measured-to-predicted load distributions and the updated (or posterior) measured-to-predicted bearing capacity distributions, resistance factors were calibrated (or updated) from the first-order reliability method (FORM) for two different target reliability indices, 2.33 and 3.0. This research attempted to use the results of proof pile load tests, which are generally conducted to verify pile designs, to update resistance factors. The updated resistance factors varied substantially depending on the proof pile load test results. Therefore, the Bayesian implementation can contribute to economical pile designs. 相似文献
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