Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Cost Prediction of Indian Green Building Projects Using Machine Learning Framework
As the world deals with environmental challenges such as resource depletion and climate change, the demand for eco-friendly and sustainable construction practices has risen significantly. In this context, the concept of "green building construction" has emerged as a game-changer, prioritizing occupant well-being, resource conservation, and energy efficiency. However, due to the relatively new nature of green building construction, stakeholders often face difficulties in accurately estimating project costs. This challenge stems from the inherent differences between green and conventional building projects, as green buildings incorporate innovative technologies and design elements to achieve environmental and social benefits, requiring a more complex approach to cost estimation than traditional construction methods. One of the significant challenges in green building projects is the potential for inaccurate cost and schedule estimations, leading to cost overruns and delays, which ultimately impact project performance. To address this issue, this research focuses on utilizing historical data from government green building projects in India to train machine learning models, specifically random forests for predicting project costs based on various project parameters. Those parameters include project type, built-up area, energy use, water use, green building rating, estimated cost, and final cost. Through this research, the construction industry can benefit from improved cost estimation strategies for green building projects, enabling better project planning, risk management, and resource utilization. The study holds significant importance for the construction industry and sustainability efforts, as it aims to provide insights into the economic implications of green building practices.
Cost Prediction of Indian Green Building Projects Using Machine Learning Framework
As the world deals with environmental challenges such as resource depletion and climate change, the demand for eco-friendly and sustainable construction practices has risen significantly. In this context, the concept of "green building construction" has emerged as a game-changer, prioritizing occupant well-being, resource conservation, and energy efficiency. However, due to the relatively new nature of green building construction, stakeholders often face difficulties in accurately estimating project costs. This challenge stems from the inherent differences between green and conventional building projects, as green buildings incorporate innovative technologies and design elements to achieve environmental and social benefits, requiring a more complex approach to cost estimation than traditional construction methods. One of the significant challenges in green building projects is the potential for inaccurate cost and schedule estimations, leading to cost overruns and delays, which ultimately impact project performance. To address this issue, this research focuses on utilizing historical data from government green building projects in India to train machine learning models, specifically random forests for predicting project costs based on various project parameters. Those parameters include project type, built-up area, energy use, water use, green building rating, estimated cost, and final cost. Through this research, the construction industry can benefit from improved cost estimation strategies for green building projects, enabling better project planning, risk management, and resource utilization. The study holds significant importance for the construction industry and sustainability efforts, as it aims to provide insights into the economic implications of green building practices.
Cost Prediction of Indian Green Building Projects Using Machine Learning Framework
Lecture Notes in Civil Engineering
Nehdi, Moncef (Herausgeber:in) / Rahman, Rahimi A. (Herausgeber:in) / Davis, Robin P. (Herausgeber:in) / Antony, Jiji (Herausgeber:in) / Kavitha, P. E. (Herausgeber:in) / Jawahar Saud, S. (Herausgeber:in) / Shende, Vaibhav D. (Autor:in) / Vyas, Gayatri S. (Autor:in)
International Conference on Structural Engineering and Construction Management ; 2024 ; Angamaly, India
29.12.2024
10 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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