Browsing by Author "Roofigari-Esfahan, Nazila"

Now showing 1 - 10 of 10
  • Thumbnail Image
    A BIM-based Interoperability Platform in Support of Building Operation and Energy Management
    Xiong, Yunjie (Virginia Tech, 2020-03-18)
    Building energy efficiency is progressively becoming a crucial topic in the architecture, engineering, and construction (AEC) sector. Energy management tools have been developed to promise appropriate energy savings. Building energy simulation (BES) is a tool mainly used to analyze and compare the energy consumption of various design/operation scenarios, while building automation systems (BAS) works as another energy management tool to monitor, measure and collect operational data, all in an effort to optimize energy consumption. By integrating the energy simulated data and actual operational data, the accuracy of a building energy model can be increased while the calibrated energy model can be applied as a benchmark for guiding the operational strategies. This research predicted that building information modeling (BIM) would link BES and BAS by acting as a visual model and a database throughout the lifecycle of a building. The intent of the research was to use BIM to document energy-related information and to allow its exchange between BES and BAS. Thus, the energy-related data exchange process would be simplified, and the productive efficiency of facility management processes would increase. A systematic literature review has been conducted in investigating the most popular used data formats and data exchange methods for the integration of BIM/BES and BAS, the results showed the industry foundation classes (IFC) was the most common choice for BIM tools mainly and database is a key solution for managing huge actual operational datasets, which was a reference for the next step in research. Then a BIM-based framework was proposed to supporting the data exchange process among BIM/BES/BAS. 4 modules including BIM Module, Operational Data Module, Energy Simulation Module and Analysis and Visualization Module with an interface were designed in the framework to document energy-related information and to allow its exchange between BES and BAS. A prototype of the framework was developed as a platform and a case study of an entire office suite was conducted using the platform to validate this framework. The results showed that the proposed framework enables automated or semi-automated multiple-model development and data analytics processes. In addition, the research explored how BIM can enhance the application of energy modeling during building operation processes as a means to improve overall energy performance and facility management productivity.
  • Thumbnail Image
    A Connected Work Zone Hazard Detection System for Highway Construction Work Zones
    Han, Wenjun (Virginia Tech, 2019-07-02)
    Roadway construction workers have to work in close proximity to construction equipment as well as high-speed traffic, exposing them to an elevated risk of collisions. This research aims to develop an innovative holistic solution to reduce the risk of collisions at roadway work zones. To this end, a connected hazard detection and prevention system is developed to detect potential unsafe proximities in highway work zones and provide warning and instructions of imminent threats. This connected system collects real-time information from all the actors inside and outside of the work zone and communicates it with a cloud server. A hazard detection algorithm is developed to identify potential proximity hazards between workers and connected/automated vehicles (CAV) and/or construction equipment. Detected imminent threats are communicated to in-danger workers and/or drivers. The trajectories and safety status of each actor is visualized on Virginia Connected Corridors (VCC) Monitor, a custom web-based situational awareness tool, in real-time. To assure the accuracy of hazard detection, the algorithm accommodates various parameters including variant threat zones for workers-on-foot, vehicles, and equipment, the direction of movement, workers' distance to the work zone border, shape of road, etc. The designed system is developed and evaluated through various experiments on the Virginia's Smart Roads located at Virginia Tech. Data regarding activities of workers-on-foot was collected during experiments and was used and classified for activity recognition using supervised machine learning methods. A demonstration was held to evaluate the usability of the developed system, and the results proved the efficacy of the algorithm in successfully detecting potential collisions and provide prompt warnings and instructions. The developed holistic system elevates safety of highway construction and maintenance workers at work sites. It also helps managers and inspectors to keep track of the real-time safety status of their work zone actors as well as the accidents occurrences. As such, with the connected work zone hazard detection system, the safety level and productivity of the workers is expected to be greatly enhanced.
