Advances in concrete construction

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Energy efficiency and concrete waste management based on machine learning in sustainable construction

  • G.V. Rambabu (Department of Mechanical Engineering, MLR Institute of Technology) ;
  • R. Ramya Swetha (Department of Civil Engineering Institute of Aeronautical Engineering) ;
  • Pritee Parwekar (Department of CSE, GIT, GITAM University) ;
  • Pradeep Jangir (Department of Biosciences, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences) ;
  • S. Amutha (Department of Computer Science and Engineering, School of Computing, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology) ;
  • V. Sivaramaraju Vetukuri (Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation)
  • Received : 2023.12.25
  • Accepted : 2024.04.24
  • Published : 2024.08.25
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Abstract

Achieving sustainability in today's society is mostly dependent on energy efficiency. The viability of smart cities hinges on the availability of services and infrastructure that use less energy. The properties of different types of concrete, including geopolymer, fiber-reinforced, conventional, and recycled aggregate concrete, are predicted using machine learning techniques. From a recycling standpoint, using plastic waste in concrete may be the best option for the building sector. this research proposes novel technique in energy efficiency with concrete waste management using machine learning model based on sustainable construction application. In this research the concrete construction energy efficiency is carried out using discriminant extreme backward fuzzy genetic neural networks. Then the concrete waste management is carried out using support vector perceptron with concrete aggregate component analysis. the experimental analysis has been carried out for various concrete construction parameters in terms of sensitivity, efficiency co-efficient, accuracy, specificity, Coefficient of Determination (R2). The proposed model attained accuracy of 98%, Efficiency co-efficient of 95%, Sensitivity of 93%, SPECIFICITY of 89%, R2 of 96%.

Keywords

References

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