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1.
Prediction of unconfined compressive strength of carbonate rocks using artificial neural networks 总被引：3，自引：2，他引：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.
Abdolazim Azimian 《Acta Geotechnica》2017,12(2):321-333
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.
以南京内秦淮河疏浚淤泥为例,通过土工试验、XRD和X射线荧光光谱试验等方法,研究了城市河道淤泥的物理性质、矿物成分、化学成分等特性。试验结果显示:秦淮河淤泥粘粒含量低、有机质含量极高,矿物成分主要有石英和少量粘土矿物等。为了实现淤泥的资源化处理,运用水泥、石灰无机固化材料对淤泥进行固化改良试验及改性土无侧限抗压强度试验,结果表明随着水泥掺量增加,水泥固化土由塑性破坏向脆性破坏过渡,破坏应变在1.8%~2.2%,而石灰固化土均表现为脆性破坏,且破坏应变小于水泥土,为1%左右。水泥固化土28d强度为670kPa,固化效果优于石灰,但略低于处理一般软土的固化土强度。研究结果对处置城市河道淤泥有一定参考价值。 相似文献
4.
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
5.
A. Kayabasi 《Environmental Earth Sciences》2012,66(8):2171-2183
The Standard Penetration Test (SPT) is one of the most frequently applied tests during the geotechnical investigation of soils. Due to its usefulness, the development of empirical equations to predict mechanical and compressibility of soil parameters from the SPT blow count has been an attractive subject for geotechnical engineers and engineering geologists. The purpose of this study is to perform regression analyses between the SPT blow counts and the pressuremeter test parameters obtained from a geotechnical investigation performed in a Mersin (Turkey) city sewerage project. In accordance with this purpose, new empirical equations between pressuremeter modulus (E M) and corrected SPT blow counts (N 60) and between limit pressure (P L) and corrected SPT blow counts (N 60) are developed in the study. When developing the empirical equations, in addition to the SPT blow counts, the role of moisture content and the plasticity index of soils on the pressuremeter parameters are also assessed. A series of simple and nonlinear multiple regression analyses are performed. As a result of the analyses, several empirical equations are developed. It is shown that the empirical equations between N 60 and E M, and N 60 and P L developed in this study are statistically acceptable. An assessment of the prediction performances of some existing empirical equations, depending on the new data, is also performed in the study. However, the prediction equations proposed in this study and the previous studies are developed using a limited number of data. For this reason, a cross-check should be applied before using these empirical equations for design purposes. 相似文献
6.
In-situ Rock Spalling Strength near Excavation Boundaries 总被引：2，自引：0，他引：2
It is widely accepted that the in-situ strength of massive rocks is approximately 0.4 ± 0.1 UCS, where UCS is the uniaxial compressive strength obtained from unconfined tests using diamond drilling core samples with a diameter around 50 mm. In addition, it has been suggested that the in-situ rock spalling strength, i.e., the strength of the wall of an excavation when spalling initiates, can be set to the crack initiation stress determined from laboratory tests or field microseismic monitoring. These findings were supported by back-analysis of case histories where failure had been carefully documented, using either Kirsch’s solution (with approximated circular tunnel geometry and hence σ max = 3σ 1 ?σ 3) or simplified numerical stress modeling (with a smooth tunnel wall boundary) to approximate the maximum tangential stress σ max at the excavation boundary. The ratio of σ max /UCS is related to the observed depth of failure and failure initiation occurs when σ max is roughly equal to 0.4 ± 0.1 UCS. In this article, it is suggested that these approaches ignore one of the most important factors, the irregularity of the excavation boundary, when interpreting the in-situ rock strength. It is demonstrated that the “actual” in-situ spalling strength of massive rocks is not equal to 0.4 ± 0.1 UCS, but can be as high as 0.8 ± 0.05 UCS when surface irregularities are considered. It is demonstrated using the Mine-by tunnel notch breakout example that when the realistic “as-built” excavation boundary condition is honored, the “actual” in-situ rock strength, given by 0.8 UCS, can be applied to simulate progressive brittle rock failure process satisfactorily. The interpreted, reduced in-situ rock strength of 0.4 ± 0.1 UCS without considering geometry irregularity is therefore only an “apparent” rock strength. 相似文献
7.
