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Enhancing Concrete Strength Prognostication Through Machine Learning and Robotics
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Author(s): A. Hema (Vellore Institute of Technology, Chennai, India), S. Geetha (Vellore Institute of Technology, Chennai, India)and S. Karthiyaini (Vellore Institute of Technology, Chennai, India)
Copyright: 2025
Pages: 18
Source title:
Exploring the Micro World of Robotics Through Insect Robots
Source Author(s)/Editor(s): U. Vignesh (Vellore Institute of Technology, Chennai, India), Annavarapu Chandra Sekhara Rao (Indian Institute of Technology (ISM), Dhanbad, India), Saleem Raja (University of Technology and Applied Sciences, Shinas, Oman)and P. Chitra (GITAM University, Bangalore, India)
DOI: 10.4018/979-8-3693-6150-4.ch009
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Abstract
Concrete is a widely used construction material globally due to its exceptional properties. The strength of concrete varies based on the composition of cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, and fine aggregate. By altering the ingredients in different proportions, the process can anticipate the concrete strength through the various machine learning techniques. The study in this chapter involves the utilization and comparison of three algorithms, viz. Random Forest, Gradient Boosting, and Linear Regression models to analyse the concrete strength. Among these models, gradient boosting yielded superior results. In order to predict concrete strength from these models, three sensors in the field paves the way for better analysis, such as, an acoustic emission sensor, a strain gauge sensor, and a wireless concrete maturity sensor. This approach proves to be highly effective in achieving optimal concrete strength using machine learning techniques.
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