Multiple regression,ANN and ANFIS models for prediction of backbreak in the open pit blasting

Backbreak is one of the undesirable effects of blasting operations causing instability in mine walls, falling down the machinery, improper fragmentation and reduction in efficiency of drilling. Backbreak can be affected by various parameters such as the rock mass properties, blasting geometry and explosive properties. In this study, the application of the artificial neural network (ANN), an adaptive neuro-fuzzy inference system (ANFIS) for prediction of backbreak, was described and compared with the traditional statistical model of multiple regression. The performance of these models was assessed through the root mean square error, correlation coefficient (R ²) and mean absolute percentage error. As a result, it was found that the constructed ANFIS exhibited a higher performance than the ANN and multiple regression for backbreak prediction.     

Prediction of unconfined compressive strength of rock surrounding a roadway using artificial neural network

The unconfined compressive strength (UCS) of rocks is an important design parameter in rock engineering and geotechnics, which is required and determined for rock mechanical studies in mining and civil projects. This parameter is usually determined through a laboratory UCS test. Since the preparation of high-quality samples is difficult, expensive and time consuming for laboratory tests, development of predictive models for determining the mechanical properties of rocks seems to be essential in rock engineering. In this study, an attempt was made to develop an artificial neural network (ANN) and multivariable regression analysis (MVRA) models in order to predict UCS of rock surrounding a roadway. For this, a database of laboratory tests was prepared, which includes rock type, Schmidt hardness, density and porosity as input parameters and UCS as output parameter. To make a database (including 93 datasets), different rock samples, ranging from weak to very strong types, are used. To compare the performance of developed models, determination coefficient (R ²), variance account for (VAF), mean absolute error (E _a) and mean relative error (E _r) indices between predicted and measured values were calculated. Based on this comparison, it was concluded that performance of the ANN model is considerably better than the MVRA model. Further, a sensitivity analysis shows that rock density and Schmidt hardness were recognized as the most effective parameters, whereas porosity was considered as the least effective input parameter on the ANN model output (UCS) in this study.     

Several non-linear models in estimating air-overpressure resulting from mine blasting

This research presents several non-linear models including empirical, artificial neural network (ANN), fuzzy system and adaptive neuro-fuzzy inference system (ANFIS) to estimate air-overpressure (AOp) resulting from mine blasting. For this purpose, Miduk copper mine, Iran was investigated and results of 77 blasting works were recorded to be utilized for AOp prediction. In the modeling procedure of this study, results of distance from the blast-face and maximum charge per delay were considered as predictors. After constructing the non-linear models, several performance prediction indices, i.e. root mean squared error (RMSE), variance account for (VAF), and coefficient of determination (R ²) and total ranking method are examined to choose the best predictive models and evaluation of the obtained results. It is obtained that the ANFIS model is superior to other utilized techniques in terms of R ², RMSE, VAF and ranking herein. As an example, RMSE values of 5.628, 3.937, 3.619 and 2.329 were obtained for testing datasets of empirical, ANN, fuzzy and ANFIS models, respectively, which indicate higher performance capacity of the ANFIS technique to estimate AOp compared to other implemented methods.     

Genetic programing and non-linear multiple regression techniques to predict backbreak in blasting operation

In addition to all benefits of blasting in mining and civil engineering applications, it has some undesirable environmental impacts. Backbreak is an unwanted phenomenon of blasting which can cause instability of mine walls, decreasing efficiency of drilling, falling down of machinery, etc. Recently, the use of new approaches such as artificial intelligence (AI) is greatly recommended by many researchers. In this paper, a new AI technique namely genetic programing (GP) was developed to predict BB. To prepare a sufficient database, 175 blasting works were investigated in Sungun copper mine, Iran. In these operations, the most influential parameters on BB including burden, spacing, stemming length, powder factor and stiffness ratio were measured and used to develop BB predictive models. To demonstrate capability of GP technique, a non-linear multiple regression (NLMR) model was also employed for prediction of BB. Value account for (VAF), root mean square error (RMSE) and coefficient of determination (R ²) were used to control the capacity performance of the predictive models. The performance indices obtained by GP approach indicate the higher reliability of GP compared to NLMR model. RMSE and VAF values of 0.327 and 97.655, respectively, for testing datasets of GP approach reveal the superiority of this model in predicting BB, while these values were obtained as 0.865 and 81.816, respectively, for NLMR model.     

