Labor productivity in building construction has long been a focused research topic due to the high contribution of labor cost in the building total costs. This study, among a few studies that used scaled data that were collected directly from measuring equipment and onsite activities, utilized neural networks to model the productivity of two main construction tasks and influencing factors. The neural networks show their ability to predict the behaviors of labor productivity of the formwork and rebar tasks in a test case of a high-rise building. A multilayer perceptron that had two layers and used sigmoid as its activation function provided the best effectiveness in predicting the relations among data. Among eleven independent factors, weather (e.g., temperature, precipitation, sun) generally played the most important role while crew factors were distributed in the mid of the ranking and the site factor (working floor height) played a mild role. This study confirms the robustness of neural networks in productivity research problems and the importance of working environments to labor productivity in building construction. Managerial implications, including careful environmental factors and crew structure deliberation, evolved from the study when labor productivity improvement is considered.

An advanced evaluation technique, helpful in the fire resistance assessment of a simple steel structure exposed to fire is presented and discussed in detail on the example of an unrestrained and uniformly heated steel beam. The proposed design methodology deals with the generalised probability-based approach in which the most probable failure point is formally identified. The random nature of all variables considered in the detailed analysis is taken into account. The critical temperature of the steel from which the considered beam is made of is accepted here as the authoritative safety measure. This temperature value is associated with the fire resistance limit state defined for the maximum acceptable value of failure probability. When forecasting the failure probability, not only the risk of a potential fire being initiated but also not being effectively extinguished is included in the calculation. Various levels of the target failure probability may be assumed in such the analysis, depending on the selected reliability class. They are specified in general by setting an appropriate value of the required reliability index β fire req. In the presented design algorithm no representative values of the considered random variables are specified. The critical temperature estimates obtained from these calculations are always less restrictive in comparison with the corresponding solutions computed after applying the conventional standard procedure.