Genetic programming approach for estimating the deformation modulus of rock mass using sensitivity analysis by neural network

Beiki, Morteza; Bashari, Ali; Majdi, Abbas

doi:10.1016/j.ijrmms.2010.07.007

Cited by 70 publications

(18 citation statements)

References 31 publications

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“…Different mechanical parameters in the same grade of surrounding rock will lead to a great difference of surrounding rock deformation when cavern buried depth and cavern size are fixed. The numerical calculation results of surrounding rock deformation have a close relationship to the selection of mechanical parameters, but the influence of each parameter on deformation is different [13]. The research showed that the sensitivities of these parameters affecting deformation in a descending order are the deformation modulus, the internal friction angle, the Poisson ratio, and the cohesion.…”

Section: Rock Mass Quality Classificationmentioning

confidence: 93%

Allowable Deformation Prediction for Surrounding Rock of Underground Caverns Based on Support Vector Machine

Tan

et al. 2016

Period. Polytech. Civil Eng.

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Section: Rock Mass Quality Classificationmentioning

confidence: 93%

Allowable Deformation Prediction for Surrounding Rock of Underground Caverns Based on Support Vector Machine

Tan

et al. 2016

Period. Polytech. Civil Eng.

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“…Therefore, in such cases, the application of artificial intelligence based methods is recognized to be an appropriate substitute. In recent years, these methods have been widely used in problems related to geosciences and geotechnical engineering (Lazzari and Salvaneschi, 1994;Beiki et al, 2010;Fragos et al, 2010;Mollahasani et al, 2011;Mousavi et al, 2012).…”

Section: Intelligent Methodsmentioning

confidence: 99%

Predicting crest settlement in concrete face rockfill dams using adaptive neuro-fuzzy inference system and gene expression programming intelligent methods

Behnia

Ahangari

Noorzad³

et al. 2013

J. Zhejiang Univ. Sci. A

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This paper deals with the estimation of crest settlement in a concrete face rockfill dam (CFRD), utilizing intelligent methods. Following completion of dam construction, considerable movements of the crest and the body of the dam can develop during the first impoundment of the reservoir. Although there is vast experience worldwide in CFRD design and construction, few accurate experimental relationships are available to predict the settlement in CFRD. The goal is to advance the development of intelligent methods to estimate the subsidence of dams at the design stage. Due to dam zonification and uncertainties in material properties, these methods appear to be the appropriate choice. In this study, the crest settlement behavior of CFRDs is analyzed based on compiled data of 24 CFRDs constructed during recent years around the world, along with the utilization of gene expression programming (GEP) and adaptive neuro-fuzzy inference system (ANFIS) methods. In addition, dam height (H), shape factor (S f ), and time (t, time after first operation) are also assessed, being considered major factors in predicting the settlement behavior. From the relationships proposed, the values of R 2 for both equations of GEP (with and without constant) were 0.9603 and 0.9734, and for the three approaches of ANFIS (grid partitioning (GP), subtractive clustering method (SCM), and fuzzy c-means clustering (FCM)) were 0.9693, 0.8657, and 0.8848, respectively. The obtained results indicate that the overall behavior evaluated by this approach is consistent with the measured data of other CFRDs.

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“…Karena kesulitan yang dihadapi selama pengujian jika harus dilakukan secara langsung, mengembangkan model evaluasi untuk memperkirakan beban puncak pada waktu yang akan datang berdasarkan parameter yang mempengaruhi akan selalu menarik untuk diteliti [1,2]. Beberapa penelitian yang dilakukan dengan metode yang berbeda telah banyak dilakukan seperti prediksi beban listrik menggunakan kecerdasan buatan.…”

Section: Pendahuluanunclassified

Metode Logika Fuzzy Sebagai Evaluasi Distribusi Daya Listrik Berdasarkan Beban Puncak Pembangkit Tenaga Listrik

2016

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AbstrakEvaluasi beban puncak pada sistem tenaga listrik yang dibangkitkan sangat berpengaruh terhadap perkembangan ketersediaan tenaga listrik di berbagai provinsi. Dengan meninjau beban puncak selama satu tahun, dapat diimplementasikan terhadap evaluasi pembangkitan energi listrik sebagai simulasi ketersediaan energi listrik untuk kedepannya. Mengevaluasi beban puncak juga bergantung terhadap beberapa faktor seperti kapasitas terpasang, daya mampu, dan hasil produksi pada beberapa sistem pembangkit. Hal tersebut dapat menjadi kontrol dari daya-daya yang dihasilkan pada masing-masing pembangkit listrik seperti PLTA, PLTU, PLTN, PLTG, dan PLTS. Metode logika fuzzy merupakan metode yang efektif yang dapat diaplikasikan untuk mengevaluasi beban puncak dengan tingkat keakuratan yang tinggi. Dengan begitu pemenuhan akan tenaga listrik akan terpenuhi dengan tingkat keandalan yang diinginkan. Dari evaluasi tersebut output yang dihasilkan dapat dijadikan sebagai kontrol untuk keamanan dari sistem pembangkit. Dengan hasil yang diperoleh adalah tingkat error tertinggi mencapai 60% dan telah dilakukan training dan testing data sebanyak 4x untuk menguji parameter dari fungsi keanggotaan yang telah ditentukan dengan hasil recognize tertinggi sebesar 12,5%.Kata kunci: Beban puncak, evaluasi, logika fuzzy. AbstractEvaluation of peak load on the power system is raised very influential on the development of electric power availability in the various provinces. By reviewing the peak load for a year, can be implemented for the evaluation of power generation as a simulation of the electrical energy supply for the future. Evaluating the peak load also depends on several factors such as installed capacity, power capacity, and production at some plants systems. It can be the control of the forces generated on each such PLTA, PLTU, PLTG, and PLTS. Fuzzy logic method is an effective method that can be applied to evaluate peak loads with high accuracy. Thus the fulfillment of the electricity will be met with the desired reliability level. The evaluation of the resulting output can be used as a control for the security of the power system. With the results obtained is the highest error rate reached 60%, and has done training and testing data is as much as 4x to test the parameters of the membership function has been determined by the highest recognize result of 12.5%.

show abstract

Genetic programming approach for estimating the deformation modulus of rock mass using sensitivity analysis by neural network

Cited by 70 publications

References 31 publications

Allowable Deformation Prediction for Surrounding Rock of Underground Caverns Based on Support Vector Machine

Allowable Deformation Prediction for Surrounding Rock of Underground Caverns Based on Support Vector Machine

Predicting crest settlement in concrete face rockfill dams using adaptive neuro-fuzzy inference system and gene expression programming intelligent methods

Metode Logika Fuzzy Sebagai Evaluasi Distribusi Daya Listrik Berdasarkan Beban Puncak Pembangkit Tenaga Listrik

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