The effect of ICA and PSO on ANN results in approximating elasticity modulus of rock material

Reliable determination/evaluation of the rock deformation can be useful prior any structural design application. Young’s modulus (E) affords great insight into the characteristics of the rock. However, its direct determination in the laboratory is costly and time-consuming. Therefore, rock deformation prediction through indirect techniques is greatly suggested. This paper describes hybrid particle swarm optimization (PSO)–artificial neural network (ANN) and imperialism competitive algorithm (ICA)–ANN to solve shortcomings of ANN itself. In fact, the influence of PSO and ICA on ANN results in predicting E was studied in this research. By investigating the related studies, the most important parameters of PSO and ICA were identified and a series of parametric studies for their determination were conducted. All models were built using three inputs (Schmidt hammer rebound number, point load index and p-wave velocity) and one output which is E. To have a fair comparison and to show the capability of the hybrid models, a pre-developed ANN model was also constructed to estimate E. Evaluation of the obtained results demonstrated that a higher ability of E prediction is received developing a hybrid ICA–ANN model. Coefficient of determination (R²) values of (0.952, 0.943 and 0.753) and (0.955, 0.949 and 0.712) were obtained for training and testing of ICA–ANN, PSO–ANN and ANN models, respectively. In addition, VAF values near to 100 (95.182 and 95.143 for train and test) were achieved for a developed ICA–ANN hybrid model. The results indicated that the proposed ICA–ANN model can be implemented better in improving performance capacity of ANN model compared to another implemented hybrid model.

     

Evaluation and prediction of flyrock resulting from blasting operations using empirical and computational methods

Mines, quarries and construction sites face environmental impacts, such as flyrock, due to blasting operations. Flyrock may cause damage to structures and injury to human. Therefore, flyrock prediction is required to determine safe blasting zone. In this regard, 232 blasting operations were investigated in five granite quarries, Malaysia. Blasting parameters comprising maximum charge per delay and powder factor were prepared to predict flyrock using empirical and intelligent methods. An empirical graph was proposed to predict flyrock distance for different powder factor values. In addition, using the same datasets, two intelligent systems, namely artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) were used to predict flyrock. Considering some model performance indices including coefficient of determination (R ²), value account for and root mean squared error and also using simple ranking procedure, the best flyrock prediction models were selected. It was found that the ANFIS model can predict flyrock with higher performance capacity compared to ANN predictive model. R ² values of testing datasets are 0.925 and 0.964 for ANN and ANFIS techniques, respectively, suggesting the superiority of the ANFIS technique in predicting flyrock.     

Combination of neural network and ant colony optimization algorithms for prediction and optimization of flyrock and back-break induced by blasting

Blasting is the process of use of explosives to excavate or remove the rock mass. The main objective of blasting operation is to provide proper rock fragmentation and to avoid undesirable environmental impacts such as ground vibration, flyrock and back-break. Therefore, proper predicting and subsequently optimizing these impacts may reduce damage on facilities and equipment. In this study, an artificial neural network (ANN) was developed to predict flyrock and back-break resulting from blasting. To do this, 97 blasting works in Delkan iron mine, Iran, were investigated and required blasting parameters were collected. The most influential parameters on flyrock and back-break, i.e. burden, spacing, hole length, stemming, and powder factor were considered as model inputs. Results of absolute error (Ea) and root mean square error (RMSE) (0.0137 and 0.063 for Ea and RMSE, respectively) reveal that ANN as a powerful tool can predict flyrock and back-break with high degree of accuracy. In addition, this paper presents a new metaheuristic approximation approach based on the ant colony optimization (ACO) for solving the problem of flyrock and back-break in Delkan iron mine. Considering changeable parameters of the ACO algorithm, blasting pattern parameters were optimized to minimize results of flyrock and back-break. Eventually, implementing ACO algorithm, reductions of 61 and 58 % were observed in flyrock and back-break results, respectively.     

