Abstract
Building scaffoldings are temporary structures that are commonly used in the construction industry. A precise determination of the number of building scaffoldings in use is a very complex task. The literature survey showed that there is a lack of scientific studies concerning the estimation of the scaffolding population in the construction industry. This observation gave rise to the need to undertake such research, the aim of which was to develop a model of a neural network set which would in turn enable the number of used building scaffoldings to be estimated. In order to carry out such a research task, an original research methodology was developed, which used the results of empirical research that involved the counting of construction scaffoldings used in selected representative areas of the studied regions of Poland (the research was carried out in the period from 2016 to 2018), and also data taken from a publication of the Central Statistical Office on socio-economic indicators that characterize the analyzed regions (data from 2010 to 2018). The main element of the developed methodology is a set of five MLP neural networks, which was used to predict the number of used construction scaffoldings. The analysis of the sensitivity of the quantitative and qualitative variables of the model showed that they have a significant impact on the final result generated by the networks. The obtained results of the research and analyses showed the size of the population of building scaffoldings used in individual regions of Poland, and also the seasonality of their occurrence. The knowledge obtained on this basis can be used, among others, in economic analyses related to the use of construction scaffolding, as well as in the process of managing occupational safety on scaffoldings. The most important scientific aspect of the article concerns the development of an original methodology for estimating the population of building scaffoldings.
Highlights
Building scaffoldings are temporary structures commonly used in the construction industry
Every artificial neural network consists of a network of interconnected elements, each of which has a certain number of inputs and one output
The adoption of five neural networks for further calculations was justified by the fact that adoption of five neural networks for further calculations was justified by the fact that these these networks were characterized by a high level of quality, as expressed by the networks were characterized by a high level of quality, as expressed by the correlation coefficient and the low value of the root-mean-square error (RMSE)
Summary
Building scaffoldings are temporary structures commonly used in the construction industry. Safety during work when erecting, maintaining, repairing or demolishing buildings and other structures; Easy access to elements and parts of construction objects located in hard-to-reach places—e.g., at heights; Support for the elements of a structure being erected during their construction; and Adequate vertical communication (e.g., temporary staircases) and horizontal communication (e.g., temporary footbridges) With such a wide area of use for scaffoldings in the construction industry, the large number of construction solutions available on the market, and the fact that the work carried out with their use are conducted at height and in changing weather conditions mean that these works are burdened with a high occupational risk [2,3] and are considered dangerous [4,5].
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