首页|Reports Outline Support Vector Machines Study Findings from Academy Science & Innovation Research (Prediction of Confined Compressive Strength of Concrete Col umn Strengthened With Frcm Composites)
Reports Outline Support Vector Machines Study Findings from Academy Science & Innovation Research (Prediction of Confined Compressive Strength of Concrete Col umn Strengthened With Frcm Composites)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-Researchers detail new data in Machine Learning - Support Vector Machines. Accordingto news originating from Ghaziaba d, India, by NewsRx correspondents, research stated, "Nowadays,retrofitting and rehabilitation of deteriorated reinforced concrete structures are becoming a gr owing needof the construction industry instead of demolishing aged structures. The application of fabric-reinforcedcementitious matrix (FRCM) on the existing concrete structures is one of the sustainable solutions toretrofit the concrete structures."Our news journalists obtained a quote from the research from Academy Science & Innovation Research,"This study used machine learning (ML) models such as linea r regression (LR), support vector machines(SVM), and adaptive neuro-fuzzy infer ence systems (ANFIS) to estimate the compressive strength (CS) ofcolumns wrappe d with FRCM. The experimental dataset of 301 column specimens was collected incl udinginput parameters such as cross-sectional properties, mechanical properties of concrete and steel, and characteristicsof FRCM material. Apart from ML mode ls, seven analytical models were also used to comparethe accuracy and precision of ML models. The results illustrate that the ANFIS model outperformed otherML models and established itself as a dependable and precise model. The R-value of the ANFIS modelwas 0.9816, whereas R-values of 0.9269 and 0.9572 were achieved by LR and SVM models, respectively.In addition, the MAPE value acquired by the ANFIS model was 1.52% which was lower than those of theLR model by 73.24%, and the SVM model by 60.60%, respectively. As the precision of the ANFIS modelwas higher as compared with SVM and LR model s, so, the developed ANFIS-based mathematical modelcan be easily used to predic t the CS of FRCM-strengthened concrete columns."
GhaziabadIndiaAsiaMachine LearningSupport Vector MachinesAcademy Science & Innovation Research
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