岩土力学数值模拟结果的概率评估方法研究

提出了一种用于评估多参数随机变异情况下岩土力学非线性数值模拟结果可靠性的多维高效直接积分型随机分析方法.它首先根据多维积分理论和可靠性分析的直接积分法确定多维随机输入参数的计值点;然后利用非线性数值模拟方法,比如有限元法或有限差分法等,完成所有计值点上的计算任务,据此就可以利用多维高效积分程序计算待评估响应量的前几阶矩(一般包括1～6阶矩);最后利用多种方法,包括曲线拟合法、加权余量法及最大熵法计算响应量的概率分布,并据此算出响应量的概率置信区间,用于定量评估响应量的可靠性.数值算例表明,此法具有精度和计算效率较高的优点.     

Moving least squares method for reliability assessment of rock tunnel excavation considering ground-support interaction

A practical approach is proposed in this paper for the reliability assessment of rock tunnel excavations using the moving least squares method (MLSM) and the uniform design. The failure probability is computed by the first-order and the second-order reliability method (FORM/SORM), which is based on the generated MLSM response surface (MLSM-RS) via an iterative algorithm. The proposed approach is first implemented in the analysis of a circular tunnel that consists of three limit state functions to illustrate the efficiency and accuracy of the approach. Then, the method is applied to a non-circular tunnel to demonstrate the feasibility and validity of the method for practical problems, in which numerical procedures are commonly employed to solve the implicit limit state functions.     

利用全概率方法分析岩土力学数值模拟结果的变异性

提出了一种用于分析在多参数随机情况下岩土力学数值模拟结果变异性的全概率方法。以特定数值模拟结果为随机隐式响应量,根据多维高效数值求积理论,计算出响应量特定函数的均值,利用快速傅里叶变换数值概率分析方法及笔者开发的最大熵分布,求解器软件一次性求解出响应量的全概率分布,用于定量评估数值模拟结果的变异性。算例表明该法具有精度和计算效率较高的优点。     

证据权模型中两种预测单元划分方式对比

证据权模型作为一种数据综合方法已被广泛应用于矿产资源定量预测与评价。在模糊证据权基础上,发展了基于地质单元思想的矢量证据图层构建和数据综合方法,并通过实例作具体阐述：它以矿点缓冲区图层作为训练图层,以各证据变量图层在空间上的叠置所形成的唯一地质单元作为评价对象,统一计算各个证据变量的证据权重,进而基于地质单元进行证据综合和后验概率成图。与基于栅格（或规则格网）的模型不同,基于矢量证据权模型以具有明确地质内涵的地质单元（而非规则网格单元）为预测单元,易于解释,并且消除了边界误差;相比基于规则格网划分所得到的成矿单元,以矿床（点）缓冲区作为训练对象,提高了已知矿点的代表性。实例表明：若预测单元大小为初始栅格大小整数倍,各缓冲等级平均面积计算误差为0.26%,否则面积平均误差达到6%;即使在预测单元大小为初始栅格大小整数倍情况下,矿点平均计算误差也达到4.78%。因此,基于地质单元思想的证据权预测单元划分方法在精度上优于基于栅格或规则格网方法。     

Application of Dempster-Shafer theory of evidence to GIS-based landslide susceptibility analysis

GIS-based spatial data integration tasks for predictive geological applications, such as landslide susceptibility analysis, have been regarded as one of the primary geological application issues of GIS. An efficient framework for proper representation and integration is required for this kind of application. This paper presents a data integration framework based on the Dempster-Shafer theory of evidence for landslide susceptibility mapping with multiple geospatial data. A data-driven information representation approach based on spatial association between known landslide occurrences and input geospatial data layers is used to assign mass functions. After defining mass functions for multiple geospatial data layers, Dempster’s rule of combination is applied to obtain a series of combined mass functions. Landslide susceptibility mapping using multiple geospatial data sets from Jangheung in Korea was conducted to illustrate the application of this methodology. The results of the case study indicated that the proposed methodology efficiently represented and integrated multiple data sets and showed better prediction capability than that of a traditional logistic regression model.     

