Agent-based evacuation modeling represents an effective tool for making predictions about evacuation aspects of buildings such as evacuation times, congestions, and maximum safe building capacity. Collection of real behavioral data for calibrating agent-based evacuation models is time-consuming, costly, and completely impossible in the case of buildings in the design phase, where predictions about evacuation behavior are especially needed. In recent years evacuation experiments conducted in virtual reality (VR) have been frequently proposed in the literature as an effective tool for collecting data about human behavior. However, empirical studies which would assess validity of VR-based data for such purposes are still rare and considerably lacking in the agent-based evacuation modeling domain. This study explores opportunities that the VR behavioral data may bring for refining outputs of agent evacuation models. To this end, this study employed multiple input settings of agent-based evacuation models (ABEMs), including those based on the data gathered from the VR evacuation experiment that mapped out evacuation behaviors of individuals within the building. Calibration and evaluation of models was based on empirical data gathered from an original evacuation exercise conducted in a real building (N = 35) and its virtual twin (N = 38). This study found that the resulting predictions of single agent models using data collected in the VR environment after proposed corrections have the potential to better predict real-world evacuation behavior while offering desirable variance in the data outputs necessary for practical applications.

The aim of this work was to verify the behavior of hybrid hardwood glue-laminated beams and the possibility of replacing Norway spruce (Picea abies) construction elements, especially in roof and ceiling structures. The samples were six lamellae homogeneous beech (Fagus sylvatica) beams and six-lamellae hybrid beech-poplar (Fagus sylvatica–Poplar spp.) beams; each beam had a cross-section of 60 × 120 mm and was glued with polyurethane adhesive. The samples were loaded using destructive four-point bending tests according to EN 408; the obtained bending strength and modulus of elasticity were statistically evaluated and compared to each other in both types of samples. The results showed that the examined properties of the hybrid beams (with a 16% weight reduction) are comparable to the properties of homogeneous beech glue-laminated beams. Based on the obtained data, the timber elements that are currently used can be successfully replaced by hardwood glue-laminated elements. Based on their higher load-bearing capacity, the cross-section depth can be reduced compared to a larger cross-section depth in spruce beams; this means that hardwood could be suitable in building renovations.

Machine learning methods and agent-based models enable the optimization of the operation of high-capacity facilities. In this paper, we propose a method for automatically extracting and cleaning pedestrian traffic detector data for subsequent calibration of the ingress pedestrian model. The data was obtained from the waiting room traffic of a vaccination center. Walking speed distribution, the number of stops, the distribution of waiting times, and the locations of waiting points were extracted. Of the 9 machine learning algorithms, the random forest model achieved the highest accuracy in classifying valid data and noise. The proposed microscopic calibration allows for more accurate capacity assessment testing, procedural changes testing, and geometric modifications testing in parts of the facility adjacent to the calibrated parts. The results show that the proposed method achieves state-of-the-art performance on a violent-flows dataset. The proposed method has the potential to significantly improve the accuracy and efficiency of input model predictions and optimize the operation of high-capacity facilities.