Automating excavator productivity measurement using deep learning

Mahamedi, Elham, Rogage, Kay, Doukari, Omar and Kassem, Mohamad (2021) Automating excavator productivity measurement using deep learning. Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction, 174 (4). pp. 121-133. ISSN 2397-8759

jsmic.21.00031.pdf - Accepted Version

Download (1MB) | Preview
Official URL:


Heavy equipment represents a major cost element and a critical resource in large infrastructure projects. Automating the measurement of their productivity is important to remove the inaccuracies and inefficiencies of current manual measurement processes and to improve the performance of projects. Existing studies have prevalently focused on equipment activity recognition using mainly vision based systems which require intrusive field installation and the application of more computationally demanding methods. This study aims to automate the measurement of equipment productivity using a combination of smartphone sensors to collect kinematic and noise data and deep learning algorithms. Different combination inputs and deep learning methods were implemented and tested in a real-world case study of a demolition activity. The results demonstrated very high accuracy (99.78%) in measuring the productivity of the excavator. Construction projects can benefit from the proposed method to automate productivity measurement, identify equipment inefficiencies in near real-time, and inform corrective actions.

Item Type: Article
Subjects: G400 Computer Science
H200 Civil Engineering
H300 Mechanical Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Faculties > Engineering and Environment > Mechanical and Construction Engineering
Depositing User: Elena Carlaw
Date Deposited: 13 Apr 2022 12:05
Last Modified: 04 Apr 2023 08:00

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics