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Sensors for Digital Construction

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 2027

Special Issue Editor

Chair of Computing in Engineering, Ruhr-Universitat Bochum, 44801 Bochum, Germany
Interests: artificial intelligence; building information modeling; digital twin; IoT for construction

Special Issue Information

Dear Colleagues,

Detailed insights into the ongoing processes of construction projects are a prerequisite for an efficient management of time, costs, and resources. However, providing relevant information requires the analysis of vast amounts of data. A consistent digitization throughout all phases of a project facilitates a proper aggregation of these data, as well as an automated evaluation. While digital building models already support decision making during project planning, other domains are only sparsely digitized.

The use of sensors helps to advance digitization in construction through the automated collection of time-dependent data. It allows for a continuous localization of resources and materials as well as the monitoring of construction machines and their states. This enables a digital monitoring of construction projects through their entire life cycle and supports the management in optimizing workflows, scheduling maintenance, improving safety, and so on.

This Special Issue focuses on the application of sensors in construction-related domains and the processing of the collected data. Relevant topics include but are not limited to:

  • Digital twin
  • Internet of Things
  • Safety
  • Productivity
  • Maintenance

Prof. Dr. Markus König
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Construction monitoring
  • Smart sensors
  • Sensor networks
  • Sensor fusion
  • Wireless technologies

Published Papers (1 paper)

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Research

22 pages, 8477 KiB  
Article
Productivity Measurement through IMU-Based Detailed Activity Recognition Using Machine Learning: A Case Study of Masonry Work
by Sungkook Hong, Youngjib Ham, Jaeyoul Chun and Hyunsoo Kim
Sensors 2023, 23(17), 7635; https://doi.org/10.3390/s23177635 - 03 Sep 2023
Viewed by 1198
Abstract
Although measuring worker productivity is crucial, the measurement of the productivity of each worker is challenging due to their dispersion across various construction jobsites. This paper presents a framework for measuring productivity based on an inertial measurement unit (IMU) and activity classification. Two [...] Read more.
Although measuring worker productivity is crucial, the measurement of the productivity of each worker is challenging due to their dispersion across various construction jobsites. This paper presents a framework for measuring productivity based on an inertial measurement unit (IMU) and activity classification. Two deep learning algorithms and three sensor combinations were utilized to identify and analyze the feasibility of the framework in masonry work. Using the proposed method, worker activity classification could be performed with a maximum accuracy of 96.70% using the convolutional neural network model with multiple sensors, and a minimum accuracy of 72.11% using the long short-term memory (LSTM) model with a single sensor. Productivity could be measured with an accuracy of up to 96.47%. The main contributions of this study are the proposal of a method for classifying detailed activities and an exploration of the effect of the number of IMU sensors used in measuring worker productivity. Full article
(This article belongs to the Special Issue Sensors for Digital Construction)
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