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1.
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.  相似文献   

2.
《Engineering Geology》2000,57(3-4):215-237
Weathering can induce a rapid change of rock material from initial rock-like properties to soil-like properties. The resistance of a rock to short-term weathering is described through a durability parameter called the slake durability index. As durability is an important engineering parameter, particularly for weak and clay-bearing rocks, it was assessed by a number of tests. The main purpose of this study is to assess the influence of the number of drying and wetting cycles and controls of mineralogical composition and strength on durability. For this purpose, 141 samples of different types of weak and clay-bearing rocks were selected from different parts of Turkey, and relationships between the above-mentioned rock characteristics were statistically investigated. The samples were subjected to multiple-cycle slake durability testing, X-ray diffraction (XRD) analysis and uniaxial compression testing. In addition, to assess the influence of mineralogical composition on durability, the mineral contents of the original material and the material passing from the drum of the slake durability apparatus after each cycle were also determined by XRD. The results indicate that the type and amount of clay minerals are the main factors influencing the variations of the slake durability index in all samples. The durability of the clay-bearing rocks studied correlates best with the amount of expandable clay minerals. A strong relationship between the uniaxial compressive strength and the fourth-cycle slake durability index is found only for the marls among the rock types studied. Assessment of gradation results of the spoil pile materials consisting of clay-bearing rocks also reveals that the increase in percentage of fines in old piles is indicative of material degradation, as is evident by multiple-cycle slaking. It is emphasized that two-cycle conventional slake durability testing did not appear to offer an acceptable indication of the durability of weak and clay-bearing rocks when compared with multiple-cyclic wetting and drying. Comments on the performance of the test are made that aim to make the testing process and interpretation of the results more reliable.  相似文献   

3.
Accurate laboratory measurement of geo-engineering properties of intact rock including uniaxial compressive strength (UCS) and modulus of elasticity (E) involves high costs and a substantial amount of time. For this reason, it is of great necessity to develop some relationships and models for estimating these parameters in rock engineering. The present study was conducted to forecast UCS and E in the sedimentary rocks using artificial neural networks (ANNs) and multivariable regression analysis (MLR). For this purpose, a total of 196 rock samples from four rock types (i.e., sandstone, conglomerate, limestone, and marl) were cored and subjected to comprehensive laboratory tests. To develop the predictive models, physical properties of studied rocks such as P wave velocity (Vp), dry density (γd), porosity, and water absorption (Ab) were considered as model inputs, while UCS and E were the output parameters. We evaluated the performance of MLR and ANN models by calculating correlation coefficient (R), mean absolute error (MAE), and root-mean-square error (RMSE) indices. The comparison of the obtained results revealed that ANN outperforms MLR when predicting the UCS and E.  相似文献   

4.
Slake durability study of shaly rock and its predictions   总被引：2，自引：0，他引：2  
More than 35% of the earths crust is comprised of clay-bearing rocks, characterized by a wide variation in engineering properties and their resistance to short term weathering by wetting and drying phenomenon. The resistance to short-term weathering can be determined by slake durability index test. There are various methods to determine the slake durability indices of weak rock. The effect of acidity of water (slaking fluid) on slake durability index of shale in the laboratory is investigated. These methods are cumbersome and time consuming but they can provide valuable information on lithology, durability and weather ability of rock. Fuzzy set theory, Fuzzy logic and Artificial Neural Networks (ANN) techniques seem very well suited for typical complex geotechnical problems. In conjunction with statistics and conventional mathematical methods, a hybrid method can be developed that may prove a step forward in modeling geotechnical problems. During this investigation a model was developed and compared with two other models i.e., Neuro-fuzzy systems (combination of fuzzy and artificial neural network systems) and artificial neural network system, for the prediction of slake durability index of shaly rock to evaluate the performance of its prediction capability.  相似文献   

