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- 저자 : Osman Şimşek (검색결과 2 건)
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Prediction of concrete compressive strength using non-destructive test results
Hamit Erdal,Mürsel Erdal,Osman Şimşek,Halil İbrahim Erdal 사단법인 한국계산역학회 2018 Computers and Concrete, An International Journal Vol.21 No.4
Concrete which is a composite material is one of the most important construction materials. Compressive strength is a commonly used parameter for the assessment of concrete quality. Accurate prediction of concrete compressive strength is an important issue. In this study, we utilized an experimental procedure for the assessment of concrete quality. Firstly, the concrete mix was prepared according to C 20 type concrete, and slump of fresh concrete was about 20 cm. After the placement of fresh concrete to formworks, compaction was achieved using a vibrating screed. After 28 day period, a total of 100 core samples having 75 mm diameter were extracted. On the core samples pulse velocity determination tests and compressive strength tests were performed. Besides, Windsor probe penetration tests and Schmidt hammer tests were also performed. After setting up the data set, twelve artificial intelligence (AI) models compared for predicting the concrete compressive strength. These models can be divided into three categories (i) Functions (i.e., Linear Regression, Simple Linear Regression, Multilayer Perceptron, Support Vector Regression), (ii) Lazy-Learning Algorithms (i.e., IBk Linear NN Search, KStar, Locally Weighted Learning) (iii) Tree- Based Learning Algorithms (i.e., Decision Stump, Model Trees Regression, Random Forest, Random Tree, Reduced Error Pruning Tree). Four evaluation processes, four validation implements (i.e., 10-fold cross validation, 5-fold cross validation, 10% split sample validation & 20% split sample validation) are used to examine the performance of predictive models. This study shows that machine learning regression techniques are promising tools for predicting compressive strength of concrete.
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Serap Duraklı-Velioğlu,İsmail Hakkı Boyacı,Osman Şimşek,Tuncay Gümüş 한국식품과학회 2013 Food Science and Biotechnology Vol.22 No.6
The combined effect of temperature, agitationspeed, and light on red pigment production by Monascuspurpureus (M. purpureus) Went DSM 1604 using bugdamaged wheat was studied using an artificial neuralnetwork (ANN). Information retrieved from the ANN wasused to determine the optimal operating conditions forpigment production by M. purpureus using bug damagedwheat meal. The developed ANN had R2 values fortraining, validation, and testing data sets of 0.993, 0.961,and 0.944, respectively. According to the model, thehighest pigment production of 1.874 absorbance units at510 nm (A510 nm) would be achieved at 29oC and 150 rpmunder light conditions. The mean value of the experimentalresults obtained under these optimum conditions was1.787±0.072 A510 nm, corresponding to a pigment yield of35.740 A510 nm/g. The study showed that bug damagedwheat can be used as a substrate for red pigment productionby M. purpureus.
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