基于P波模量的岩石单轴抗压强度预测EI北大核心CSCD

传统获取岩石单轴抗压强度参数需要钻进取样、加工制作等严格的试验步骤,需要建立一种参数易于获取且准确的岩石单轴抗压强度预测公式。基于岩石物理力学参数的内在联系,建立了岩石单轴抗压强度与岩石P波模量的关系式。根据英安斑岩和页岩两种岩石的干密度、P波速度及单轴抗压强度的测试数据,采用线性拟合的方法建立了岩石基于P波模量的单轴抗压强度预测公式,并采用统计检验的方法对上述预测公式与传统基于P波速度的预测公式进行了对比分析。结果表明,所建立的强度预测通式简单、精度高,模量容易获取,具有很强的实用性。     

一种确定各向异性单轴抗压强度的经验公式

岩石的单轴抗压强度可以通过相对简单且经济的方法直接测试得到,在岩土工程领域应用十分广泛。提出了一种确定岩石各向异性单轴抗压强度的经验公式。通过该经验公式对砂岩、千枚岩、板岩和页岩的各向异性单轴抗压强度数据进行了拟合。采用决定系数R~2、相对误差D_p和平均绝对相对误差AAREP这3种统计参数评价经验式的预测能力,结果表明,预测的单轴抗压强度与试验值吻合较好。基于包含274个各向异性单轴抗压强度的数据库,通过统计分析评价了经验公式的预测能力,并与3种常用方法的预测能力进行了对比。统计分析表明,仅有3个方位角(β分别为0°、30°和90°)的试验数据可用时,提出的经验式也能够很好地预测各向异性单轴抗压强度。     

基于灰色理论的脆性岩石抗压强度尺寸效应试验研究

采用相同直径不同高度的圆柱形白色大理岩及灰岩试样进行单轴压缩试验研究,验证了脆性岩石抗压强度在一定范围内随高径比的增大而减小的规律。依据岩石单轴压缩试验结果,基于灰色预测GM（1,1）模型建立了脆性岩石的单轴抗压强度与试件高径比之间的非线形关系式,该经验公式对脆性岩石具有统一的形式,同时通过式中参数a值反映了由于岩性、试验条件等不同而引起的强度差异。利用该关系式可以很方便地在相关岩土工程中为相似岩石估算其强度值提供参考、借鉴。     

基于量纲分析的岩石相似材料抗压强度计算模型

物理相似模型试验是复杂、难采矿技术研究的有效手段,而岩石相似材料物理力学特性的研究是试验有效进行的前提。根据相似原理和岩石的力学特性,论证了岩石相似材料单轴抗压强度的力学代表性。统计并分析了影响岩石相似材料单轴抗压强度的一系列因素,基于量纲分析的方法构建了岩石相似材料单轴抗压强度与砂粒径、配比材料总量、充填材料（砂）用量、水用量、养护方式等重要因素间的无量纲计算模型,结合岩石相似材料配比试验得到了定量关系式。选取3组具有代表性的岩石相似材料配比试验对所建立的单轴抗压强度计算关系式进行验证,其计算结果与实验室实测结果相吻合,平均误差为4.20%,定量关系式在岩石相似材料参数的研究上具有一定的合理性。研究结果可以为相似材料物理力学参数的预测及岩土工程物理模型试验相似材料的高效选取提供参考。     

砂岩强度的估算研究及应用——以南水北调中线安阳段为例

为了获得合理的施工设计岩石强度参考值,以南水北调安阳段砂岩为例开展了3种岩石强度估算方法的研究:1)引进标准条件下风钎钻进时间作为指标来估算岩石的单轴抗压强度;2)收集已有成果给出利用波速估算岩石单轴抗压强度的公式;3)根据试验数据拟合出安阳地区点荷载强度与岩石单轴抗压强度间的经验关系式。通过上述方法得到了3种不同的岩石强度估算公式,并以AY33+600为例,综合利用所得公式进行了砂岩强度估算,发现这3种方法吻合度很高,且与砂岩室内试验强度值间的误差为2%~9%。上述公式在安阳段的应用解决了原设计方案中围岩分级不准的问题。     

