Comparison of ordinary and disjunctive kriging in an Indian iron ore deposit

This paper presents the results of disjunctive kriging applied to a supergene iron ore deposit of Bailadila Range of India. Disjunctive kriging is applied firstly to compare estimates of the blocks by ordinary kriging and secondly to estimate benchwise local recoverable reserves of the orebody. Good agreement exists between block estimates by ordinary kriging and disjunctive kriging except for peripheral blocks with less borehole information. Estimation of benchwise reserves shows that the behavior of the distribution of grades is different in various benches. The study shows that disjunctive kriging can be applied successfully for estimation of local recoverable reserves in the case of a good grade hematite iron ore deposit.     

专题制图决策支持系统的设计

专题制图中有很多决策问题,如何建立一个专题制图决策支持系统是本文要讨论的问题。本文对下面的问题进行了讨论和介绍;专题制图决策支持系统的总体设计、子系统间的信息交换、地图表达手段、数据处理方法等。     

Roof bolting in underground mining: a state-of-the-art review

Conclusions With continuing investigations and developments, roof bolting today can in most cases successfully reinforce the mine roof in underground mining. In order to cope with the increasing use of roof bolts, efforts should be made to maximize the safety and minimize the cost. With regard to mechanical bolting, two important parameters need more advanced improvements and study, namely, optimum design of the shape and type of expansion shell and optimum bolt tension for a specific bolt pattern. For the fully grouted resin bolt, the most critical requirement is to develop a fast-setting, low-cost, intoxic, inflammable grouting material that can be used in the high speed mining cycle. It should be noted that geological conditions such as the strata type, rock properties,in situ stress, and planes of weakness play an important role in the successful application of any roof bolting system. These factors should be specified as accurately and quantitatively as possible in the design of any roof bolting system. Finally, proper and careful installation and continuous monitoring are imperative for the success of any roof bolting system.     

Non‐linear system identification of the versatile‐typed structures by a novel signal processing technique

Non‐linear structural identification problems have raised considerable research efforts since decades, in which the Bouc–Wen model is generally utilized to simulate non‐linear structural constitutive characteristic. Support vector regression (SVR), a promising data processing method, is studied for versatile‐typed structural identification. First, a model selection strategy is utilized to determine the unknown power parameter of the Bouc–Wen model. Meanwhile, optimum SVR parameters are selected automatically, instead of tuning manually. Consequently, the non‐linear structural equation is rewritten in linear form, and is solved by the SVR technique. A five‐floor versatile‐type structure is studied to show the effectiveness of the proposed method, in which both power parameter known and unknown cases are investigated. Copyright © 2007 John Wiley & Sons, Ltd.     

3S技术在黑龙江省防汛指挥系统中的应用

文章简要介绍了黑龙江省防汛指挥系统建设的目标 ,论述了 3S技术是该系统应用的关键技术 ,以及展现了 3S技术在该系统应用的发展趋势。     

隧道支护新技术——水平旋喷成拱法

介绍了水平旋喷成拱工艺，分析了工艺参数对旋喷的影响，并采用该方法在鞍子河电站成功地进行了工程施工，为软弱地层隧道预衬砌提供了一个新途径。     

美军数字化战场剖析及对我军测绘保障发展的几点认识

本文剖析了美军数字化部队和数字化战场的内涵，阐述了对发展我军测绘保障的一些认识。     

典型类比分析法的原理与实践

本文以我国隧道工程应用新奥法的典型工程经验和原位测试资料为基础,综合应用系统科学、工程地质力学和岩石力学方法,阐述在隧道工程锚喷支护设计与施工中,普及应用围岩稳定分析预测技术的原理和方法典型类比分析法的提出、原理框架与工程验证。     

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.