Design of backfilled thin-seam coal pillars using earth pressure theory

In underground coal mining any increase in coal recovery rate is dependent on a decrease in pillar size. Backfilling is one way of reducing the required size of pillars and hence the volume of coal left underground. Therefore any comparisons made between a self-supported mine layout and backfill supported mine layout are based directly on pillar design. The most effective way to examine the effect of backfill on pillar support, and subsequently the rate of recovery, would be to incorporate the mechanisms of backfill support directly into the current design procedure for coal pillars. This paper presents a review of the mechanics of backfill support, a method of estimating the magnitude of that support based on earth pressure theory, and an example that incorporates backfill support into current coal pillar design.     

城市非点源污染管理的制度、信息和决策支撑

城市非点源污染对中国水环境的威胁日益加剧。制度不健全、信息基础薄弱和缺少决策支撑是当前城市非点源污染管理的主要制约因素。短期内,改善城市非点源污染管理的首要任务是夯实信息基础。从长远来看,制度的完善是落实城市非点源污染管理的根本保障。发达国家在信息基础建设和制度设计方面的成果和经验对于中国城市非点源污染管理工作有重要的参考价值。此外,先进的决策支撑技术是管理获得预期成效的重要保障。国际上决策支撑技术研究的最新发展动态值得国内相关领域借鉴。     

临海浅埋富水明挖隧道底板合理支护参数研究

沿海地区深基坑设计施工都十分重视其抗浮问题,在深基坑开挖前掌握抗拔桩对于基坑隆起和围护结构变形及受力规律十分重要,其研究成果对于节约工程成本和保证基坑施工安全具有重要的指导意义。以港珠澳大桥侧接线拱北海关隧道明挖段为依托,首先建立抗拔桩与土体的硬接触模型,并验证该模型对于抗拔桩模拟的合理性,采用极差分析法研究底板不同桩长和桩径加固对基坑围护结构稳定性的影响规律。分析结果表明,增大桩长可更有效地减小地表沉降,增大桩径可更好地控制连续墙的变形,增大桩长和桩径都可以很好地控制坑底隆起。     

Monthly suspended sediment load prediction using artificial intelligence: testing of a new random subspace method

ABSTRACT

The predictive capability of a new artificial intelligence method, random subspace (RS), for the prediction of suspended sediment load in rivers was compared with commonly used methods: random forest (RF) and two support vector machine (SVM) models using a radial basis function kernel (SVM-RBF) and a normalized polynomial kernel (SVM-NPK). Using river discharge, rainfall and river stage data from the Haraz River, Iran, the results revealed: (a) the RS model provided a superior predictive accuracy (NSE = 0.83) to SVM-RBF (NSE = 0.80), SVM-NPK (NSE = 0.78) and RF (NSE = 0.68), corresponding to very good, good, satisfactory and unsatisfactory accuracies in load prediction; (b) the RBF kernel outperformed the NPK kernel; (c) the predictive capability was most sensitive to gamma and epsilon in SVM models, maximum depth of a tree and the number of features in RF models, classifier type, number of trees and subspace size in RS models; and (d) suspended sediment loads were most closely correlated with river discharge (PCC = 0.76). Overall, the results show that RS models have great potential in data poor watersheds, such as that studied here, to produce strong predictions of suspended load based on monthly records of river discharge, rainfall depth and river stage alone.     

A minimalistic approach for evapotranspiration estimation using the Prophet model

ABSTRACT

This study aimed to evaluate the potential of the recently introduced Prophet model for estimating reference evapotranspiration (ETo). A comparative study was conducted for benchmarking the model results with support vector regression (SVR) and temperature-based empirical models (Thornthwaite and Hargreaves) in southern Japan. The performance of the Prophet, SVR and temperature-based empirical models was evaluated by Nash–Sutcliffe efficiency (NSE) and coefficient of determination (R²). The results indicate that temperature-based Prophet and SVR models have greater accuracy than the empirical models. The Prophet model with sole input of relative humidity, sunshine hours or windspeed showed acceptable accuracy (NSE > 0.80; R² > 0.80), while SVR models with similar inputs showed greater errors. Accuracy improved with increasing number of input parameters, giving excellent performance (NSE > 0.95; R² > 0.95) with all input parameters. Hence, the Prophet model is a new promising approach for modelling ETo with limited input variables.     