A new technique to predict fly-rock in bench blasting based on an ensemble of support vector regression and GLMNET

Fly-rock caused by blasting is one of the dangerous side effects that need to be accurately predicted in open-pit mines. This study proposed a new technique to predict the distance of fly-rock based on an ensemble of support vector regression models (SVRs) and Lasso and elastic-net regularized generalized linear model (GLMNET), called SVRs–GLMNET. It was developed based on a combination of six SVR models and a GLMNET model. Accordingly, the dataset including 210 experimental data was divided into three parts, i.e., training, validating, and testing. Of the whole dataset, 70% was used for the development of the six SVR models first as the sub-models. Subsequently, 20% of the entire dataset (the validating dataset) was used to predict fly-rock based on the six developed SVR models. The predicted results from the six developed SVR models were used as the input variables to establish the GLMNET model (i.e., SVRs–GLMNET model). Finally, the remaining 10% of the dataset was used for testing the performance of the proposed SVRs–GLMNET model. A comparison and evaluation of the six developed SVR models and the proposed SVRs–GLMNET model were implemented based on five statistical criteria, such as mean absolute error (MAE), mean absolute percentage error (MAPE), root-mean-square error (RMSE), variance account for (VAF), and determination of correlation (R²). The results indicated that the proposed SVRs–GLMNET model provided the most dominant performance in predicting the distance of fly-rock caused by bench blasting in this study with an RMSE of 3.737, R² of 0.993, MAE of 3.214, MAPE of 0.018, and VAF of 99.207. Whereas, the other models yielded poorer accuracy with RMSE of 7.058–12.779, R² of 0.920–0.972, MAE of 3.438–7.848, MAPE of 0.021–0.055, and VAF of 90.538–97.003.

     

ANN based simulation and experimental verification of analytical four- and five-parameters models of PV modules

In this article, artificial neural network (ANN) is adopted to predict photovoltaic (PV) panel behaviors under realistic weather conditions. ANN results are compared with analytical four and five parameter models of PV module. The inputs of the models are the daily total irradiation, air temperature and module voltage, while the outputs are the current and power generated by the panel. Analytical models of PV modules, based on the manufacturer datasheet values, are simulated through Matlab/Simulink environment. Multilayer perceptron is used to predict the operating current and power of the PV module. The best network configuration to predict panel current had a 3–7–4–1 topology. So, this two hidden layer topology was selected as the best model for predicting panel current with similar conditions. Results obtained from the PV module simulation and the optimal ANN model has been validated experimentally. Results showed that ANN model provide a better prediction of the current and power of the PV module than the analytical models. The coefficient of determination (R²), mean square error (MSE) and the mean absolute percentage error (MAPE) values for the optimal ANN model were 0.971, 0.002 and 0.107, respectively. A comparative study among ANN and analytical models was also carried out. Among the analytical models, the five-parameter model, with MAPE = 0.112, MSE = 0.0026 and R² = 0.919, gave better prediction than the four-parameter model (with MAPE = 0.152, MSE = 0.0052 and R² = 0.905). Overall, the 3–7–4–1 ANN model outperformed four-parameter model, and was marginally better than the five-parameter model.     

A comparative study of artificial neural network and adaptive neurofuzzy inference system for prediction of compressional wave velocity

In this study, two solutions for prediction of compressional wave velocity (p wave) are presented and compared: artificial neural network (ANN) and adaptive neurofuzzy inference system (ANFIS). Series of analyses were performed to determine the optimum architecture of utilized methods using the trial and error process. Several ANNs and ANFISs are constructed, trained and validated to predict p wave in the investigated carbonate reservoir. A comparative study on prediction of p wave by ANN and ANFIS is addressed, and the quality of the target prediction was quantified in terms of the mean-squared errors (MSEs), correlation coefficient (R ²) and prediction efficiency error. ANFIS with MSE of 0.0552 and R ² of 0.9647, and ANN with MSE of 0.042 and R ² of 0.976, showed better performance in comparison with MLR methods. ANN and ANFIS systems have performed comparably well and accurate for prediction of p wave.     

