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.

The architecture, engineering, and construction industry has increased the adoption of virtual reality (VR) tools and applications in recent years. VR is an extremely versatile tool for creating an environment through data entry. This paper addresses design-change issues during the preliminary design phase and leverages end-user satisfaction through the use of collaborative functions in a VR environment for material cost estimation. In this regard, the research proposes a VR-based framework for interior finish material selection, incorporating both visual aesthetics and associated cost impacts. A systematic approach for converting building information models into interactive VR models is presented. The output of this system is validated in a case study where through an interactive and immersive environment, stakeholders can interact with the three-dimensional model and visually receive an updated cost estimate in real time. The proposed approach can effectively assist different stakeholders in selecting their desired interior finish materials and change orders. The basis of the developed model benefits and practical applications such as end-user design involvement, end-user satisfaction, and reduction of design changes are further discussed throughout the paper.

Effective shared autonomy requires a clear understanding of driver’s behavior, which is governed by multiple psychophysiological and environmental variables. Disentangling this intricate web of interactions requires understanding the driver’s state and behaviors in different real-world scenarios, longitudinally. Naturalistic Driving Studies (NDS) have shown to be an effective approach to understanding the driver’s state and behavior in real-world scenarios. However, due to the lack of technological and computing capabilities, former NDS only focused on vision-based approaches, ignoring important psychophysiological factors such as cognition and emotion. The main objective of this paper is to introduce HARMONY, a human-centered multimodal naturalistic driving study, where driver’s behaviors and states are monitored through (1) in-cabin and outside video streams (2) physiological signals including driver’s heart rate and hand acceleration (IMU data), (3) ambient noise, light, and the vehicle’s GPS location, and (4) music logs, including song features such as tempo. HARMONY is the first study that collects long-term naturalistic facial, physiological, and environmental data simultaneously. This paper summarizes HARMONY’s goals, framework design, data collection and analysis, and the on-going and future research efforts. Through a presented case study, we first demonstrate the importance of longitudinal driver state sensing through using Kernel Density Estimation Methods. Second, we leverage the application of Bayesian Change Point detection methods to demonstrate how we can identify driver behaviors and responses to the environmental conditions by fusing psychophysiological information with features extracted from video streams.

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.

Any change in the geometric configuration of the existing road affects other dependent elements such as travel time, fuel consumption, and existing pavement. This paper quantifies these effects and finds an optimum geometric configuration. Three separate models are developed in this research: (1) the travel time model which predicts vehicles flow speed on the specified geometric condition, traffic composure, and evaluates the imposed costs to the road users; (2) the fuel consumption model, which estimates needed propulsive force and anticipated fuel for vehicles passing through the road; and (3) the pavement rehabilitation cost model considering two main constraints of existing pavement condition and project line elevation. The developed pavement rehabilitation model proposes the best solution for pavement rehabilitation practice and computes associated costs. Particle Swarm Optimization (PSO) is used to find the optimum solution in a minimization problem search space. The proposed model is applied to a real-world case study. Results show that there is an extremely less tendency for change, even with the existence of adverse geometric conditions, when there is a relatively good pavement condition. In the case of deteriorated pavement conditions, economic justification for geometric modification is required.