Predicting the Performance of Chain Saw Machines Based on Shore Scleroscope Hardness

Shore hardness has been used to estimate several physical and mechanical properties of rocks over the last few decades. However, the number of researches correlating Shore hardness with rock cutting performance is quite limited. Also, rather limited researches have been carried out on predicting the performance of chain saw machines. This study differs from the previous investigations in the way that Shore hardness values (SH₁, SH₂, and deformation coefficient) are used to determine the field performance of chain saw machines. The measured Shore hardness values are correlated with the physical and mechanical properties of natural stone samples, cutting parameters (normal force, cutting force, and specific energy) obtained from linear cutting tests in unrelieved cutting mode, and areal net cutting rate of chain saw machines. Two empirical models developed previously are improved for the prediction of the areal net cutting rate of chain saw machines. The first model is based on a revised chain saw penetration index, which uses SH₁, machine weight, and useful arm cutting depth as predictors. The second model is based on the power consumed for only cutting the stone, arm thickness, and specific energy as a function of the deformation coefficient. While cutting force has a strong relationship with Shore hardness values, the normal force has a weak or moderate correlation. Uniaxial compressive strength, Cerchar abrasivity index, and density can also be predicted by Shore hardness values.     

Applying Optimized Support Vector Regression Models for Prediction of Tunnel Boring Machine Performance

One of the main factors in the effective application of a tunnel boring machine (TBM) is the ability to accurately estimate the machine performance in order to determine the project costs and schedule. Predicting the TBM performance is a nonlinear and multivariable complex problem. The aim of this study is to predict the performance of TBM using the hybrid of support vector regression (SVR) and the differential evolution algorithm (DE), artificial bee colony algorithm (ABC), and gravitational search algorithm (GSA). The DE, ABC and GSA are combined with the SVR for determining the optimal value of its user defined parameters. The optimization implementation by the DE, ABC and GSA significantly improves the generalization ability of the SVR. The uniaxial compressive strength (UCS), average distance between planes of weakness (DPW), the angle between tunnel axis and the planes of weakness (α), and intact rock brittleness (BI) were considered as the input parameters, while the rate of penetration was the output parameter. The prediction models were applied to the available data given in the literature, and their performance was assessed based on statistical criteria. The results clearly show the superiority of DE when integrated with SVR for optimizing values of its parameters. In addition, the suggested model was compared with the methods previously presented for predicting the TBM penetration rate. The comparative results revealed that the hybrid of DE and SVR yields a robust model which outperforms other models in terms of the higher correlation coefficient and lower mean squared error.     

Expert System for Applicability of Tunnel Boring Machines in Japan

Summary ¶This paper outlines the development of a new expert system for assessing the applicability of tunnel boring machine (TBM) tunneling in Japan. Although a great deal of research on TBMs has been published, and the applicability of TBMs has been discussed, considerable differences in opinion still exist between authors. In this paper, we review previous studies and outline the present situation with particular focus on disc-cutter TBMs. Based on the knowledge acquired, we present an expert system for the applicability of TBMs, for use in pre-feasibility studies. Originally, we planned to construct the expert system on the basis of unified knowledge or rules without contradictions. However, it was found after several attempts that it is very difficult to unify knowledge because opinions vary considerably and TBMs are under continuous development. As a result, the expert system was divided into three stages. In stage A, the fulfillment of fundamental requirements is checked. Stage B is a simple expert system consisting of a minimal set of suitable rules as judged by the authors. Stage C incorporates the opinions of various other experts and the over-simplified and omitted points in stage B. The system is applied to 18 tunnels in Japan, and while the results provided by the expert system can certainly be improved, the method for accumulating knowledge and rules makes the system simple and easy to use, with very large scope for improvement and expansion.Received July 1, 2001; accepted December 9, 2002 Published online April 29, 2003     

导向钻进非开挖铺管技术

介绍了导向钻进非开挖铺管技术的工作原理，施工技术和工程施工实例，总结出一套导向孔轨迹设计和钻进方向控制的方法，并对我国的导向钻进非开挖技术的发展提出了建议。     

老石旦煤矿缓坡斜井大口径通风立眼钻孔关键技术

通过老石旦煤矿缓坡斜井通风立眼的施工,叙述了钻遇采空区的几项施工关键技术。针对大口径通风井过采空区、井较深、井径较大的特点,采用大孔径反井成孔工艺;导向孔采用无线随钻定向技术,后改用无磁单点测斜潜孔锤定向技术;采用技术套管隔离的方法进行了管底特殊处理,保证技术套管顺利起拔;悬空固井技术,大直径套管人工柔性井底的制作等;对大孔径反井施工工艺技术进行了系统全面介绍,并对反井施工过程中发现的问题及取得成果进行的总结,为类似工程的施工具有示范效应。     

非饱和土壤水力参数预测的分形模型

综述了利用分形几何理论,可在土壤水力性质,包括土壤水分特征曲线及水力传导系数与土壤结构分维之间建立起一定的函数关系式.这些函数关系式大多与Campbell定律具有相同或相似的幂定律形式,一方面揭示了Campbell定律的物理实质,另一方面可用于土壤持水量及水力传导系数的预测.     

