AI-based cloud computing application for smart earthmoving operations

Abstract

This paper introduces a newly developed 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 utilizes data analytics and a dashboard to provide project managers with actionable data on the status of these operations in near-real time. The model consists of two modules; the first is a low-cost open-source remote sensing data acquisition module for collecting data throughout the execution of earthmoving operations. The collected data are sent to a cloud-based MySQL database, in which the second module is designed to (1) measure actual productivity in near-real-time, (2) detecting the location and condition of hauling roads, and (3) monitoring and reporting driving conditions over these roads. Artificial neural network (ANN) is used in cloud computing for analyzing the productivity to determine and prioritize causes behind experienced loss of productivity from that planned. This paper presents cloud computing over a web-based platform (Knowi®). Productivity measurement and analysis outputs are retrieved through any web browser. The work encompassed field and scaled laboratory experiments in the development and validation processes of the developed model. The laboratory experiments 1:24 scaled loader and dumping truck to simulate loading, hauling, and dumping operations. The data collected from the lab experiments and field work was used as input for the developed model. The results obtained highlight 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.