5.
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.  相似文献   

6.
Elastic properties of rocks play a major and crucial role for the design of any engineering structure. Determination of elastic properties in laboratory is tedious, laborious, very time consuming, as well as expertise is required, whereas determination of uniaxial compressive strength (UCS) and tensile strength in laboratory is simple, easy, and less expertise is required. Here, an attempt has been made to predict the elastic properties (Poisson’s ratio and Young’s modulus) of the schistose rocks from unconfined strength (UCS and tensile strength) using artificial neural network (ANN). A three-layer feed-forward back propagation neural network with 2-5-2 architecture was trained up to 855 epochs to predict the elastic properties of rock mass. The network was trained and tested by 120 data sets, and validation of the network was done by 20 new randomly selected data sets of UCS and tensile strength. The samples were collected from the schistose rocks of Nathpa-Jhakri hydropower project site, SJVNL, Himachal Pradesh, India. To check the validity and suitability of the artificial neural network technique, multivariate regression analysis (MVRA) is also performed, and comparison has been made. It was found that ANN gives closer values of predicted Poisson’s ratio and Young’s modulus as compared to MVRA. The coefficient of determination for Poisson’s ratio was 0.9809 and 0.843 by ANN and MVRA, respectively, whereas 0.9922 and 0.9362 for Young’s modulus by ANN and MVRA, respectively. The mean absolute percentage error (MAPE) for Young’s modulus is 11.13 and 28.21 by ANN and MVRA, respectively; whereas MAPE for Poisson’s ratio is 3.64 and 9.23 by ANN and MVRA, respectively.  相似文献   

7.
The uniaxial compressive strength (UCS) of rocks is a critical parameter required for most geotechnical projects. However, it is not always possible for direct determination of the parameter. Since determination of such a parameter in the lab is not always cost and time effective, the aim of this study is to assess and estimate the general correlation trend between the UCS and indirect tests or indexes used to estimate the value of UCS for some granitoid rocks in KwaZulu-Natal. These tests include the point load index test, Schmidt hammer rebound, P-wave velocity (Vp) and Brazilian tensile strength (σt). Furthermore, it aims to assess the reliability of empirical equations developed towards estimating the value of UCS and propose useful empirical equations to estimate the value of UCS for granitoid rocks. According to the current study, the variations in mineralogy, as well as the textural characteristics of granitoid rocks play an important role in influencing the strength of the rock. Simple regression analyses exhibit good results, with all regression coefficients R2 being greater than 0.80, the highest R2 of 0.92 being obtained from UCS versus σt. Comparison of equations produced in the current study as well as empirical equations derived by several researchers serves as a validation. Also illustrate that the reliability of such empirical equations are dependent on the rock type as well as the type of index tests employed, where variation in rock type and index tests produces different values of UCS. These equations provide a practical tool for estimating the value of UCS, and also gives further insight into the controlling factors of the strength of the granitoid rocks, where the strength of a rock is a multidimensional parameter.  相似文献   

8.
A new rock mass classification for Coal Measures Rocks   总被引：2，自引：0，他引：2  
This paper examines a new rock mass classification system (RMCR) for Coal Measures Rocks which is based on extensive laboratory testing results. The new system has been developed using 12 parameters which consist of mineral content index, uniaxial compressive strength, uniaxial tensile strength, Young's modulus of elasticity, shear strength, cohesion of rocks, angle of internal friction, point load index, cone indenter index, Cerchar index, Shore schleroscope hardness and specific energy index. The RMCR value was obtained by a number of laboratory and in situ testing results which were obtained from the coal site. The objective of the RMCR is to estimate the rock mass properties for engineering purposes.  相似文献   

9.
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 (Leq) produced during drilling for input parameters, and uniaxial compressive strength (UCS), Schmidt rebound number (SRN), dry density (ρ), P-wave velocity (Vp), 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.  相似文献   

10.
Preparing high-quality samples, which can fulfill testing standards, from weak and block-in-matrix conglomerate for laboratory tests, is a big challenge in engineering projects. Hence, using indirect methods seems to be indispensable for determination uniaxial compressive strength (UCS). The main objective of this study is to estimate the relation between sonic velocity (Vp), Schmidt hammer rebound number (SCH) and UCS. For this reason, some samples of weak conglomeratic rock were collected from two different sites of dam in Iran (Bakhtiari and Hezardareh Formations). In order to evaluate the correlation, the measured and predicted values utilizing simple and multivariate regression techniques were examined. To control the performance of the proposed equation, root mean square error (RMSE) and value accounts for (VAF%) were determined. The VAF% and RMSE indices were computed as 94.34 and 1.56 for the relation between Vp and UCS from simple regression model. These were 94.39 and 1.6 between SCH and UCS, while these were 97.24 and 1.34 for uniaxial compressive strengths obtained from multivariate regression model.  相似文献   

