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    Deep learning and machine learning-based prediction of capillary water absorption of hybrid fiber reinforced self-compacting concrete
    (2022) Kına, Ceren; Turk, Kazım; Tanyildizi,Harun
    Deep auto-encoders and long short-term memory methodology (LSTM) basedon deep learning as well as support vector regression (SVR) and k-nearestneighbors (kNN) based on machine learning models for the capillary waterabsorption prediction of self-compacting concrete (SCC) with single andbinary, ternary, and quaternary fiber hybridization were developed. A macroand two types of micro steel fibers having different aspect ratios, and PVA fiberwere used. One hundred and sixty-eight specimens produced from 24 mixtureswere used in the prediction models. The input was the content of cement, flyash, silica fume, fine and coarse aggregate, water, superplasticizer (SP), macroand micro steel fibers, PVA, time that the specimen was immersed in water,and splitting tensile strength. Water absorption was used as output. As per theANOVA analysis of the experiment results, the most effective parameters weremacro steel fiber and time for tensile strength and water absorption, respec-tively. Finally, binary hybridization of 1% macro steel fiber and PVA improvedthe splitting tensile strength while the use of PVA as binary, ternary, and qua-ternary fiber hybridization increased the water absorption of SCC specimens.The auto-encoder, LSTM, SVR, and kNN models predicted the water absorp-tion of fiber reinforced SCC with 99.99%, 99.80%, 94.57%, and 95.50% accuracy,respectively. The performance of deep autoencoder in the estimation of waterabsorption of fiber-reinforced SCC was superior to the other predictionmodels.
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    Estimation of strengths of hybrid FR-SCC by using deep-learning and support vector regression models
    (2021) Kına, Ceren; Turk, Kazım; Tanyildizi,Harun
    In this work, to estimate the compressive, splitting tensile, and flexuralstrength of self-compacting concrete (SCC) having single fiber and binary, ter-nary, and quaternary fiber hybridization, the deep-learning (DL) and supportvector regression (SVR) models were devised. The fiber content and coarseaggregate/total aggregate ratio (CA/TA) were the variables for 24 designedmixtures. Four different fibers, which were a macro steel fiber, two types ofmicro steel fibers with different aspect ratio, and polyvinyl alcohol (PVA) fiber,were used in SCC mixtures. The specimens of each mixture were tested to mea-sure the engineering properties for 7, 28, and 90 days. The amount of cement,fly ash, fine aggregate, CA, high-range water-reducing admixture, water, macrosteel fiber, PVA fiber, two types of micro steel fibers, and curing time wereselected as input layers while the output layers were strength results. Theexperimental results were compared with the estimation results. The engineer-ing properties were estimated using the SVR model with 95.25%, 87.81%, and93.89% accuracy, respectively. Furthermore, the DL model estimated compres-sive strength, tensile strength, and flexural strength with 99.27%, 98.59%, and99.15% accuracy, respectively. It was found that the DL estimated the engineer-ing properties of hybrid fiber–reinforced SCC with higher accuracy than SVR