I-Corps: Automated Activity Monitoring of Construction Machinery Using a Hybrid Approach

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The broader impact/commercial potential of this I-Corps project will be to provide a cost-efficient, and easy-to-use tool for construction managers and equipment owners to monitor performance of their machines, measure productivity rates and quantities of completed work, record active vs. idle times, and recognize abnormal conditions for considering preventive/corrective actions. The system will eventually operate on all equipment pieces in a construction project and necessary managerial information about the project will be generated. The project is expected to provide a transformational re-thinking about the concept of "construction equipment monitoring" and translate it from "location tracking" tools into informative "performance monitoring and control systems." Considering the significant costs of maintenance/repair/fuel/operator expenses in overall costs of construction heavy equipment, the technology can provide considerable savings to the U.S. architecture, engineering, and construction industries. Activities of this project will take place in Salt Lake City, Utah, one of America's emerging tech startup hubs and a setting for rapid construction. This may facilitate the commercialization phase of the project and also provide excellent educational and training opportunities, especially for the graduate student involved in the project. This I-Corps project aims to develop and empirically test the feasibility of a hybrid system for automatically detecting, tracking, and recording activities of construction heavy equipment. The system works based on collecting audio, kinematic, and location data of construction machines using a specially designed hardware unit. The collected data will be transferred into a central processing unit through Wi-Fi links and a chronological list of activities and productivity rates will be automatically generated during each working shift. As the next step, the information collected and processed from individual machines will be further integrated and processed to generate managerial information at project and company levels. The novel aspects of the project are twofold: 1) The hybrid use of audio and kinematic signals capable of producing more accurate results and overcoming the limitations and barriers of implementing each signal individually; and 2) the concept of fusing information from multiple machines and automatically generating managerial information for the entire project (and company), which could replace the current manual procedures for updating the project schedule, budget, and inventory management systems.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.