Boundary element approach to the dynamic stiffness functions of circular foundations

A boundary element approach for time harmonic axisymmetric problems using the complete space point load fundamental solution is presented. The fundamental solution is integrated numerically along the azimuthal co-ordinate of each axisymmetric element. To increase the accuracy of the numerical integration a simple co-ordinate transformation is proposed. The approach is applied to the computation of the dynamic stiffness functions of rigid circular foundations on layered viscoelastic soils. Three different sites are considered: a uniform half-space, a soil layer on a half-space, and a soil consisting of four horizontal layers and a compliant half-space. The numerical results obtained by the proposed approach for surface circular foundations are very close to corresponding published results obtained by different procedures.     

Evaluation of rain gauge network in arid regions using geostatistical approach: case study in northern Oman

A geostatistical approach based on ordinary kriging is presented for the evaluation and the augmentation of an existing rain gauge network. The evaluation is based on estimating the percentage of the area that achieves a targeted level of acceptable accuracy. The variances of kriging estimation erros at un-gauged locations were assumed to be normally distributed. Kriging estimation erros with a probability that equals to or exceeds a given threshold value of acceptance probability were assumed to have satisfactory accuracies. The percentage of the area that achieved the targeted probability of acceptance is delineated and used to judge the overall performance of the existing rain gauge network. A study area in northern Oman located in Sohar governorate is selected as the pilot case. The area has 34 rain gauges and it is characterized by a terrain surface that varies from coastal plain to mountains. For a threshold value of 0.85, and 0.90 of acceptance probability, the existing network achieved area of acceptable probability of 88.71 and 77.72 %, respectively. For a success criterion of 80 %, the existing rain gauge network indicated acceptable performance for acceptance probability threshold of 0.85 and inadequate performance is noticed in the case of probability threshold of 0.90, which necessitated further network augmentation. A sequential algorithm for ranking and prioritization of the existing rain gauges is used to classify the existing rain gauges into base and non-base rain gauges. The base rain gauge network for mean annual rainfall comprised about 29 of the existing rain gauges. The identified non-base rain gauges were sequentially relocated to achieve higher levels of percentage of area with acceptable accuracy. The percentage of area with acceptable accuracy increased from 88.71 % for the original rain gauge network to about 94.51 % for the augmented network by adding four rain gages at probability acceptance threshold of 0.85. It also increased from 77.72 % for the existing network to 90.50 % for the augmented rain gauge network at acceptance threshold of 0.9.     

A GIS framework for evaluating the implications of urban road network failure due to earthquakes: Bucharest (Romania) case study

Through this paper we propose and test a GIS framework that addresses the issue of seismic risk due to urban road network failure. The approach relies on full GIS integration, on Monte Carlo simulations for generating potentially disrupted network configurations, considering also the damage probability due to direct earthquake implications, and on traffic considerations (both in typical and post-earthquake situations). The damage probability can be obtained using fragility functions for critical structures like bridges and tunnels or by determining empirically the possibility of affected buildings to generate debris leading to road obstruction. Multiple performance indicators such as travel time and distance under various conditions are combined, in order to quantify the risks inflicted by dysfunctionalities in the emergency intervention process. The framework considers at the same time temporal and spatial dimensions, being able to cope with traffic dynamics or reconfigurable network configurations. The ArcGIS Network Analyst Module is used for model integration, and full city scale analysis is performed in order to test the capabilities. Bucharest (capital of Romania) is selected for the case study; this 2 million inhabitant city is one of the most endangered in Europe, due to earthquakes that occur in the Vrancea Area, at intermediate depth, with moment magnitudes > 7, but also due to the vulnerable building stock. Beside this, it is one of Europe’s top cities when it comes to traffic congestion. The results of the study provide initial insights on the deficiencies of the city’s road network and connectivity limitations, showing the high impact of road obstructions and traffic congestion on intervention times, for ambulances and firefighters, in case of an earthquake.     

