Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Subject: search.filters.subject.Regression model

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    Regression model of oxidation behavior of 6061 Al/SiC composite with and without protective coatings
    (2011) Priyamvada, B.; Rajasekaran, S.; Udayashankar, N.K.; Nayak, J.
    This paper analyses the variation of weight gain, Δm, of 6061 Al/SiC composite due to oxidation with time, t, using regression model. Using curve fitting technique, the mathematical equations for the oxidation behavior of the composite are formulated. The generated data according to the mathematical equations are analyzed and compared with the experimental data. More specifically, regression analysis helps in understanding how the typical value of the mass gain (dependent variable) changes when the time of oxidation (independent variable) is varied, while the other independent variable (Temperature) held fixed. Since the oxidation resistance of the 6061Al/SiC composite decreases due to the presence of alloying elements precipitates in the matrix, the effect of aging treatment and protective coatings like Aluminium and AlCrN on the oxidation behavior of the composite is studied. It is observed that the coatings increase the oxidation resistance of the composite. The regression analysis carried out shows a threefold linear variation of weight gain (Dependent variable) with respect to time and temperature of oxidation (Independent variables). © 2011 IEEE.
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    A Comparative Study of Data-Driven Models for Shear Strength Prediction of FRP-RC Beam Using Machine Learning Techniques
    (Springer Science and Business Media Deutschland GmbH, 2024) Jangid, M.S.; Jayalekshmi, B.R.
    Nowadays machine learning techniques are effectively used as a means of resolving issues in civil and structural engineering. Accurately evaluating the shear strength of a reinforced concrete beam with fibre reinforced polymer (FRP) is crucial to ensure a secure design and effectively assess its performance. However, the accuracy of the predictions made by current shear models is generally constrained by the use of a limited database and complex parameters. The aim of this study is to create a model based on machine learning techniques that can predict the shear strength of reinforced concrete beams containing fibre reinforced polymer bars, both with and without stirrups, by utilizing data-driven approaches. A comprehensive database of 491 shear strength tests on FRP beams was collected from the public literature for developing framework’s training and testing sets. In order to prepare the data for machine learning algorithms, exploratory data analysis (EDA) has been carried out to investigate the correlation and identify collinearity between several independent parameters. Further, different models for linear regression, decision tree regression, random forest regression, gradient boost, and XGBoost have been developed for prediction of shear strength based on twelve different independent parameters and dependent output parameters. Root mean square error (RMSE), R2 score, and mean absolute error (MAE) are used to check the performance of all the models, and the best model is chosen for forecasting the shear strength of FRP reinforced concrete beam. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.