11.
One of the most important quality and design parameters of natural rock materials is uniaxial compressive strength (UCS). UCS value of a building stone determines its application area such as cladding, roofing, facing, and coverings. In rock mechanics and engineering practice determination of UCS values of rock materials is suggested on core specimens whereas in construction and building stone sector, cubic specimens are suggested. In this experimental study, the effect of cubic specimen size on UCS values of some carbonate rocks which are being used as dimension stones are investigated. A total of 299 cubic specimens at five different edge sizes (3, 5, 7, 9, and 11 cm) from limestone, marble, and travertine are prepared. Chemical, petrographic analyses and physical properties of specimens are determined and after that UCS tests are carried out. It is observed that as the specimen sizes increase from 3 to 11 cm, average UCS values decrease about 7% for the tested carbonate rocks. In the light of this finding, results of UCS tests could be interpreted considering cubic specimen sizes for the same rock types in various fields.  相似文献   

12.
Durability is one of the most important engineering properties of weak and clay-bearing rocks. Weathering can induce a rapid change in rock material from initial properties to soil-like properties. The sensitivity of a rock type against weatherability is usually described by a durability parameter, such as the slake durability index. However, marl resistance is not detected satisfactorily by the durability indices by using slake durability test as suggested by ISRM for two wetting–drying cycles. The results of this study are obtained from samples of compact or laminated eocene marls from region of Dalmatia, Croatia. The samples were subjected to 4 cycles of slake durability, point load tests, determination of dry density, determination of carbonate content and absorption of water. The scatter of data suggests that strength probably has no influence on the durability of marls. On the other hand a separate group of marl samples have a second-cycle slake durability index higher than approximately 85%, and the durability of these samples is classified as “medium-high” to “high”, although the visual inspection of samples after testing, suggests that they should have “medium” to “low” durability classification. According to obtained results these samples of marl fulfil the criterions for the durability classification: a carbonate content lower than approximately 65%, a dry density lower than 2.4 Mg/m3, and values of water absorption higher than 5%.  相似文献   

13.
This study aims to establish new correlations to assess uniaxial compressive strength (UCS) of northern Algeria sedimentary rocks. This estimation is based on the measurements of density, porosity, and Schmidt hammer hardness. To achieve this goal, a geological and geotechnical characterization campaign was conducted on 19 types of sandstone and carbonate rocks which have been collected from different geological areas of the Maghrebides chain. Petrographic analyses were conducted to identify the geological characteristics of each kind of rock. Subsequently, physico-mechanical tests (i.e., density, porosity, hardness, and uniaxial compressive strength) were carried out for all the sampled rocks. The results were then used to develop correlations between UCS values and the other parameter values determined. Finally, the UCS predictive equations which have the best predictive powers (coefficient of determination R 2 of 0.75 to 0.94) were discussed taking into account the geological specificities of the rocks, and then compared to similar studies developed by other authors in different areas of the world.  相似文献   

14.
This paper describes two artificial intelligence techniques for prediction of maximum dry density (MDD) and unconfined compressive strength (UCS) of cement stabilized soil. The first technique uses various artificial neural network (ANN) models such as Bayesian regularization method (BRNN), Levenberg- Marquardt algorithm (LMNN) and differential evolution algorithm (DENN). The second technique uses the support vector machine (SVM) that is firmly based on the theory of statistical learning theory, uses regression technique by introducing ε-insensitive loss function has been adopted. The inputs of both models are liquid limit (LL), plasticity index (PI), clay fraction (CF)%, sand (S)%, gravel Gr (%), moisture content (MC) and cement content (Ce). The sensitivity analyses of the input parameters have been also done for both models. Based on different statistical criteria the SVM models are found to be better than ANN models for the prediction of MDD and UCS of cement stabilized soil.  相似文献   

15.
The durability is a measure of the rock’s ability to resist degradation during its working life. Rock durability is greatly related to the mineralogical composition of rocks, rock texture (crystal interlocking, crystal shape and size), and the nature of fluids that are in contact with rock. Marbles have been among the most important building materials since ancient times. The main aim of this study is to evaluate the durability of Al Masjid Al-Haram marble and Ordinary white marble “Carrara” (M1 and M2) and develop some correlations among the physical and mechanical properties such as P-wave velocity, slake durability index, dry uniaxial compressive strength (UCSDry), abrasion resistance, point load index, impact strength index, Brazilian tensile strength, and Shore hardness. After testing and the evaluation of the test results, strong statistical correlations were found between P-wave velocity and other rock properties. Statistical correlations between the UCSDry other tests were also carried out. The coefficients of regressions (R2) range from 0.6177 to 0.997. The study shows that the UCSDry values of M1 and M2 have positive relationship with P wave velocities. Concluding remark is that the rocks tested in the study have good durability characteristics and can be reliably used for construction projects. On the other hand, the derived empirical equations can be used for the estimation purposes for similar rock types.  相似文献   

