2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 17 , Issue 2 , PP: 197-210, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Saif Mohammed Ali 1 , Omar Al-Boridi 2 *
Doi: https://doi.org/10.54216/FPA.170215
Green buildings are those that use sustainable methods of construction to either maintain or improve the local quality of life. Decisions affecting a project's quality, safety, profitability, and timetable are made using Artificial Intelligence (AI) in Green Construction by analyzing data gathered from monitoring the construction site and using predictive analytics. For instance, increased accuracy in weather predictions might lead to more production, less waste, lower costs, and less greenhouse gas emissions. Green building construction is a significant source of carbon dioxide released through the breakdown of carbonates. Researchers have concluded that integrating industrial wastes is crucial in green concrete making due to its benefits, such as reducing the requirement for cement. When planning with concrete, its compressive strength must be considered. Due to their high predictive power, AI algorithms may be used to determine the compressive strength of concrete mixtures. Existing artificial intelligence (AI) models may be evaluated for their modeling process and accuracy to inform the creation of new models that more accurately represent the comprehensive evaluation of setting parameters on model performance and boost accuracy. Potential sources of conflict in this anthropocentric future include climate change and the availability of renewable energy sources. Scientists think there is a connection between the increased emission of greenhouse gases like carbon dioxide (Co2) from the combustion of fossil fuels and the acceleration of climate change and global warming. Research has demonstrated that the building sector is a significant source of atmospheric carbon dioxide (Co2). Construction, building activities, and subpar energy sources have all significantly increased atmospheric CO2. The proposed research set out to measure how well AI in Green Building Construction (AI-GBC) might reduce carbon emissions and utility bills. Artificial intelligence uses SVM and GA to reduce energy use and carbon dioxide emissions. Several statistical metrics, such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Root Mean Squared Log Error (RMSLE), are used to evaluate the AI-GBC's precision. Both Machine Learning (ML) models yielded positive results, with prediction accuracies above 95%. Regarding predicting Co_2, GA models were close to the mark, with an R2 of 0.95. Ninety-six percent will complete a performance analysis, and 97% will conduct a k-fold cross-validation analysis. Cross-validation is used to ensure that the findings of the extended modeling technique are accurate and prevent overfitting.
Green Building , Support Vector Machine , Genetic Programming , Energy Consumption , Artificial Intelligence , Sustainable Environment
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