Embedded Files
This research will develop a DAta Sensing Learning EnvironmenT (called DASLET) to investigate how students? computational thinking can be developed in addressing construction industry challenges with data sensing technologies and improve their engagement and attitudes toward STEM-related careers. Within DASLET, students can learn how to safely work with different data sensing technologies on a virtual construction site, translate sensor data into computational rules and extract meaningful information to support decision-making.
This project aims to advance the innovation needed to enhance the work environments for construction workforce interacting with robots. As the construction industry gears towards widespread acceptance of robots, there is a need to prepare Construction Engineering and Management (CEM) students to excel in highly technological work environments. However, the current curriculum is inadequate to equip CEM students with the required competencies for working alongside construction robots. This project intends to investigate an immersive virtual reality (VR)-based learning environment to develop students' experiential skills so that the students would interact safely with robots in the construction industry after they graduate. The proposed project has the potential to provide the CEM communities with tangible benefits for advancing robotics in engineering education.
This interdisciplinary research seeks to integrate advances across a diverse spectrum of critical innovations, including immersive technologies, physiological sensing, wearable robots, and organizational psychology, to identify the underlying physical, psychological, and socio-technical risks of exoskeletons in the construction sector. The technical aims of this project are divided into three thrusts. The first thrust will utilize a socio-technical perspective to identify barriers and facilitators to the adoption of exoskeletons in the construction industry. The second thrust will generate an immersive and interactive virtual-reality testbed for the feasible simulation of different construction tasks executed using these exoskeletons. In particular, this thrust will develop a user-centered, simulated workspace to seamlessly examine different interactions for a safe and feasible evaluation of pertinent physical and psychological risks. The third thrust will design a novel worker-centered risk assessment framework for evaluating the physical and psychological risks of using these exoskeletons for construction workers. Specifically, this thrust will integrate artificial intelligence and objective evaluations to develop a new interpretive pipeline between physiological and psychophysiological data with local muscular fatigue, fall risk, joint hyperextension, cognitive workload, trust, and vigilance of workers during the construction tasks. These three research aims will be complemented by a comprehensive evaluation plan featuring three intermediate evaluations to ensure the reliability of the project.
This project is designed to create a collaborative network (called ConPEC) to investigate how the accessibility of construction industry practitioners to instructors, influences construction engineering students’ disciplined perception and professional identity development. The ConPEC platform will employ machine learning algorithms and complex data analysis to allow for pairing instructors with their community of practice. To achieve this, the research will first investigate the practical course-support needs of construction engineering instructors and the characteristics of industry practitioners. Next, the ConPEC framework will be designed to include learning-driven algorithms to exploit dynamic matching patterns between instructors and industry practitioners. Finally, the team will use semi-structured interviews with both students and industry practitioners to gather qualitative data that will be analyzed using Grounded Theory. The research team will examine changes in students’ disciplined perception and professional identity development.
This project aims to help students learn computational thinking skills in construction engineering and management courses. The project focuses on active learning experiences in which students learn how to extract meaningful information from large datasets and use the results to make informed engineering decisions. These experiences can help better prepare students to address construction industry needs, such as increasing productivity, reducing waste, and improving worker safety. The use of sensors on construction sites is a growing trend because they provide real-time data showing what is happening on a site. Students need to develop skills in data analytics and computational thinking so that they can process sensor data, perform data analyses, and develop an understanding of construction site operations. To accomplish these aims, the project team will develop a web application that provides students with a graphical interface to select, analyze, and display sensor data. Students will be able to explore a construction site in real-time to understand behaviors and relationships between objects on a site and how they relate to construction project safety and productivity. The web application software will be made available to the engineering education community through public software repositories. By addressing the computational skills gap in the construction industry, this project will benefit construction workers and the economic competitiveness of construction companies.
The rapidly emerging technology of occupational exoskeletons has the potential to benefit construction workers. The ability of exoskeletons to provide assistive forces, especially for movements involving the back or shoulder, can reduce demands on areas of the body most affected by work-related musculoskeletal disorders. Exoskeletons could also make construction work accessible to a broader population. Passive exoskeletons , which require no actuators or power supply, are the main focus of the study, as they are more likely to be widely adopted, being lighter, simpler, and more cost-effective. Currently, however, evidence is insufficient to support the safe and effective use of exoskeletons in construction. This project conducts a mixed-methods assessment of exoskeleton to: understand the perspectives of a broad set of industry stakeholders, quantify the benefits and risks of exoskeleton use, facilitate adoption, and prevent unexpected consequences.