Summary of Numerical Models for Predicting Productivity of Shale Gas Horizontal Wells

Shale gas production has gradually achieved high and stable output, which makes it possible to make up for the shortage of oil and gas energy as an alternative energy source. Shale reservoir is compact, with well-developed nano-pore, and has the characteristics of adsorption and desorption, diffusion and slippage. At the same time, there are a large number of natural cracks, bedding and foliation. Hydraulic fractures expand irregularly after volume fracturing in horizontal wells. The whole system has multi-field coupling and cross-scale flow effects. Productivity prediction of shale gas is difficult and uncertain, which restricts the efficient development and evaluation of shale reservoirs. In this paper, the development status of productivity numerical models for shale gas horizontal wells is reviewed in consideration of the multi-scale transport characteristics of shale gas. These models include dual media capacity models, multiple media capacity models, and complex seam productivity models. It is considered that the dual medium and multi-media productivity models weaken the large permeable flow area and channel provided by the complex seam network system after shale reservoir lamination, and cannot comprehensively characterize the full-scale coupled transport characteristics of shale gas. The numerical model for productivity prediction of shale gas horizontal wells based on complex fracture network provides a multi-scale flow embedded fracture network system, which solves the problem of systematic flow without losing the ability to accurately characterize each scale flow. It is necessary to obtain the complex fracture network morphological characterization which conforms to reservoir geological characteristics, rock mechanical behavior and fluid-solid coupling mechanism. Fracture network characterization is the key to the productivity prediction of shale gas horizontal wells.     

Characterization of Geochemical Distributions Using Multifractal Models

The use of multifractals in the applied sciences has proven useful in the characterization and modeling of complex phenomena. Multifractal theory has also been recently applied to the study and characterization of geochemical distributions, and its relation to spatial statistics clearly stated. The present paper proposes a two-dimensional multifractal model based on a trinomial multiplicative cascade as a proxy to some geochemical distribution. The equations for the generalized dimensions, mass exponent, coarse Lipschitz–Hölder exponent, and multifractal spectrum are derived. This model was tested with an example data set used for geochemical exploration of gold deposits in Northwest Portugal. The element used was arsenic because a large number of sample assays were below detection limit for gold. Arsenic, however, has a positive correlation with gold, and the two generations of arsenopyrite identified in the gold quartz veins are consistent with different mineralizing events, which gave rise to different gold grades. Performing the multifractal analysis has shown problems arising in the subdivision of the area with boxes of constant side length and in the uncertainty the edge effects produce in the experimental estimation of the mass exponent. However, it was possible to closely fit a multifractal spectrum to the data with enrichment factors in the range 2.4–2.6 and constant K₁ = 1.3. Such parameters may give some information on the magnitude of the concentration efficiency and heterogeneity of the distribution of arsenic in the mineralized structures. In a simple test with estimated points using ordinary lognormal kriging, the fitted multifractal model showed the magnitude of smoothing in estimated data. Therefore, it is concluded that multifractal models may be useful in the stochastic simulation of geochemical distributions.     

利用MDT资料预测油气产能

准确地预测油气产能关系到油气田开发方案的有效制定。目前,常用模块式地层动态测试(MDT)资料进行产能预测。由于MDT的测试时间只有几秒钟到几分钟,因此直接使用MDT测试数据计算储层产量会导致较大的误差。针对这一问题,利用小波分析手段提取了MDT压力测试数据的小波系数之后,采用神经网络方法建立了小波系数与钻杆地层测试(DST)产能间的数学模型,进而用该模型对其产能进行了预测。将所构建的这一整套MDT产能预测方法应用于XJ油田的产能预测中,预测结果与DST测试产能的对比表明,该方法能够较好地对工区内的储层产能进行预测,其预测精度能够满足油气田生产的要求。     

Some Attempts for Estimating Rock Strength and Rock Mass Classification from Cutting Force and Investigation of Optimum Operation of Tunnel Boring Machines

Summary. Tunnel face and wall collapse are common during excavations performed by tunnel boring machines (TBMs) due to the difficulty of correctly identifying the properties of the excavated rock. This identification, however, can be simplified by using the cutting force to estimate rock strength, a method that has already proved quite successful in Japanese tunnel excavations. This paper summarizes knowledge relating to the cutting force obtained through tunnel excavation experience, and the relationship between rock strength and TBM operation is discussed. Although TBM operators rely on intuition to set the cutter head speed appropriately, this decision process represents a logical method of operation that takes advantage of the variable speed capability of the cutter head. Selection of appropriate support methods for the excavated face is also a critical issue in tunnel excavation. This selection process is based on the condition of the rock, which is difficult to determine quickly and accurately during tunnel excavation. The present paper uses the excavation of two tunnels to demonstrate that it is possible to assign rock mass classifications accurately based on rock strength when boring a uniform rock type. It is also shown that the rock mass can be classified from the rock strength normalized by the uniaxial compressive strength when boring through mixed rock types.     

