Prediction of abrasiveness index of some Indian rocks using soft computing methods

Tripathy, Ashutosh; Singh, T. N.; Kundu, Jagadish

doi:10.1016/j.measurement.2015.03.009

Cited by 46 publications

(7 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The purpose of multiple regressions is to learn more about the relationships between several independent variables and dependent variable(s) (Verma and Singh 2013b;Tripathy et al 2015;Ghiasi et al 2016). In the NLMR technique, both nonlinear and linear relationships, e.g., exponential, logarithmic, and power, can be employed.…”

Section: Ppv Prediction By Nlmr Modelmentioning

confidence: 99%

Prediction of ground vibration due to quarry blasting based on gene expression programming: a new model for peak particle velocity prediction

Faradonbeh

Armaghani

Majid

et al. 2016

Int. J. Environ. Sci. Technol.

149

View full text Add to dashboard Cite

Blasting is a widely used technique for rock fragmentation in opencast mines and tunneling projects. Ground vibration is one of the most environmental effects produced by blasting operation. Therefore, the proper prediction of blast-induced ground vibrations is essential to identify safety area of blasting. This paper presents a predictive model based on gene expression programming (GEP) for estimating ground vibration produced by blasting operations conducted in a granite quarry, Malaysia. To achieve this aim, a total number of 102 blasting operations were investigated and relevant blasting parameters were measured. Furthermore, the most influential parameters on ground vibration, i.e., burden-to-spacing ratio, hole depth, stemming, powder factor, maximum charge per delay, and the distance from the blast face were considered and utilized to construct the GEP model. In order to show the capability of GEP model in estimating ground vibration, nonlinear multiple regression (NLMR) technique was also performed using the same datasets. The results demonstrated that the proposed model is able to predict blast-induced ground vibration more accurately than other developed technique. Coefficient of determination values of 0.914 and 0.874 for training and testing datasets of GEP model, respectively show superiority of this model in predicting ground vibration, while these values were obtained as 0.829 and 0.790 for NLMR model.

show abstract

Section: Ppv Prediction By Nlmr Modelmentioning

confidence: 99%

Prediction of ground vibration due to quarry blasting based on gene expression programming: a new model for peak particle velocity prediction

Faradonbeh

Armaghani

Majid

et al. 2016

Int. J. Environ. Sci. Technol.

149

View full text Add to dashboard Cite

show abstract

“…Many scholars have attempted to develop the MLR in various fields in rock and geotechnical engineering [45][46][47][48]. As an example, Enayatollahi et al [49] developed MLR to predict rock fragmentation at Gol-e-Gohar mine, Iran.…”

Section: Ppv Prediction Using Mlrmentioning

confidence: 99%

Prediction of blast-produced ground vibration using particle swarm optimization

Hasanipanah

Naderi

Kashir

et al. 2016

Engineering with Computers

124

View full text Add to dashboard Cite

work, a database including 80 data sets was collected, and the values of the maximum charge weight used per delay (W), distance between blast-point and monitoring station (D) and peak particle velocity (PPV) were measured. To develop the PSO models, PPV was used as output parameter, while W and D were used as input parameters. To check the performance of the proposed PSO models, multiple linear regression (MLR) model and United States Bureau of Mines (USBM) equation were also developed. Accuracy of models established was evaluated using statistical criteria, i.e., coefficient of correlation (R 2 ) and root mean square error (RMSE), variance absolute relative error (VARE) and Nash & Sutcliffe (NS). Finally, it was found that the PSO power form provided more accurate predictions in comparison with PSO linear form, MLR and USBM models.

show abstract

“…Carbonate rocks of different origins have been examined against P-wave velocity and a linear relation between densities, Young's modulus and UCS has been suggested (Yasar and Erdogan 2004). Soft computing techniques have also been used for estimation of Cercher abrasive index and penetration rate using geomechanical properties for varying rock types (Tripathy et al 2015).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Significant amount of research has been done to use this simple technique for developing correlation between various geomechanical properties of rocks (Kahraman 2001b;Entwisle et al 2005;Khandelwal Fig. 6 Correlation between physico-mechanical properties of quartzite with P-wave velocity Geotech Geol Eng and Singh 2009;Azimian et al 2014;Tripathy et al 2015). The main drawback of all regression techniques is that one can only establish a general idea about the relationships between different parameters but the underlying mechanism seems to be disputable.…”

Section: Regression Analysismentioning

confidence: 99%

Prediction of Strength Parameters of Himalayan Rocks: A Statistical and ANFIS Approach

et al. 2015

View full text Add to dashboard Cite

The knowledge of geomechanical properties of rocks is essential for planning and design of structures in civil, mining, and other allied industries. Direct assessment of these properties is quite expensive and time consuming. It also requires fine precision in sample preparation and high-end testing instruments. To overcome these problems, 597 rock samples of four different Himalayan rock types have been tested for their strength attributes in a laboratory. The test results were correlated with their P-wave velocity using linear regression analysis for estimation of compressive, tensile, cohesive strengths and angle of internal friction. Subsequently, an adaptive neurofuzzy inference system (ANFIS) technique was applied to find its efficiency as a predictor against normal regression system. The results indicated that the ANFIS is comparatively better at prediction of geotechnical strength parameters when pitted against regression techniques.

show abstract

Prediction of abrasiveness index of some Indian rocks using soft computing methods

Cited by 46 publications

References 26 publications

Prediction of ground vibration due to quarry blasting based on gene expression programming: a new model for peak particle velocity prediction

Prediction of ground vibration due to quarry blasting based on gene expression programming: a new model for peak particle velocity prediction

Prediction of blast-produced ground vibration using particle swarm optimization

Prediction of Strength Parameters of Himalayan Rocks: A Statistical and ANFIS Approach

Contact Info

Product

Resources

About