岩石-钢纤维混凝土复合层抗压强度预测模型

为了研究岩石-钢纤维混凝土复合层单轴抗压强度的计算方法,对岩石、钢纤维混凝土和岩石-钢纤维混凝土(R-SFRC)复合层试件进行单轴压缩试验,分析混凝土强度等级(C30、C40和C50)和纤维掺量(0、40、60和80 kg/m3)对钢纤维混凝土和复合层单轴抗压强度的影响规律,应用RFPA2D模拟复合层单轴压缩损伤过程和应力-应变曲线,基于Mohr-Coulomb屈服准则建立R-SFRC复合层抗压强度预测模型。结果表明,复合层试件单轴抗压强度介于岩石和混凝土抗压强度之间,复合层中岩石和混凝土界面的相互约束改变了各层的受力状态,复合层中岩石强度降低而混凝土强度增大,复合层极限抗压强度为复合层中的混凝土强度。复合层试件的抗压强度随混凝土基体强度和钢纤维掺量的增大而增大,混凝土基体强度对复合层试件的抗压强度影响更显著;不同材料的复合层单轴抗压强度数值模拟值和理论计算值相对于试验值的误差范围分别为-5.41%～-0.69%和-8.67%～-1.21%,数值模拟和理论计算模型可用于复合层单轴抗压强度预测。     

不同高径比下极软岩(泥岩)单轴抗压强度尺寸效应试验研究

以成都东部地区某工程项目勘察岩石试验成果为例，通过对岩石数据统计分析，发现泥岩在不同高径比下岩石单轴抗压强度数据呈现的差异性较大，对于岩石参数取值建议造成了很大影响。为进一步验证和分析尺寸效应对极软岩岩石单轴抗压强度的影响，通过建立对比试验模型、取样试验，分析了不同高径比的极软岩岩石单轴抗压强度差异特征，并对差异较大的原因和影响因素进行了阐述，对于工程勘察中极软岩单轴抗压强度试验的应用有一定借鉴意义。     

基于P-R相关性研究的岩石地基承载力优化取值

岩石地基承载力是建筑工程中一个重要的力学指标,大多数建筑工程主要依据岩石饱和单轴抗压强度的标准值R确定,重要建筑工程还需要通过岩体载荷试验的承载力特征值P确定,那么两种方法必然存在一定的相关性。实践经验表明,用R确定承载力的方法简单易行,但R代表的是岩石材料的特性,与工程岩体的情况有显然的不同,而原位载荷试验是在半无限条件下进行的,与工程实际条件更为接近,因此,用P确定地基承载力比用R要合理。在总结多达43点的岩体载荷试验及对应部位至少3组的单轴抗压强度对比试验成果与收集248组重庆地区岩石的三轴强度试验成果的基础上,建立了P-R相关关系及围压条件下的强度特征。通过揭示它们之间的相关性,提出采用岩石饱和单轴抗压强度标准值的1.5倍作为地基承载力特征值的重要结论,为岩石地基承载力优化取值提供了重要依据。     

基于H-B准则的公路桥梁桩基嵌岩深度计算

基于Hoek-Brown强度准则,推导了在微小转动下嵌岩段桩侧法向应力及水平摩阻力计算模型,采用静力平衡原理建立了水平荷载作用下公路桥梁桩基嵌岩深度的计算公式,提出了嵌岩深度计算的新方法。参数敏感性及影响因素分析表明： （1）水平荷载引起的力矩 、桩径d、岩层上覆压力 、岩石单轴抗压强度 、岩体类别参数 、岩体地质力学分类指标RMR均对嵌岩深度有一定影响,在 、d不变的情况下,岩体质量和岩石单轴抗压强度对嵌岩深度的确定最为敏感; （2）岩体质量越差,所需嵌岩深度越大;岩体质量越好,岩层上覆压力对嵌岩深度影响越小,反之越大。（3）嵌岩深度随岩石单轴抗压强度的提高呈非线性缓慢地减小,在相同的单轴抗压强度下,岩体质量越好,嵌岩深度越小,反之越大。     