基于离散Gabor变换的磁暴识别

以地磁秒数据为研究对象,通过离散Gabor变换将时域的地磁数据转换至二维时频面,提取Gabor变换谱图的均值和方差作为特征值,使用支持向量机实现地磁正常数据与磁暴干扰数据的自动分类识别。对5个地磁台的200组地磁秒数据进行计算分析,结果表明该方法对测试样本数据的识别率可达94%。     

Deformation Mechanism and Stability of a Rocky Slope

A high slope is located on the side of the spillway at a hydropower station in Southwest China, which has some weak inter-layers inclining outwards. Parts of the slope show heavy weathering and unloading. There appeared deformation and tensile crack either on the surface or on the afteredge of the slope during excavation, and under a platform （elev. 488 m）, two levels of slopes collapsed on the downriver side. Based on the investigation in situ and the analysis of the geological structure, the conceptual model of deformation and failure mechanism was erected for this slope. Furthermore, the deformation characteristics were studied with FLAC^3D numerical simulation. Comprehensive analysis shows that the whole deformation of the slope is unloading rebound in certain depth scope and the whole body does not slide along any weak interlayer. In addition, two parts with prominent local deformation in the shallow layer of the slope show the models of ＂creep sfiding-tensile cracking＂ and ＂slidlng-tensile cracking＂, respectively. Based on the above analysis, the corresponding project of support and reinforcement is proposed to make the slope more stable.     

参数优化LSSVM的巷道围岩松动圈预测研究

最小二乘支持向量机方法(LSSVM)在处理小样本、高维数、非线性的问题时,具有求解速度快、易于描述非线性关系的优良特性。但是,该方法得到的模型拟合精度和泛化能力依赖于其相关参数,因此,提出基于粒子群优化算法(PSO)的LSSVM参数优选方法。最后,用该模型对巷道围岩松动圈进行了预测研究。结果表明,PSO优化的LSSVM模型具有收敛速度快、计算精度高的特点,说明该模型是合理、有效的。     

空间决策支持系统开发平台及其应用实例

空间决策支持系统（Ｓｐａｔｉａｌ Ｄｅｃｉｓｉｏｎ Ｓｕｐｐｏｒｔ Ｓｙｓｔｅｍ ＳＤＳＳ）作为在地理信息系统（ＧＩＳ）和决策支持系统（ＤＳＳ）基础上发展起来的一个新光科学技术领域,自本世纪８０年代中后期后来,在国内外已引起越来越广泛的关注和重视。通过大量文献的分析表明：对这种系统目前尚有许多不同的理解和认识。作者认为只有那些能够帮助决策者生成、比较和选择空间决策方案的信息系统才能纳入ＳＤＳＳ的范     

A support vector machine classifier based on a new kernel function model for hyperspectral data

The kernel function is a key factor to determine the performance of a support vector machine (SVM) classifier. Choosing and constructing appropriate kernel function models has been a hot topic in SVM studies. But so far, its implementation can only rely on the experience and the specific sample characteristics without a unified pattern. Thus, this article explored the related theories and research findings of kernel functions, analyzed the classification characteristics of EO-1 Hyperion hyperspectral imagery, and combined a polynomial kernel function with a radial basis kernel function to form a new kernel function model (PRBF). Then, a hyperspectral remote sensing imagery classifier was constructed based on the PRBF model, and a genetic algorithm (GA) was used to optimize the SVM parameters. On the basis of theoretical analysis, this article completed object classification experiments on the Hyperion hyperspectral imagery of experimental areas and verified the high classification accuracy of the model. The experimental results show that the effect of hyperspectral image classification based on this PRBF model is apparently better than the model established by a single global or local kernel function and thus can greatly improve the accuracy of object identification and classification. The highest overall classification accuracy and kappa coefficient reached 93.246% and 0.907, respectively, in all experiments.