Sensitivity analysis of support safety factor for predicting the effects of contributing parameters on roof falls in underground coal mines

Roof falls accounted for 18.18% of all fatal accidents in Indian coal mines, contributing about 35.29% of all fatal accidents in below-ground operations in 2005. The support safety factor, always preferred in support planning and design of underground coal mines, may be an important predictor for roof falls. In this paper, geotechnical data were collected from 14 roof fall incident places in an underground coal mine, located in the Eastern India, which has bord and pillar method of workings. The mean value of probabilistic support safety factor for the case study mine was found to be 1.24. However, the probability, of the estimated support safety factor of less than or equal to one, was found to be 0.246. Sensitivity analysis was conducted to analyze the effects of the contributing parameters on support safety factor and the likelihood of the roof fall. The multi-variate regression analysis was carried out for the data generated by Monte Carlo method to correlate the contributing factors to support safety factor. It ranked gallery width as the first parameter to control the support safety factor.     

随机激励下非线性海洋结构物响应分析方法研究进展

对在随机载荷作用下非线性海洋结构物的运动响应分析的各种方法进行了综述,对有些方法的基本原理做了阐述,并指出一些最新的研究进展和今后进一步研究的方向。     

蒙特卡罗法在天然气水合物资源量计算中的应用

天然气水合物甲烷资源量的估算是天然气水合物研究中的热点问题。运用体积法计算含水合物沉积物中的甲烷资源量时,存在参数赋值不确定的问题,从而影响了计算结果的可信度。采用蒙特卡罗法,通过计算样本的频率可以较好地评价和描述计算结果的信度,弥补体积法的不足。对采用蒙特卡罗法估算甲烷资源量的原理进行了分析和探讨,并以麦肯齐三角洲和南海海域水合物为例,计算了水合物赋存区的甲烷资源量。     

A Time Monte Carlo method for addressing uncertainty in land-use change models

One of the main objectives of land-use change models is to explore future land-use patterns. Therefore, the issue of addressing uncertainty in land-use forecasting has received an increasing attention in recent years. Many current models consider uncertainty by including a randomness component in their structure. In this paper, we present a novel approach for tuning uncertainty over time, which we refer to as the Time Monte Carlo (TMC) method. The TMC uses a specific range of randomness to allocate new land uses. This range is associated with the transition probabilities from one land use to another. The range of randomness is increased over time so that the degree of uncertainty increases over time. We compare the TMC to the randomness components used in previous models, through a coupled logistic regression-cellular automata model applied for Wallonia (Belgium) as a case study. Our analysis reveals that the TMC produces results comparable with existing methods over the short-term validation period (2000–2010). Furthermore, the TMC can tune uncertainty on longer-term time horizons, which is an essential feature of our method to account for greater uncertainty in the distant future.     

超声回弹综合法检测单一构件混凝土强度推定值的保证率分析

在实际工程检测数据的基础上,利用M on te C arlo试验对单一构件混凝土强度推定值的保证率问题进行了分析。30片梁板实测数据分析结果表明:将构件各测区混凝土强度换算值的最小值作为该构件的混凝土强度推定值的保证率范围为79.0%~94.2%,小于《超声回弹综合法检测混凝土强度技术规程》(CECS 02:88)规定的95%保证率要求,因此,对结构性能鉴定而言该混凝土强度推定值是偏于不安全的,应引起试验检测人员的充分重视。     

原子钟噪声的蒙特卡络模拟方法

讨论了原子钟噪声的蒙特卡络模拟方法．具体过程是：先产生模拟所需的正态分布随机数，从实际钟的阿仑方差估算出噪声电平，然后用１组递点函数产生高质量的模拟钟时差的随机序列．在对１台商品铯钟进行具体模拟后，给出了真实数据与模拟值的阿仑方差之问的相符程度，最后对实际问题进行了分析．     

一种生成大周期伪随机数的新算法——改进的混合同余法

为了克服当前所生成的伪随机数周期小、每个随机数仅能出现1次而不能重复的缺点,本文提出了一种生成伪随机数的新方法,称为改进的混合同余法。     

Quantifying uncertainty in remote sensing-based urban land-use mapping

Land-use/land-cover information constitutes an important component in the calibration of many urban growth models. Typically, the model building involves a process of historic calibration based on time series of land-use maps. Medium-resolution satellite imagery is an interesting source for obtaining data on land-use change, yet inferring information on the use of urbanised spaces from these images is a challenging task that is subject to different types of uncertainty. Quantifying and reducing the uncertainties in land-use mapping and land-use change model parameter assessment are therefore crucial to improve the reliability of urban growth models relying on these data. In this paper, a remote sensing-based land-use mapping approach is adopted, consisting of two stages: (i) estimating impervious surface cover at sub-pixel level through linear regression unmixing and (ii) inferring urban land use from urban form using metrics describing the spatial structure of the built-up area, together with address data. The focus lies on quantifying the uncertainty involved in this approach. Both stages of the land-use mapping process are subjected to Monte Carlo simulation to assess their relative contribution to and their combined impact on the uncertainty in the derived land-use maps. The robustness to uncertainty of the land-use mapping strategy is addressed by comparing the most likely land-use maps obtained from the simulation with the original land-use map, obtained without taking uncertainty into account. The approach was applied on the Brussels-Capital Region and the central part of the Flanders region (Belgium), covering the city of Antwerp, using a time series of SPOT data for 1996, 2005 and 2012. Although the most likely land-use map obtained from the simulation is very similar to the original land-use map – indicating absence of bias in the mapping process – it is shown that the errors related to the impervious surface sub-pixel fraction estimation have a strong impact on the land-use map's uncertainty. Hence, uncertainties observed in the derived land-use maps should be taken into account when using these maps as an input for modelling of urban growth.     

Safety assessment of infrastructures using a new Bayesian Monte Carlo method

A recently developed Bayesian Monte Carlo (BMC) method and its application to safety assessment of structures are described in this paper. We use a one-dimensional BMC method that was proposed in 2009 by Rajabalinejad in order to develop a weighted logical dependence between successive Monte Carlo simulations. Our main objective in this research is to show that the extended BMC can dramatically improve simulation efficiency by using prior information from modelling and outcomes of preceding simulations. We provide theory and numerical algorithms for an extended BMC method for multi-dimensional problems, integrate it with a probabilistic finite element model and apply these coupled models to assessment of reliability of a flood defence for the 17th Street Flood Wall system in New Orleans. This is the first successful demonstration of the BMC method to a complex system. We provide a comparison of the numerical efficiency for the BMC, Monte Carlo (MC) and Dynamic Bounds methods that are used in reliability assessment of complex infrastructures.