  • Thumbnail Image
    A Cyber-Physical System (CPS) Approach to Support Worker Productivity based on Voice-Based Intelligent Virtual Agents
    Linares Garcia, Daniel Antonio (Virginia Tech, 2022-08-16)
    The Architecture, Engineering, and Construction (AEC) industry is currently challenged by low productivity trends and labor shortages. Efforts in academia and industry alike invested in developing solutions to this pressing issue. The majority of such efforts moved towards modernization of the industry, making use of digitalization approaches such as cyber-physical systems (CPS). In this direction, various research works have developed methods to capture information from construction environments and elements and provide monitoring capabilities to measure construction productivity at multiple levels. At the root of construction productivity, the productivity at the worker level is deemed critical. As a result, previous works explored monitoring the productivity of construction workers and resources to address the industry's productivity problems. However, productivity trends are not promising and show a need to more rigorously address productivity issues. Labor shortages also exacerbated the need for increasing the productivity of the current labor workers. Active means to address productivity have been explored as a solution in recent years. As a result, previous research took advantage of CPS and developed systems that sense construction workers' actions and environment and enable interaction with workers to render productivity improvements. One viable solution to this problem is providing on-demand activity-related information to the workers while at work, to decrease the need for manually seeking information from different sources, including supervisors, thereby improving their productivity. Especially, construction workers whose activities involve visual and manual limitations need to receive more attention, as seeking information can jeopardize their safety. Multiple labor trades such as plumbing, steel work, or carpenters are considered within this worker classification. These workers rely on knowledge gathered from the construction project documentation and databases, but have difficulties accessing this information while doing their work. Research works have explored the use of knowledge retrieval systems to give access to construction project data sources to construction workers through multiple methods, including information booths, mobile devices, and augmented reality (AR). However, these solutions do not address the need of this category of workers in receiving on-demand activity related information during their work, without negatively impacting their safety. This research focuses on voice, as an effective modality most appropriate for construction workers whose activities impose visual and manual limit actions. to this end, first, a voice-based solution is developed that supports workers' productivity through providing access to project knowledge available in Building Information Modeling (BIM) data sources. The effect of the selected modality on these workers' productivity is then evaluated using multiple user studies. The work presented in this dissertation is structured as follows: First, in chapter 2, a literature review was conducted to identify means to support construction workers and how integration with BIM has been done in previous research. This chapter identified challenges in incorporating human factors in previous systems and opportunities for seamless integration of workers into BIM practices. In chapter 3, voice-based assistance was explored as the most appropriate means to provide knowledge to workers while performing their activities. As such, Chapter 3 presents the first prototype of a voice-based intelligent virtual agent, aka VIVA, and focuses on evaluating the human factors and testing performance of voice as a modality for worker support. VIVA was tested using a user study involving a simulated construction scenario and the results of the performance achieved through VIVA were compared with the baseline currently used in construction projects for receiving activity-related information, i.e., blueprints. Results from this assessment evidenced productivity performance improvements of users using VIVA over the baseline. Finally, chapter 4 presents an updated version of VIVA that provides automatic real-time link to BIM project data and provides knowledge to the workers through voice. This system was developed based on web platforms, allowing easier development and deployment and access to more devices for future deployment. This study contributes to the productivity improvements in the AEC industry by empowering construction workers through providing on-demand access to project information. This is done through voice as a method that does not jeopardize workers' safety or interrupt their activities. This research contributes to the body of knowledge by developing an in-depth study of the effect of voice-based support systems on worker productivity, enabling real-time BIM-worker integration, and developing a working worker-level productivity support solution for construction workers whose activities limit them in manually accessing project knowledge.
  • Thumbnail Image
    Design and Evaluation of a Connected Work Zone Hazard Detection and Communication System for Connected and Automated Vehicles (CAVs)
    Mollenhauer, Michael A.; White, Elizabeth E.; Roofigari-Esfahan, Nazila (SAFE-D: Safety Through Disruption National University Transportation Center, 2019-08)
    Roadside work zones (WZs) present imminent safety hazards for roadway workers as well as passing motorists. In 2016, 764 fatalities occurred in WZs in the United States due to motor vehicle traffic crashes, which are the second most common cause of worker fatalities. The advent of connected and connected automated vehicles (CVs/CAVs) is driving WZ safety practitioners and vehicle designers towards implementing solutions that will more accurately describe activity in WZs to help identify and communicate imminent safety hazards that elevate crash risks. A viable solution to this problem is to accurately localize, monitor, and predict WZ actors’ collision threats based on their movements and activities. This information along with CV/CAVs’ trajectories can be used to detect potential proximity conflicts and provide advanced warnings to workers, passing drivers, and CAV control systems. This project aims to address WZ safety by delivering a real-time threat detection and warning algorithm that can be used in wearable WZ communication solutions in conjunction with CVs/CAVs. As a result, this research provides a key element required to significantly improve the safety conditions of roadside WZs through prompt detection and communication of hazardous situations to workers and CVs/CAVs alike.
