List of Titles

A new model based on gene expression programming to estimate air flow in a single rock joint

- Khandelwal, Manoj, Armaghani, Danial, Faradonbeh, Roohollah, Ranjith, Pathegama, Ghoraba, Saber

Authors: Khandelwal, Manoj , Armaghani, Danial , Faradonbeh, Roohollah , Ranjith, Pathegama , Ghoraba, Saber
Date: 2016
Type: Text , Journal article
Relation: Environmental Earth Sciences Vol. 75, no. 9 (2016), p.
Full Text: false
Reviewed:
Description: This paper is aimed to introduce and validate a gene expression programming (GEP) model to estimate the rate of air flow in triaxial conditions at various confining pressures incorporating cell pressure, air inlet pressure and air outlet pressure. To achieve the aim of this study, a series of laboratory experiments were designed and carried out and then a database comprising 47 datasets was prepared to develop new predictive models. A gene expression programming (GEP) model for prediction of air flow was proposed using the prepared datasets. In this regard, a series of sensitivity analyses were performed to choose the best GEP model. For comparison purposes, multiple regression (MR) analysis was also employed for air flow estimation. Several performance indices, i.e., coefficient of determination (CoD), mean absolute error (MAE), root mean square error (RMSE) and variance account for (VAF) were considered and calculated to evaluate the performance prediction of the developed models. Considering both training and testing datasets, the developed GEP model can provide higher performance prediction of rate of air flow in comparison to the MR model. © 2016, Springer-Verlag Berlin Heidelberg.

An expert system based on hybrid ICA-ANN technique to estimate macerals contents of Indian coals

- Khandelwal, Manoj, Mahdiyar, Amir, Armaghani, Danial, Singh, Trilok, Fahimifar, Ahmad, Faradonbeh, Roohollah

Authors: Khandelwal, Manoj , Mahdiyar, Amir , Armaghani, Danial , Singh, Trilok , Fahimifar, Ahmad , Faradonbeh, Roohollah
Date: 2017
Type: Text , Journal article
Relation: Environmental Earth Sciences Vol. 76, no. 11 (2017), p. 1-14
Full Text: false
Reviewed:
Description: Coal, as an initial source of energy, requires a detailed investigation in terms of ultimate analysis, proximate analysis, and its biological constituents (macerals). The rank and calorific value of each type of coal are managed by the mentioned properties. In contrast to ultimate and proximate analyses, determining the macerals in coal requires sophisticated microscopic instrumentation and expertise. This study emphasizes the estimation of the concentration of macerals of Indian coals based on a hybrid imperialism competitive algorithm (ICA)–artificial neural network (ANN). Here, ICA is utilized to adjust the weight and bias of ANNs for enhancing their performance capacity. For comparison purposes, a pre-developed ANN model is also proposed. Checking the performance prediction of the developed models is performed through several performance indices, i.e., coefficient of determination (R2), root mean square error and variance account for. The obtained results revealed higher accuracy of the proposed hybrid ICA-ANN model in estimating macerals contents of Indian coals compared to the pre-developed ANN technique. Results of the developed ANN model based on R2 values of training datasets were obtained as 0.961, 0.955, and 0.961 for predicting vitrinite, liptinite, and inertinite, respectively, whereas these values were achieved as 0.948, 0.947, and 0.957, respectively, for testing datasets. Similarly, R2 values of 0.988, 0.983, and 0.991 for training datasets and 0.989, 0.982, and 0.985 for testing datasets were obtained from developed ICA-ANN model. © 2017, Springer-Verlag Berlin Heidelberg.

Classification and regression tree technique in estimating peak particle velocity caused by blasting

- Khandelwal, Manoj, Armaghani, Danial, Faradonbeh, Roohollah, Yellishetty, Mohan, Abd Majid, Muhd, Monjezi, Masoud

Authors: Khandelwal, Manoj , Armaghani, Danial , Faradonbeh, Roohollah , Yellishetty, Mohan , Abd Majid, Muhd , Monjezi, Masoud
Date: 2017
Type: Text , Journal article
Relation: Engineering with Computers Vol. 33, no. 1 (2017), p. 45-53
Full Text: false
Reviewed:
Description: Blasting is a widely used technique for rock fragmentation in surface mines and tunneling projects. The ground vibrations produced by blasting operations are the main concern for the industries undertaking blasting operations, which can damage the surrounding structures, adjacent rock masses, roads and slopes in the vicinity. Therefore, proper prediction of blast-induced ground vibrations is essential to demarcate the safety area of blasting. In this research, classification and regression tree (CART) as a rule-based method was used to predict the peak particle velocity through a database comprising of 51 datasets with results of maximum charge per delay and distance from the blast face were fixed as model inputs. For comparison, the empirical and multiple regression (MR) models were also applied and proposed for peak particle velocity prediction. Performance of the proposed models were compared and evaluated using three statistical criteria, namely coefficient of correlation (R (2)), root mean square error (RMSE) and variance account for (VAF). Comparison of the obtained results demonstrated that the CART technique is more reliable for predicting the peak particle velocity than the MR and empirical models and it can be introduced as a new technique in this field.

