## 2 Background and State-Of-The-Art
Several papers deal with thermal comfort as a factor, such as research on thermal environment variations and their influence on the loss of workers' productivity, along with statistical models developed for the purpose of loss predictions, presented in [19]. This is followed by similar work from the same authors [20], which proposes productivity-thermal environment relationship models for three different types of construction works. A more specific analysis, presented in [21], reports an investigation of construction rebar workers' productivity against heat stress. The modelled dependency explains how heat stress reduces productivity. Another paper [22] explores the relationships between the work motivation of construction workers and their productivity. The study reveals motivation-related factors influencing productivity. In [23], research on the efficiency of construction workers in the carpentry trade is presented. The authors report the results of a two-year study of workers' efficiency with regard to human issues. One of the publications [24] presents an analysis that aims to reveal how unsatisfactory working conditions negatively affect the efficiency of workers, as well as the image of the construction industry. An important trend in research is the development of models that can predict labour efficiency, productivity, or performance using various mathematical methods. Fuzzy logic, presented in [25], is believed to aid in the determination of construction workers' productivity. A regression model that explains the dependencies between identified factors and construction workers' labour productivity in Vietnam is presented in [26]. The study focused on 17 factors influencing productivity (including environmental factors) and was ordered into five groups relevant to construction workers, site operation and management, motivation, working time, working tools, working conditions, health and safety, project information, natural environment, and socio-economic conditions. Another study introduces the application of artificial neural networks for formwork labour productivity prediction [27]. Among the investigated types of models, including backpropagation networks, general regression networks, radial basis function networks, and adaptive neuro-fuzzy networks, the first mentioned appears to outperform the others for modelling construction labour productivity. The concept of labour efficiency modelling based on an ensemble approach and combining several neural networks into one predictive model is presented in [28]. The analysis is provided for steel reinforcement works and is based on data records from construction sites. The research resulted in the development of a model based on a generalised ensemble averaging approach capable of predicting efficiency with satisfactory accuracy within an acceptable range of errors. The application of a broad range of machine learning tools for the analysis and prediction of construction masonry works productivity is presented and discussed in [29]. The authors propose using subjective measures referring to the compatibility of the personality of workers, together with other workers' characteristics, external conditions, and site conditions, as efficiency driving factors. In the course of the research, k-nearest neighbours, deep neural networks, logistic regression, support vector machines, and convolutional neural networks were examined to discover the mapping between driving factors, and masonry works efficiency. Artificial neural networks are also a tool used to build a predictive model in another study [30]. The authors focus on the construction works efficiency prediction problem in Iran as a developing country and a specific type of project--namely, commercial-office complex projects. Nineteen factors ordered into five groups, including individual, managerial, economic, technical, and environmental aspects, were used as efficiency drivers.