A generic method for rock mass classification

Rock mass classification (RMC) is of critical importance in support design and applications to mining, tunneling and other underground excavations. Although a number of techniques are available, there exists an uncertainty in application to complex underground works. In the present work, a generic rock mass rating (GRMR) system is developed. The proposed GRMR system refers to as most commonly used techniques, and two rock load equations are suggested in terms of GRMR, which are based on the fact that whether all the rock parameters considered by the system have an influence or only few of them are influencing. The GRMR method has been validated with the data obtained from three underground coal mines in India. Then, a semi-empirical model is developed for the GRMR method using artificial neural network (ANN), and it is validated by a comparative analysis of ANN model results with that by analytical GRMR method.     

Prediction of blast-induced flyrock in Indian limestone mines using neural networks

Frequency and scale of the blasting events are increasing to boost limestone production. Mines areapproaching close to inhabited areas due to growing population and limited availability of land resourceswhich has challenged the management to go for safe blasts with special reference to opencast mining.The study aims to predict the distance covered by the flyrock induced by blasting using artificial neuralnetwork （ANN） and multi-variate regression analysis （MVRA） for better assessment. Blast design andgeotechnical parameters, such as linear charge concentration, burden, stemming length, specific charge,unconfined compressive strength （UCS）, and rock quality designation （RQD）, have been selected as inputparameters and flyrock distance used as output parameter. ANN has been trained using 95 datasets ofexperimental blasts conducted in 4 opencast limestone mines in India. Thirty datasets have been used fortesting and validation of trained neural network. Flyrock distances have been predicted by ANN, MVRA,as well as further calculated using motion analysis of flyrock projectiles and compared with the observeddata. Back propagation neural network （BPNN） has been proven to be a superior predictive tool whencompared with MVRA.
2014 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting byElsevier B.V. All rights reserved.     

Application of artificial neural networks for predicting the impact of rolling dynamic compaction using dynamic cone penetrometer test results

Rolling dynamic compaction (RDC), which involves the towing of a noncircular module, is now widespread and accepted among many other soil compaction methods. However, to date, there is no accurate method for reliable prediction of the densification of soil and the extent of ground improvement by means of RDC. This study presents the application of artificial neural networks (ANNs) for a priori prediction of the effectiveness of RDC. The models are trained with in situ dynamic cone penetration (DCP) test data obtained from previous civil projects associated with the 4-sided impact roller. The predictions from the ANN models are in good agreement with the measured field data, as indicated by the model correlation coefficient of approximately 0.8. It is concluded that the ANN models developed in this study can be successfully employed to provide more accurate prediction of the performance of the RDC on a range of soil types.     

响应面法计算结构可靠度的回顾

体系可靠度已经成为可靠度研究的重点,由于其功能函数大都为隐式功能函数,响应面法已成为计算可靠指标的主要方法,响应面法主要分为迭代序列响应面、混合响应面、神经网络响应面、模糊神经网络响应面,就最近几年响应面法的理论发展和工程运用作一总结。     

大体积混凝土温升预测的非线性模型

大体积混凝土的水化热若不能及时散发,会产生很大的温度应力,导致出现温度裂缝。为了避免温度裂缝的产生,人们必须预测和控制大体积混凝土的温度形成。针对大体积混凝土温度场的非稳态特性,提出了一种基于灰色人工神经网络的温升预测模型,介绍了灰色神经网络预测方法在工程中的应用,采用Matlab进行计算。预测结果表明,该模型收敛速度快,预测精度较高,实现了对大体积混凝土温升的准确预测。说明了灰色人工神经网络方法的可行性和实用性。     

神经网络及其在岩爆灾害预测中的应用

岩爆是高地应力区岩质隧道开挖施工过程中发生的主要施工地质灾害之一,它的发生对隧道施工企业的安全生产构成很大的威胁,对岩爆发生可能性及其程度的预测是这类隧道设计、施工及安全生产所面临的重大问题。依据隧道岩爆发生的条件,基于国内外隧道及地下工程岩爆实例,应用人工神经网络方法,建立了岩爆危险性预测的评价模型并应用vc＋＋6.0实现了该评价模型,将其运用到武隆隧道的岩爆预测中,取得了良好的效果。     

An ANN-based approach to predict blast-induced ground vibration of Gol-E-Gohar iron ore mine,Iran

Blast-induced ground vibration is one of the inevitable outcomes of blasting in mining projects and may cause substantial damage to rock mass as well as nearby structures and human beings.In this paper,an attempt has been made to present an application of artificial neural network(ANN)to predict the blast-induced ground vibration of the Gol-E-Gohar(GEG)iron mine,Iran.A four-layer feed-forward back propagation multi-layer perceptron(MLP)was used and trained with Levenberg–Marquardt algorithm.To construct ANN models,the maximum charge per delay,distance from blasting face to monitoring point,stemming and hole depth were taken as inputs,whereas peak particle velocity(PPV)was considered as an output parameter.A database consisting of69data sets recorded at strategic and vulnerable locations of GEG iron mine was used to train and test the generalization capability of ANN models.Coefficient of determination(R2)and mean square error(MSE)were chosen as the indicators of the performance of the networks.A network with architecture4-11-5-1and R2of0.957and MSE of0.000722was found to be optimum.To demonstrate the supremacy of ANN approach,the same69data sets were used for the prediction of PPV with four common empirical models as well as multiple linear regression(MLR)analysis.The results revealed that the proposed ANN approach performs better than empirical and MLR models.     