Yuantian Sun Junfei Zhang Guichen Li Yuhang Wang Junbo Sun Chao Jiang 《国际地质力学数值与分析法杂志》2019,43(4):801-813
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
8.
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
9.
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
10.
This paper is likely one of the very recent researches based on an experimental study, which aims to investigate some geotechnical performances of fine-grained soil treated with industrial wastewater sludge. The experimental program conducts the standard compaction, direct shear, California bearing ratio (CBR), and unconfined compressive strength (UCS) tests. The sludge proportions in samples of the soil + sludge mixtures are 0, 5, 10, 20, 30, 40, 50, 60, 70, and 80 % by dry weight of the mixture. The results indicate that the internal friction angle of untreated soil is significantly enhanced at most of the sludge dosages (p < 0.05). The CBR values offer that the soil quality can be improved to “good” rating quality to use “base” layers in stabilizations up to the 50 % sludge dosage. The contribution is also obtained by the UCS values that increase with the sludge addition. Moreover, the stress–strain responses promise to develop the ductility behavior due to the sludge inclusion. Consequently, the soil mixtures treated with the sludge have exhibited satisfactory geotechnical characteristics. Thus, this study suggests that the industrial wastewater sludge can be potentially employed for improvement of fine-grained soil in the stabilizations. The proposed soil stabilization with locally available industrial wastewater sludge can also provide recycling and sustainability to the environment. 相似文献
11.
Gökhan Saygili 《Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards》2017,11(2):197-207
The conventional liquefaction potential assessment methods (also known as simplified methods) profoundly rely on empirical correlations based on observations from case histories. A probabilistic framework is developed to incorporate uncertainties in the earthquake ground motion prediction, the cyclic resistance prediction, and the cyclic demand prediction. The results of a probabilistic seismic hazard assessment, site response analyses, and liquefaction potential analyses are convolved to derive a relationship for the annual probability and return period of liquefaction. The random field spatial model is employed to quantify the spatial uncertainty associated with the in-situ measurements of geotechnical material. 相似文献
12.
Sarat Kumar Das Pijush Samui Shakilu Zama Khan Nagarathnam Sivakugan 《Central European Journal of Geosciences》2011,3(4):449-461
Stability with first time or reactivated landslides depends upon the residual shear strength of soil. This paper describes prediction of the residual strength of soil based on index properties using two machine learning techniques. Different Artificial Neural Network (ANN) models and Support Vector Machine (SVM) techniques have been used. SVM aims at minimizing a bound on the generalization error of a model rather than at minimizing the error on the training data only. The ANN models along with their generalizations capabilities are presented here for comparisons. This study also highlights the capability of SVM model over ANN models for the prediction of the residual strength of soil. Based on different statistical parameters, the SVM model is found to be better than the developed ANN models. A model equation has been developed for prediction of the residual strength based on the SVM for practicing geotechnical engineers. Sensitivity analyses have been also performed to investigate the effects of different index properties on the residual strength of soil. 相似文献
13.
重庆沙溪庙组地层岩石单轴抗压强度研究 总被引：6，自引：0，他引：6
岩石单轴抗压强度是工程勘察中最基本的岩体力学参数之一,已广泛应用于岩石地基设计、隧洞围岩分类、岩体质量分级、土石开挖分级和岩石地基验收中。重庆市区约70%面积坐落于侏罗系中统沙溪庙组地层之上,研究沙溪庙组地层岩石单轴抗压强度具有十分重要的意义。从工程勘察的实践出发,通过对重庆市沙溪庙组岩石抗压强度的统计和对软化系数的研究,建立了饱和抗压强度与天然抗压强度之间的经验公式。通过对计算值和试验值的大量对比,结果显示,计算值的可靠性高,可替代试验值,应用前景广阔。 相似文献
14.
Amin Jamshidi Mashalah Khamehchiyan Reza Zarei Sahamieh 《Geomechanics and Geoengineering》2016,11(1):13-19
Estimation of uniaxial compressive strength (UCS) by P-wave velocity (VP) 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 VP. In this study, the diameter size of specimens that effect UCS and VP is investigated. Moreover, the correlation between UCS and VP 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 VP. Moreover, it was found that the best correlation between relevant parameters obtained for the specimen diameter of 38 mm. 相似文献
15.