用声波参数确定岩石加载破坏过程的不同阶段

本文研究了岩石在单轴和真三轴加载的破坏过程与声波参数及动力学参数之间的关系。在单轴和三轴压缩实验中,通过测量纵横波速度和振幅的变化,有效地判断岩石加载和结构的变化情况,确定岩石加载破坏过程中的几个阶段,实现对岩石破裂前兆的预测。本文还研究了三轴压缩实验中中间主应力σ_2的改变对声波速度和振幅变化曲线的影响。     

Experimental Study on Acoustoelastic Character of Rock Under Uniaxial Compression

This paper made a research about the change rule of elastic wave velocity with stress applied on rock from theoretical and experimental aspect. Firstly, a mathematical model of P-wave velocity and confining pressure of rock was set up from the point of acoustoelastic character. Effect of axial stress on P-wave velocity in granite and sandstone during uniaxial compression process was studied experimentally by using GAW-2000 rock mechanical testing system and RSM-SY5 ultrasonic wave testing system, and the relation curves of axial stress with P-wave velocity were obtained. Based on test data, acoustoelastic theoretical formulas of granite and sandstone were established and the best empirical formulas were fitted by using regression method. Meanwhile, a comparative analysis of the empirical and theoretical calculated values was carried out. Finally, the reliability of applying acoustoelastic theoretical formula in hard rock range was further verified based on the experimental data of granitic gneiss. The results show that the P-wave velocity experiences a rapid increase, gentle increase and then a sharp fall during the uniaxial compression process. The B-value in acoustoelastic theoretical formula (proportion coefficient determined by elastic modulus and third-order elastic constant) decreases exponentially with axial stress. The acoustoelastic theoretical formula can effectively reflect the relationship between rock acoustic velocity and stress within the allowable error, which can be the theoretical foundation of acoustoelastic geo-stress measurement of subsurface rock mass.     

The effect of specimen diameter size on uniaxial compressive strength,P-wave velocity and the correlation between them

Estimation of uniaxial compressive strength (UCS) by P-wave velocity (V_P) is of great interest to geotechnical engineers in various design projects. The specimen diameter size is one of the main factors that influence rock parameters such as UCS and V_P. In this study, the diameter size of specimens that effect UCS and V_P is investigated. Moreover, the correlation between UCS and V_P are examined via empirical analysis. For this purpose, 15 travertine samples were collected and core specimens with a diameters size of 38, 44, 54, 64 and 74 mm were prepared. Then, uniaxial compressive strength and P-wave velocity tests were conducted according to the procedure suggested by ISRM (1981). It is concluded that the diameter size of the specimen has a significant effect on UCS and V_P. Moreover, it was found that the best correlation between relevant parameters obtained for the specimen diameter of 38 mm.     

Artificial neural networks and nonlinear regression techniques to assess the influence of slake durability cycles on the prediction of uniaxial compressive strength and modulus of elasticity for carbonate rocks

Understanding rock material characterizations and solving relevant problems are quite difficult tasks because of their complex behavior, which sometimes cannot be identified without intelligent, numerical, and analytical approaches. Because of that, some prediction techniques, like artificial neural networks (ANN) and nonlinear regression techniques, can be utilized to solve those problems. The purpose of this study is to examine the effects of the cycling integer of slake durability index test on intact rock behavior and estimate some rock properties, such as uniaxial compressive strength (UCS) and modulus of elasticity (E) from known rock index parameters using ANN and various regression techniques. Further, new performance index (PI) and degree of consistency (Cd) are introduced to examine the accuracy of generated models. For these purposes, intact rock dataset is established by performing rock tests including uniaxial compressive strength, modulus of elasticity, Schmidt hammer, effective porosity, dry unit weight, p‐wave velocity, and slake durability index tests on selected carbonate rocks. Afterward, the models are developed using ANN and nonlinear regression techniques. The concluding remark given is that four‐cycle slake durability index (I_d4) provides more accurate results to evaluate material characterization of carbonate rocks, and it is one of the reliable input variables to estimate UCS and E of carbonate rocks; introduced performance indices, both PI and Cd, may be accepted as good indicators to assess the accuracy of the complex models, and further, the ANN models have more prediction capability than the regression techniques to estimate relevant rock properties. Copyright © 2011 John Wiley & Sons, Ltd.     

