Publications

Detailed Information

A hybrid kinematic-acoustic system for automated activity detection of construction equipment

Cited 26 time in Web of Science Cited 34 time in Scopus
Authors

Sherafat, Behnam; Rashidi, Abbas; Lee, Yong-Cheol; Ahn, Changbum R.

Issue Date
2019-10
Publisher
Multidisciplinary Digital Publishing Institute (MDPI)
Citation
Sensors, Vol.19 No.19, p. 4286
Abstract
Automatically recognizing and tracking construction equipment activities is the first step towards performance monitoring of a job site. Recognizing equipment activities helps construction managers to detect the equipment downtime/idle time in a real-time framework, estimate the productivity rate of each equipment based on its progress, and efficiently evaluate the cycle time of each activity. Thus, it leads to project cost reduction and time schedule improvement. Previous studies on this topic have been based on single sources of data (e.g., kinematic, audio, video signals) for automated activity-detection purposes. However, relying on only one source of data is not appropriate, as the selected data source may not be applicable under certain conditions and fails to provide accurate results. To tackle this issue, the authors propose a hybrid system for recognizing multiple activities of construction equipment. The system integrates two major sources of data—audio and kinematic—through implementing a robust data fusion procedure. The presented system includes recording audio and kinematic signals, preprocessing data, extracting several features, as well as dimension reduction, feature fusion, equipment activity classification using Support Vector Machines (SVM), and smoothing labels. The proposed system was implemented in several case studies (i.e., ten different types and equipment models operating at various construction job sites) and the results indicate that a hybrid system is capable of providing up to 20% more accurate results, compared to cases using individual sources of data.
ISSN
1424-8220
URI
https://hdl.handle.net/10371/203460
DOI
https://doi.org/10.3390/s19194286
Files in This Item:
There are no files associated with this item.
Appears in Collections:

Related Researcher

  • College of Engineering
  • Department of Architecture & Architectural Engineering
Research Area Computing in Construction, Management in Construction

Altmetrics

Item View & Download Count

  • mendeley

Items in S-Space are protected by copyright, with all rights reserved, unless otherwise indicated.

Share