Development of next-generation methodologies of resource scheduling, augmented reality visualization, and resource tracking for construction project management

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4D modeling (3D CAD plus time) and building information modeling (BIM) technologies will turn mainstream in the construction industry in the foreseeable future. Yet, their application effectiveness in construction engineering largely hinges on development of cost-effective, dynamic-data-driven methodologies for scheduling, tracking, and visualizing construction resources. In order to cater for application needs in planning and control of construction projects, the present research is intended to develop next-generation methodologies for scheduling, tracking, positioning, real-time 3D modeling of critical construction resources on site, including heavy equipment, materials, laborers and tools. This research is proposed based on the PI's previous research achievements in (1) advancing critical path method for construction scheduling; (2) applying robotic total station to track and position tunnel boring machine during microtunneling and pipe-jacking; and (3) utilizing digital camera and photogrammetry to realize augmented reality (AR) visualization for subsurface infrastructure construction. The research will combine various real-time computing analyses with advanced technologies for surveying automation and data communications. It is anticipated (1) the resulting resource scheduling analysis will address fundamental limitations inherent in established project scheduling methodologies; (2) implementation of the resource allocation plan derived from scheduling analysis can be closely monitored and dynamically adjusted to actual site situations; and (3) the state of resource deployment and utilization can be mapped in AR visualization in real time- which is also instrumental in improving efficiency, safety, and job costing onsite. With this grant, the PI will leverage existing strengths and contribute to establishing a more broadly based construction research program at the U of A. In the long run, this research will lay a solid foundation for future development and deployment of an engineering-analysis-integrated, dynamic-data-driven platform for construction engineering and project management.