Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Building life-span prediction for life cycle assessment and life cycle cost using machine learning: A big data approach
https://orcid.org/0000-0002-7236-3068
https://orcid.org/0000-0003-1784-8983
Abstract Life cycle assessment (LCA) and life cycle cost (LCC) are two primary methods used to assess the environmental and economic feasibility of building construction. An estimation of the building's life span is essential to carrying out these methods. However, given the diverse factors that affect the building's life span, it was estimated typically based on its main structural type. However, different buildings have different life spans. Simply assuming that all buildings with the same structural type follow an identical life span can cause serious estimation errors. In this study, we collected 1,812,700 records describing buildings built and demolished in South Korea, analysed the actual life span of each building, and developed a building life-span prediction model using deep-learning and traditional machine learning. The prediction models examined in this study produced root mean square errors of 3.72–4.6 and the coefficients of determination of 0.932–0.955. Among those models, a deep-learning based prediction model was found the most powerful. As anticipated, the conventional method of determining a building's life expectancy using a discrete set of specific factors and associated assumptions of life span did not yield realistic results. This study demonstrates that an application of deep learning to the LCA and LCC of a building is a promising direction, effectively guiding business planning and critical decision making throughout the construction process.
Highlights Actual life span of building is vastly different from mainframe-based life span. The computational models were trained to predict building life span using big data. The proposed computational approach is superior over the mainframe-based approach.
Building life-span prediction for life cycle assessment and life cycle cost using machine learning: A big data approach
https://orcid.org/0000-0002-7236-3068
https://orcid.org/0000-0003-1784-8983
Abstract Life cycle assessment (LCA) and life cycle cost (LCC) are two primary methods used to assess the environmental and economic feasibility of building construction. An estimation of the building's life span is essential to carrying out these methods. However, given the diverse factors that affect the building's life span, it was estimated typically based on its main structural type. However, different buildings have different life spans. Simply assuming that all buildings with the same structural type follow an identical life span can cause serious estimation errors. In this study, we collected 1,812,700 records describing buildings built and demolished in South Korea, analysed the actual life span of each building, and developed a building life-span prediction model using deep-learning and traditional machine learning. The prediction models examined in this study produced root mean square errors of 3.72–4.6 and the coefficients of determination of 0.932–0.955. Among those models, a deep-learning based prediction model was found the most powerful. As anticipated, the conventional method of determining a building's life expectancy using a discrete set of specific factors and associated assumptions of life span did not yield realistic results. This study demonstrates that an application of deep learning to the LCA and LCC of a building is a promising direction, effectively guiding business planning and critical decision making throughout the construction process.
Highlights Actual life span of building is vastly different from mainframe-based life span. The computational models were trained to predict building life span using big data. The proposed computational approach is superior over the mainframe-based approach.
Building life-span prediction for life cycle assessment and life cycle cost using machine learning: A big data approach
https://orcid.org/0000-0002-7236-3068
https://orcid.org/0000-0003-1784-8983
Abstract Life cycle assessment (LCA) and life cycle cost (LCC) are two primary methods used to assess the environmental and economic feasibility of building construction. An estimation of the building's life span is essential to carrying out these methods. However, given the diverse factors that affect the building's life span, it was estimated typically based on its main structural type. However, different buildings have different life spans. Simply assuming that all buildings with the same structural type follow an identical life span can cause serious estimation errors. In this study, we collected 1,812,700 records describing buildings built and demolished in South Korea, analysed the actual life span of each building, and developed a building life-span prediction model using deep-learning and traditional machine learning. The prediction models examined in this study produced root mean square errors of 3.72–4.6 and the coefficients of determination of 0.932–0.955. Among those models, a deep-learning based prediction model was found the most powerful. As anticipated, the conventional method of determining a building's life expectancy using a discrete set of specific factors and associated assumptions of life span did not yield realistic results. This study demonstrates that an application of deep learning to the LCA and LCC of a building is a promising direction, effectively guiding business planning and critical decision making throughout the construction process.
Highlights Actual life span of building is vastly different from mainframe-based life span. The computational models were trained to predict building life span using big data. The proposed computational approach is superior over the mainframe-based approach.
Building life-span prediction for life cycle assessment and life cycle cost using machine learning: A big data approach
Ji, Sukwon (Autor:in) / Lee, Bumho (Autor:in) / Yi, Mun Yong (Autor:in)
Building and Environment ; 205
17.08.2021
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Life Cycle Assessment for Modular Roof Systems of Large-Span Building
| Springer Verlag | 2020