Application of improved support vector regression model for prediction of deformation modulus of a rock mass

Deformation modulus of a rock mass is one of the crucial parameters used in the design of surface and underground rock engineering structures. Determination of this parameter by testing cylindrical core samples is almost impossible due to the presence of discontinuities. Due to the problems in determining the deformability of jointed rock masses at the laboratory-scale, various in situ test methods such as plate loading tests, dilatometer etc. have been developed. Although these methods are currently the best techniques, they are expensive and time-consuming, and present operational problems. To overcome this difficulty, in this paper, presents the results of the application of hybrid support vector regression (SVR) with harmony search algorithm , differential evolution algorithm and particle swarm optimization algorithm (PSO). The optimized models were applied to available data given in open source literature and the performance of optimization algorithm was assessed by virtue of statistical criteria. In these models, rock mass rating (RMR), depth, uniaxial compressive strength of intact rock (UCS) and elastic modulus of intact rock (E _i) were utilized as the input parameters, while the deformation modulus of a rock mass was the output parameter. The comparative results revealed that hybrid of PSO and SVR yield robust model which outperform other models in term of higher squared correlation coefficient (R ²) and variance account for (VAF) and lower mean square error (MSE), root mean squared error (RMSE) and mean absolute percentage error (MAPE).     

Prediction of seismic slope stability through combination of particle swarm optimization and neural network

One of the main concerns in geotechnical engineering is slope stability prediction during the earthquake. In this study, two intelligent systems namely artificial neural network (ANN) and particle swarm optimization (PSO)–ANN models were developed to predict factor of safety (FOS) of homogeneous slopes. Geostudio program based on limit equilibrium method was utilized to obtain 699 FOS values with different conditions. The most influential factors on FOS such as slope height, gradient, cohesion, friction angle and peak ground acceleration were considered as model inputs in the present study. A series of sensitivity analyses were performed in modeling procedures of both intelligent systems. All 699 datasets were randomly selected to 5 different datasets based on training and testing. Considering some model performance indices, i.e., root mean square error, coefficient of determination (R ²) and value account for (VAF) and using simple ranking method, the best ANN and PSO–ANN models were selected. It was found that the PSO–ANN technique can predict FOS with higher performance capacities compared to ANN. R ² values of testing datasets equal to 0.915 and 0.986 for ANN and PSO–ANN techniques, respectively, suggest the superiority of the PSO–ANN technique.     

Overbreak prediction and optimization in tunnel using neural network and bee colony techniques

Overbreak is an undesirable phenomenon in blasting operations. The causing factors of overbreak can be generally divided as blasting and geological parameters. Due to multiplicity of effective parameters and complexity of interactions among these parameters, empirical methods may not be fully appropriated for blasting pattern design. In this research, artificial neural network (ANN) as a powerful tool for solving such complicated problems is developed to predict overbreak induced by blasting operations in the Gardaneh Rokh tunnel, Iran. To develop an ANN model, an established database comprising of 255 datasets has been utilized. A three-layer ANN was found as an optimum model for prediction of overbreak. The coefficient of determination (R²) and root mean square error (RMSE) values of the selected model were obtained as 0.921, 0.4820, 0.923 and 0.4277 for training and testing, respectively, which demonstrate a high capability of ANN in predicting overbreak. After selecting the best model, the selected model was used for optimization purpose using artificial bee colony (ABC) algorithm as one of the most powerful optimization algorithms. Considering this point that overbreak is one of the main problems in tunneling, reducing its amount causes to have a good tunneling operation. After making several models of optimization and variations in its weights, the optimum amount for the extra drilling was 1.63 m², which is 47% lower than the lowest value (3.055 m²). It can be concluded that ABC algorithm can be introduced as a new optimizing algorithm to minimize overbreak induced by tunneling.

