(A) study on big data-based building life-span prediction method for life cycle assessment and life cycle cost using machine learning

Abstract

Various methods are used to make major decisions in the construction industry. Among them, life cycle assessment (LCA) method and life cycle cost (LCC) analysis are mainly used for environmental and economic evaluation of buildings, construction methods, and materials. To perform these methods properly, it is essential to estimate the realistic lifespan of the building. However, since it is practically impossible to estimate the lifespan by considering various factors affecting the lifespan of a building, most LCA and LCC studies have assumed the building lifespan uniformly according to the major structural types of buildings. However, the lifespan of buildings is in fact very variable, and a simple assumption that all buildings of the same structural type follow the same lifespan may lead to completely erroneous analysis of the results of LCA and LCC studies. In the first study, 1,812,700 records of buildings constructed and demolished in South Korea were collected, the actual lifespan of each building was analyzed, and a building lifespan prediction model using deep learning and traditional machine learning was developed. As a result, in the case of a reinforced concrete building, which is generally known to have a durability of about 50 years, the actual average building lifespan was only 22.8 years. In addition, the prediction model investigated in this study showed root mean square error (RMSE) of 3.72~4.6 and coefficient of determination of 0.932~0.955. Among them, the deep learning-based prediction model was found to have the best performance. Therefore, these results mean that realistic LCA and LCC analysis results cannot be obtained by simply estimating the lifespan of a building in the existing method of determining the life cycle of a building with several specific factors. The second study empirically demonstrates the effect of the application of big data-based realistically predicted lifespan on LCA and LCC analysis via LCA and LCC analysis on waterproofing methods of building models and actual buildings. In the LCA and LCC analysis of the waterproofing method of the architectural model, the application of the building lifespan from 5 to 39 years decreased carbon emissions by a maximum of 78% to a minimum of 24% in all phases relative to the result when a building lifespan of 50 years was assumed. During the maintenance phase, the maximum and minimum reductions were 100% and 31 %, respectively. As a result of the LCA analysis of the waterproofing method of 17 real building cases, the accuracy of the LCA analysis results revealed a tendency to be irregular as the predicted lifespan percent error increased. Moreover, the percent error of the LCA analysis results of the buildings with a predicted life percent error of 6% or less was 0%. Evidently, depending on the research purpose, a predictive building lifespan model that can guarantee a certain percent error or less is required for accurate LCA results. Based on the above research results, it is evident that the building life prediction method that applies deep learning based on big data is the most realistic building life prediction method thus far, and it is necessary to apply an accurately predicted lifespan to secure the accuracy of building-related LCA and LCC analysis. Therefore, this study demonstrates that a big data-based building lifespan prediction method is an essential and promising direction for effectively guiding business planning and critical decision-making throughout the construction process.