Machine learning based energy efficiency analysis with concrete waste reduction techniques and carbon footprint modelling

All evidence-based waste management endeavour needs accurate data on construction waste creation, but because many developing nations have outdated recording systems, this data is still hard to come by. Around 50% of global carbon dioxide (CO2) emissions connected to energy use in buildings have historically come from this industry. Thus, in the global endeavour to decarbonise the energy system, it garners a great deal of attention. In order to anticipate CO2 emissions from buildings over the long term, this research introduces and compares several Machine Learning (ML)-based methods. This research proposes novel technique in concrete waste reduction based on energy efficiency analysis and carbon footprint modelling using machine learning algorithms. Here the concrete construction waste reduction with energy efficiency is carried out using Bayesian multilayer reinforcement neural networks. then the carbon footprint analysis in smart building construction using fuzzy Gaussian linear hidden markov vector model. the experimental analysis has been carried out based on various concrete composition and CO2 analysis in terms of MAPE (mean average energy efficiency error), detection accuracy, correlation coefficient values (R), root mean square error (RMSE), energy efficiency. Proposed method produced 98% detection accuracy, 97% correlation coefficient values, 95% energy efficiency, 68% RMSE, and 58% MAPE.