Enhanced machine learning classification accuracy for scaffolding safety using increased features

Despite regular safety inspections and safety planning, numerous fatal accidents related to scaffold take place at construction sites. Current practices relying on human inspection are not only impractical but also ineffective due to dynamic construction activities. Furthermore, a scaffold typically consists of multiple bays and stories, which leads to complexity in its structural behaviors with various modes of failure. However, previous studies considered only a limited number of failure cases for a simple one-bay scaffold while exploring machine-learning (ML) approaches to predict safety conditions. Thus, the authors have proposed an approach to monitor a complicated scaffolding structure in real time. This study explored a method of classifying scaffolding failure cases and reliably predicting safety conditions based on strain data sets from scaffolding columns. Furthermore, the research team successfully enhanced the predicting accuracy of ML classification by the proposed self-multiplication method to increase the number of features such as strain data sets. Implementation of the proposed methodology is expected to enable the monitoring of a large, complex system at construction sites.