     

A new combination of artificial neural network and <Emphasis Type="Italic">K</Emphasis>-nearest neighbors models to predict blast-induced ground vibration and air-overpressure

Blasting operation is widely used method for rock excavation in mining and civil works. Ground vibration and air-overpressure (AOp) are two of the most detrimental effects induced by blasting. So, evaluation and prediction of ground vibration and AOp are essential. This paper presents a new combination of artificial neural network (ANN) and K-nearest neighbors (KNN) models to predict blast-induced ground vibration and AOp. Here, this combination is abbreviated using ANN-KNN. To indicate performance of the ANN-KNN model in predicting ground vibration and AOp, a pre-developed ANN as well as two empirical equations, presented by United States Bureau of Mines (USBM), were developed. To construct the mentioned models, maximum charge per delay (MC) and distance between blast face and monitoring station (D) were set as input parameters, whereas AOp and peak particle velocity (PPV), as a vibration index, were considered as output parameters. A database consisting of 75 datasets, obtained from the Shur river dam, Iran, was utilized to develop the mentioned models. In terms of using three performance indices, namely coefficient correlation (R ²), root mean square error and variance account for, the superiority of the ANN-KNN model was proved in comparison with the ANN and USBM equations.     

Developing a new uncertain rule-based fuzzy approach for evaluating the blast-induced backbreak

This study proposes a new uncertain rule-based fuzzy approach for the evaluation of blast-induced backbreak. The proposed approach is based on rock engineering systems (RES) updated by the fuzzy system. Additionally, a genetic algorithm (GA) and imperialist competitive algorithm (ICA) were employed for the prediction aim. The most key step in modeling of fuzzy RES is the coding of the interaction matrix. This matrix is responsible for analyzing the interrelationships among the parameters influencing the rock engineering activities. The codes of the interaction matrix are not unique; thus, probabilistic coding can be done non-deterministically, which allows the uncertainties to be considered in the RES analysis. To achieve the objective of this research, 62 blasts in Shur River dam region, located in south of Iran, were investigated and the required datasets were measured. The performance of the proposed models was then evaluated in accordance with the statistical criteria such as coefficient of determination (R²). The results signify the effectiveness of the proposed GA- and ICA-based models in the simulating process. R² of 0.963 and 0.934 obtained from ICA- and GA-based models, respectively, revealed that both models were capable of predicting the backbreak. Further, the fuzzy RES was introduced as a powerful uncertain approach to evaluate and predict the backbreak.

     

Application of several non-linear prediction tools for estimating uniaxial compressive strength of granitic rocks and comparison of their performances

Uniaxial compressive strength (UCS) of rock is crucial for any type of projects constructed in/on rock mass. The test that is conducted to measure the UCS of rock is expensive, time consuming and having sample restriction. For this reason, the UCS of rock may be estimated using simple rock tests such as point load index (I _s(50)), Schmidt hammer (R _n) and p-wave velocity (V _p) tests. To estimate the UCS of granitic rock as a function of relevant rock properties like R _n, p-wave and I _s(50), the rock cores were collected from the face of the Pahang–Selangor fresh water tunnel in Malaysia. Afterwards, 124 samples are prepared and tested in accordance with relevant standards and the dataset is obtained. Further an established dataset is used for estimating the UCS of rock via three-nonlinear prediction tools, namely non-linear multiple regression (NLMR), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). After conducting the mentioned models, considering several performance indices including coefficient of determination (R ²), variance account for and root mean squared error and also using simple ranking procedure, the models were examined and the best prediction model was selected. It is concluded that the R ² equal to 0.951 for testing dataset suggests the superiority of the ANFIS model, while these values are 0.651 and 0.886 for NLMR and ANN techniques, respectively. The results pointed out that the ANFIS model can be used for predicting UCS of rocks with higher capacity in comparison with others. However, the developed model may be useful at a preliminary stage of design; it should be used with caution and only for the specified rock types.     

