Purpose: The construction site operates under a hazardous environment that requires a high level of understanding in building systems to minimise accidents. However, the current building education generally adopts paper-based learning approaches that lack hands-on experiences. Furthermore, to achieve Industrial Revolution 4.0 in line with any unforeseen pandemic, the most optimum solution is to transition from physical to technological-based building education. This paper aims to address the problems by proposing a game-based virtual reality (GBVR) for building utility inspection training.Design/methodology/approach: The feasibility of the GBVR for building the utility inspection training approach is validated on a sample of undergraduate engineering students through user experience (survey) and performance-based comparisons against traditional paper-based training method.Findings: The results show that the developed GBVR training has higher system usability in terms of visual output and knowledge retention than paper-based training due to visualisation technologies. The GBVR training method has also higher user-friendliness because of the higher motivational and engagement factors through the adoption of virtual reality and game-based learning.Research limitations/implications: GBVR training required a longer training duration and achieved a lower performance score (effectiveness) but can be improved by transitioning into hands-on tasks. This study has the potentials to be extended to vocational training platforms for competency development in the construction workforce by using cutting-edge extended reality technologies.Originality/value: This paper portrays the benefits of integrating virtual reality technology in building education to overcome the low practicality and engagement of paper-based training.

While VR-based training has been proven to improve learning effectiveness over conventional methods, there is a lack of study on its learning effectiveness due to the implementation of training modes. This study aims to investigate the learning effectiveness of engineering students under different training modes in VR-based construction design training. Three VR scenarios with varying degrees of immersiveness were developed based on Dale’s cone of learning experience, including (1) Audio-visual based training, (2) Interactive-based training, and (3) Contrived hands-on experience training. Sixteen students with varying backgrounds participated in this study. The results posit a positive correlation between learning effectiveness and the degree of immersiveness, with a mean score of 77.33%, 81.33%, and 82.67% in each training scenario, respectively. Participants with lower academic performance tend to perform significantly better in audio-visual and interactive-based training. Meanwhile, participants with experience in gaming tend to outperform the latter group. Results also showed that participants with less experience in gaming benefited the most from hands-on VR training. The findings suggest that the general audience retained the most information via hands-on VR training; however, training scenarios should be contextualized toward the targeted group to maximize learning effectiveness.

In recent years, mixed reality (MR) technology has gained popularity in construction management due to its real-time visualisation capability to facilitate on-site decision-making tasks. The semantic segmentation of building components provides an attractive solution towards digital construction monitoring, reducing workloads through automation techniques. Nevertheless, data shortages remain an issue in maximizing the performance potential of deep learning segmentation methods. The primary aim of this study is to address this issue through synthetic data generation using Building Information Modelling (BIM) models. This study presents a point-cloud-based deep learning segmentation approach to a 3D light steel framing (LSF) system through synthetic BIM models and as-built data captured using MR headsets. A standardisation workflow between BIM and MR models was introduced to enable seamless data exchange across both domains. A total of five different experiments were set up to identify the benefits of synthetic BIM data in supplementing actual as-built data for model training. The results showed that the average testing accuracy using solely as-built data stood at 82.88%. Meanwhile, the introduction of synthetic BIM data into the training dataset led to an improved testing accuracy of 86.15%. A hybrid dataset also enabled the model to segment both the BIM and as-built data captured using an MR headset at an average accuracy of 79.55%. These findings indicate that synthetic BIM data have the potential to supplement actual data, reducing the costs associated with data acquisition. In addition, this study demonstrates that deep learning has the potential to automate construction monitoring tasks, aiding in the digitization of the construction industry.