PFI-TT: Prototyping a Hybrid System for Automated Activity Detection and Monitoring of Construction Equipment

Award ID: 2016514

The broader impact/commercial potential of this Partnerships for Innovation - Technology Translation (PFI-TT) is in addressing the lack of cost-effective and easy-to-use systems for recognizing and monitoring jobsite operations in the construction industry. The proposed system is a novel solution expected to assist jobsite managers and equipment owners to better assess the working status of equipment, to evaluate safety conditions and take preventive/corrective measures, when necessary. The ability of the system in automatically integrating and processing data collected from multiple machines will be particularly beneficial for medium and large size companies where fleets of construction equipment are utilized to execute larger size projects. This technology development project will advance the concept of automated construction performance monitoring from simple location tracking tools into more informative and comprehensive action tracking and control systems. Research shows that heavy commercial vehicles used in construction and mining represent approximately 6% ($42 million) of the total North America fleet management system market size. The proposed research focuses on the automated tracking and monitoring component of the fleet management system. The proposed project intends to create and empirically test the feasibility of a hybrid system capable of recognizing, tracking, and recording activities conducted by construction equipment at jobsites and then process and integrate the captured data from individual machines to automatically generate and update managerial information at project (and company) levels. The system consists of a hardware unit and the data analysis. The hardware unit, specifically designed and fabricated during this project, will be mounted on each piece of construction equipment. This hardware unit is capable of capturing three major sources of data (audio, kinematic, and location data) produced by machines while performing different tasks. The hardware unit is also capable of wirelessly communicating the collected data into a central processing unit located at any physical or virtual location such as a project office. The collected data for each piece of machinery will be processed and the different activities of the machine will be recognized based on the sounds and kinematic patterns. The chronological list of activities will be converted into semantic information regarding productivity rates and quantities of completed work. The information collected and computed for individual machines will be integrated and processed at the project (and company) level. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Program Director: Samir Iqbal