Application of genetic programming to predict the uniaxial compressive strength and elastic modulus of carbonate rocks

Beiki, Morteza; Majdi, Abbas; Givshad, A. Dadi

doi:10.1016/j.ijrmms.2013.08.004

Cited by 114 publications

(23 citation statements)

References 29 publications

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“…Crossover chooses between parents and mutation works in a range of 0-1. This process is repeated until creation of the best generations evaluated based on their performance [53,54].…”

Section: Genetic Algorithmmentioning

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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“…Crossover chooses between parents and mutation works in a range of 0-1. This process is repeated until creation of the best generations evaluated based on their performance [53,54].…”

Section: Genetic Algorithmmentioning

confidence: 99%

Three hybrid intelligent models in estimating flyrock distance resulting from blasting

Koopialipoor

Fallah

Armaghani

et al. 2018

Engineering with Computers

152

View full text Add to dashboard Cite

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“…Ozbeka sa suradnicima opisao je upotrebu GEP modela za procjenu UCS bazalta i tufa te pokazao dobro slaganje između eksperimentalno utvrđenih podataka i rezultata postignutih procjenom [49]. Beiki i suradnici su na temelju genetskog programiranja načinili modele za procjenu UCS i E karbonatnih stijena na temelju rezultata ispitivanja uzoraka prikupljenih na Asmary formaciji u Iranu [50]. U budućnosti se očekuje više radova koji će uspoređivati modele evolucijskog programiranja s ostalim složenim modelima, a zasad su oni dokazano bolji od modela višestruke regresije.…”

Section: Procjena Pomoću Evolucijskog Programiranjaunclassified

Modeli za procjenu jednoosne tlačne čvrstoće i modula elastičnosti

Briševac¹,

Hrženjak²,

Buljan³

2016

JCE

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Modeli za procjenu jednoosne tlačne čvrstoće i modula elastičnosti U ovom radu ukratko je izložen pregled najznačajnijih metoda za procjenu jednoosne tlačne čvrstoće i Yangovog modula elastičnosti intaktnog stijenskog materijala koje su nastale u okviru mnogobrojnih istraživanja. Iznesen je prijedlog podjele metoda prema kojemu se one u osnovi mogu podijeliti na jednostavne i složene metode. Jednostavne metode uključuju različite dijagrame i tablice te primjenu jednadžbi jednostruke regresije, a složene metode uključuju primjene jednadžbi višestruke regresije, modela neizrazite logike, neuronskih mreža, evolucijskog programiranja i regresijskog stabla. Models for estimating uniaxial compressive strength and elastic modulusThe most significant methods for estimating the uniaxial compressive strength and Young's modulus of intact rock material, formulated in the scope of numerous previous studies, are briefly presented in the paper. The proposal for classification of these methods, according to which they can generally be divided into simple and complex methods, is also presented. Simple methods include various diagrams and tables and the use of simple regression equations, while complex methods comprise the use of multiple regression equations, fuzzy logic models, neural networks, evolutionary programming, and regression trees.

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“…ANN approach has been used widely in geotechnical and geomechanical engineering problems [30][31][32][33][34][35][36][37][38][39][40][41][42]. Due to above mentioned defects of numerical, experimental and analytical methods in design of segmental tunnel lining, application of ANN methods seems to be a new alternative solution as prediction tools.…”

Section: Introductionmentioning

confidence: 99%