# Calculation of the Population of Construction Scaffoldings Using Neural Networks
## 1 Introduction  
Building scaffoldings are temporary structures commonly used in the construction industry. According to [1], they can be used, to provide:  
* 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 particularly dangerous [4,5]. A precise determination of the number of building scaffoldings in use is a very time-consuming and costly task, and in the case of a large geographical area of analysis, it is nearly impossible to perform. An in-depth study of the literature 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 and fill the identified research gap. Neural networks were used to solve the problem. Neural networks are commonly used for the classification, prediction and analysis of data sets [6,7]. The operation of a neural network is based on the functioning of a natural biological network of nerve cells [8, 9, 10]. Artificial neural networks are tools that are willingly used in various fields of science, including construction engineering [11]. One example of their practical implementation can be found in paper [12], in which a model of a neural network was proposed. This model enabled the compressive strength of a cement composite containing ground granular blast furnace slag (GGBFS) to be predicted. The developed model allowed for important strength parameters of the tested composite to be accurately estimated. The usefulness of neural networks for predicting the parameters of building materials can also be found in paper [13]. In this case, a neural network model was used to predict the strength of rubberized concrete containing silica dust and zeolite. In turn, in study [14], a model of a neural network was proposed in order to determine the effect of freezing and thawing on the occurrence of damage to fiber-cement boards. Neural networks can also be used in the area of managing construction projects, e.g., for predicting time and cost claims in construction projects [15], for modeling the work process of construction teams [16,17] and construction equipment [18], and for estimating construction costs [19,20]. One example of such an implementation is a paper [21] in which the proposed neural network model enabled general construction costs to be estimated. In the above studies, a database containing 143 cases of completed construction projects was used to build the model. The developed model enabled the general building cost index to be predicted with a high degree of accuracy. Other research in which artificial neural networks were used include a study [22] in which the authors focused on the analysis of the efficiency of earthmoving machines. The neural network presented allowed for the prediction of efficiency, unit losses, and unit costs on the basis of parameters, such as the number of excavators and trucks, and also the capacity of both the truck and excavator buckets. In analyses related to occupational safety, neural networks can be used to predict and evaluate the behavior of employees during the execution of a building project [23,24], to predict the level of risk of work carried out at heights [25], and to identify risk factors [26].