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
16.
The influence of mineral grain and grain boundary strength is investigated using a calibrated intact (non-jointed) brittle rock specimen subjected to direct shear with a particle-based distinct element method and its embedded grain-based method. The adopted numerical approach allows one to independently control the grain boundary and mineral grain strength. The investigation reveals that, in direct shear, the normal stress (σ n) applied to a rock specimen relative to its uniaxial compressive strength (UCS) determines the resulting rupture mechanism, the ultimate rupture zone geometry, and thus its shear stress versus horizontal displacement response. This allows one to develop a rupture matrix based on this controlling parameter (i.e., σ n/UCS). Mineral grain strength reductions result in the lowering of the apparent cohesion intercept of the peak linear Coulomb strength envelope, while grain boundary strength reductions change the peak linear Coulomb strength envelope to a bi-linear or curved shape. The impact of grain boundary strength is only relevant at σ n/UCS ratios <0.17 where tensile and dilatant rupture mechanisms dominate. Once shear rupture begins to be the dominant rupture mechanism in a brittle rock (i.e., at σ n/UCS ratios >0.17), the influence of weakened grain boundaries is minimized and strength is controlled by that of the mineral grains. 相似文献
17.
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
18.
Compression index Ccis an essential parameter in geotechnical design for which the effectiveness of correlation is still a challenge.This paper suggests a novel modelling approach using machine learning(ML)technique.The performance of five commonly used machine learning(ML)algorithms,i.e.back-propagation neural network(BPNN),extreme learning machine(ELM),support vector machine(SVM),random forest(RF)and evolutionary polynomial regression(EPR)in predicting Cc is comprehensively investigated.A database with a total number of 311 datasets including three input variables,i.e.initial void ratio e0,liquid limit water content wL,plasticity index Ip,and one output variable Cc is first established.Genetic algorithm(GA)is used to optimize the hyper-parameters in five ML algorithms,and the average prediction error for the 10-fold cross-validation(CV)sets is set as thefitness function in the GA for enhancing the robustness of ML models.The results indicate that ML models outperform empirical prediction formulations with lower prediction error.RF yields the lowest error followed by BPNN,ELM,EPR and SVM.If the ranges of input variables in the database are large enough,BPNN and RF models are recommended to predict Cc.Furthermore,if the distribution of input variables is continuous,RF model is the best one.Otherwise,EPR model is recommended if the ranges of input variables are small.The predicted correlations between input and output variables using five ML models show great agreement with the physical explanation. 相似文献
19.
Accurate assessment of undrained shear strength(USS)for soft sensitive clays is a great concern in geotechnical engineering practice.This study applies novel data-driven extreme gradient boosting(XGBoost)and random forest(RF)ensemble learning methods for capturing the relationships between the USS and various basic soil parameters.Based on the soil data sets from TC304 database,a general approach is developed to predict the USS of soft clays using the two machine learning methods above,where five feature variables including the preconsolidation stress(PS),vertical effective stress(VES),liquid limit(LL),plastic limit(PL)and natural water content(W)are adopted.To reduce the dependence on the rule of thumb and inefficient brute-force search,the Bayesian optimization method is applied to determine the appropriate model hyper-parameters of both XGBoost and RF.The developed models are comprehensively compared with three comparison machine learning methods and two transformation models with respect to predictive accuracy and robustness under 5-fold cross-validation(CV).It is shown that XGBoost-based and RF-based methods outperform these approaches.Besides,the XGBoostbased model provides feature importance ranks,which makes it a promising tool in the prediction of geotechnical parameters and enhances the interpretability of model. 相似文献
20.
Application of Artificial Intelligence to Maximum Dry Density and Unconfined Compressive Strength of Cement Stabilized Soil 总被引：2，自引：1，他引：1
Sarat Kumar Das Pijush Samui Akshaya K. Sabat 《Geotechnical and Geological Engineering》2011,29(3):329-342
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