Abstract
A traditional structural analysis of scaffolding structures requires loading conditions that are only possible during design, but not in operation. Thus, this study proposes a method that can be used during operation to make an automated safety prediction for scaffolds. It implements a divide-and-conquer technique with deep learning. As a test scaffolding, a four-bay, three-story scaffold model was used. Analysis of the model led to 1411 unique safety cases for the model. To apply deep learning, a test simulation generated 1,540,000 datasets for pre-training, and an additional 141,100 datasets for testing purposes. The cases were then sub-divided into 18 categories based on failure modes at both global and local levels, along with a combination of member failures. Accordingly, the divide-and-conquer technique was applied to the 18 categories, each of which were pre-trained by a neural network. For the test datasets, the overall accuracy was 99%. The prediction model showed that 82.78% of the 1411 safety cases showed 100% accuracy for the test datasets, which contributed to the high accuracy. In addition, the higher values of precision, recall, and F1 score for the majority of the safety cases indicate good performance of the model, and a significant improvement compared with past research conducted on simpler cases. Specifically, the method demonstrated improved performance with respect to accuracy and the number of classifications. Thus, the results suggest that the methodology could be reliably applied for the safety assessment of scaffolding systems that are more complex than systems tested in past studies. Furthermore, the implemented methodology can easily be replicated for other classification problems.
Highlights
IntroductionScaffolds possess various safety hazards, such as workers falling from a height, being struck by equipment or materials, or being electrocuted, as well as the scaffold collapsing [1]
To alleviate the problem associated with a large number of based on a limited number of features, divide-and-conquer technique technique is applied.is This technique classifications based on a limited numberaof features, a divide-and-conquer applied
Duringofthe pre-training of the neural network (NN) models,1411 it was observed that highly imbalanced trainingTotal datanumber sizes resulted in a large number of misclassifications
Summary
Scaffolds possess various safety hazards, such as workers falling from a height, being struck by equipment or materials, or being electrocuted, as well as the scaffold collapsing [1] These potential hazards endanger the lives of 65% of all construction laborers working on scaffolds in the United States [2]. Despite regular safety inspections and safety training at construction sites, many laborers are exposed to fatal accidents every year [2] To reduce this safety problem, researchers have investigated various methods to improve worker safety in the early stages of construction through the automation of scaffolding structure design [3,4], as well as planning [5,6] and the application of building information modeling [5,7,8,9]. These studies have advanced steps forward in automated safety planning and design, they are not applicable during the construction stage
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