Prediction of self-compacting concrete elastic modulus using two symbolic regression techniques

yesThis paper introduces a novel symbolic regression approach, namely biogeographical-based programming (BBP), for the prediction of elastic modulus of self-compacting concrete (SCC). The BBP model was constructed directly from a comprehensive dataset of experimental results of SCC available in the literature. For comparison purposes, another new symbolic regression model, namely artificial bee colony programming (ABCP), was also developed. Furthermore, several available formulas for predicting the elastic modulus of SCC were assessed using the collected database. The results show that the proposed BBP model provides slightly closer results to experiments than ABCP model and existing available formulas. A sensitivity analysis of BBP parameters also shows that the prediction by BBP model improves with the increase of habitat size, colony size and maximum tree depth. In addition, among all considered empirical and design code equations, Leemann and Hoffmann and ACI 318-08’s equations exhibit a reasonable performance but Persson and Felekoglu et al.’s equations are highly inaccurate for the prediction of SCC elastic modulus

Efficiency of VR-Based Safety Training for Construction Equipment: Hazard Recognition in Heavy Machinery Operations

Machinery operations on construction sites result in many serious injuries and fatalities. Practical training in a virtual environment is the key to improving the safety performance of machinery operators on construction sites. However, there is limited research focusing on factors responsible for the efficiency of virtual training in increasing hazard identification ability among novice trainees. This study analyzes the efficiency of virtual safety training with head-mounted VR displays against flat screen displays among novice operators. A cohort of tower crane operation trainees was subjected to multiple simulations in a virtual towards this aim. During the simulations, feedback was collected using a joystick to record the accuracy of hazard identification while a post-simulation questionnaire was used to collect responses regarding factors responsible for effective virtual training. Questionnaire responses were analyzed using interval type-2 fuzzy analytical hierarchical process to interpret the effect of display types on training efficiency while joystick response times were statistically analyzed to understand the effect of display types on the accuracy of identification across different types of safety hazards. It was observed that VR headsets increase the efficiency of virtual safety training by providing greater immersion, realism and depth perception while increasing the accuracy of hazard identification for critical hazards such as electric cables