2019 CSCE Annual Conference - Laval (Greater Montreal)

2019 CSCE Annual Conference - Laval (Greater Montreal) Conference

Low-cost Smart Productivity Tracking Model For Earthmoving Operations

Dr. Ashraf Salem, Concordia University (Presenter)
Dr. Osama Moselhi, Concordia University, BCEE

Ashraf Salem and Osama Moselhi

Construction Automation Laboratory

Department of Building, Civil and Environmental Engineering

Gina Cody School of Engineering and Computer Science

Concordia University, Montreal, Quebec, Canada

This paper introduces a model for automated monitoring and control of productivity in earthmoving operations. The model makes use of advancements in wireless sensing networks, Internet of Things (IoT), and artificial intelligence. It consists of two modules; the first is a low-cost open-source remote sensing data acquisition module for collecting data throughout earthmoving operations. The collected data is sent to a cloud-based MySQL database, in which the second module is designed to (1) measure actual productivity in near-real-time, (2) detect the location and condition of hauling roads and (3) monitoring and reporting driving conditions over these roads via short email messages. The work encompassed field and scaled laboratory experiments in the development and validation processes of the developed model. The laboratory experiments utilized 1:24 scaled loader and dumping truck to simulate loading, hauling and dumping operations. The truck was instrumented with the microcontroller equipped with accelerometer, GPS module, load cell and soil water content sensor. Fifteen simulated earthmoving cycles were conducted using the scaled equipment. The field work was carried out in the city of Saint-Laurent, Montreal, Canada using a passenger vehicle to mimic the hauling truck operational modes. Fifteen Field simulated earthmoving cycles were performed. The data collected from the lab experiments and field work was used as input for the developed model. The results will be presented, highlighting the accuracy of the developed model in recognition of the status of the hauling truck, traveled road condition and in the estimated duration of the simulated earthmoving cycles.