E3S Web Conf.
Volume 220, 2020Sustainable Energy Systems: Innovative Perspectives (SES-2020)
|Number of page(s)||6|
|Published online||19 February 2021|
Development of Prediction models for Bond Strength of Steel Fiber Reinforced Concrete by Computational Machine Learning
Department of Civil Engineering, Amity School of Engineering & Technology, Amity University Uttar Pradesh, Noida, India
2 Department of Computer Science & Engineering, Amity School of Engineering & Technology, Amity University Uttar Pradesh, Noida, India
3 Department of Mechanical Engineering, Amity School of Engineering & Technology, Amity University Uttar Pradesh, Noida, India
4 Department of Agricultural Engineering, Udai Pratap College, Varanasi, Uttar Pradesh- 221 002, India
Sustainable construction contributed to the usage of recycled and waste materials to substitute conventional concrete. This research focuses on prediction of normalized bond strength of cement concrete substituted by large amounts of waste materials and products with strong mechanical properties and sustainability. It also emphases on using analytical model for the prediction of bond strength of the green concrete, so that there is a reduction in the cost of construction, con-serve energy, and it will lead to a reduction of CO2 production from cement industries within reliable limits. In this paper machine learning approach has been used to predict the normalized bond strength of green and sustainable concrete. Machine learning empowers machines to learn from their experiences and data provided. The system analyses the datasets and finds different patterns formed in the given data. Then, based on its learnings the machine can make certain predictions. In civil engineering application, a special computing technique called the Machine learning (ML) is in huge demand. ANN is a soft computing technique that learns from previous situations and adapts without constraints to a new environment. In this work, a ML network model for prediction of normalized bond strength of concrete has been illustrated. Different sets of data based upon several concrete design mixes were taken from technical literature and were fed to the model. The model is then trained for prediction, which are being influenced by several input attributes and were jotted down a linear regression analysis.
© The Authors, published by EDP Sciences, 2020
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