Wave scattering and diffraction of subsurface cavities in layered half-space for incident SV-P and SH waves

A novel approach is proposed to deal with the problem of wave scattering and diffraction of subsurface cavities embedded in stratified half-space. The subsurface cavity with complex surroundings is treated as a substructure. The continuity condition at the interface between the substructure and the far field of stratified half-space is maintained by applying a free-field approach. As the boundary of the free-field ground is regular, the construction of the dynamic matrices and the evaluation of the wave input on the interface become considerably easier. Based on the previous work with some improvement, a novel approach for evaluation of Green's functions in stratified half-space is presented. The wave equation is decoupled into the one for SV-P wave components and the other one for SH wave component. The precise integration technique ensures high accuracy of the solution of wave equations. The layer merging technique and the dual form equation make it possible to obtain Green's function in closed-form solution of matrix equations. Numerical examples validate accuracy and efficiency of the proposed approach.     

基于广义子集模拟的土坡系统可靠度分析

如何有效地评价边坡的系统可靠度并识别出对边坡稳定性具有重要影响的关键滑面一直是边坡稳定性分析的关键问题。提出了基于广义子集模拟的边坡系统可靠度分析方法及代表性滑面识别方法,并推导了基于广义子集模拟的边坡系统可靠度计算公式及边坡中滑面对边坡系统失效的相对贡献量化公式。基于广义子集模拟计算结果,采用概率网络评价方法识别边坡代表性滑面。以一个双层黏性土坡和芝加哥国会切坡算例验证了所提方法的有效性。结果表明：提出的基于广义子集模拟的边坡系统可靠度分析方法可有效地估计边坡系统及其单一滑面的失效概率,对于具有低失效概率水平边坡可靠度的求解,其计算效率明显优于传统蒙特卡洛模拟方法。此外,对于单个失效模式而言,广义子集模拟与子集模拟计算效率相当。对于多个失效模式的失效概率计算问题,广义子集模拟不需要重复对每个失效模式失效概率进行计算,计算效率明显优于子集模拟。提出的代表性滑面选择方法是在系统失效概率及单滑面失效概率的高效计算基础上实现的,代表性滑动面能够较好地代表边坡系统失效,从而有效地降低了边坡系统失效概率对代表性滑面数目及代表性滑面失效概率估计准确性的依赖性。     

On the use of enriched finite element method to model subsurface features in porous media flow problems

In this paper, a new enrichment scheme is proposed to model fractures and other conduits in porous media flow problems. Inserting this scheme into a partition of unity based method results in a new numerical method that does not require the mesh to honor the specific geometry of these subsurface features. The new scheme involves a specially designed integration procedure and enrichment functions, which can capture effects of local heterogeneity introduced by subsurface features on the pressure solution. The new method is also capable of modeling fractures with low as well as high conductivity. Another feature of the proposed scheme is that, even though two enrichment functions are used to model the permeability change at the two rock/fracture interfaces of a fracture, only one element partition is made for numerical integration. To demonstrate the accuracy and effectiveness of the proposed approach, production problems for wells that were stimulated or completed by longitudinal fracture, transverse fractures, and perforations are studied.     

A GIS framework for evaluating the implications of urban road network failure due to earthquakes: Bucharest (Romania) case study

Through this paper we propose and test a GIS framework that addresses the issue of seismic risk due to urban road network failure. The approach relies on full GIS integration, on Monte Carlo simulations for generating potentially disrupted network configurations, considering also the damage probability due to direct earthquake implications, and on traffic considerations (both in typical and post-earthquake situations). The damage probability can be obtained using fragility functions for critical structures like bridges and tunnels or by determining empirically the possibility of affected buildings to generate debris leading to road obstruction. Multiple performance indicators such as travel time and distance under various conditions are combined, in order to quantify the risks inflicted by dysfunctionalities in the emergency intervention process. The framework considers at the same time temporal and spatial dimensions, being able to cope with traffic dynamics or reconfigurable network configurations. The ArcGIS Network Analyst Module is used for model integration, and full city scale analysis is performed in order to test the capabilities. Bucharest (capital of Romania) is selected for the case study; this 2 million inhabitant city is one of the most endangered in Europe, due to earthquakes that occur in the Vrancea Area, at intermediate depth, with moment magnitudes > 7, but also due to the vulnerable building stock. Beside this, it is one of Europe’s top cities when it comes to traffic congestion. The results of the study provide initial insights on the deficiencies of the city’s road network and connectivity limitations, showing the high impact of road obstructions and traffic congestion on intervention times, for ambulances and firefighters, in case of an earthquake.

