Okan Önal | Gazi University (original) (raw)

Address: Ankara, Turkey

less

Uploads

Papers by Okan Önal

Research paper thumbnail of Artificial neural network application on microstructure-compressive strength relationship of cement mortar

Advances in Engineering Software, 2010

Research paper thumbnail of Nondestructive evaluation of volumetric shrinkage of compacted mixtures using digital image analysis

Engineering Geology, 2006

Adapting image processing technology to engineering disciplines can be useful in the evaluation o... more Adapting image processing technology to engineering disciplines can be useful in the evaluation of the mechanical behavior of materials. Not only characteristics of granular materials, but also particulate levels of colloids can be studied using image analysis. Attempts to identify the ...

Research paper thumbnail of Predicting Uniaxial Compressive Strengths of Brecciated Rock Specimens using neural networks and different learning models

Calculation of the uniaxial compressive strength (UCS) of Breccia rock specimens (BRS) is require... more Calculation of the uniaxial compressive strength (UCS) of Breccia rock specimens (BRS) is required for the correct determination of material strengths of marble specimens. However, this procedure is expensive and difficult since destructive laboratory tests (DLT) are needed to be done. Therefore, the results of non-destructive laboratory tests (NDLT) combined with different features that are extracted by using image processing techniques can be used instead of DLT to predict UCS of BRS. The goal of this study is to predict the results of DLT by using the results of NDLT, extracted features and artificial neural networks (ANN). Unfortunately, having enough number of specimens for training of ANN is often impossible since the preparation of the standard BRS is extraordinarily difficult. Hence, it is very important to use a learning methodology that prevents deficient evaluation practices. Therefore, different well-known learning methodologies are tested to train the ANN. Then, their effects on error estimation for our small size sample set of BRS are evaluated. The results of simulations show the importance of learning strategies for accurate evaluation of an ANN with a low error rate in prediction of UCS of BRS.

Research paper thumbnail of Cascaded and Hierarchical Neural Networks for Classifying Surface Images of Marble Slabs

IEEE Transactions on Systems, Man, and Cybernetics, 2009

Research paper thumbnail of A Visual Basic program for analyzing oedometer test results and evaluating intergranular void ratio

Computers & Geosciences, 2006

Research paper thumbnail of Artificial neural network application on microstructure-compressive strength relationship of cement mortar

Advances in Engineering Software, 2010

Research paper thumbnail of Nondestructive evaluation of volumetric shrinkage of compacted mixtures using digital image analysis

Engineering Geology, 2006

Adapting image processing technology to engineering disciplines can be useful in the evaluation o... more Adapting image processing technology to engineering disciplines can be useful in the evaluation of the mechanical behavior of materials. Not only characteristics of granular materials, but also particulate levels of colloids can be studied using image analysis. Attempts to identify the ...

Research paper thumbnail of Predicting Uniaxial Compressive Strengths of Brecciated Rock Specimens using neural networks and different learning models

Calculation of the uniaxial compressive strength (UCS) of Breccia rock specimens (BRS) is require... more Calculation of the uniaxial compressive strength (UCS) of Breccia rock specimens (BRS) is required for the correct determination of material strengths of marble specimens. However, this procedure is expensive and difficult since destructive laboratory tests (DLT) are needed to be done. Therefore, the results of non-destructive laboratory tests (NDLT) combined with different features that are extracted by using image processing techniques can be used instead of DLT to predict UCS of BRS. The goal of this study is to predict the results of DLT by using the results of NDLT, extracted features and artificial neural networks (ANN). Unfortunately, having enough number of specimens for training of ANN is often impossible since the preparation of the standard BRS is extraordinarily difficult. Hence, it is very important to use a learning methodology that prevents deficient evaluation practices. Therefore, different well-known learning methodologies are tested to train the ANN. Then, their effects on error estimation for our small size sample set of BRS are evaluated. The results of simulations show the importance of learning strategies for accurate evaluation of an ANN with a low error rate in prediction of UCS of BRS.

Research paper thumbnail of Cascaded and Hierarchical Neural Networks for Classifying Surface Images of Marble Slabs

IEEE Transactions on Systems, Man, and Cybernetics, 2009

Research paper thumbnail of A Visual Basic program for analyzing oedometer test results and evaluating intergranular void ratio

Computers & Geosciences, 2006

Log In