A Review on Quantum Deep Machine Learning Model for Predicting Rice Husk Ash Compressive Strength

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Author(s): Dorothy Blessing Agboola (Landmark University, Omu-Aran, Nigeria), Micheal Olaolu Arowolo (University of Missouri, Columbia, USA)and Amit Kumar Tyagi (National Institute of Fashion Technology, New Delhi, India)
Copyright: 2023
Pages: 18
Source title: Handbook of Research on Quantum Computing for Smart Environments
Source Author(s)/Editor(s): Amit Kumar Tyagi (National Institute of Fashion Technology, New Delhi, India)
DOI: 10.4018/978-1-6684-6697-1.ch006

Keywords: Engineering Science Reference / High Performance Computing / Knowledge Management and Library Science / Quantum Information & Computing

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Abstract

Concrete formulation for qualities is difficult with advances in concrete science. Using rice husk ash as a partial substitute of cement is a strategy to lower the environmental impact worldwide. Compressive strength of concrete made with rice husk ash has not been reliably predicted; diverse quantum events in condensed matter and atomic physics may be traced back, however, their underlying mechanisms and their dynamical management remain elusive to researchers, while machine learning's task-solving abilities have shown potentials in the burgeoning computational machine learning for concrete mixture design. Reducing cement used in concrete can lessen the industry's environmental impact and boost efficiency. The expanding data volumes provide openings for cutting-edge machine learning data analysis methods. This study reviews prediction models for compressive strength of cement replacement concrete with rice husk ash using quantum deep machine learning algorithm models for experimental data on concrete's compressive and flexural strengths.

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A Review on Quantum Deep Machine Learning Model for Predicting Rice Husk Ash Compressive Strength

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