     

Integrated model for earthquake risk assessment using neural network and analytic hierarchy process:Aceh province,Indonesia

Catastrophic natural hazards,such as earthquake,pose serious threats to properties and human lives in urban areas.Therefore,earthquake risk assessment(ERA)is indispensable in disaster management.ERA is an integration of the extent of probability and vulnerability of assets.This study develops an integrated model by using the artificial neural network–analytic hierarchy process(ANN–AHP)model for constructing the ERA map.The aim of the study is to quantify urban population risk that may be caused by impending earthquakes.The model is applied to the city of Banda Aceh in Indonesia,a seismically active zone of Aceh province frequently affected by devastating earthquakes.ANN is used for probability mapping,whereas AHP is used to assess urban vulnerability after the hazard map is created with the aid of earthquake intensity variation thematic layering.The risk map is subsequently created by combining the probability,hazard,and vulnerability maps.Then,the risk levels of various zones are obtained.The validation process reveals that the proposed model can map the earthquake probability based on historical events with an accuracy of 84%.Furthermore,results show that the central and southeastern regions of the city have moderate to very high risk classifications,whereas the other parts of the city fall under low to very low earthquake risk classifications.The findings of this research are useful for government agencies and decision makers,particularly in estimating risk dimensions in urban areas and for the future studies to project the preparedness strategies for Banda Aceh.     

A surge response function approach to coastal hazard assessment. Part 2: Quantification of spatial attributes of response functions

In response to the 2004 and 2005 hurricane seasons, surge risk assessment approaches have been re-evaluated to develop more rapid, reliable methods for predicting the risk associated with extreme hurricanes. Here, the development of dimensionless surge response functions relating surge to hurricane meteorological parameters is presented. Such response functions present an opportunity to maximize surge data usage and to improve statistical estimates of surge probability by providing a means for defining continuous probability density functions. A numerical modeling investigation was carried out for the Texas, USA coastline to develop physical scaling laws relating storm surge response with hurricane parameters including storm size, intensity, and track. It will be shown that these scaling laws successfully estimate the surge response at any arbitrary location for any arbitrary storm track within the study region. Such a prediction methodology has the potential to decrease numerical computation requirements by 75% for hurricane risk assessment studies.     

A fuzzy set based approach for integration of thematic maps for landslide susceptibility zonation

Spatial prediction of landslides is termed landslide susceptibility zonation (LSZ). In this study, an objective weighting approach based on fuzzy concepts is used for LSZ in a part of the Darjeeling Himalayas. Relevant thematic layers pertaining to landslide causative factors have been generated using remote sensing and geographic information system (GIS) techniques. The membership values for each category of thematic layers have been determined using the cosine amplitude fuzzy similarity method and are used as ratings. The integration of these ratings led to the generation of LSZ map. The integration of different ratings to generate an LSZ map has been performed using a fuzzy gamma operator apart from the arithmetic overlay approach. The process is based on determination of combined rating known as the landslide susceptibility index (LSI) for all the pixels using the fuzzy gamma operator and classification using the success rate curve method to prepare the LSZ map. The results indicate that as the gamma value increases, the accuracy of the LSZ map also increases. It is observed that the LSZ map produced by the fuzzy algebraic sum has reflected a more real situation in terms of landslides in the study area.     

Successive Nonparametric Estimation of Conditional Distributions

Spatial characterization of non-Gaussian attributes in earth sciences and engineering commonly requires the estimation of their conditional distribution. The indicator and probability kriging approaches of current nonparametric geostatistics provide approximations for estimating conditional distributions. They do not, however, provide results similar to those in the cumbersome implementation of simultaneous cokriging of indicators. This paper presents a new formulation termed successive cokriging of indicators that avoids the classic simultaneous solution and related computational problems, while obtaining equivalent results to the impractical simultaneous solution of cokriging of indicators. A successive minimization of the estimation variance of probability estimates is performed, as additional data are successively included into the estimation process. In addition, the approach leads to an efficient nonparametric simulation algorithm for non-Gaussian random functions based on residual probabilities.     

