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Open AccessArticle

A Hybrid Kinematic-Acoustic System for Automated Activity Detection of Construction Equipment

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Department of Civil and Environmental Engineering, University of Utah, Salt Lake City, UT 84112, USA
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Department of Construction Management, Louisiana State University, Baton Rouge, LA 70803, USA
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Department of Construction Science, Texas A&M University, College Station, TX 77843, USA
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(19), 4286; https://doi.org/10.3390/s19194286
Received: 13 August 2019 / Revised: 24 September 2019 / Accepted: 1 October 2019 / Published: 3 October 2019
(This article belongs to the Special Issue Sensors for Construction Automation and Management)
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. View Full-Text
Keywords: construction equipment; audio and kinematic signals; sensor fusion; dimension reduction; activity detection; support vector machines construction equipment; audio and kinematic signals; sensor fusion; dimension reduction; activity detection; support vector machines
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Sherafat, B.; Rashidi, A.; Lee, Y.-C.; Ahn, C.R. A Hybrid Kinematic-Acoustic System for Automated Activity Detection of Construction Equipment. Sensors 2019, 19, 4286.

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