Predicting roadheader performance by using artificial neural network

With growing use of roadheaders in the world and its significant role in the successful accomplishment of a tunneling project, it is a necessity to accurately predict performance of this machine in different ground conditions. On the other hand, the existence of some shortcomings in the prediction models has made it necessary to perform more research on the development of the new models. This paper makes an attempt to model the rate of roadheader performance based on the geotechnical and geological site conditions. For achieving the aim, an artificial neural network (ANN), a powerful tool for modeling and recognizing the sophisticated structures involved in data, is employed to model the relationship between the roadheader performance and the parameters influencing the tunneling operations with a high correlation. The database used in modeling is compiled from laboratory studies conducted at Azad University at Science and Research Branch, Tehran, Iran. A model with architecture 4-10-1 trained by back-propagation algorithm is found to be optimum. A multiple variable regression (MVR) analysis is also applied to compare performance of the neural network. The results demonstrate that predictive capability of the ANN model is better than that of the MVR model. It is concluded that roadheader performance could be accurately predicted as a function of unconfined compressive strength, Brazilian tensile strength, rock quality designation, and alpha angle R ² = 0.987. Sensitivity analysis reveals that the most effective parameter on roadheader performance is the unconfined compressive strength.     

Flood flow forecasting using ANN,ANFIS and regression models

Flood prediction is an important for the design, planning and management of water resources systems. This study presents the use of artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), multiple linear regression (MLR) and multiple nonlinear regression (MNLR) for forecasting maximum daily flow at the outlet of the Khosrow Shirin watershed, located in the Fars Province of Iran. Precipitation data from four meteorological stations were used to develop a multilayer perceptron topology model. Input vectors for simulations included the original precipitation data, an area-weighted average precipitation and antecedent flows with one- and two-day time lags. Performances of the models were evaluated with the RMSE and the R ². The results showed that the area-weighted precipitation as an input to ANNs and MNLR and the spatially distributed precipitation input to ANFIS and MLR lead to more accurate predictions (e.g., in ANNs up to 2.0 m³ s^?1 reduction in RMSE). Overall, the MNLR was shown to be superior (R ² = 0.81 and RMSE = 0.145 m³ s^?1) to ANNs, ANFIS and MLR for prediction of maximum daily flow. Furthermore, models including antecedent flow with one- and two-day time lags significantly improve flow prediction. We conclude that nonlinear regression can be applied as a simple method for predicting the maximum daily flow.     

Artificial neural network–genetic algorithm for estimation of crop evapotranspiration in a semi-arid region of Iran

This study compares the daily potato crop evapotranspiration (ET_C) estimated by artificial neural network (ANN), neural network–genetic algorithm (NNGA) and multivariate nonlinear regression (MNLR) methods. Using a 6-year (2000–2005) daily meteorological data recorded at Tabriz synoptic station and the Penman–Monteith FAO 56 standard approach (PMF-56), the daily ET_C was determined during the growing season (April–September). Air temperature, wind speed at 2 m height, net solar radiation, air pressure, relative humidity and crop coefficient for every day of the growing season were selected as the input of ANN models. In this study, the genetic algorithm was applied for optimization of the parameters used in ANN approach. It was found that the optimization of the ANN parameters did not improve the performance of ANN method. The results indicated that MNLR, ANN and NNGA methods were able to predict potato ET_C at desirable level of accuracy. However, the MNLR method with highest coefficient of determination (R ² > 0.96, P value < 0.05) and minimum errors provided superior performance among the other methods.     

Prediction of rock fragmentation due to blasting using artificial neural network

Prediction of rock fragmentation is essential for optimizing blasting operation. Fragmentation depends on many parameters such as rock mass properties, blast geometry and explosive properties. In this paper, artificial neural network (ANN) method is implemented to develop a model to predict rock fragmentation due to blasting in an iron ore mine. In the developing of the proposed model eight parameters such as hole diameter, burden, powder factor, blastability index, etc., were incorporated. Training of the model was performed by back-propagation algorithm using 220 datasets. A four-layer ANN was found to be optimum with architecture 10-9-7-1. Sensitivity analysis revealed that the most effective parameters on rock fragmentation are blastability index (G), charge per delay (J), burden (C), SMR (F) and powder factor (E).