Stochastic analysis of beams on reinforced earth beds

The present study comprises Monte-Carlo simulation assisted analysis of foundations resting on reinforced earth beds using the concept of beams on an elastic foundation, treating the modulus of subgrade reaction (MSR) as a stationary stochastic field characterised by mean, variance, autocorrelation function (ACF) and the autocorrelation distance (ACD). Realisations of the MSR, generated by solving a stochastic differential equation, are fed to a deterministic distributed parameter model to generate realisations of two dependent stochastic fields, namely deflection and bending moment in the foundation beam, and two random variables, namely the location of occurrence of maximum deflection and the bending moment. Subsequently these realisations are analysed to evolve probability distribution functions, variance and ACF of the dependent stochastic fields and the random variables. It is revealed that the ACF of these fields is independent of the ACF of the MSR. Further, variance of deflection is found to increase as the ACD of the MSR increases, implying requirement of a larger factor of safety when random soils display low frequency (macro level) variations. On the other hand, variance of the bending moment is larger at smaller ACDs of the MSR, indicating that for bending moments a larger factor of safety is required when the random soils display high frequency (micro level) variations.     

Stochastic model for settlement: footings on cohesionless soil

In this paper, given an estimate of the bearing capacity of the soil, by treating settlement at a given load as a random variable and the evolution of settlement of footing on cohesionless soil with the increasing load as a stochastic process, a tri-level homogeneous Markov chain (TLHMC) model is proposed for prediction of settlement. Comparison of the predicted mean and bounds on settlements, obtained using TLHMC, with the respective field values obtained from literature shows that the stochastic evolution can be modelled using TLHMC with a correlation coefficient of 0.90. A methodology for reliability-based design of footings is also presented and its use is demonstrated through a numerical example.     

基于灰色最小二乘支持向量机的大坝变形预测

提出一种基于灰色最小二乘支持向量机的大坝变形预测新算法。通过对原始大坝序列进行一次累加,弱化序列中随机扰动的影响,增强数据的规律性,进而建立最小二乘支持向量机预测模型,并采用网格搜索法选取最优参数。算法充分利用了最小二乘支持向量机泛化能力强、非线性拟合性好等优良特性,避免了灰色方法及模型存在的理论缺陷。与灰色GM(1,1)和单一最小二乘支持向量机对比表明,新算法能保证较优的局部预测值和较好的全局预测精度,应用于短期大坝变形预测是可行的。     

随机微分方程的Euler数值解法

基于常微分方程(ODEs)的Euler数值解法,提出了求解一类随机常微分方程(ODEs)的3种Euler格式:显Euler格式,半隐Euler格式和隐Euler格式.讨论了3种Euler格式的T-稳定条件,并给出了部分数值实验结果.     

一种基于知识的非DHT资源搜索算法

描述了一种适用于分布式系统基于知识的非DHT结构的资源搜索算法(KBS).该算法能够从实际环境中自动获取知识,完成知识体系的构建,知识量得到了有效的控制,基于KBS算法的系统具有良好的可扩展性,可以保证系统的负载均衡.     

截短后平衡的扩频码搜索方法

为了能够高效地找出平衡的扩频码(平衡性的扩频码有较好地抑制载波的性能),研究了一种不交叠平衡性扩频码字的搜索方法。在这种方法中,首先生成母码,然后计算、查找平衡不交叠的子码。该方法在计算平衡码时与其它方法有相同的时间复杂度。计算机仿真表明,该母码生成方法较为高效。在采用分割方法后,搜索过程得到了进一步的优化。     

如何提高论文被SCI、EI、ISTP收录的几率

SCI、EI、ISTP是国际公认的三大权威检索工具，SCI还被国内外学术界当做制定学科发展规划和进行学术排名的重要依据。因此，减少具名误差，提高学校排名位次已成为不少高校的努力目标。要提高我校科研人员在三大检索系统的收录率，论文应发表在检索机构的源期刊上，论文编写格式要符合国际标准和SCI、EI有关规定，此外，还要注意本单位的属名问题。     

基于分层分块数据组织的双向A*算法

研究了路网的数据组织方法,采用了C STL实现道路数据的分层分块存储,设计了道路网络分析中的双向A*算法,试验验证了其正确性。     

Features of the geological history of the Iultin Massif determined by stochastic modeling

Petrographic data from the Iultin Massif (Chuckotka) were analyzed for crystallization and postmagmatic transformation using a Markov stochastic model. The goal was to ascertain whether this model can be used to divide the massif into distinctive portions. A comparison was made between the stochastic approach and conventional geological methods for understanding the historical development of granitic massifs.     

Ideal granites and models of their metasomatic transformations: Theory, experience, and current problems

A definition of the ideal granite and its metasomatic transformation are discussed and the corresponding matrices of transition probabilities are given, along with guidelines for accepting models. Some results are presented using these models to examine granitic massifs and to search for high-temperature ore deposits. Current problems in investigation of granitic massifs on the basis of stochastic models are reviewed.