Conference Papers

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    Experimental studies on shear strength characteristics of alkali activated slag concrete mixes
    (Elsevier Ltd, 2020) Manjunath, R.; Narasimhan, M.C.; Shashanka, M.; Vijayanand, S.D.; Vinayaka, J.
    In the present study an attempt has made to study the shear strength characteristics of alkali activated slag concrete mixes developed using steel slag sand and Electric Arc Furnace (EAF) slag aggregates, respectively, as the fine and coarse aggregates. These mixes use the ground granulated blast furnace slag (GGBFS) as the primary source material. Thus it is to be recognized that all the three materials used-GGBFS, slag sand and EAF slag aggregates are by-products of the Iron and Steel Industry, and are available in very large quantities demanding safe disposal. Different amounts of Sodium silicate solutions, with specified amounts of Sodium hydroxide flakes dissolved in them, are used as alkaline solutions. The test results indicate higher compressive strengths values for all the mixes in the range of 50-70 MPa with their shear strength values ranging between 7.5 and 12.0 MPa. Further the relationship between shear strength and compressive strength of the AASC mixes was also developed. © 2019 Elsevier Ltd. All rights reserved.
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    Shear Strength Characteristics of One-Part Alkali Activated Concrete Mixes—A DOE Approach
    (Springer Science and Business Media Deutschland GmbH, 2024) Mahendra, K.; Prakash, G.B.; Shetty, S.; Narasimhan, M.C.
    Utilization of one-part alkali-activated concrete (OPAAC) mixes is an advantageous option for large-scale construction applications. In the present investigation, the main objective was to investigate the shear strength characteristics of OPAAC mixes that were made using GGBFS and fly ash as precursors and sodium meta-silicate as solid activator. Taguchi’s DOE approach has been used to reduce the experimental effort and to find the optimum parameters. An initial set of nine OPAAC mixes was identified based on an L-9 array, with three representative levels considered for each of three principal mix parameters and experiments were conducted to test their compressive and shear strengths. The test results revealed that the OPAAC mixes exhibited 28-day compressive strength values ranging from 55 to 70 MPa, with shear strengths varying in the range of 8.5–12.67 MPa. Multi-linear regression equations were then developed to predict the 28-day compressive and shear strengths using MINITAB 21 statistical software. The predictions of these were verified by conducting actual strength experiments on a new set of three verification mixes. Further, additionally, a generalized correlation was developed to predict the 28-day shear strength of OPAAC mixes based on the known 28-day compressive strength. Again, an examination of microstructures was carried out through the utilization of FESEM analysis, to get a general appreciation of the microstructure (morphology) and elemental composition using EDX analysis of these mixes. The outcomes of this study are anticipated to promote the extensive adoption of environmentally friendly and sustainable materials within the construction industry. The findings of this study are anticipated to promote the extensive adoption of environmentally friendly and sustainable materials in the construction industry. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
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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.