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Smart Sensing and Recognition Systems in the Construction Industry
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Smart Sensing and Recognition Systems in the Construction Industry

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 1921

Special Issue Editor

Prof. Dr. Ren-Jye Dzeng

E-Mail Website
Guest Editor
Department of Civil Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
Interests: construction management; AI and IoT applications; digital twins; building information modeling

Special Issue Information

Dear Colleagues,

Smart sensing and recognition systems and technologies demonstrate promising potential to provide the construction industry with a safe, productive, and high-quality process. This Special Issue of Sensors is dedicated to pushing the horizon of research and exploring the practical applications of smart sensing and recognition systems. The primary objective of the Special Issue is to investigate how sensor-driven or automatic recognition systems can augment and revolutionize collaborative and cooperative practices while addressing the accompanying challenges and risks.

The majority of sensing or recognition technologies in the construction research area have been focused on construction automation research in relation to prefabrication, on-site operation, and logistics. With industry 4.0 and the realization of digital twins now within reach, these advancements have substantial implications for the construction industry. This Special Issue aims to bring to the forefront the ways in which new developments in sensor technologies can be applied to the construction industry.

Potential topics applicable in the construction industry, include but are not limited to:

  • Smart sensing;
  • Smart image recognition;
  • Automatic decision making;
  • Internet of Things (IoT);
  • Construction 4.0 and beyond;
  • Digital twins;
  • Visualization of sensing data;
  • Mobile sensing.

Prof. Dr. Ren-Jye Dzeng
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

  • sensing technologies
  • image recognition technologies
  • construction automation
  • Internet of Things
  • radio frequency identification
  • ultra-wideband technology
  • fiber optic sensing technology

Published Papers (2 papers)

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Research

28 pages, 17976 KiB  
Article
Untethered Ultra-Wideband-Based Real-Time Locating System for Road-Worker Safety
by Aitor Ochoa-de-Eribe-Landaberea, Leticia Zamora-Cadenas and Igone Velez
Sensors 2024, 24(8), 2391; https://doi.org/10.3390/s24082391 - 09 Apr 2024
Viewed by 732
Abstract
In order to reduce the accident risk in road construction and maintenance, this paper proposes a novel solution for road-worker safety based on an untethered real-time locating system (RTLS). This system tracks the location of workers in real time using ultra-wideband (UWB) technology [...] Read more.
In order to reduce the accident risk in road construction and maintenance, this paper proposes a novel solution for road-worker safety based on an untethered real-time locating system (RTLS). This system tracks the location of workers in real time using ultra-wideband (UWB) technology and indicates if they are in a predefined danger zone or not, where the predefined safe zone is delimited by safety cones. Unlike previous works that focus on road-worker safety by detecting vehicles that enter into the working zone, our proposal solves the problem of distracted workers leaving the safe zone. This paper presents a simple-to-deploy safety system. Our UWB anchors do not need any cables for powering, synchronisation, or data transfer. The anchors are placed inside safety cones, which are already available in construction sites. Finally, there is no need to manually measure the positions of anchors and introduce them to the system thanks to a novel self-positioning approach. Our proposal, apart from automatically estimating the anchors’ positions, also defines the limits of safe and danger zones. These features notably reduce the deployment time of the proposed safety system. Moreover, measurements show that all the proposed simplifications are obtained with an accuracy of 97%. Full article
(This article belongs to the Special Issue Smart Sensing and Recognition Systems in the Construction Industry)
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13 pages, 4615 KiB  
Article
A GRU-Based Model for Detecting Common Accidents of Construction Workers
by Ren-Jye Dzeng, Keisuke Watanabe, Hsien-Hui Hsueh and Chien-Kai Fu
Sensors 2024, 24(2), 672; https://doi.org/10.3390/s24020672 - 21 Jan 2024
Viewed by 558
Abstract
Fall accidents in the construction industry have been studied over several decades and identified as a common hazard and the leading cause of fatalities. Inertial sensors have recently been used to detect accidents of workers in construction sites, such as falls or trips. [...] Read more.
Fall accidents in the construction industry have been studied over several decades and identified as a common hazard and the leading cause of fatalities. Inertial sensors have recently been used to detect accidents of workers in construction sites, such as falls or trips. IMU-based systems for detecting fall-related accidents have been developed and have yielded satisfactory accuracy in laboratory settings. Nevertheless, the existing systems fail to uphold consistent accuracy and produce a significant number of false alarms when deployed in real-world settings, primarily due to the intricate nature of the working environments and the behaviors of the workers. In this research, the authors redesign the aforementioned laboratory experiment to target situations that are prone to false alarms based on the feedback obtained from workers in real construction sites. In addition, a new algorithm based on recurrent neural networks was developed to reduce the frequencies of various types of false alarms. The proposed model outperforms the existing benchmark model (i.e., hierarchical threshold model) with higher sensitivities and fewer false alarms in detecting stumble (100% sensitivity vs. 40%) and fall (95% sensitivity vs. 65%) events. However, the model did not outperform the hierarchical model in detecting coma events in terms of sensitivity (70% vs. 100%), but it did generate fewer false alarms (5 false alarms vs. 13). Full article
(This article belongs to the Special Issue Smart Sensing and Recognition Systems in the Construction Industry)
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