  • Thumbnail Image
    Development of a Connected Smart Vest for Improved Roadside Work Zone Safety
    Roofigari-Esfahan, Nazila; White, Elizabeth E.; Mollenhauer, Michael A.; Talledo Vilela, Jean Paul (SAFE-D: Safety Through Disruption National University Transportation Center, 2021-04)
    Roadside work zones (WZs) present imminent safety threats for roadway workers as well as passing motorists. In 2016, 764 fatalities occurred in WZs in the United States due to motor vehicle traffic crashes. A number of factors (aging highway infrastructure, increased road work, increased levels of traffic and more nighttime WZs) have led to an increase in WZ crashes in the past few years. The standard WZ safety signage and personal protective equipment worn by workers at roadside WZs have not been completely effective in controlling WZ crashes. This project aims to address this issue by designing a wearable device to accurately localize, monitor, and predict potential collisions between WZ actors based on their movements and activities, and communicate potential collisions to workers, passing drivers, and connected and automated vehicles (CAVs). Through this project, a wearable worker localization and communication device (i.e., Smart Vest) was developed that utilizes the previously developed Threat Detection Algorithm to communicate workers’ locations to passing CAVs and proactively warn workers and passing motorists of potential collisions. As a result, this research is expected to significantly improve the safety conditions of roadside WZs through prompt detection and communication of hazardous situations to workers and drivers.
  • Thumbnail Image
    Group-based VR Training to Improve Hazard Recognition, Evaluation, and Control for Highway Construction Workers
    Roofigari-Esfahan, Nazila; Porterfield, Curt; Ogle, Jeffrey; Upthegrove, Tanner; Lee, Sang; Jeon, Myounghoon (IEEE, 2022-03-11)
    The construction industry spends approximately 15billion/year for occupational injuries, and highway sector is the most dangerous. Highway construction workers have to work in close proximity to construction equipment and high-speed traffic, exposing them to an elevated risk of serious injuries/fatalities. Safety training has a direct impact on the prevention of construction accidents. The traditional lecture-based construction training curriculum has not been revisited and is designed to train the workers individually, thus the benefits of collective engagement in worker training is ignored. High-engagement Virtual Reality (VR) environments offer a more effective learning experience for training workers to identify hazards in the job site. We present a training platform for instructor-in-the-loop, group-based VR training to complement and increase the effectiveness of the current training program for highway workers. We develop a VR platform in which an instructor can create and improvise on work zone scenarios and share the virtual scenario easily with the entire class.
  • Human Factors Considerations for Teaming between Construction Workers and Voice-based Intelligent Virtual Agent (VIVA)
    Rahimi Movassagh, Maryam; Roofigari-Esfahan, Nazila; Lee, Sang Won; Evia, Carlos; Hicks, David; Jeon, Myounghoon (SAGE Publications, 2021-09)
    Construction sites experience low productivity due to particular characteristics such as unique designs in each project, sporadic arrival of projects, and complexity of the process. Another contributing factor to low productivity is poor communication among workers, supervisors, and a site’s centralized knowledge hub. Research shows that introducing advanced artificial intelligence (AI) technology in construction can tackle these problems. In this paper, we analyzed human factors considerations–user, task, environment, and technology and identified their characteristics and challenges to design an interactive AI system to facilitate communication between workers and other stakeholders. Based on the analysis, we propose a voice-based intelligent virtual agent (VIVA) as a multi-purpose AI system on construction sites with a further research agenda. We hope that this effort can guide the design of construction-specific AI systems and that this worker-AI teaming can improve overall work processes, enhance productivity, and promote safety in construction.
