This study examines the degree to which construction sector employees perceive that safety is important in their organizations/sites and how job satisfaction affects these perceptions when age is introduced as a moderator variable. Two-way analysis of variance demonstrated that job satisfaction has a strong effect on perceived management commitment to work safety and that this relationship was moderated by respondents' age. Job satisfaction was associated with perceived accident rate and safety inspection frequency, but the proposed role of age in this linkage was not confirmed. Consequently, the findings indicated that by increasing the level of job satisfaction, perceptions of these safety climate aspects proved to be more positive. The conclusion is that these relationships could further lead to a lower percentage of accidents and injuries in the workplace and better health among employees. A significant relationship between job satisfaction, age and perceived co-workers' commitment to work safety was not found.

PurposeIntegrating the aspects of sustainability into facilities design has become a designers’ challenge, and the early design phase is seen as the most important in implementing sustainability into facilities design. Therefore, this paper aims to analyze the factors that influence sustainability assessment of preliminary design of facilities and predicts sustainability assessment depending on those factors. Design/methodology/approachData were collected by survey questionnaire distributed to project managers using a six-point Likert scale. Obtained data were modeled with general regression neural network (GRNN) using DTREG software. In total, 27 factors were chosen for determining the most accurate predictive model, and their importance was computed. FindingsThe six most important factors for sustainability assessment of facilities design are: work experience, work on several outline design proposals, resolving issues between stakeholders, prioritization of participants in the design phase, procurement management and defining projects’ program and goals. The predictive model that was used for prediction of the sustainability assessment was shown to be highly accurate, with MAPE (mean absolute percentage error) amounting to 2.58 per cent. Practical implicationsUsing the same approach, assessment of every other factor for the preliminary design can be predicted and the factors that are most influential to its sustainability can be obtained. Originality/valueThe paper supports the sustainability improvement of the preliminary design of future facilities’ projects, as well as support during the decision-making process.

A model for early construction cost prediction is useful for all construction project participants. This paper presents a combination of process-based and data-driven model for construction cost prediction in early project phases. Bromilow’s “time-cost” model is used as process-based model and general regression neural network (GRNN) as data-driven model. GRNN gave the most accurate prediction among three prediction models using neural networks which were applied, with the mean absolute percentage error (MAPE) of about 0.73% and the coefficient of determination R2 of 99.55%. The correlation coefficient between the predicted and the actual values is 0.998. The model is designed as an integral part of the cost predicting system (CPS), whose role is to estimate project costs in the early stages. The obtained results are used as Cost Model (CM) input being both part of the Decision Support System (DSS) and part of the wider Building Management Information System (BMIS). The model can be useful for all project participants to predict construction cost in early project stage, especially in the phases of bidding and contracting when many factors, which can determine the construction project implementation, are yet unknown.

The energy consumption of buildings can directly affect the buildings users' budget and their satisfaction with the investment in the property. Vice versa, buildings energy consumption has a social implication on the buildings' users. Additionally, building energy consumption is connected with the buildings influence on the environment due to the CO2 emission. Thus, having a model for energy usage prediction is of crucial importance. Data for sixty real-built buildings were collected. Using support vector machine, a model was developed for prediction of energy consumption. The mean absolute percentage error of the model is 2,44% and the coefficient of determination of the model R 2 is 94,72%, which expresses the global fit of the model. The model is useful for all participants in the designs of buildings, particularly in the early phases. It can serve as a decision support model during the process of selection of optimal building design.

This paper investigates the construction managers’ perception of sustainability contributing factors in construction. Respondents worked at 102 construction companies in the R. Macedonia and 102 in the Federation of Bosnia and Herzegovina (B&H). Using support vector machine, prediction models were designed. For classification of the target variable “familiarity with sustainable construction industry”, 25 predictors were chosen. Depending on the validation method used, the accuracy of the B&H model was from 93.14% to 100%, and of the Macedonian model – from 91.18% to 94.12%. General conclusion is that construction managers should increase their knowledge about sustainability contributing factors.