Intellectual Tool to Compute Embodied Energy and Carbon Dioxide Emission for Building Construction Materials. Issue 1 (August 2021)

• Record Type: Journal Article
• Authors: Mukherjee, Abhilash; Sumit, ; Deepmala, ; Dhiman, Vishal Kumar; Srivastava, Prateek; Kumar, Ajay
• Is Part Of: Journal of physics. Volume 1950:Issue 1(2021)
• Publication Date: 2021-08
• Subjects: Residential Building -- Construction Material -- Embodied Energy -- Embodied Carbon (CO2) -- Convolutional Neural Network; Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1950/1/012025
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Every material has its capacity to consume energy called embodied energy (EE) and to emit CO2 during the manufacturing process. The estimation procedure of EE and CO2 emission is predominantly based on the existing case examples from various nations across the globe. There is an impressive aperture in instances from developing nations concerning an appraisal of embodied energy and emission of CO2 for used building construction materials. The available tools are also not much user friendly and hence not everyone is capable to use it easily. From this point of view, this research aims to develop an intellectual tool to find out embodied energy and CO2 emission for building materials. This research presents a model for estimation of EE and emission of CO2 (E4-Model) for various building materials used in construction. This research focuses on construction of building in the Indian Construction Section. For the assessment of EE and CO2 emission, the concept of Convolutional Neural Network (CNN) is used and all the values are by weight of material. From the experimental analysis, it is observed that the estimation accuracy of the system is more than 99.62% for small as well as large buildings. The research concluded that the use of CNN is far better than the artificial neural networks in terms of system response time and complexity to estimate the EE and CO2 emission rates. In the research proposed E4-Model is applied and developed using the concept of deep learning and curve-fitting toolboxes within MATLAB Software. The actual emission for all the tested projects were found to be lesser than the estimated emission. The tool developed using CNN proved to work with high accuracy.