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The construction industry is witnessing an increasing adoption of virtual reality (VR) technology for training and education purposes. Given this trend, it becomes essential to critically investigate the impact it has on learners, especially when compared to traditional paper-based learning method. In this paper, the authors developed a close-to-reality virtual system using the Unity3D game engine. Participants engage in learning safety protocols, operating a virtual crane, and assembling a steel structure within this environment. Corresponding paper-based instructional materials were also developed for comparison. The study involved 16 participants who were randomly assigned to either the VR training or the traditional paper-based training, their brainwaves data were recorded through electroencephalography (EEG) headset during the training progress to assess their emotions. Results show that an individual is most likely to experience exciting emotions when they are training in the VR system compared with the traditional training method. The correlation with actual safety performance, however, remains unclear and requires further investigation. 

A building must meet various requirements during the design and construction process to ensure the benefits of stakeholders and well-being of construction workers and occupants. These requirements may come from different functional areas such as structure, electricity, and fire protection, and focus on different building materials, such as concrete, steel, and glass. They may overlap or even conflict with each other. In order to identify the sources and focus of building code requirements and further clarify the relationships between them, this paper presents some recent results on using graphic convolutional networks (GCN) to classify building code requirements. One hundred building code provisions were randomly selected from the International Building Code 2015 and labeled into six categories manually, and a cutting-edge GCN model was trained to classify them. Experimental results showed an average precision of 91.67% and an average recall of 94.44% when 10% of the data was used for testing, which is comparable to the 84.30% precision and 97.30% recall of the state-of-the-art machine learning-based approaches applied on construction document classification. The effect of the size of training data on testing accuracy was also discussed in this paper. 

Locational information in building information models (BIMs) is essential for providing geographical context to a project as well as the relative spatial context to each and every individual building element that the project is composed of. From a construction automation perspective, one main application is the use of locational data as input for robot-assisted operations in the construction of building components. Nevertheless, obtaining locational information is a time-intensive, laborious, and error-susceptible process. To address this gap, the authors proposed a logic-based approach for examining BIMs and retrieving the positional data of building elements. A duplex apartment model was used to test the proposed method, which achieved 100% precision and 92.31% recall compared to a gold standard. Building elements, such as columns and beams, from the model were successfully extracted. Results show that logic representation and reasoning can be effectively used for extracting locational information in the context of construction automation. 

Challenged by the prevalent workforce shortage, the construction industry is picking up interest in using robotic arms in construction operations, especially in the context of modular construction and prefabrication. However, the lack of systematic investigations into integrating robotic arms with mobile systems to enhance mobility and operational range has been identified as one main research gap. Stationary robotic arms have inherent limitations in their range, making mobility a critical need. To address that issue, in this paper, the authors proposed a mobile construction robotic system to facilitate their use in the automation of timber frame assembly operation. The authors simulated the system to assess the interactions and coordination among its various components, and to identify potential areas for improvement. This study showcased the effectiveness of the new system design in improving the timber construction automation process and reveals its potential for further exploration in the realm of mobile construction robotics. 

One main challenge in the full automation of building code compliance checking is in the extraction and transformation of building code requirements into computable representations. Semantic rule-based approach has been taken mainly due to its expected better performance than machine learning-based approach on this particular task. With the recent advancement in deep learning AI, particularly the launch of ChatGPT by OpenAI, there is a potential for this landscape to be shifted given the highly regarded capabilities of ChatGPT in processing (i.e., understanding and generating) natural language texts and computer codes. In this paper, the author preliminarily explored the use of ChatGPT in converting (i.e., extracting and transforming) building code requirements into computer codes, and compared it with the results from cutting-edge semantic rule-based approach. It was found that comparing to the semantic rule-based approach, the conversion results from ChatGPT still has limitations, but there is a great potential for it to help speed up the implementation and scale-up of automated building code compliance checking systems. 

Traditional manual building code compliance checking is costly, time-consuming, and human error-prone. With the adoption of Building Information Modeling (BIM), automation in such a checking process becomes more feasible. However, existing methods still face limited automation when applied to different building codes. To address that, in this paper, the authors proposed a new framework that requires minimal input from users and strives for full automation, namely, the Invariant signature, logic reasoning, and Semantic Natural language processing (NLP)-based Automated building Code compliance Checking (I-SNACC) framework. The authors developed an automated building code compliance checking (ACC) prototype system under this framework and tested it on Chapter 10 of the International Building Codes 2015 (IBC 2015). The system was tested on two real projects and achieved 95.2% precision and 100% recall in non-compliance detection. The experiment showed that the framework is promising in building code compliance checking. Compared to the state-of-the-art methods, the new framework increases the degree of automation and saves manual efforts for finding non-compliance cases.