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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.
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.  相似文献   

3.
Burden prediction is a vital task in the production blasting. Both the excessive and insufficient burden can significantly affect the result of blasting operation. The burden which is determined by empirical models is often inaccurate and needs to be adjusted experimentally. In this paper, an attempt was made to develop an artificial neural network (ANN) in order to predict burden in the blasting operation of the Mouteh gold mine, using considering geomechanical properties of rocks as input parameters. As such here, network inputs consist of blastability index (BI), rock quality designation (RQD), unconfined compressive strength (UCS), density, and cohesive strength. To make a database (including 95 datasets), rock samples are used from Iran’s Mouteh goldmine. Trying various types of the networks, a neural network, with architecture 5-15-10-1, was found to be optimum. Superiority of ANN over regression model is proved by calculating. To compare the performance of the ANN modeling with that of multivariable regression analysis (MVRA), mean absolute error (E a), mean relative error (E r), and determination coefficient (R 2) between predicted and real values were calculated for both the models. It was observed that the ANN prediction capability is better than that of MVRA. The absolute and relative errors for the ANN model were calculated 0.05 m and 3.85%, respectively, whereas for the regression analysis, these errors were computed 0.11 m and 5.63%, respectively. Moreover, determination coefficient of the ANN model and MVRA were determined 0.987 and 0.924, respectively. Further, a sensitivity analysis shows that while BI and RQD were recognized as the most sensitive and effective parameters, cohesive strength is considered as the least sensitive input parameters on the ANN model output effective on the proposed (burden).  相似文献   

4.
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   

5.
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   

6.
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   

7.
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 (Id4) 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.  相似文献   

8.
Summary. Uniaxial Compressive Strength (UCS), considered to be one of the most useful rock properties for mining and civil engineering applications, has been estimated from some index test results by fuzzy and multiple regression modelling. Laboratory investigations including Uniaxial Compressive Strength (UCS), Point Load Index test (PL), Schmidt Hammer Hardness test (SHR) and Sonic velocity (Vp) test have been carried out on nine different rock types yielding to 305 tested specimens in total. Average values along with the standard deviations (Stdev) as well as Coefficients of variation (CoV) have been calculated for each rock type. Having constructed the Mamdani Fuzzy algorithm, UCS of intact rock samples was then predicted using a data driven fuzzy model. The predicted values derived from fuzzy model were compared with multi-linear statistical model. Comparison proved that the best model predictions have been achieved by fuzzy modelling in contrast to multi-linear statistical modelling. As a result, the developed fuzzy model based on point load, Schmidt hammer and sonic velocity can be used as a tool to predict UCS of intact rocks.  相似文献   

9.
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   

10.
Accurate and reliable prediction of shallow groundwater level is a critical component in water resources management. Two nonlinear models, WA–ANN method based on discrete wavelet transform (WA) and artificial neural network (ANN) and integrated time series (ITS) model, were developed to predict groundwater level fluctuations of a shallow coastal aquifer (Fujian Province, China). The two models were testified with the monitored groundwater level from 2000 to 2011. Two representative wells are selected with different locations within the study area. The error criteria were estimated using the coefficient of determination (R 2), Nash–Sutcliffe model efficiency coefficient (E), and root-mean-square error (RMSE). The best model was determined based on the RMSE of prediction using independent test data set. The WA–ANN models were found to provide more accurate monthly average groundwater level forecasts compared to the ITS models. The results of the study indicate the potential of WA–ANN models in forecasting groundwater levels. It is recommended that additional studies explore this proposed method, which can be used in turn to facilitate the development and implementation of more effective and sustainable groundwater management strategies.  相似文献   

11.
The transfer of energy between two adjacent parts of rock mainly depends on its thermal conductivity. Knowledge of the thermal conductivity of rocks is necessary for the calculation of heat flow or for the longtime modeling of geothermal resources. In recent years, considerable effort has been made to develop artificial intelligence techniques to determine these properties. Present study supports the application of artificial neural network (ANN) in the study of thermal conductivity along with other intrinsic properties of rock due to its increasing importance in many areas of rock engineering, agronomy, and geoenvironmental engineering field. In this paper, an attempt has been made to predict the thermal conductivity (TC) of rocks by incorporating uniaxial compressive strength, density, porosity, and P-wave velocity using artificial neural network (ANN) technique. A three-layer feed forward back propagation neural network with 4-7-1 architecture was trained and tested using 107 experimental data sets of various rocks. Twenty new data sets were used for the validation and comparison of the TC by ANN. Multivariate regression analysis (MVRA) has also been done with same data sets of ANN. ANN and MVRA results were compared based on coefficient of determination (CoD) and mean absolute error (MAE) between experimental and predicted values of TC. It was found that CoD between measured and predicted values of TC by ANN and MVRA were 0.984 and 0.914, respectively, whereas MAE was 0.0894 and 0.2085 for ANN and MVRA, respectively.  相似文献   