Efficient system reliability analysis of rock slopes based on Subset simulation

How to efficiently assess the system reliability of rock slopes is still challenging. This is because when the probability of failure is low, a large number of deterministic slope stability analyses are required. Based on Subset simulation, this paper proposes an efficient approach for the system reliability analysis of rock slopes. The correlations among multiple potential failure modes are properly accounted for with the aid of the “max” and “min” functions. A benchmark rock slope and a real engineered rock slope with multiple correlated failure modes are used to demonstrate the effectiveness of the proposed approach.     

Adaptive analysis of infinite beams dynamics problems using the periodic configuration update method in the time domain

This paper proposes a new approach for the assessment of the dynamic response of continuously supported infinite beams under high‐speed moving loads. A change in the representation of equations of motion in the dynamics of discrete structures is proposed to obtain an improved accuracy of the numerical integration in the time domain. The proposed numerical method called the “periodic configuration update” or “PCU method” is applied to solve the problem of a vertical moving harmonic load on an infinite classical Euler‐Bernoulli beam resting on a continuous viscoelastic foundation. This study shows the superiority of the proposed method in comparison with other methods presented in the literature that suffer from the material time derivative, i.e., convective terms, that arises from the Galilean transformation. To confront this numerical problem, the PCU method retains the principle of the spatial follow of loads while zeroing the relative velocity with the traversed beam via a step‐by‐step adaptive integration of the equation of structural dynamics. The dynamic load is modeled with high theoretical velocities that can reach the critical velocity of the studied beam with different angular frequencies belonging to moderate frequency range. A parametric study is carried out to analyze the influence of key parameters on the convergence. The obtained results show a high efficiency of the PCU method for solving these types of problems relative to the dynamics of high speed trains/tracks.     

Numerical integration method for computing reliability index of geotechnical system

To account for the uncertainties in the design of a geotechnical system, reliability-based design approach is often adopted, in which the main task is to evaluate reliability index of the system based on a performance function (or limit state function). In this paper, we propose a new method for computing the reliability index, based upon the numerical integration of the cumulative distribution function (CDF) of the performance function. This numerical integration method requires only a deterministic evaluation of the system performance and the joint probability of the uncertain input parameters. The effectiveness and the efficiency of the proposed method, measured in terms of the accuracy and the computational effort, respectively, are demonstrated with two geotechnical problems: a drilled shaft in sand and a semi-gravity retaining wall. The new method is found valid regardless of the type of distribution of uncertain input parameters, whether the correlations exist among these input parameters, whether the system involves single or multiple failure modes, and how the performance function is formulated.     

Earthquake risk assessment in NE India using deep learning and geospatial analysis

Earthquake prediction is currently the most crucial task required for the probability, hazard, risk mapping, and mitigation purposes. Earthquake prediction attracts the researchers' attention from both academia and industries. Traditionally, the risk assessment approaches have used various traditional and machine learning models. However, deep learning techniques have been rarely tested for earthquake probability mapping. Therefore, this study develops a convolutional neural network (CNN) model for earthquake probability assessment in NE India. Then conducts vulnerability using analytical hierarchy process (AHP), Venn's intersection theory for hazard, and integrated model for risk mapping. A prediction of classification task was performed in which the model predicts magnitudes more than 4 Mw that considers nine indicators. Prediction classification results and intensity variation were then used for probability and hazard mapping, respectively. Finally, earthquake risk map was produced by multiplying hazard, vulnerability, and coping capacity. The vulnerability was prepared by using six vulnerable factors, and the coping capacity was estimated by using the number of hospitals and associated variables, including budget available for disaster management. The CNN model for a probability distribution is a robust technique that provides good accuracy. Results show that CNN is superior to the other algorithms, which completed the classification prediction task with an accuracy of 0.94, precision of 0.98, recall of 0.85, and F1 score of 0.91. These indicators were used for probability mapping, and the total area of hazard (21,412.94 km²), vulnerability (480.98 km²), and risk (34,586.10 km²) was estimated.