16.
Weathering and durability are the key factors of the rock in the suitability and usefulness of different construction materials, building materials and engineering structures. A single test never predicts the entire factor for suitability of rock stone and aggregate in different uses. Thus, variety of physical, mechanical and chemical tests and indices of rocks are widely used to estimate and evaluate the rocks for the suitability of the required purpose. In all the cases, knowledge of durability and weathering properties are the most important along with the strength of the rock. Micropetrographic index and rock durability indicators (dynamic and static) are the one of the best methods to evaluate the rock for weathering and durability. To estimate these indices, variety of tests are performed such as petrographic examination test, point load index, sulfate soundness test, water absorption test, modified aggregate impact value test and test for specific gravity. Slake durability index and impact strength index tests were also performed for correlation with static and dynamic rock durability indicators due to its application and usefulness in the durability and strength of the rock materials. Micropetrographic index was obtained by petrographic examination test and correlated with all the physical and mechanical properties used for find out the durability indicators. The present study is to express the usefulness of these three indices in the classification of weathering and durability classes and estimation of durability indices by slake durability index, impact strength index and micropetrographic index.  相似文献   

17.
《地学前缘(英文版)》2018,9(6):1609-1618
Rock properties exhibit spatial variabilities due to complex geological processes such as sedimentation,metamorphism, weathering, and tectogenesis. Although recognized as an important factor controlling the safety of geotechnical structures in rock engineering, the spatial variability of rock properties is rarely quantified. Hence, this study characterizes the autocorrelation structures and scales of fluctuation of two important parameters of intact rocks, i.e. uniaxial compressive strength(UCS) and elastic modulus(EM).UCS and EM data for sedimentary and igneous rocks are collected. The autocorrelation structures are selected using a Bayesian model class selection approach and the scales of fluctuation for these two parameters are estimated using a Bayesian updating method. The results show that the autocorrelation structures for UCS and EM could be best described by a single exponential autocorrelation function. The scales of fluctuation for UCS and EM respectively range from 0.3 m to 8.0 m and from 0.3 m to 8.4 m.These results serve as guidelines for selecting proper autocorrelation functions and autocorrelation distances for rock properties in reliability analyses and could also be used as prior information for quantifying the spatial variability of rock properties in a Bayesian framework.  相似文献   

18.
充分认识岩石的地质本质性是准确描述其物理力学特性的桥梁。岩石的地质本质性涵盖了岩石的物质性、结构性和赋存状态3个方面的内容。在综合考虑岩石上述3方面特征及其与单轴试验联系的基础上,以矿物组成、密度、纵波波速和含水状态为基本指标,采用回归和BP神经网络的方法对碳酸盐岩单轴抗压强度进行预测,并采用灰色关联分析法验证本研究所选用的预测基本指标的合理性。实例应用表明：本次采用的回归方法对该类岩石强度预测的最大误差为15.3%,BP神经网络方法预测的最大误差为8.5%。预测误差出现的原因为碳酸盐岩物质组成复杂,所选预测基本指标是实际情况的简化,同时泥灰质岩石所具有的膨胀性也导致实测和预测结果具有一定的差异。  相似文献   

19.
张建明  唐志成  刘泉声 《岩土力学》2015,36(Z2):595-602
单轴压缩强度是岩石工程建设中广泛使用的力学参数,直接确定单轴压缩强度相对耗时且较为麻烦。点荷载试验可以间接估算岩石的单轴压缩强度（UCS）,试验方式简洁。通过收集到的岩浆岩点荷载试验成果,分析结果表明,（1）大部分转换公式在点荷载强度指数较大时得到偏高的单轴压缩强度值,特别是对部分幂函数;（2）ISRM（American Society for Testing and Materials）建议的取值范围仍然高估了岩石的单轴压缩强度值。为更准确地估算岩石的单轴压缩强度,建议采用点荷载强度指数 的二次函数（见式（1））估算岩石的单轴压缩强度,适用范围为0.0 MPa < <15.0 MPa。  相似文献   

20.
李文  谭卓英 《岩土力学》2016,37(Z2):381-387
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。  相似文献   

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