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Implementing an ANN model optimized by genetic algorithm for estimating cohesion of limestone samples

- Khandelwal, Manoj, Marto, Aminaton, Fatemi, Seyed, Ghoroqi, Mahyar, Armaghani, Danial, Singh, Trilok, Tabrizi, Omid

  • 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
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  • 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.

Feasibility of ANFIS model for prediction of ground vibrations resulting from quarry blasting

- Armaghani, Danial, Momeni, Ehsan, Abad, Seyed, Khandelwal, Manoj

  • Authors: Armaghani, Danial , Momeni, Ehsan , Abad, Seyed , Khandelwal, Manoj
  • Date: 2015
  • Type: Text , Journal article
  • Relation: Environmental Earth Sciences Vol. 74, no. 4 (2015), p. 2845-2860
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  • Description: One of the most significant environmental issues of blasting operations is ground vibration, which can cause damage to the surrounding residents and structures. Hence, it is a major concern to predict and subsequently control the ground vibration due to blasting. This paper presents two artificial intelligence techniques, namely, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network for the prediction of ground vibration in quarry blasting site. For this purpose, blasting parameters as well as ground vibrations of 109 blasting operations were measured in ISB granite quarry, Johor, Malaysia. Moreover, an empirical equation was also proposed based on the measured data. Several AI-based models were trained and tested using the measured data to determine the optimum models. Each model involved two inputs (maximum charge per delay and distance from the blast-face) and one output (ground vibration). To control capacity performances of the predictive models, the values of root mean squared error (RMSE), value account for (VAF), and coefficient of determination (R2) were computed for each model. It was found that the ANFIS model can provide better performance capacity in predicting ground vibration in comparison with other predictive techniques. The values of 0.973, 0.987 and 97.345 for R2, RMSE and VAF, respectively, reveal that the ANFIS model is capable to predict ground vibration with high degree of accuracy. © 2015, Springer-Verlag Berlin Heidelberg.

Finding optimal architecture and weights for ANN : A combined hierarchical approach

- Ghosh, Ranadhir

Analysis of the relation between crude oil futures prices and spot price using nonlinear artificial neural networks

- Haidar, Imad, Pan, Heping, Kulkarni, Siddhivinayak

  • Authors: Haidar, Imad , Pan, Heping , Kulkarni, Siddhivinayak
  • Date: 2008
  • Type: Text , Conference paper
  • Relation: Paper presented at 37th Annual Conference of Economists, Gold Coast, Queensland : 30th September - 4th October 2008
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  • Description: As the oil demand continues to surge ahead and production continues to decline, it is believed that oil prices will continue to rise to unprecedented levels. As a reference, in 2004, the crude oil price was averaging $41 per barrel while it is above $130 in today’s market. This oil price increase is affecting the economy from both developing and developed countries. This paper investigates the possibility of using oil futures price to forecast spot price direction for short term, one day ahead using multilayer feedforward neural networks. The data was pre-processed to reflect the direction and the turning point of the price. Our approach is to create a benchmark based on lagged value of pre-processed spot price, then add pre-processed futures prices 1, 2, 3 and 4 months for maturity one by one and also altogether. For all the experiments, that include futures data as an input, the results show that on the short term, one day ahead, there is weak evidence to support futures price do hold new information on the spot price direction. This evidence is stronger for futures 1, 2 months to maturity.
  • Description: 2003007719

Artificial neural network modeling and sensitivity analysis of performance and emissions in a compression ignition engine using biodiesel fuel

- Jaliliantabar, Farzad, Ghobadian, Barat, Najafi, Gholamhassan, Yusaf, Talal

  • Authors: Jaliliantabar, Farzad , Ghobadian, Barat , Najafi, Gholamhassan , Yusaf, Talal
  • Date: 2018
  • Type: Text , Journal article
  • Relation: Energies Vol. 11, no. 9 (2018), p. 1-24
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  • Description: In the present research work, a neural network model has been developed to predict the exhaust emissions and performance of a compression ignition engine. The significance and novelty of the work, with respect to existing literature, is the application of sensitivity analysis and an artificial neural network (ANN) simultaneously in order to predict the engine parameters. The inputs of the model were engine load (0, 25, 50, 75 and 100%), engine speed (1700, 2100, 2500 and 2900 rpm) and the percent of biodiesel fuel derived from waste cooking oil in diesel fuel (B0, B5, B10, B15 and B20). The relationship between the input parameters and engine cylinder performance and emissions can be determined by the network. The global sensitivity analysis results show that all the investigated factors are effective on the created model and cannot be ignored. In addition, it is found that the most emissions decreased while using biodiesel fuel in the compression ignition engine.
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