12.
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   

13.
The Equotip hardness tester (EHT) is a portable and non-destructive instrument used mainly for the dynamic rebound hardness testing of metals. Although various versions of the ‘single impacts’ and ‘repeated impacts’ testing procedures have been employed by different authors for different applications, it is not yet known whether a particular testing procedure is more relevant for a specific application in rock engineering. To be able to contribute to the subject, the present study was carried out to determine the suitability of different rebound testing procedures with this instrument for uniaxial compressive strength (UCS) estimations of some selected carbonate rocks. To achieve this goal, as well as four different existing rebound testing procedures, a newly proposed testing methodology involving the parameter hybrid dynamic hardness (HDH) was also employed. The statistical analyses performed on the experimental data, on the whole, showed that the test procedures which are based on single impacts test procedures outperformed the repeated impacts test procedures in terms of UCS prediction accuracy. The prediction capability of the newly introduced testing methodology was found to be superior to those of other procedures considered in this work, suggesting that it could be an efficient tool in practice for preliminary estimates of rock strength. The statistical analyses also indicated that, in practical applications of the EHT using different test procedures, it may be possible to predict the UCS more accurately when apparent density data is available. For the range of specimen sizes considered, no clear evidence of size effect was observed in the mean rebound values. The argument raised by some other authors that the EHT might not be a convenient instrument for the dynamic rebound hardness determination of relatively high-porosity rocks was not confirmed in this study.  相似文献   

14.
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   

15.
Adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) models have been extensively used to predict different soil properties in geotechnical applications. In this study, it was aimed to develop ANFIS and ANN models to predict the unconfined compressive strength (UCS) of compacted soils. For this purpose, 84 soil samples with different grain-size distribution compacted at optimum water content were subjected to the unconfined compressive tests to determine their UCS values. Many of the test results (for 64 samples) were used to train the ANFIS and the ANN models, and the rest of the experimental results (for 20 samples) were used to predict the UCS of compacted samples. To train these models, the clay content, fine silt content, coarse silt content, fine sand content, middle sand content, coarse sand content, and gravel content of the total soil mass were used as input data for these models. The UCS values of compacted soils were output data in these models. The ANFIS model results were compared with those of the ANN model and it was seen that the ANFIS model results were very encouraging. Consequently, the results of this study have important findings indicating reliable and simple prediction tools for the UCS of compacted soils.  相似文献   

16.
Learning from data is a very attractive alternative to “manually” learning. Therefore, in the last decade the use of machine learning has spread rapidly throughout computer science and beyond. This approach, supported on advanced statistics analysis, is usually known as Data Mining (DM) and has been applied successfully in different knowledge domains. In the present study, we show that DM can make a great contribution in solving complex problems in civil engineering, namely in the field of geotechnical engineering. Particularly, the high learning capabilities of Support Vector Machines (SVMs) algorithm, characterized by it flexibility and non-linear capabilities, were applied in the prediction of the Uniaxial Compressive Strength (UCS) of Jet Grouting (JG) samples directly extracted from JG columns, usually known as soilcrete. JG technology is a soft-soil improvement method worldwide applied, extremely versatile and economically attractive when compared with other methods. However, even after many years of experience still lacks of accurate methods for JG columns design. Accordingly, in the present paper a novel approach (based on SVM algorithm) for UCS prediction of soilcrete mixtures is proposed supported on 472 results collected from different geotechnical works. Furthermore, a global sensitivity analysis is applied in order to explain and extract understandable knowledge from the proposed model. Such analysis allows one to identify the key variables in UCS prediction and to measure its effect. Finally, a tentative step toward a development of UCS prediction based on laboratory studies is presented and discussed.  相似文献   

17.
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   

18.
An application of artificial intelligence for rainfall-runoff modeling   总被引：5，自引：0，他引：5  
This study proposes an application of two techniques of artificial intelligence (AI) for rainfall-runoff modeling: the artificial neural networks (ANN) and the evolutionary computation (EC). Two different ANN techniques, the feed forward back propagation (FFBP) and generalized regression neural network (GRNN) methods are compared with one EC method, Gene Expression Programming (GEP) which is a new evolutionary algorithm that evolves computer programs. The daily hydrometeorological data of three rainfall stations and one streamflow station for Juniata River Basin in Pennsylvania state of USA are taken into consideration in the model development. Statistical parameters such as average, standard deviation, coefficient of variation, skewness, minimum and maximum values, as well as criteria such as mean square error (MSE) and determination coefficient (R 2) are used to measure the performance of the models. The results indicate that the proposed genetic programming (GP) formulation performs quite well compared to results obtained by ANNs and is quite practical for use. It is concluded from the results that GEP can be proposed as an alternative to ANN models.  相似文献   

19.
Prediction of engineering properties of rocks from microscopic data   总被引：1，自引：0，他引：1  
The purpose of this study is to develop the empirical equations for the prediction of the physical and mechanical properties of limestone and marble from microscopic data including their mineralogical and petrographical properties and to test the validity of model equations by using multivariate statistical methods. This study was performed on 15 different rocks, composed of six limestone and nine marble samples. Stepwise multiple regression analysis was applied to predict the engineering properties of both the marble and limestone rock samples considering petrographical properties as inputs. In order to determine the overall significance of the empirical equations for prediction of the physical and mechanical properties of marble and limestone samples, the F test was also performed. As a result of this study, it is found that the empirical equations developed in this study are statistically significant.  相似文献   

20.
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   

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