کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6479017 1428282 2017 14 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Activity analysis of construction equipment using audio signals and support vector machines
ترجمه فارسی عنوان
تجزیه و تحلیل فعالیت تجهیزات ساخت و ساز با استفاده از سیگنال های صوتی و دستگاه های بردار پشتیبانی
کلمات کلیدی
تجهیزات سنگین ساخت و ساز، سیگنال های صوتی، ماشین آلات بردار پشتیبانی، تجزیه و تحلیل فعالیت، بهره وری،
موضوعات مرتبط
مهندسی و علوم پایه سایر رشته های مهندسی مهندسی عمران و سازه
چکیده انگلیسی


- An automatic solution for construction equipment activity analysis problem is proposed.
- The focus of this research is on activity analysis of single machines.
- The proposed system consists of multiple steps and is based on machine learning techniques.
- The machine learning model and parameters need to be tuned separately for each machine.
- The performance of the proposed system for automatically recognizing activities of single machines is very promising.

In the construction industry, especially for civil infrastructure projects, a large portion of overall project expenses are allocated towards various costs associated with heavy equipment. As a result, continuous tracking and monitoring of tasks performed by construction heavy equipment is vital for project managers and jobsite personnel. The current approaches for automated construction equipment monitoring include both location and action tracking methods. Current construction equipment action recognition and tracking methods can be divided into two major categories: 1) using active sensors such as accelerometers and gyroscopes and 2) implementing computer vision algorithms to extract information by processing images and videos. While both categories have their own advantages, the limitations of each mean that the industry still suffers from the lack of an efficient and automatic solution for the construction equipment activity analysis problem. In this paper we propose an innovative audio-based system for activity analysis (and tracking) of construction heavy equipment. Such equipment usually generates distinct sound patterns while performing certain tasks, and hence audio signal processing could be an alternative solution for solving the activity analysis problem within construction jobsites. The proposed system consists of multiple steps including filtering the audio signals, converting them into time-frequency representations, classifying these representations using machine learning techniques (e.g., a support vector machine), and window filtering the output of the classifier to differentiating between different patterns of activities. The proposed audio-based system has been implemented and evaluated using multiple case studies from several construction jobsites and the results demonstrate the potential capabilities of the system in accurately recognizing various actions of construction heavy equipment.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Automation in Construction - Volume 81, September 2017, Pages 240-253
نویسندگان
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