Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition

Prediction of uniaxial compressive strength of rock samples using hybrid particle swarm optimization-based artificial neural networks

This paper presents a new hybrid artificial neural network (ANN) optimized by imperialist competitive algorithm (ICA) to predict peak particle velocity (PPV) resulting from quarry blasting. For this purpose, 95 blasting works were precisely monitored in a granite quarry site in Malaysia and PPV values were accurately recorded in each operation. Furthermore, the most influential parameters on PPV were measured and used to train the ICA-ANN model. Considering the measured data from the granite quarry site, a new empirical equation was developed to predict PPV. For comparison, a pre-developed ANN model was developed for PPV prediction. The results demonstrated that the proposed ICA-ANN model is able to predict blasting-induced PPV better than other presented techniques.

Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm

The potential surface settlement, especially in urban areas, is one of the most hazardous factors in subway and other infrastructure tunnel excavations. Therefore, accurate prediction of maximum surface settlement (MSS) is essential to minimize the possible risk of damage. This paper presents a new hybrid model of artificial neural network (ANN) optimized by particle swarm optimization (PSO) for prediction of MSS. Here, this combination is abbreviated using PSO-ANN. To indicate the performance capacity of the PSO-ANN model in predicting MSS, a pre-developed ANN model was also developed. To construct the mentioned models, horizontal to vertical stress ratio, cohesion and Young's modulus were set as input parameters, whereas MSS was considered as system output. A database consisting of 143 data sets, obtained from the line No. 2 of Karaj subway, in Iran, was used to develop the predictive models. The performance of the predictive models was evaluated by comparing performance prediction parameters, including root mean square error (RMSE), variance account for (VAF) and coefficient correlation (R2). The results indicate that the proposed PSO-ANN model is able to predict MSS with a higher degree of accuracy in comparison with the ANN results. In addition, the results of sensitivity analysis show that the horizontal to vertical stress ratio has slightly higher effect of MSS compared to other model inputs.

Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling

https://www.uvm.edu/giee/pubpdfs/Besaw_2010_Journal_of_Hydrology.pdf

Advances in ungauged streamflow prediction using artificial neural networks

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.

Prediction of unconfined compressive strength of carbonate rocks using artificial neural networks

Application of support vector machines and relevance vector machines in predicting uniaxial compressive strength of volcanic rocks

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

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

SUMMARY

In the predicting of geological variables, artificial neural networks (ANNs) have some drawbacks including possibility of getting trapped in local minima, over training, subjectivity in the determining of model parameters and the components of its complex structure. Recently, support vector machines (SVM) has been found to be popular in prediction studies due to its some advantages over ANNs. Because the least squares SVM (LS-SVM) provides a computational advantage over SVM by converting quadratic optimization problem into a system of linear equations, LS-SVM method is also tried in study. The main purpose of this study is to examine the capability of these two SVM algorithms for the prediction of tensile strength of rock materials and to compare its performance with ANN and linear regression (MLR) models. Total porosity, sonic velocity, slake durability index and aggregate impact value were used as input in modeling applications. Favorite performance evaluation measures were employed to assess developed models. The results determined in study indicate that the SVM, LS-SVM and ANN methods are successful tools for prediction of tensile strength variable and can give good prediction performances than MLR model. Although these three methods are powerful artificial intelligence techniques, LS-SVM makes the running time considerably faster with the higher accuracy. In terms of accuracy, the LS-SVM model resulted in error reductions relative to that of the other models. Copyright © 2012 John Wiley & Sons, Ltd.

Modeling of tensile strength of rocks materials based on support vector machines approaches

Uniaxial compressive strength (UCS) of rock material is very important parameter for rock engineering applications such as rock mass classification, numerical modelling bearing capacity, mechanical excavation, slope stability and supporting with respect to the engineering behaviors’ of rock. UCS is obtained directly or can be predicted by different methods including using existing tables and diagrams, regression, Bayesian approach and soft computing methods. The main purpose of this study is to examine the applicability and capability of the Extreme Learning Machine (ELM), Minimax Probability Machine Regression (MPMR) for prediction of UCS of the volcanic rocks and to compare its performance with Least Square Support Vector Machine (LS-SVM). The samples tested were taken from the volcanic rock masses exposed at the eastern Pontides (NE Turkey). In the soft computing model to estimate UCS of the samples investigated, porosity and slake durability index were used as input parameters. In this study, the root mean square error (RMSE), variance account factor (VAF), maximum determination coefficient value (R2), adjusted determination coefficient (Adj. R2) and performance index (PI), regression error characteristic (REC) curve and Taylor diagram were used to determine the accuracy of the ELM, MPMR and LS-SVM models developed.

Nurcihan Ceryan

Papers

Prediction of unconfined compressive strength of carbonate rocks using artificial neural networks

Application of support vector machines and relevance vector machines in predicting uniaxial compressive strength of volcanic rocks

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

Modeling of tensile strength of rocks materials based on support vector machines approaches

Application of soft computing methods in predicting uniaxial compressive strength of the volcanic rocks with different weathering degree