EMBODIED ENERGY ASSESSMENT OF CONSTRUCTION MATERIAL IN INDIA USING ARTIFICIAL NEURAL NETWORK

Abstract

The increase in demand for urban areas resulted in an unprecedented increase in production as well as consumption of construction materials in the building sector. The production of materials requires considerable energy and contributes to pollution and greenhouse gas emissions. Efforts to minimize energy consumption and pollution-related to the construction materials is done by measuring the embodied energy produced by those materials. Embodied energy is evaluated by measuring the total energy required by the raw material up to the transportation process. This article deals with the embodied energy of common building materials used in India's construction industry. The construction material with their embodied energy is provided as an input data to the neural network. On the basis of which, ANN trained the system to suggest an appropriate material that release less embodied energy to the environment. The performance in terms of accuracy, mean square error and embodied energy are determined. From the experiment, it is observed that the average accuracy of the designed embodied energy tool is 98.52 %