Predicting Tunnel Squeezing Using the SVM-BP Combination Model

Geotechnical and Geological Engineering - Rock squeezing has a large influence on tunnel construction safety; thus, when designing and constructing tunnels it is highly important to use a reliable...     

Analysis and Improvement of Fitting Models for Predicting Subsidence Under High-Speed Railway Lines

A number of methods for predicting land subsidence and monitoring deformation under high-speed railway tracks exist, and are divided into three categories: layer-wise summation, numerical calculations based on consolidation theory, and curve fitting. One of these, curve fitting, including the hyperbola, expanded hyperbola, three-point fitting and Asaoka methods, is widely used because it is computationally simple and applicable in many situations. In this paper, we analyze the performance of the four classical curve fitting methods using field data and propose a novel approach to estimate land subsidence. The new method integrates three-point fitting, which is computationally simple whilst stringent in terms of correlation restrictions, with the Asaoka method to significantly improve performance in practical applications. Our experimental results indicate the average relative error of the modified method is reduced by 35.3 % than that of three-point fitting, and the mean correlation coefficient remains within acceptable bounds and even was enhanced by 1.48 %, so that this modified method can substantially improve prediction precision.     

应用灰色预测模型解决水文地质问题的思考

介绍了磷以预测模型在解决水文地质问题过程中所存在的诸多问题,并针对灰色预测模型的适用条件,提出了波动时间序列均值化的方法,在此基础上不文地质问题的特点,提出用周围外延的方法修正残差。计算结果表明,结合采用上述两种方法,将会收到较好的效果。     

水文模型中不同目标函数的影响分析比较

在水文模型中,目标函数的选择对参数率定至关重要,不同的目标函数可以得到不同的模拟结果.本文以三水源新安江模型为例,采用SCE-UA算法,选定三个不同的目标函数(平方均方误,对数均方误和平方根均方误)最小为目标函数分别进行参数优化,比较其优化结果在高、低水期的精度,最后分析不同目标函数对模拟结果的影响.研究发现:以平方均方误为目标函数的时候,高水期的模拟效果较好;以对数均方误为目标函数的时候,低水期的模拟效果较好;以平方根均方误为目标函数的时候,在整体上的模拟效果较好.     

Study on Numerical Models in Predicting Surface Deformation Caused by Underground Coal Mining

Geotechnical and Geological Engineering - Accurate prediction of surface deformation due to the extraction of underground coal seams is a significant challenge in geotechnical engineering. This...     

Improvements in Blast Fragmentation Models Using Digital Image Processing

One of the fundamental requirements for being able to optimise blasting is the ability to predict fragmentation. An accurate blast fragmentation model allows a mine to adjust the fragmentation size for different downstream processes (mill processing versus leach, for instance), and to make real time adjustments in blasting parameters to account for changes in rock mass characteristics (hardness, fracture density, fracture orientation, etc). A number of blast fragmentation models have been developed in the past 40 years such as the Kuz-Ram model [1]. Fragmentation models have a limited usefulness at the present time because: 1. The input parameters are not the most useful for the engineer to determine and data for these parameters are not available throughout the rock mass. 2. Even if the input parameters are known, the models still do not consistently predict the correct fragmentation. This is because the models capture some but not all of the important rock and blast phenomena. 3. The models do not allow for 'tuning' at a specific mine site. This paper describes studies that are being conducted to improve blast fragmentation models. The Split image processing software is used for these studies [2, 3].     

Automatic Identification of Rock Fracture Sets Using Finite Mixture Models

The clustering and classification of fracture orientation data are crucial tasks in geotechnical engineering and rock engineering design. The explicit simulation of fracture orientations is always applied to compensate for the lack of direct measurements over the entire rock mass. In this study, a single step approach based on the theory of finite mixture models, where the component distributions are Fisher distributions, is proposed for automatic clustering and simulation of fracture orientation data. In the proposed workflow, the spherical K-means algorithm is applied to select the initial cluster centers, and the component-wise expectation–maximization algorithm using the minimum message length criterion is used to automatically determine the optimal number of fracture sets. An additional advantage of the proposed method is the representation of orientation data using a full sphere, instead of the conventional hemispherical characterization. The use of a full spherical representation effectively solves the issue of clustering for fractures with high dip angles. In addition, the calculation process of the mean direction is also simplified. The effectiveness of the model-based clustering method is tested with a complicated artificial data set and two real world data sets. Cluster validity is introduced to evaluate the clustering results. In addition, two other clustering algorithms are also presented for comparison. The results demonstrate that the proposed method can successfully detect the optimal number of clusters, and the parameters of the distributions are well estimated. In addition, the proposed method also exhibits good computational performance.     

纠斜钻具的初步应用

斜度超限的钻孔,不但影响地质勘探质量,而且会给钻探施工在技术上造成许多困难,严重者甚至导致钻孔中途报废。对此钻探工作者普遍较为重视,在长期的实践中,也探索、积累了不少防斜纠斜的方法。