An Empirical Correlation of Index Geomechanical Parameters with the Compressional Wave Velocity

The geomechanical strength of rockmass plays a key role in planning and design of mining and civil construction projects. Determination of geomechanical properties in the field as well as laboratory is time consuming, tedious and a costly affair. In this study, density, slake durability index, uniaxial compressive strength (UCS) and P-wave velocity tests were conducted on four igneous, six sedimentary and three metamorphic rock varieties. These properties are crucial and used extensively in geotechnical engineering to understand the stability of the structures. The main aim of this study is to determine the various mechanical properties of 13 different rock types in the laboratory and establish a possible and acceptable correlation with P-wave velocity which can be determined in the field as well as laboratory with ease and accuracy. Empirical equations were developed to calculate the density, slake durability index and UCS from P-wave velocities. Strong correlations among P-wave velocity with the physical properties of different rock were established. The relations mainly follow a linear trend. Student’s ‘t’ test and ‘F’ test were performed to ensure proper analysis and validation of the proposed correlations. These correlations can save time and reduce cost during design and planning process as they represent a reliable engineering tool.     

Application of statistical methods for predicting uniaxial compressive strength of limestone rocks using nondestructive tests

Uniaxial compressive strength (UCS) of an intact rock is an important geotechnical parameter for engineering applications. Using standard laboratory tests to determine UCS is a difficult, expensive and time-consuming task. The main purpose of this study is to develop a general model for predicting UCS of limestone samples and to investigate the relationships among UCS, Schmidt hammer rebound and P-wave velocity (V _P). For this reason, some samples of limestone rocks were collected from the southwestern Iran. In order to evaluate a correlation, the measured and predicted values were examined utilizing simple and multivariate regression techniques. In order to check the performance of the proposed equation, coefficient of determination (R ²), root-mean-square error, mean absolute percentage error, variance accounts for (VAF %), Akaike Information Criterion and performance index were determined. The results showed that the proposed equation by multivariate regression could be applied effectively to predict UCS from its combinations, i.e., ultrasonic pulse velocity and Schmidt hammer hardness. The results also showed that considering high prediction performance of the models developed, they can be used to perform preliminary stages of rock engineering assessments. It was evident that such prediction studies not only provide some practical tools but also contribute to better understanding of the main controlling index parameters of UCS of rocks.     

Artificial neural network model for prediction of rock properties from sound level produced during drilling

In many rock engineering applications such as foundations, slopes and tunnels, the intact rock properties are not actually determined by laboratory tests, due to the requirements of high quality core samples and sophisticated test equipments. Thus, predicting the rock properties by using empirical equations has been an attractive research topic relating to rock engineering practice for many years. Soft computing techniques are now being used as alternative statistical tools. In this study, artificial neural network models were developed to predict the rock properties of the intact rock, by using sound level produced during rock drilling. A database of 832 datasets, including drill bit diameter, drill bit speed, penetration rate of the drill bit and equivalent sound level (L_eq) produced during drilling for input parameters, and uniaxial compressive strength (UCS), Schmidt rebound number (SRN), dry density (ρ), P-wave velocity (V_p), tensile strength (TS), modulus of elasticity (E) and percentage porosity (n) of intact rock for output, was established. The constructed models were checked using various prediction performance indices. Goodness of the fit measures revealed that recommended ANN model fitted the data as accurately as experimental results, indicating the usefulness of artificial neural networks in predicting rock properties.     

综合成岩作用和孔隙形状的岩石物理模型及其应用

横波速度对于地震模拟、AVO分析以及流体识别具有重要意义,实际测井数据中横波速度信息缺乏,因此横波速度预测已经成为岩石物理研究的一个焦点。综合Xu-White模型以及Pride模型,提出了一种新的用于计算干岩石模量的岩石物理模型。该模型综合考虑了孔隙形状和成岩作用对干岩石体积模量、剪切模量的影响,因此该模型更加合理并具有更高的精度。同时联合Gassmann理论,建立了饱和流体岩石的纵波、横波速度计算模型。将该模型成功地应用于实验室测量数据和实际测井数据的横波速度预测中,预测结果表明,基于本文提出的岩石物理模型的横波速度预测方法是行之有效的。     

