Estimation of rock modulus: For intact rocks with an artificial neural network and for rock masses with a new empirical equation

Sönmez, H.; Gökçeoğlu, Candan; Nefeslioğlu, Hakan A.; Kayabaşı, Ali

doi:10.1016/j.ijrmms.2005.06.007

Cited by 363 publications

(119 citation statements)

References 58 publications

(51 reference statements)

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“…Similar approach was already used in [18,21,[32][33]. Na osnovu predloga Rumelharta i dr. [34], Lipmana [35] i Sonmeza i dr. [36], analizirana je vešta ka neuronska mreža sa samo jednim skrivenim slojem, dok je broj jedinica u skrivenom sloju odre en koriš enjem heuristi kih obrazaca [36]. Kao što se u Tabeli 7 može videti, na osnovu broja ulaznih i izlaznih podataka, broj jedinica u skrivenom sloju je u rasponu od 1 do 12.…”

Section: Estimation Of Concrete Compressive Strengthunclassified

“…Following the suggestion of Rumelhart et al [34], Lippmann [35] and Sonmez et al [36] one hidden layer was chosen in present study, while the number of hidden neurons was determined using heuristics [36]. As it is clear from Table 7, the number of neurons that may be used in the hidden layer varies between 1 and 12.…”

Section: Estimation Of Concrete Compressive Strengthmentioning

confidence: 99%

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Estimation of concrete compressive strength using artificial neural network

Kostic¹,

Vasović²

2015

Gra mat konstru

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Analiza vrsto e betona pri pritisku predstavlja jedan od primarnih zadataka laboratorijskih ispitivanja za razli ite potrebe inženjerske prakse, pre svega u zgradarstvu, tunelogradnji, putarstvu, pri izgradnji brana i mostova, kao i pri izvo enju razli itih podzemnih i površinskih konstrukcija u rudarstvu. Zavisno od zahteva projekta, ispitivanje vrsto e betona izvodi se za uzorke razli ite starosti s promenljivim vodocementnim faktorom, raznim tipovima i koli inom aditiva (lete i pepeo, silikatna prašina, metakaolin i mlevena granulisana zgura iz visokih pe i kao mineralni aditivi, odnosno plastifikatori, razli iti akceleratori i retarderi, kao hemijski aditivi), te u razli itim spoljašnjim uslovima ugradnje betona (s naglaskom na otpornost betona pri izlaganju mrazu). Pri tome, pouzdanost dobijenih rezultata naj eš e predstavlja direktnu funkciju broja ispitanih uzoraka, tj. ve i broj ispitanih uzoraka betona doprinosi pouzdanijem odre ivanju njegovih svojstava. Me utim, sredstva predvi ena programom istraživanja, u najveem broju slu ajeva, nisu dovoljna za analizu brojnih uzoraka, ve se naj eš e pristupa interpolaciji malog, vrlo esto i nedovoljnog broja podataka ispitivanja. U tom smislu, modeli za procenu vrsto e betona predstavljaju posebno važnu tehniku koja omogu ava utvr ivanje relacije izme u zrelosti betona i njegove vrsto e, Sr an Kosti , Departman za geologiju, Univerzitet u Beogradu, Rudarsko-geološki fakultet, Srbija, srdjan.kostic@rgf.bg.ac.rs Dejan Vasovi Departman za arhitektonske tehnologije, Univerzitet u Beogradu, Arhitektonski fakultet, Srbija,

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Section: Estimation Of Concrete Compressive Strengthunclassified

Section: Estimation Of Concrete Compressive Strengthmentioning

confidence: 99%

Estimation of concrete compressive strength using artificial neural network

Kostic¹,

Vasović²

2015

Gra mat konstru

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“…The usage of a number of hidden layers in the ANN depends on the degree of complexity in the pattern recognition problem, and one or two hidden layers are found to be quite useful for most problems [13][14][15]. Also, the number of neurons in the hidden layers depends on the nature of the problem, and various methods have been employed by several researchers to determine them [16][17][18][19][20][21].…”

Section: Details Of the Neural Networkmentioning

confidence: 99%

Artificial neural network models for predicting soil thermal resistivity

Erzin

Rao

Singh

2008

International Journal of Thermal Sciences

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“…The datasets were normalized between zero and 1 considering the maximum values of input variables. In this study, a computer code, namely ANNES written by Sonmez et al (2006) was used to construct the ANN structure. The relation between the number of training cycles and the RMSE values of the models obtained by ANNES for each random data set are given in Fig.…”

Section: Application Of Ann Architecture To Produce Potential Debris mentioning

confidence: 99%

“…In recent years, artificial neural networks (ANN) have been applied to many studies in engineering geology and geomorphology (Meulenkamp and Alvarez Grima, 1999;Singh et al, 2001;Zhang and Govindaraju, 2003;Lee et al, 2003aLee et al, , b, 2004Gomez and Kavzoglu, 2005;Ermini et al, 2005;Sarangi and Bhattacharya, 2005;Yesilnacar and Topal, 2005;Sonmez et al, 2006;Lee and Evangelista, 2006) because an ANN based prediction model has a high prediction capacity due to its high performance in the modeling of non-linear multivariate problems. For this reason, ANN has become an attractive and important tool for engineering geologists and geomorphologists because both engineering geological and geomorphological problems are generally non-linear and multivariate problems.…”

Section: Introductionmentioning

confidence: 99%

An artificial neural network application to produce debris source areas of Barla, Besparmak, and Kapi Mountains (NW Taurids, Turkey)

Tunusluoglu

Gökçeoğlu

Sönmez

et al. 2007

Nat. Hazards Earth Syst. Sci.

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Abstract. Various statistical, mathematical and artificial intelligence techniques have been used in the areas of engineering geology, rock engineering and geomorphology for many years. However, among the techniques, artificial neural networks are relatively new approach used in engineering geology in particular. The attractiveness of ANN for the engineering geological problems comes from the information processing characteristics of the system, such as nonlinearity, high parallelism, robustness, fault and failure tolerance, learning, ability to handle imprecise and fuzzy information, and their capability to generalize. For this reason, the purposes of the present study are to perform an application of ANN to a engineering geology problem having a very large database and to introduce a new approach to accelerate convergence. For these purposes, an ANN architecture having 5 neurons in one hidden layer was constructed. During the training stages, total 40 000 training cycles were performed and the minimum RMSE values were obtained at approximately 10 000th cycle. At this cycle, the obtained minimum RMSE value is 0.22 for the second training set, while that of value is calculated as 0.064 again for the second test set. Using the trained ANN model at 10 000th cycle for the second random sampling, the debris source area susceptibility map was produced and adjusted. Finally, a potential debris source susceptibility map for the study area was produced. When considering the field observations and existing inventory map, the produced map has a high prediction capacity and it can be used when assessing debris flow hazard mitigation efforts.

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Estimation of rock modulus: For intact rocks with an artificial neural network and for rock masses with a new empirical equation

Cited by 363 publications

References 58 publications

Estimation of concrete compressive strength using artificial neural network

Estimation of concrete compressive strength using artificial neural network

Artificial neural network models for predicting soil thermal resistivity

An artificial neural network application to produce debris source areas of Barla, Besparmak, and Kapi Mountains (NW Taurids, Turkey)

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