  • Thumbnail Image
    Immersive Cross-platform X3D Training: Elevating Construction Safety Education
    Roofigari-Esfahan, Nazila; Polys, Nicholas F.; Johnson, Ashley; Ogle, J. Todd; Sandbrook, Ben (ACM, 2023-10-09)
    A multi-platform Virtual Reality (VR) approach is proposed to complement the traditional approaches for construction safety training. Visual simulations of a highway construction project were developed and presented through the developed platforms, aiming at giving students immersive experience of actual construction environments. The simulated worksite scenarios included active traffic, multiple worker roles and heavy equipment, and was rendered at different times of day and weather conditions. We used this material in an undergraduate class activity with 50 students. During a session in our visualization lab, students experienced the scenarios presenting day shift, afternoon shift with adverse weather and night shift and were asked to develop daily report of their job site observation. The scenrios were presented via the following platforms: TV projection, Mobile Phone, Head-Mounted Display (HMD), and CAVE projection room. The results demonstrates that the multi-platform immersive experience has the potential to significantly improve hazard recognition skill of construction students.
  • Thumbnail Image
    A Novel Approach to Indoor Environment Assessment: Artificial Intelligence of Things (AIoT) Framework for Improving Occupant Comfort and Health in Educational Facilities
    Lee, Min Jae (Virginia Tech, 2024-05-09)
    Maintaining the quality of indoor environments in educational facilities is crucial for student comfort, health, well-being, and learning performance. Amidst the growing recognition of the impact of indoor environmental conditions on occupant comfort, health, and well-being, there has been an increasing focus on the assessment and modeling of Indoor Environmental Quality (IEQ). Despite considerable advancements, current IEQ modeling and assessment methodologies often prioritize and limit to singular comfort metrics, potentially neglect- ing the comprehensive and holistic factors associated with occupant comfort and health. Furthermore, existing indoor environment maintenance practices and building systems for educational facilities often fail to include feedback from occupants (e.g., students and fac- ulty) and exhibit limited adaptability to their needs. This calls for more inclusive and occupant-centric IEQ assessment models that cover a broader spectrum of environmental parameters and occupant needs. To address the gaps, this thesis proposes a novel Artificial Intelligence of Things (AIoT)-based IEQ assessment framework that bridges gaps by uti- lizing multimodal data fusion and deep learning-based prediction and classification models. These models are developed to utilize real-time multidimensional IEQ data, non-intrusive occupant feedback (MFCC features from audio recordings, video/thermal features extracted by Vision Transformer (ViT)), and self-reported comfort and health levels, placing a focus on occupant-centric and data-driven decision-making for intelligent educational facilities. The proposed framework was evaluated and validated at Virginia Tech Blacksburg campus, achieving a 91.9% in R2 score in predicting future IEQ conditions and 97% and 96% accuracy in comfort and health-based IEQ conditions classifications.
  • Thumbnail Image
    Vibrotactile Alerting to Prevent Accidents in Highway Construction Work Zones: An Exploratory Study
    Yang, Xiang; Roofigari-Esfahan, Nazila (MDPI, 2023-06-16)
    Struck-by accidents are the leading cause of injuries in highway construction work zones. Despite numerous safety interventions, injury rates remain high. As workers’ exposure to traffic is sometimes unavoidable, providing warnings can be an effective way to prevent imminent threats. Such warnings should consider work zone conditions that can hinder the timely perception of alerts, e.g., poor visibility and high noise level. This study proposes a vibrotactile system integrated into workers’ conventional personal protective equipment (PPE), i.e., safety vests. Three experiments were conducted to assess the feasibility of using vibrotactile signals to warn workers in highway environments, the perception and performance of vibrotactile signals at different body locations, and the usability of various warning strategies. The results revealed vibrotactile signals had a 43.6% faster reaction time than audio signals, and the perceived intensity and urgency levels on the sternum, shoulders, and upper back were significantly higher than the waist. Among different notification strategies used, providing a moving direction imposed significantly lower mental workloads and higher usability scores than providing a hazard direction. Further research should be conducted to reveal factors that affect alerting strategy preference towards a customizable system to elicit higher usability among users.