Rock strength estimation: a PSO-based BP approach

Mohamad, Edy Tonnizam; Armaghani, Danial Jahed; Momeni, Ehsan; Yazdavar, Amir Hossein; Ebrahimi, Monireh

doi:10.1007/s00521-016-2728-3

Cited by 149 publications

(43 citation statements)

References 46 publications

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“…The equations were evaluated with taking into consideration some prediction intervals (PIs) such as variance accounted for (VAF), root mean square error (RMSE), and R 2 , which have been recommended by lots of researchers such as [24,59,61,63]. In addition, the formulas in regard to such PIs were taken from Mohamad et al's [64] research. It is noted that the best fit is the one with a VAF of 100%, RMSE of 0, and R 2 of 1.…”

Section: Laboratory Experiments and Regression Analysismentioning

confidence: 99%

“…The literature contains numerous studies conducted to improve ANN using optimization algorithms such as PSO, genetic algorithm (GA), ICA, and ABC (see [38,[61][62][63][64][65]). The back-propagation (BP) does not act strongly in exploring the accurate global minimum; as a result, the ANN model might obtain unwanted results [82][83][84][85][86][87][88].…”

Section: Hybrid Algorithmsmentioning

confidence: 99%

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Invasive Weed Optimization Technique-Based ANN to the Prediction of Rock Tensile Strength

et al. 2019

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In many site investigation phases of civil and mining engineering projects, the tensile strength of the rocks is one of the most significant parameters that must be identified. This parameter can be determined directly through laboratory tests. However, conducting such laboratory tests is costly and time consuming. In this paper, a new artificial neural network (ANN)-based model is developed to predict rock tensile strength, using the invasive weed optimization (IWO) technique. Granite samples for the purpose of this research were selected from a tunnel located in Malaysia and underwent appropriate laboratory tests (i.e., Schmidt hammer, point load, dry density, as well as the Brazilian tensile strength (BTS) as system output). A simple regression analysis was carried out, and the obtained results confirmed the need for developing a model with multiple inputs, rather than one with only a single input, in order to predict BTS values. Aiming to highlight the capability of an IWO-ANN model in estimating BTS, artificial bee colony (ABC)-ANN and imperialism competitive algorithm (ICA)-ANN were also applied and developed. The parameters required for the ANN-based models were obtained using different parametric studies. According to calculated performance indices, a new hybrid IWO-ANN model can provide a higher accuracy level for the prediction of BTS compared to the ABC-ANN and ICA-ANN models. The results showed that the IWO-ANN model is a suitable alternative solution for a robust and reliable engineering design.

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Section: Laboratory Experiments and Regression Analysismentioning

confidence: 99%

Section: Hybrid Algorithmsmentioning

confidence: 99%

Invasive Weed Optimization Technique-Based ANN to the Prediction of Rock Tensile Strength

et al. 2019

View full text Add to dashboard Cite

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“…Such networks are good tools for forecasting issues, however, they have several limitations such as low learning speed and falling into local minima [32][33][34]. As mentioned in literatures [20,23,[35][36][37][38][39], using efficient optimization algorithms (OAs), these limitations can be overcome. Various OAs such as particle swarm optimization (PSO), imperialism competitive algorithm (ICA) and genetic algorithm (GA) can be applied to solve continuous and discontinuous problems.…”

Section: Introductionmentioning

confidence: 99%

Three hybrid intelligent models in estimating flyrock distance resulting from blasting

Koopialipoor

Fallah

Armaghani

et al. 2018

Engineering with Computers

152

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Flyrock is an adverse effect produced by blasting in open-pit mines and tunnelling projects. So, it seems that the precise estimation of flyrock is essential in minimizing environmental effects induced by blasting. In this study, an attempt has been made to evaluate/predict flyrock induced by blasting through applying three hybrid intelligent systems, namely imperialist competitive algorithm (ICA)-artificial neural network (ANN), genetic algorithm (GA)-ANN and particle swarm optimization (PSO)-ANN. In fact, ICA, PSO and GA were used to adjust weights and biases of ANN model. To achieve the aim of this study, a database composed of 262 datasets with six model inputs including burden to spacing ratio, blast-hole diameter, powder factor, stemming length, the maximum charge per delay, and blast-hole depth and one output (flyrock distance) was established. Several parametric investigations were conducted to determine the most effective factors of GA, ICA and PSO algorithms. Then, at the end of modelling process of each hybrid model, eight models were constructed and their results were checked considering two performance indices, i.e., root mean square error (RMSE) and coefficient of determination (R 2 ). The obtained results showed that although all predictive models are able to approximate flyrock, PSO-ANN predictive model can perform better compared to others. Based on R 2 , values of (0.943, 0.958 and 0.930) and (0.958, 0.959 and 0.932) were found for training and testing of ICA-ANN, PSO-ANN and GA-ANN predictive models, respectively. In addition, RMSE values of (0.052, 0.045 and 0.057) and (0.045, 0.044 and 0.058) were achieved for training and testing of ICA-ANN, PSO-ANN and GA-ANN predictive models, respectively. These results show higher efficiency of the PSO-ANN model in predicting flyrock distance resulting from blasting. Moreover, sensitivity analysis shows that hole diameter is more effective than others.

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“…ANN has many applications in the petroleum industry as summarized by Al-Bulushi et al [23]. ANN has been applied in different aspects of petroleum engineering such as production forecasting [24,25], PVT (Pressure, volume, temperature) parameter prediction [26], well integrity evaluation [27], drilling fluid properties [28][29][30], reservoir, rock mechanics [31][32][33][34][35], drilling optimization [36][37][38][39][40][41], and permeability determination from well logs [42].…”

Section: Artificial Neural Network and Its Application In Drilling Opmentioning

confidence: 99%

A New Model for Predicting Rate of Penetration Using an Artificial Neural Network

Elkatatny

Al-AbdulJabbar

Abdelgawad

2020

Sensors

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The drilling rate of penetration (ROP) is defined as the speed of drilling through rock under the bit. ROP is affected by different interconnected factors, which makes it very difficult to infer the mutual effect of each individual parameter. A robust ROP is required to understand the complexity of the drilling process. Therefore, an artificial neural network (ANN) is used to predict ROP and capture the effect of the changes in the drilling parameters. Field data (4525 points) from three vertical onshore wells drilled in the same formation using the same conventional bottom hole assembly were used to train, test, and validate the ANN model. Data from Well A (1528 points) were utilized to train and test the model with a 70/30 data ratio. Data from Well B and Well C were used to test the model. An empirical equation was derived based on the weights and biases of the optimized ANN model and compared with four ROP models using the data set of Well C. The developed ANN model accurately predicted the ROP with a correlation coefficient (R) of 0.94 and an average absolute percentage error (AAPE) of 8.6%. The developed ANN model outperformed four existing models with the lowest AAPE and highest R value.

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Rock strength estimation: a PSO-based BP approach

Cited by 149 publications

References 46 publications

Invasive Weed Optimization Technique-Based ANN to the Prediction of Rock Tensile Strength

Invasive Weed Optimization Technique-Based ANN to the Prediction of Rock Tensile Strength

Three hybrid intelligent models in estimating flyrock distance resulting from blasting

A New Model for Predicting Rate of Penetration Using an Artificial Neural Network

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