The demands for innovative approaches to delivering safer, less intrusive and more resilient infrastructure projects are pushing modern construction industries into investing in visualization and sensing technologies (e.g. mixed reality, radio frequency identification systems, global positioning systems, drones and laser scanners). Sadly, there is a shortfall of graduating construction engineering and management engineers, or existing workforce equipped with the required knowledge and skills for developing and implementing sustainable solutions with these technologies. This proposed work seeks to create and assess a pedagogical framework (based on holographic scenes and objects, a concept of mixed reality) for equipping CEM students with the competencies required for deploying data sensing technologies on construction projects.
Digital Twin Data Driven Musculoskeletal Injury Prevention System
Digital Twin Data Driven Musculoskeletal Injury Prevention System
The repetitive and physically demanding nature of construction work makes workers vulnerable to work-related musculoskeletal injuries. Performing construction work in awkward postures imposes a significant strain on the body parts and can result in fatigue, injuries, or in severe cases permanent disabilities. One of the effective methods of preventing these injuries is to empower workers with instant information regarding the ergonomic consequences of their working postures so as to enable them control or self-manage the exposures. With digital twin, workers can be dynamically mapped to their virtual replica such that their working postures can be captured, assessed and feedback can be provided via a mixed reality head-mounted display. This study aims to create and assess a framework for augmenting construction workspaces with digital twin representation of real-time ergonomic exposures of construction workers to facilitate self-management of the risks. The proposed approach obtains posture data using wearable sensors, evaluates the risk factors on body segments and projects these as a color gradient digital twin overlaid on the gaze points of construction workers.
Cyber-Physical Postural Training System for Construction Workers
Cyber-Physical Postural Training System for Construction Workers
Risks of work-related musculoskeletal injuries can be reduced by adequately training construction workers on performing work in safe postures. Traditional training approaches provide limited support for performing work tasks and receiving real-time feedback. This study develops a cyber-physical postural training environment where workers can practice to perform work with reduced ergonomic risks. The system uses wearable sensors, Vive trackers, machine learning and virtual reality to track body kinematics, and engagement with physical construction resources, and provides feedback via an interactive user interface.
Deep Learning-Enabled Annotated Videos for Virtual Site Visits
Deep Learning-Enabled Annotated Videos for Virtual Site Visits
Engaging students in site visits is sometimes challenging due to cost, safety, weather, and schedule constraints, in addition to the logistical challenges of accommodating large class sizes. The prevalence of cheap video technology and easy accessibility of video-recordings of construction projects afford opportunities for field experiences without physical construction site visits. However, videos alone are insufficient for drawing students’ attention to practical knowledge typically provided by field personnel during physical site visits. Annotations can be used to attract students’ attention to practical knowledge while reducing misconceptions or improper inferences. Inspired by recent advances of deep learning in object detection and classification, this study investigates a deep learning-based annotation of construction concepts in site videos to recognize and illustrate construction workflows for equipping students with site visit experiences.
Towards Unmanned Self-Navigable Real Time Location Sensing System for Construction Safety
Towards Unmanned Self-Navigable Real Time Location Sensing System for Construction Safety
Despite increasing efforts to address safety concerns in the construction industry, construction sites still have high accident rates. Integrating information technologies with construction activities and environments can provide opportunities for real-time monitoring of resources, access to data on workers’ behavior, and prediction of construction accidents. This project evaluates the performance of a commercially available real-time location sensing system that provides access to the location of workers, materials and equipment, enabling the design and development of an unmanned location tracking system that can self-navigate indoor construction environments.
Michigan Truck Strategic Plan for 2016-2019
Michigan Truck Strategic Plan for 2016-2019
This project aims to develop a safety strategic plan that sets focus areas , define priorities, resources and processes for managing the attributes of the road, the driver, and the vehicle so as to achieve the highest level of highway safety.
Enhancing Non-motorized Mobility within Construction Zone
Enhancing Non-motorized Mobility within Construction Zone
Mobility of a wide range of pedestrians of all ages, including people with hearing, visual,cognitive, and mobility disabilities; bicyclists; and emergency crew is affected due to construction.Mobility restrictions also affect the local businesses. The impact is significant when construction zones are located in urban areas and involves roads with non-motorized access, and/or pedestrian generators such as schools, shopping areas, community or senior centers, transit facilities, etc.Even on the roads in rural or suburban areas, an abrupt termination of non-motorized facilities cause immense inconvenience to pedestrians and cyclists. Typically, pedestrians and cyclists are reluctant to add distance or out-of-the-way travel to a destination. Also, emergency response teams need the quickest access route to a location/facility. This study reviews and synthesizes the best practices in access management policies/procedures, and use of infrastructure and technology to provide safe access through construction zones.
Page updated
Report abuse