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It is essential to create a comfortable and healthy indoor microclimate in order to assure proper conditions for work or recreation as people are spending 80-90% of their life in buildings. Therefore the building performance can help in achieving such sustainable living area, but also one should look into at other sources of temperature and humidity generators such as occupants’ habits. Based on gathered data from observed apartments an insight into preliminary analysis of indoor temperatures and humidity in urban social housing is given. The analysis of the results confirm the set hypotheses that the occupants’ habit significantly influence the indoor temperatures and humidity in urban social housing.

Cost prediction in early stages of construction projects is one of the crucial problems of project planning. Previous research has been directed to process based and data driven model development by using various techniques, e.g. regression analysis, support vector machine (SVM), neural networks etc. According to the research results, neither one of the techniques could be considered the best for all circumstances. Therefore, the research has been redirected towards the hybrid modelling, i.e. combination of different techniques. In this research, for prediction of the target variable – real construction cost of road structures, using available variables: contracted construction cost, contracted construction time and real construction time and cost, hybrid model – combination of SVM technique (data- driven model) and Bromilow time-cost model (TCM) (process-based model) has been used. Five hybrid models have been built for comparison purposes: SVM-Bromilow TCM, LR-Bromilow TCM, RBFNN-Bromilow TCM, MLP-Bromilow TCM and GRNN- Bromilow TCM, combining Bromilow TCM with SVM, LR (linear regression), RBFNN (radial basis neural network), MLP (Multilayer perceptron) and GRNN (general regression neural network), respectively. The best accuracy has been obtained with SVM-Bromilow TCM with mean absolute percentage error (MAPE) 1.01% and coefficient of determination (R2) 97.61%.