With advances in Building Information Modeling (BIM), Virtual Reality (VR) and Augmented Reality (AR) technologies have many potential applications in the Architecture, Engineering, and Construction (AEC) industry. However, the AEC industry, relative to other industries, has been slow in adopting AR/VR technologies, partly due to lack of feasibility studies examining the actual cost of implementation versus an increase in profit. The main objectives of this paper are to understand the industry trends in adopting AR/VR technologies and identifying gaps within the industry. The identified gaps can lead to opportunities for developing new tools and finding new use cases. To achieve these goals, two rounds of a survey at two different time periods (a year apart) were conducted. Responses from 158 industry experts and researchers were analyzed to assess the current state, growth, and saving opportunities for AR/VR technologies for the AEC industry. The findings demonstrate that older generations are significantly more confident about the future of AR/VR technologies and they see more benefits in AR/VR utilization. Furthermore, the research results indicate that Residential and commercial sectors have adopted these tools the most, compared to other sectors and institutional and transportation sectors had the highest growth from 2017 to 2018. Industry experts anticipated a solid growth in the use of AR/VR technologies in 5 to 10 years, with the highest expectations towards healthcare. Ultimately, the findings show a significant increase in AR/VR utilization in the AEC industry from 2017 to 2018.

Smart city programs provide a range of technologies that can be applied to solve infrastructure problems associated with ageing infrastructure and increasing demands. The potential for infrastructure and urban improvement remains unrealized, however, due to technical, financial, and social constraints and criticisms that limit the implementation of smart cities concepts for infrastructure management. The discussion presented here provides a review of smart technologies including sensors, crowdsourcing and citizen science, actuators, data transmission, Internet of Things, big data analytics, data visualization, and blockchain, which can be used for infrastructure management. Smart infrastructure programs are reviewed to explore how enabling technologies have been applied across civil engineering domains, including transportation systems, water systems, air quality, energy infrastructure, solid waste management, construction engineering and management, structures, and geotechnical systems. Gaps in the application of smart technologies for infrastructure systems are identified, and we highlight how the civil engineering profession can adopt new roles toward the development of smart cities applications. These roles are: (1) master designer: civil engineers can identify ready applications of enabling technologies to improve the delivery of urban resources and services; (2) steward: civil engineers must account for both the environmental and societal impacts of smart infrastructure applications; (3) innovator and integrator: civil engineers should integrate across diverse sectors and groups of experts to develop smart infrastructure programs; (4) manager of risk: civil engineers should manage existing and growing risks of natural disasters, emergencies, and climate change; they should also manage new vulnerabilities in the privacy and security of individuals and households that are introduced through smart technologies; and (5) leader and decision maker: civil engineers can take a lead in smart infrastructure discussions and policy development.

Construction performance monitoring has been identified as a key component that leads to the success of a construction project. Real-time and frequent monitoring will enable early detection of potential schedule delays and facilitate the communication of progress information accurately and quickly. To facilitate as-built and as-planned data comparison, this paper proposes an automated registration of a video sequence (i.e., a series of image frames) to an as-planned building information model (BIM) in real time. This method discovers the camera poses of image frames in the BIM coordinate system by performing an augmented monocular simultaneous localization and mapping (SLAM) and perspective detecting and matching between the image frames and their corresponding BIM views. The results demonstrate the effectiveness of real-time registration of images with BIMs. The presented method can potentially fully automate past studies that automate progress inference, given visual representation of as-built models aligned with BIM. Moreover, it will facilitate communication on jobsites by associating quality and progress with visuals that are in the BIM coordinate system.

Hazard recognition is vital to achieving effective safety management. Unmanaged or unrecognized hazards on construction sites can lead to unexpected accidents. Recent research has identified cognitive failures among workers as being a principal factor associated with poor hazard recognition levels. Therefore, understanding cognitive correlates of when individuals recognize hazards versus when they fail to recognize hazards will be useful in combating poor hazard recognition. Such efforts are now possible with recent advances in electroencephalograph (EEG) and eye-tracking technologies. This paper presents a feasibility study that combines EEG and eye tracking in an immersive virtual environment (IVE) to predict when safety hazards will be successfully recognized during hazard recognition efforts using machine learning techniques. Workers wear a virtual reality (VR) head-mounted device (HMD) that is equipped with an eye-tracking sensor. Together with an EEG sensor, brain activities and eye movements are recorded as the workers navigate a simulated virtual construction site and recognize safety hazards. Through an experiment and a feature extraction and selection process, 13 best features out of 306 features from EEG and eye tracking were selected to train a machine learning model. The results show that EEG and eye tracking together can be leveraged to predict when individuals will recognize safety hazards. The developed IVE can be potentially used to first identify hazard types that are correlated with higher arousal and valence. Also, the developed IVE can be potentially used to evaluate the correlation among arousal, valence, and hazard recognition.

One of the main desired qualities of an efficient project management in the construction industry is the ability to visualize a construction project in 2D drawings and execute them accurately and efficiently on-site. However, there are several types of delays that significantly influence project durations, often resulting from misunderstanding and miscommunications among parties (owners, contractors, and other stakeholders). One of the more common delays is change orders that are usually attributed as one of the major reasons behind delays in construction. Making changes after a building enters the construction phase can be very expensive, conflicting, and time-consuming. Such delays may arise due to the lack of communication and coordination among the owner and the contractors regarding the change orders. In most instances, it is highly conflicting to form a mutual agreement when there is a predicted price difference to accept a change order. To minimize the impact of change orders on the design and total cost, an approach that allows communication and interaction with 3D models through advanced visualization tools (i.e., virtual and augmented reality environments) can be effective. To further identify the influence of immersive virtual environments (IVEs) on project management, a systematic approach is proposed through which stakeholders can: (1) visualize and interact with 3D models in one-to-one scaled realistic virtual environments (fully immersive); and (2) visualize the dollar amount changes as the results of change orders. The results of the presented case study show that clients can exercise the ability to make changes virtually well before actual construction begins.