A correlation between Schmidt hammer rebound numbers with impact strength index, slake durability index and P-wave velocity
- Authors: Sharma, Pramod , Khandelwal, Manoj , Singh, Trilok
- Date: 2011
- Type: Text , Journal article
- Relation: International Journal of Earth Sciences Vol. 100, no. 1 (2011), p. 189-195
- Full Text: false
- Reviewed:
- Description: The main objective of this study was to establish statistical relationship between Schmidt hammer rebound numbers with impact strength index (ISI), slake durability index (SDI) and P-wave velocity. These are important properties to characterize a rock mass and are being widely used in geological and geotechnical engineering. Due to its importance, Schmidt hammer rebound number is considered as one of the most important property for the determination of other properties, like ISI, SDI and P-wave velocity. Determination of these properties in the laboratory is time consuming and tedious as well as requiring expertise, whereas Schmidt hammer rebound number can be easily obtained on site which in addition is non-destructive. So, in this study, an attempt has been made to determine these index properties in the laboratory and each index property was correlated with Schmidt hammer rebound values. Empirical equations have been developed to predict ISI, SDI and P-wave velocity using rebound values. It was found that Schmidt hammer rebound number shows linear relation with ISI and SDI, whereas exponential relation with P-wave velocity. To check the sensitivity of empirical relations, Student's t test was done to verify the correlation between rebound values and other rock index properties. © 2010 Springer-Verlag.
Application of an expert system to predict maximum explosive charge used per delay in surface mining
- Authors: Khandelwal, Manoj , Singh, Trilok
- Date: 2013
- Type: Text , Journal article
- Relation: Rock Mechanics and Rock Engineering Vol. 46, no. 6 (2013), p. 1551-1558
- Full Text: false
- Reviewed:
- Description: The present paper mainly deals with the prediction of maximum explosive charge used per delay (Q MAX) using an artificial neural network (ANN) incorporating peak particle velocity (PPV) and distance between blast face to monitoring point (D). One hundred and fifty blast vibration data sets were monitored at different vulnerable and strategic locations in and around major coal producing opencast coal mines in India. One hundred and twenty-four blast vibrations records were used for the training of the ANN model vis-à-vis to determine site constants of various conventional vibration predictors. The other 26 new randomly selected data sets were used to test, evaluate and compare the ANN prediction results with widely used conventional predictors. Results were compared based on coefficient of correlation (R), mean absolute error and mean squared between measured and predicted values of Q MAX. It was found that coefficient of correlation between measured and predicted Q MAX by ANN was 0.985, whereas it ranged from 0.316 to 0.762 by different conventional predictor equations. Mean absolute error and mean squared error was also very small by ANN, whereas it was very high for different conventional predictor equations. © 2013 Springer-Verlag Wien.
An expert system based on hybrid ICA-ANN technique to estimate macerals contents of Indian coals
- 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.
Implementing an ANN model optimized by genetic algorithm for estimating cohesion of limestone samples
- Authors: Khandelwal, Manoj , Marto, Aminaton , Fatemi, Seyed , Ghoroqi, Mahyar , Armaghani, Danial , Singh, Trilok , Tabrizi, Omid
- Date: 2018
- Type: Text , Journal article
- Relation: Engineering with Computers Vol. 34, no. 2 (2018), p. 307-317
- Full Text: false
- Reviewed:
- Description: Shear strength parameters such as cohesion are the most significant rock parameters which can be utilized for initial design of some geotechnical engineering applications. In this study, evaluation and prediction of rock material cohesion is presented using different approaches i.e., simple and multiple regression, artificial neural network (ANN) and genetic algorithm (GA)-ANN. For this purpose, a database including three model inputs i.e., p-wave velocity, uniaxial compressive strength and Brazilian tensile strength and one output which is cohesion of limestone samples was prepared. A meaningful relationship was found for all of the model inputs with suitable performance capacity for prediction of rock cohesion. Additionally, a high level of accuracy (coefficient of determination, R2 of 0.925) was observed developing multiple regression equation. To obtain higher performance capacity, a series of ANN and GA-ANN models were built. As a result, hybrid GA-ANN network provides higher performance for prediction of rock cohesion compared to ANN technique. GA-ANN model results (R2 = 0.976 and 0.967 for train and test) were better compared to ANN model results (R2 = 0.949 and 0.948 for train and test). Therefore, this technique is introduced as a new one in estimating cohesion of limestone samples. © 2017, Springer-Verlag London Ltd., part of Springer Nature.