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Bond strength prediction of steel bars in low strength concrete by using ANN
Sohaib Ahmad,Kypros Pilakoutas,Muhammad M. Rafi,Qaiser U. Zaman 사단법인 한국계산역학회 2018 Computers and Concrete, An International Journal Vol.22 No.2
This paper presents Artificial Neural Network (ANN) models for evaluating bond strength of deformed, plain and cold formed bars in low strength concrete. The ANN models were implemented using the experimental database developed by conducting experiments in three different universities on total of 138 pullout and 108 splitting specimens under monotonic loading. The key parameters examined in the experiments are low strength concrete, bar development length, concrete cover, rebar type (deformed, cold-formed, plain) and diameter. These deficient parameters are typically found in non-engineered reinforced concrete structures of developing countries. To develop ANN bond model for each bar type, four inputs (the low strength concrete, development length, concrete cover and bar diameter) are used for training the neurons in the network. Multi- Layer-Perceptron was trained according to a back-propagation algorithm. The ANN bond model for deformed bar consists of a single hidden layer and the 9 neurons. For Tor bar and plain bars the ANN models consist of 5 and 6 neurons and a single hidden layer, respectively. The developed ANN models are capable of predicting bond strength for both pull and splitting bond failure modes. The developed ANN models have higher coefficient of determination in training, validation and testing with good prediction and generalization capacity. The comparison of experimental bond strength values with the outcomes of ANN models showed good agreement. Moreover, the ANN model predictions by varying different parameters are also presented for all bar types.
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N. R. Khalid,Samavia Ilyas,Faisal Ali,Tahir Iqbal,M. Rafi que,Muhammad Imran,Mohammad A. Assiri 대한금속·재료학회 2024 ELECTRONIC MATERIALS LETTERS Vol.20 No.1
The organic pollutants are polluting the drinking water so, it is a fi eld of great interest to clean this water by using somesophisticated materials. For this purpose, the nanostructured materials are playing vital role to attain sustainable and puredrinking water by degrading organic pollutants. The synthesis of such photocatalytic material without using harmful chemicals,is one of the important existing challenges. Thus, to tackle this challenge, we have prepared green synthesized Sn-dopedWO 3 nanomaterials by varying the content of Sn from 2 to 6 wt% and assisting from moringa oleifera seeds’ extract. Thecrystal structure, morphology, optical and photoluminescence properties of as prepared samples were investigated throughx-ray diff raction (XRD), scanning electron microscopy (SEM), ultraviolet visible spectroscopy (UV-vis) and photoluminescencespectroscopy (PL) techniques. Among of as prepared samples, the 4Sn-WO 3 (4 wt% Sn doped WO 3 ) sample hasexhibited the reduced optical band gap value i.e. 2.80 eV than 3.02 eV for pure WO 3 sample. This optimized sample has alsoshown the lowest e-h recombination rate. To test the photocatalytic performance, the methylene blue was used as a modeldye. Out of all samples, 4Sn-WO 3 sample has shown 95% degradation activity against this water pollutant. These fi ndingsspecify that the as mentioned novel photocatalytic nanomaterial will provide a signifi cant advancement in the environmentalfi eld to degrade the organic pollutants.
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