Showing items 1 - 3 of 3

Your selections:

Assessing cohesion of the rocks proposing a new intelligent technique namely group method of data handling

- Chen, Wusi, Khandelwal, Manoj, Murlidhar, Bhatawdekar, Bui, Dieu, Tahir, Mahmood, Katebi, Javad

  • Authors: Chen, Wusi , Khandelwal, Manoj , Murlidhar, Bhatawdekar , Bui, Dieu , Tahir, Mahmood , Katebi, Javad
  • Date: 2020
  • Type: Text , Journal article
  • Relation: Engineering with Computers Vol. 36, no. 2 (2020), p. 783-793
  • Full Text:
  • Reviewed:
  • Description: In this study, evaluation and prediction of rock cohesion is assessed using multiple regression as well as group method of data handling (GMDH). It is a well-known fact that cohesion is the most crucial rock shear strength parameter, which is a key parameter for the stability evaluation of some geotechnical structures such as rock slope. To fulfill the aim of this study, a database of three model input parameters, i.e., p wave velocity, uniaxial compressive strength and Brazilian tensile strength and one model output, which is cohesion of limestone samples was prepared and utilized by GMDH. Different GMDH models with neurons and layers and selection pressure were tested and assessed. It was found that GMDH model number 4 (with 8 layers) shows the best performance among all of tested models between the input and output parameters for the prediction and assessment of rock cohesion with coefficient of determination (R2) values of 0.928 and 0.929, root mean square error values of 0.3545 and 0.3154 for training and testing datasets, respectively. Multiple regression analysis was also performed on the same database and R2 values were obtained as 0.8173 and 0.8313 between input and output parameters for the training and testing of the models, respectively. The GMDH technique developed in this study is introduced as a new model in field of rock shear strength parameters. © 2019, Springer-Verlag London Ltd., part of Springer Nature.

Stability evaluation of dump slope using artificial neural network and multiple regression

- Bharati, , Ashutosh, Ray, Arunava, Khandelwal, Manoj, Rai, Rajesha, Jaiswal, , Ashok

  • Authors: Bharati, , Ashutosh , Ray, Arunava , Khandelwal, Manoj , Rai, Rajesha , Jaiswal, , Ashok
  • Date: 2022
  • Type: Text , Journal article
  • Relation: Engineering with Computers Vol. 38, no. (2022), p. 1835-1843
  • Full Text:
  • Reviewed:
  • Description: The present paper focuses on designing an artificial neural network (ANN) model and a multiple regression analysis (MRA) that could be used to predict factor of safety of dragline dump slope. To implement these two models, the dataset was utilized from the numerical simulation results of dragline dump slopes, wherein 216 dragline dump slope models were simulated using a numerical modeling technique employed with the finite element method. The finite element model was incorporated a combination of three geometrical parameters, namely, coal-rib height (Crh), dragline dump slope height (Sh), and dragline dump slope angle (Sa) of the dump slope. The predicted results derived from the MRA and ANN models were compared with the results obtained from the numerical simulation of the dump slope models. Moreover, to compare the validity of both the models, various performance indicators, such as variance account for (VAF), determination coefficient (R2), root mean square error (RMSE), and residual error were calculated. Based on these performance indicators, the ANN model has shown a higher prediction accuracy than the MRA model. The study reveals that the ANN model developed in this research could be handy in designing the dragline dump slopes at the preliminary stage. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature.

Blasting pattern optimization using gene expression programming and grasshopper optimization algorithm to minimise blast-induced ground vibrations

- Bayat, Parichehra, Monjezi, Mejrdamesj, Mehrdanesh, Amirhosseina, Khandelwal, Manoj

  • Authors: Bayat, Parichehra , Monjezi, Mejrdamesj , Mehrdanesh, Amirhosseina , Khandelwal, Manoj
  • Date: 2022
  • Type: Text , Journal article
  • Relation: Engineering with Computers Vol. 38, no. 4 (2022), p. 3341-3350
  • Full Text: false
  • Reviewed:
  • Description: Blast-induced ground vibration is considered as one of the most hazardous phenomena of mine blasting, which can even cause casualties and severe damages to the adjacent properties. Measuring peak particle velocity (PPV) is helpful to know the actual vibration level but prediction of blast vibration prior to the blast is a tedious job due to involvement of blast design, explosive and rock parameters. Nowadays, efficient application of intelligent systems has been approved in different branches of science and technology. In this paper, a gene expression programming (GEP) model was developed to predict PPV using various blasting patterns as model inputs, which showed a high level of accuracy for the implemented model. Also, to optimize blast pattern attaining minimum ground vibration during blasting operation, the developed functional GEP model was taken as objective function for grasshopper optimization algorithm (GOA). Construction of GOA model was performed using a trial and error mechanism to find out the best possible pertinent GOA parameters. Finally, it was observed that utilizing GOA technique, PPV can be reduced by 67% with optimized blast parameters including burden of 3.21 m, spacing of 3.75 m, and charge per delay of 225 kg. A sensitivity analysis was also performed to understand the influence of each input parameters on the blast vibrations. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature.
  • 1