Application of Generalized Regression Neural Networks in Predicting the Unconfined Compressive Strength of Carbonate Rocks

Measuring unconfined compressive strength (UCS) using standard laboratory tests is a difficult, expensive, and time-consuming task, especially with highly fractured, highly porous, weak rock. This study aims to establish predictive models for the UCS of carbonate rocks formed in various facies and exposed in Tasonu Quarry, northeast Turkey. The objective is to effectively select the explanatory variables from among a subset of the dataset containing total porosity, effective porosity, slake durability index, and P-wave velocity in dry samples and in the solid part of samples. This was based on the adjusted determination coefficient and root-mean-square error values of different linear regression analysis combinations using all possible regression methods. A prediction model for UCS was prepared using generalized regression neural networks (GRNNs). GRNNs were preferred over feed-forward back-propagation algorithm-based neural networks because there is no problem of local minimums in GRNNs. In this study, as a result of all possible regression analyses, alternative combinations involving one, two, and three inputs were used. Through comparison of GRNN performance with that of feed-forward back-propagation algorithm-based neural networks, it is demonstrated that GRNN is a good potential candidate for prediction of the unconfined compressive strength of carbonate rocks. From an examination of other applications of UCS prediction models, it is apparent that the GRNN technique has not been used thus far in this field. This study provides a clear and practical summary of the possible impact of alternative neural network types in UCS prediction.     

Optimization of the Rock Physical Model in Tight Sandstone Reservoir

Krief model, Nur model and Pride-Lee model are usually used to calculate dry rock modulus of sandstone reservoirs, but they are not effective for tight sandstone reservoirs. Based on Krief model and Nur model, and minimizing the difference between predicted P-wave or S-wave velocities and measured velocities, we acquireed lithologic index m in Krief model and critical porosity ?_c in Nur model by Gassmann relationship. The empirical parameters used in the models are expressed as the values changing with depth, so the accuracy of Krief and Nur models to estimate the P-wave and S-wave velocities was improved, and these two models are called as the variable parameter Krief model and the variable parameter Nur model. In addition, comparing with prediction accuracy of P-wave and S-wave velocities under different constraints, we can see that the shear modulus formulas in the three models are more accurate and more suitable in the tight sandstone reservoir. Han’s relationship about K_dry and u_dry is not affected by porosity, lithology and other factors, and the paper established dry rock model by Han’s relationship and any one of the above three models. The new dry rock model was applied in the Gassmann relationship to predict S-wave velocity of H8 tight sandstone reservoir in Sulige Gas Filed, Ordos Basin, which improved the accuracy of predicting S-wave velocity. At the same time, lithology index m in Krief model, critical porosity ?_c in Nur model and consolidation parameters c in Pride-Lee model which are corresponding to each sample can be obtained. The values of these parameters can reflect lithology difference, pore structure, compaction degree and other characteristics, which indicate the geological characteristics of the reservoir.     

水电工程坝基玄武岩体波速与变形模量关系

岩体变形模量是岩体工程设计最重要的参数之一.由于受到资金、时间、尺寸效应等限制, 在工程勘察设计阶段往往不可能大量开展岩体现场和室内变形模量试验, 试验结果也不具有普遍代表性.因此, 水电工程常常采用岩体纵波波速与变形模量之间的相关关系来估算大范围及深部岩体变形模量.根据波动微分方程从理论上解释了岩体纵波波速与变形模量之间内在的联系.以我国西南金沙江干流上坝基主要为玄武岩体的4个大型水电工程为例, 根据132组现场变形模量试验结果与同向波速测试结果建立玄武岩体波速与变形模量相关方程, 并与也有的研究成果对比分析.研究成果表明, 玄武岩体波速与变形模量具有较好的相关性.当波速小于4 500 m/s时, 不同类型玄武岩根据波速计算变形模量差别较小; 当波速大于4 500 m/s时, 差别逐渐增加.选取最优的相关方程用于估算坝基玄武岩体变形模量, 为水电工程坝基玄武岩体变形模量的快捷评价提供科学依据.