Development of a precise model for prediction of blast-induced flyrock using regression tree technique

- Hasanipanah, Mahdi, Faradonbeh, Roohollah, Armaghani, Danial, Amnieh, Hassan, Khandelwal, Manoj

Authors: Hasanipanah, Mahdi , Faradonbeh, Roohollah , Armaghani, Danial , Amnieh, Hassan , Khandelwal, Manoj
Date: 2017
Type: Text , Journal article
Relation: Environmental Earth Sciences Vol. 76, no. 1 (2017), p. 1-10
Full Text: false
Reviewed:
Description: Drilling and blasting is the predominant rock fragmentation method in open-cast mines and civil construction works. Flyrock is one of the most hazardous effects caused by blasting operation. Therefore, the ability to make accurate predictions of the blast-induced flyrock is essential to reduce the environmental problems. This paper aimed to develop a precise and applicable model based on regression tree (RT) to predict blast-produced flyrock distance in Ulu Tiram quarry, Malaysia. In this regard, 65 blasting operations were investigated and the most influential factors on the flyrock, i.e. blast-hole length, spacing, burden, stemming, maximum charge used per delay and powder factor, were measured. Also, the flyrock distance values for the considered blasting events were carefully measured. In order to check the precision of the proposed RT model, multiple linear regression (MLR) model was also developed and both of the predictive models were compared. For this work, some statistical functions, i.e. median absolute error, coefficient of determination (R2) and root mean squared error, were used and computed. The results revealed that the RT can be introduced as a powerful technique to predict flyrock distance and the proposed RT model can estimate flyrock distance better than MLR model. Also, sensitivity analysis was performed and it was found that the powder factor is the most influential parameter on the flyrock in the studied case. © 2016, Springer-Verlag Berlin Heidelberg.

Function development for appraising brittleness of intact rocks using genetic programming and non-linear multiple regression models

- Khandelwal, Manoj, Faradonbeh, Roohollah, Monjezi, Masoud, Armaghani, Danial, Bin Abd Majid, Muhd, Yagiz, Saffet

Authors: Khandelwal, Manoj , Faradonbeh, Roohollah , Monjezi, Masoud , Armaghani, Danial , Bin Abd Majid, Muhd , Yagiz, Saffet
Date: 2017
Type: Text , Journal article
Relation: Engineering with Computers Vol. 33, no. 1 (2017), p. 13-21
Full Text: false
Reviewed:
Description: Brittleness of rock is one of the most critical features for design of underground excavation project. Therefore, proper assessing of rock brittleness can be very useful for designers and evaluators of geotechnical applications. In this study, feasibility of genetic programming (GP) model and non-linear multiple regression (NLMR) in predicting brittleness of intact rocks is examined. For this purpose, a dataset developed by conducting various rock tests including uniaxial compressive strength, Brazilian tensile strength, unit weight and brittleness via punch penetration on rock samples gathered from 48 tunnels projects around the world is utilized herein. Considering multiple inputs, several GP models were constructed to estimate brittleness index of the rock and finally, the best GP model was selected. Note that, GP can make an equation for predicting output of the system using model inputs. To show applicability of the developed GP model, non-linear multiple regression (NLMR) was also applied and developed. Considering some model performance indices, performance prediction of the GP and NLMR models were evaluated and it was found that the GP model is superior to NLMR one. Based on coefficient of determination (R (2)) of testing datasets, by proposing GP model, it can be improved from 0.882 (obtained by NLMR model) to 0.904. It is worth mentioning that the proposed predictive models in this study should be planned and used for the similar types of rock and the established inputs ranges.

Risk assessment and prediction of flyrock distance by combined multiple regression analysis and Monte Carlo simulation of quarry blasting

- Armaghani, Danial, Mahdiyar, Amir, Hasanipanah, Mahdi, Faradonbeh, Roohollah, Khandelwal, Manoj, Amnieh, Hassan

Authors: Armaghani, Danial , Mahdiyar, Amir , Hasanipanah, Mahdi , Faradonbeh, Roohollah , Khandelwal, Manoj , Amnieh, Hassan
Date: 2016
Type: Text , Journal article
Relation: Rock Mechanics and Rock Engineering Vol. 49, no. 9 (2016), p. 3631-3641
Full Text: false
Reviewed:
Description: Flyrock is considered as one of the main causes of human injury, fatalities, and structural damage among all undesirable environmental impacts of blasting. Therefore, it seems that the proper prediction/simulation of flyrock is essential, especially in order to determine blast safety area. If proper control measures are taken, then the flyrock distance can be controlled, and, in return, the risk of damage can be reduced or eliminated. The first objective of this study was to develop a predictive model for flyrock estimation based on multiple regression (MR) analyses, and after that, using the developed MR model, flyrock phenomenon was simulated by the Monte Carlo (MC) approach. In order to achieve objectives of this study, 62 blasting operations were investigated in Ulu Tiram quarry, Malaysia, and some controllable and uncontrollable factors were carefully recorded/calculated. The obtained results of MC modeling indicated that this approach is capable of simulating flyrock ranges with a good level of accuracy. The mean of simulated flyrock by MC was obtained as 236.3 m, while this value was achieved as 238.6 m for the measured one. Furthermore, a sensitivity analysis was also conducted to investigate the effects of model inputs on the output of the system. The analysis demonstrated that powder factor is the most influential parameter on fly rock among all model inputs. It is noticeable that the proposed MR and MC models should be utilized only in the studied area and the direct use of them in the other conditions is not recommended.

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