Enhanced Machine Learning Classification Accuracy for Scaffolding Safety using Increased Features

Document Type

Article

Publication Date

11-30-2018

Publication Title

Journal of Construction Engineering and Management

Volume

145

Issue

2

First page number:

1

Last page number:

9

Abstract

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.

Keywords

Construction site; Machine learning; Safety; Support vector machine (SVM)

Disciplines

Civil and Environmental Engineering

Language

English

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