首页 | 本学科首页   官方微博 | 高级检索  
相似文献
 共查询到20条相似文献，搜索用时 62 毫秒
1.
The unconfined compressive strength (UCS) of intact rocks is an important geotechnical parameter for engineering applications. Determining UCS using standard laboratory tests is a difficult, expensive and time consuming task. This is particularly true for thinly bedded, highly fractured, foliated, highly porous and weak rocks. Consequently, prediction models become an attractive alternative for engineering geologists. The objective of study is to select the explanatory variables (predictors) from a subset of mineralogical and index properties of the samples, based on all possible regression technique, and to prepare a prediction model of UCS using artificial neural networks (ANN). As a result of all possible regression, the total porosity and P-wave velocity in the solid part of the sample were determined as the inputs for the Levenberg–Marquardt algorithm based ANN (LM-ANN). The performance of the LM-ANN model was compared with the multiple linear regression (REG) model. When training and testing results of the outputs of the LM-ANN and REG models were examined in terms of the favorite statistical criteria, which are the determination coefficient, adjusted determination coefficient, root mean square error and variance account factor, the results of LM-ANN model were more accurate. In addition to these statistical criteria, the non-parametric Mann–Whitney U test, as an alternative to the Student’s t test, was used for comparing the homogeneities of predicted values. When all the statistics had been investigated, it was seen that the LM-ANN that has been developed, was a successful tool which was capable of UCS prediction.  相似文献   

2.
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   

3.
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   

4.
This study aims to express the relationships between Schmidt rebound number (N) with unconfined compressive strength (UCS) and Young's modulus (Et) of the gypsum by empirical equations. As known, the Schmidt hammer has been used worldwide as an index test for a quick rock strength and deformability characterisation due to its rapidity and easiness in execution, simplicity, portability, low cost and nondestructiveness. In this study, gypsum samples have been collected from various locations in the Miocene-aged gypsum of Sivas Basin and tested. The tests include the determination of Schmidt hammer rebound number (N), tangent Young's modulus (Et) and unconfined compressive strength. Finally, obtained parameters were correlated and regression equations were established among Schmidt hammer rebound hardness, tangent Young's modulus and unconfined compressive strength, presenting high coefficients of correlation. It appears that there is a possibility of estimating unconfined compressive strength and Young's modulus of gypsum, from their Schmidt hammer rebound number by using the proposed empirical relationships of UCS=exp(0.818+0.059N) and Et=exp(1.146+0.054N). However, the equations must be used only for the gypsum with an acceptable accuracy, especially at the preliminary stage of designing a structure. Finally, by using the obtained Schmidt hammer rebound number from this study, unconfined compressive strength was calculated and compared with the calculated value from different empirical equations proposed by different authors. It can be said that it is impossible to obtain only one relation for all types of the rocks.  相似文献   

5.
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   

6.
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   

7.
致密砂岩气层压裂产能及等级预测方法   总被引：1，自引：0，他引：1  
致密砂岩储层孔隙度小、渗透率低、含气饱和度低,基本上没有自然产能,需要进行压裂,因此进行压裂产能的预测很有必要。笔者研究了鄂尔多斯盆地苏里格东部地区盒8段致密砂岩气层的压裂产能及等级预测。利用反映储层流动性质的测井参数(电阻率、自然伽马、声波时差、中子、密度)与反应压裂施工情况的压裂施工参数(单位厚度砂体积、砂比、砂质量浓度、单位厚度排量、单位厚度入井总液量),选择数学统计方法神经网络法进行致密砂岩气层压裂产能等级预测。分析比较Elman神经网络、支持向量回归(SVR)、广义回归神经网络(GRNN)3个神经网络预测致密砂岩气层压裂产能模型的网络结构参数、回判及预测精度以及运行耗费时间。结果表明:3个模型中,GRNN网络参数只有1个,回判和预测精度最高,运行时间小于1 s。因此,选择GRNN模型预测致密砂岩气层压裂产能,并用于苏里格东部地区致密砂岩气层压裂产能的等级预测。等级预测准确率达到90%。  相似文献   

