Estymacja mechanicznych właściwości wapienia przy zastosowaniu analizy regresji i sztucznych sieci neuronowych

• Autorzy: Teomete, E. , Tayfur, G. , Aktaş, E.

Warianty tytułu

EN

Estimation of mechanical properties of limestone using regression analyses and ANN

• Języki publikacji: PL

Abstrakty

PL

Estymacja mechanicznych właściwości skał jest ważna dla badaczy i inżynierów pracujących w przemyśle cementowym i betonowym. W niniejszej pracy właściwości sprężyste badane przy użyciu młotka Schmidta, szybkość impulsu ultradźwiękowego, porowatość, ściskanie jednoosiowe i pośrednia próba rozciągania zostały przeprowadzone na wapieniu otrzymanym z historycznej budowli. Do konstrukcji modeli mechanicznych właściwości wapienia zastosowano analizę regresji. Sztuczne sieci neuronowe (SSN) zastosowano do wyznaczenia właściwości mechanicznych. Dopasowanie modeli regresji i SSN jest porównane z modelami znanymi z literatury. Otrzymane wyniki wskazują zadawalające dopasowanie modeli regresji i SSN z minimalnym błędem. Modele regresji między wytrzymałością na rozciąganie i porowatością, szybkością fali i porowatością opisano po raz pierwszy w literaturze. Również po raz pierwszy w literaturze opisano zastosowanie SSN do określenia mechanicznych właściwości wapienia. Po raz pierwszy zastosowano oddzielne zbiory danych do nauki i weryfikacji wyników nauki w analizie regresji mechanicznych właściwości wapienia. Modele skonstruowane w niniejszej pracy mogą być stosowane przez naukowców i inżynierów do ustalania związków między mechanicznymi właściwościami wapienia.

EN

Estimation of mechanical properties of rocks is important for researchers and field engineers working in cement and concrete industry. Limestone is used in cement production. In this study, Schmidt hammer, ultrasonic pulse velocity, porosity, uniaxial compression and indirect tension tests were conducted on limestone obtained from a historical structure. Regression analyses were used to develop models relating mechanical properties of limestone. Artificial Neural Network (ANN) was performed to determine the mechanical properties. The performance of regression models and ANN were compared by existing models in the literature. The results showed that the regression models and ANN yield satisfactory performance with minimum error. The regression models between tensile strength and wave velocity, tensile strength and porosity, wave velocity and porosity have been developed for the first time in literature. The ANN is used for the first time to estimate the mechanical properties of limestone. The use of separate training and testing sets in the regression analyses of mechanical properties of limestone is conducted for the first time. The models developed in this study can be used by researchers and field engineers to relate the mechanical properties of limestone.

Słowa kluczowe

PL

wapień; właściwości mechaniczne; szacowanie; badanie nieniszczące; analiza regresji; sieć neuronowa

EN

limestone; mechanical properties; estimation; nondestructive testing; regression analysis; neural network

• Czasopismo: Cement Wapno Beton
• Rocznik: 2012
• Tom: R. 17/79, nr 6
• Strony: 373-389
• Opis fizyczny: Bibliogr. 43 poz., il.

Twórcy

autor

Teomete, E.

autor

Tayfur, G.

autor

Aktaş, E.

• Civil Engineering Department, Dokuz Eylul University, Izmir, Turcja

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