Applied Sciences (May 2023)
Construction Work Efficiency Analysis—Application of Probabilistic Approach and Machine Learning for Formworks Assembly
Abstract
Analyses of efficiency are vital for planning and monitoring the duration and costs of construction works, as well as the entire construction project. This paper introduces a combined quantitative (probabilistic) and qualitative (machine learning-based) approach to the problem. The proposed approach covers probabilistic analysis based on fitting a triangular distribution to empirical data, followed by the application of support vector machines (SVM). Following the theoretical assumptions, the paper also presents an application of the proposed approach for formwork assembly as an exemplary construction work. This is based on real-life data, including conditions, characteristics, and features of formwork assembly work recorded on a construction site. As a result of the study, triangular distributions were fitted to data representing efficiencies of formwork assembly for three different types of structural members made of reinforced concrete. The parameters (a—minimum, m—peak and b—maximum values of efficiency measured as square meters of an assembled formwork per hour) of the fitted distributions for the particular real-life data were as follows: for columns a = 0.100, m = 1.450, b = 1.900, for walls a = 0.700, m = 1.995, b = 3.300 and for slabs a = 0.200, m = 2.125, b = 3.200. The obtained distributions allow us to assess the probability of achieving efficiency not less than a certain assumed critical value. The study also developed two SVM models—the first based on so-called C-classification and the second based on ν-classification—capable of recognising with satisfactory accuracy whether the efficiency of formworks assembly works for certain conditions, characteristics, and features of works are above or below median values computed based on previously fitted distributions. The performance of both developed models in terms of proper classification, either for training or testing, was above 80%.
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