8.
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   

9.
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   

10.
Slake durability index (I d2) is an important engineering parameter to assess the resistance of clay-bearing and weak rocks to erosion and degradation. Standard test sample preparation for slake durability test is difficult for some rock types and the test is time-consuming. The paper reports an attempt to define I d2 using other parameters that are simpler to obtain. In this study, three different artificial neural network approaches, namely feed-forward back propagation (FFBP), radial basis function based neural network (RBNN), and generalized regression neural networks (GRNN) were used for estimating I d2. The determination coefficient (R 2), root mean square error and mean absolute relative error statistics were used as evaluation criteria of the FFBP, RBNN, and GRNN models. The experimental results were compared with these models. The comparison results indicate that the GRNN models are superior to the FFBP and RBNN models in modeling of the slake durability index (I d2).  相似文献   

11.
The effect of grain size distribution on the unconfined compressive strength (UCS) of bio-cemented granular columns is examined. Fine and coarse aggregates were mixed in various percentages to obtain five different grain size distributions. A four-phase percolation strategy was adopted where a bacterial suspension and a cementation solution (urea and calcium chloride) were percolated sequentially. The results show that a gap-graded particle size distribution can improve the UCS of bio-cemented coarser granular materials. A maximum UCS of approximately 575 kPa was achieved with a particle size distribution containing 75% coarse aggregate and 25% fine aggregate. Furthermore, the minimum UCS obtained has applications where mitigation of excessive bulging of stone/sand columns, and possible slumping that might occur during their installation, is needed. The finding also implies that the amount of biochemical treatments can be reduced by adding fine aggregate to coarse aggregate resulting in effective bio-cementation within the pore matrix of the coarse aggregate column as it could substantially reduce the cost associated with bio-cementation process. Scanning electron microscopy results confirm that adding fine aggregate to coarse aggregate provides more bridging contacts (connected by calcium carbonate precipitation) between coarse aggregate particles, and hence, the maximum UCS achieved was not necessarily associated with the maximum calcium carbonate precipitation.  相似文献   

12.
This investigation studied the coalcrete, a new supporting material produced by jet grouting (JG) for supporting surrounding coal seams. For support design, the unconfined compressive strength (UCS) of the coalcrete is an essential parameter to evaluate the jet grouting effect in coal mines. In this study, an intelligent technique was proposed for predicting the UCS of the coalcrete by combining back propagation neural network (BPNN) and beetle antennae search (BAS). The architecture of BPNN was first tuned by BAS, and then, the optimized BPNN-BAS model was subsequently used for nonlinear relationship modeling. Several crucial influencing variables including water-cement ratio, coal-grout ratio, and curing time were selected as the inputs. By combining these variables, 360 coalcrete samples were prepared in a controlled laboratory environment and tested for establishing the dataset. The results demonstrate that BAS can tune the BPNN architecture more efficiently compared with random selection. Moreover, in comparison with multiple regression (MLR) and logistic regression (LR), and support vector machine (SVM), the optimized BPNN-BAS model is more reliable and accurate for predicting coalcrete strength. Sensitivity analysis (SA) was used to obtain the variable importance, and the results demonstrate that curing time affects the UCS of the coalcrete most strongly, followed by water-cement ratio and coal-grout ratio. The success of this study provides an accurate and brief approach to coalcrete strength prediction.  相似文献   

13.
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   

14.
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   

15.
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   

16.
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   

17.
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   

18.
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   

19.
  相似文献   

20.
Specific energy (SE) measurements of circular saws were conducted on 12 different carbonate rocks. Rock samples were collected from the factories for laboratory tests. Bulk density, apparent porosity, uniaxial compressive strength, Brazilian tensile strength, flexural strength, Schmidt rebound hardness, Shore hardness, point load strength index, Los Angeles abrasion values, and P-wave velocity values were determined in the laboratory. SE and rock properties were evaluated using simple regression analysis and empirical equations were developed. The equations were verified by statistical tests. Regression analysis showed that high correlations exist between SE and uniaxial compressive strength, Shore and Schmidt hardness, bulk density, apparent porosity, and flexural strength. It was found that the SE value of rocks in cutting process was highest for those rocks having the high density, compressive strength, flexural strength, Schmidt and Shore hardness, point load strength index, and P-wave velocity values.  相似文献   

设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号