An ANN-based approach to predict blast-induced ground vibration of Gol-E-Gohar iron ore mine,Iran简

Blast-induced ground vibration is one of the inevitable outcomes of blasting in mining projects and may cause substantial damage to rock mass as well as nearby structures and human beings. In this paper, an attempt has been made to present an application of artificial neural network (ANN) to predict the blast-induced ground vibration of the Gol-E-Gohar (GEG) iron mine, Iran. A four-layer feed-forward back propagation multi-layer perceptron (MLP) was used and trained with Levenberg–Marquardt algorithm. To construct ANN models, the maximum charge per delay, distance from blasting face to monitoring point, stemming and hole depth were taken as inputs, whereas peak particle velocity (PPV) was considered as an output parameter. A database consisting of 69 data sets recorded at strategic and vulnerable locations of GEG iron mine was used to train and test the generalization capability of ANN models. Coefficient of determination (R²) and mean square error (MSE) were chosen as the indicators of the performance of the networks. A network with architecture 4-11-5-1 and R² of 0.957 and MSE of 0.000722 was found to be optimum. To demonstrate the supremacy of ANN approach, the same 69 data sets were used for the prediction of PPV with four common empirical models as well as multiple linear regression (MLR) analysis. The results revealed that the proposed ANN approach performs better than empirical and MLR models.     

遗传算法BP神经网络在隧洞围岩分类中的应用

针对目前神经网络训练易陷入局部极小点问题，用遗传算法优化神经网络的连接权，并在遗传进化过程中采取保留最优个体的策略，建立了基于遗传算法的BP神经网络的模型，并应用于解决水工隧洞围岩分类这一非线性和不确定性较大的实际问题，证明了这种方法是科学可行的。     

Investigation of mechanical properties of soft rock due to laboratory reproduction of physical weathering process

The focus of the present study is on soft rocks (moderately weathered granite and artificial rock) that have suffered physical weathering due to changes in temperature and confining pressure. Unconfined compression tests were conducted on moderately weathered granite, and triaxial compression tests were conducted on artificial rocks. Two test plans were conducted to study the effect of weathering. In the first plan, the specimens suffered weathering process cycles under unconfined conditions, followed by triaxial tests with different confining pressures (0 kPa, 30 kPa, 60 kPa, and 90 kPa). In the second plan, the specimens suffered weathering process cycles under a certain confining pressure (0 kPa, 30 kPa, 60 kPa, and 90 kPa), and the shear strength and initial Young's modulus in each weathering cycle was then studied. Finally, based on the formula of the shear wave velocity and initial Young's modulus, the relationships between normalized shear strength and normalized shear wave velocity were found. These relationships can be used in a further study to understand rock strength on site by detecting the shear wave velocity.The results of this study show that artificial rocks (cement treated sand, CTS) can be used as a homogeneous material to simulate soft rock. In the stress-strain curves, the initial Young's modulus showed no significant change when increasing the confining pressure. The initial Young's modulus showed a nonlinear decrease when the weathering process cycle increased. When soft rocks suffer the weathering process at a certain confining pressure, the relationship between normalized shear strength and normalized shear wave velocity was linear. When soft rocks suffer the weathering process at different confining pressures, the normalized shear strength under a lower confining pressure dropped faster than when the confining pressure was higher.     

断续节理岩体声学力学特性试验研究

断续节理对岩体强度的影响及其评价方法一直是岩体力学领域研究的热点和难点。采用石膏制作含有不同节理倾角、密度及连通率组合的断续节理岩体试样,共计45组,每组试样先后开展超声波波速测试和单轴压缩试验,分析试样力学参数和声学参数间的关联特性,探索节理分布特征对岩体破坏模式及单轴抗压强度的影响,最终提出断续节理岩体单轴抗压强度的取值方法。结果表明,纵波波速与节理连通率呈正相关,随节理倾角增大近似\"V\"型先减后增;单轴抗压强度和弹性模量随节理连通率的增大而增大;单轴抗压强度随节理倾角的增大近似\"U\"型先减后增;总结提出了断续节理岩体的4种破坏模式,并认为节理与加载方向成45°夹角时最易破坏;最终提出了基于岩体及岩石纵波波速、岩石内摩擦角、节理倾角的岩体单轴抗压强度与岩石单轴抗压强度之间的拟合关系。     

基于人工神经网络的投标报价决策模型

利用Matlab编程语言构造了三层BP神经网络结构,建立了基于人工神经网络的投标报价模型,通过仿真模拟确定标高金水平,并用实例验证了其可靠性,为承包商作出合理报价决策提供了科学依据。     

人工神经网络技术在火因鉴定中的应用设想

文章在综合论述人工神经网络应用理论的基础上，提出了将人工神经网络应用于火灾原因的设想，分析了人工神经网络应用于火灾原因鉴定中的优势，构造了相应的结构框架，并提出了应用中应注意的问题。     

BP人工神经网络在混凝土耐久性评价上的应用

混凝土耐久性评价与预测一直是学术界与工程界的研究热点,常规的预测模型主要基于某几项指标,形式因个人的理解不同而各异.一种仿生模型--人工神经网络则能很好地解决这个难题,试验尝试用BP人工神经网络对多种配后比的混凝土进行耐久性评价与预测,结果表明此模型的可靠度很高,可以用以优化混凝土的配合比设计.