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Special Issue "Sensors for Construction Automation and Management"

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

Deadline for manuscript submissions: 31 May 2020.

Special Issue Editors

Dr. Reza Maalek
E-Mail Website
Guest Editor
Affiliation: Department of Civil Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
Interests: construction automation; as-built building information modeling (BIM); point cloud processing; progress monitoring; fabrication verification; machine learning; laser scanning; real-time location systems (RTLS); virtual and augmented reality (VR/AR)
Prof. Dr. Derek Lichti
E-Mail Website
Guest Editor
Department of Geomatics Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
Tel. +1 403 210 9495
Interests: laser scanning; photogrammetry; self-calibration; bundle adjustment; registration; point cloud processing; network design; multi-sensor systems; sensor integration; imaging metrology; deformation measurement
Special Issues and Collections in MDPI journals
Dr. Shahrokh Maalek
E-Mail Website
Guest Editor
Faculty of Civil Engineering, University of Tehran, Tehran, 1417466191, Iran
Interests: non-destructive testing and evaluation (NDT and E); structural health monitoring; remote sensing; remote bridge monitoring; bridge engineering; finite element modeling; remote fatigue monitoring; fracture mechanics, space structures; earthquake engineering

Special Issue Information

Dear Colleagues,

With the recent and on-going technological advancements, the application of sensors for construction project automation has grown markedly. Automatic data collection and analysis provide immense opportunities to improve, evaluate, and automate construction processes, a significant advantage over traditional manual practices. New and innovative approaches that foster the application of sensors and remote sensing technologies on construction projects are, hence, eminently desirable for the growth and development of the construction industry.

The “Sensors for Construction Automation and Management” Special Issue addresses a wide range of research topics focused on the application of sensors, so as to automate construction processes. Relevant topics include, but are not limited to, the following:

  • Application of laser scanners and camera/thermal imagery for construction automation;
  • Automated segmentation and feature extraction of point clouds and images related to construction, including scan-to-BIM;
  • Remote sensor-based damage detection, and health monitoring of real-world civil/infrastructure projects;
  • Automated sensor-based progress monitoring, modular fabrication verification, and on-site quality control;
  • Artificial intelligence, machine learning, and deep learning for the analysis of field sensor data;
  • Multi-sensor systems and data fusion in construction, including virtual and augmented reality (VR/AR);
  • Radio frequency-based real-time location systems (RTLS) in construction.

We look forward to receiving your contributions.

Kind regards,

Dr. Reza Maalek
Prof. Dr. Derek Lichti
Dr. Shahrokh Maalek
Guest Editors

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 papers will be 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 2000 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 automation
  • as-built building information modelling (BIM)
  • construction monitoring and control
  • non-destructive testing and evaluation (NDT and E)
  • real-time location systems (RTLS)
  • smart construction
  • machine-learning
  • artificial intelligence (AI)
  • virtual and augmented reality (VR/AR)
  • laser scanning technology

Published Papers (3 papers)

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Research

Open AccessArticle
Evaluation of HoloLens Tracking and Depth Sensing for Indoor Mapping Applications
Sensors 2020, 20(4), 1021; https://doi.org/10.3390/s20041021 - 14 Feb 2020
Abstract
The Microsoft HoloLens is a head-worn mobile augmented reality device that is capable of mapping its direct environment in real-time as triangle meshes and localize itself within these three-dimensional meshes simultaneously. The device is equipped with a variety of sensors including four tracking [...] Read more.
The Microsoft HoloLens is a head-worn mobile augmented reality device that is capable of mapping its direct environment in real-time as triangle meshes and localize itself within these three-dimensional meshes simultaneously. The device is equipped with a variety of sensors including four tracking cameras and a time-of-flight (ToF) range camera. Sensor images and their poses estimated by the built-in tracking system can be accessed by the user. This makes the HoloLens potentially interesting as an indoor mapping device. In this paper, we introduce the different sensors of the device and evaluate the complete system in respect of the task of mapping indoor environments. The overall quality of such a system depends mainly on the quality of the depth sensor together with its associated pose derived from the tracking system. For this purpose, we first evaluate the performance of the HoloLens depth sensor and its tracking system separately. Finally, we evaluate the overall system regarding its capability for mapping multi-room environments. Full article
(This article belongs to the Special Issue Sensors for Construction Automation and Management)
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Open AccessArticle
Incorporating Worker Awareness in the Generation of Hazard Proximity Warnings
Sensors 2020, 20(3), 806; https://doi.org/10.3390/s20030806 - 02 Feb 2020
Abstract
Proximity warning systems for construction sites do not consider whether workers are already aware of the hazard prior to issuing warnings. This can generate redundant and distracting alarms that interfere with worker ability to adopt timely and appropriate avoidance measures; and cause alarm [...] Read more.
Proximity warning systems for construction sites do not consider whether workers are already aware of the hazard prior to issuing warnings. This can generate redundant and distracting alarms that interfere with worker ability to adopt timely and appropriate avoidance measures; and cause alarm fatigue, which instigates workers to habitually disable the system or ignore the alarms; thereby increasing the risk of injury. Thus, this paper integrates the field-of-view of workers as a proxy for hazard awareness to develop an improved hazard proximity warning system for construction sites. The research first developed a rule-based model for the warning generation, which was followed by a virtual experiment to evaluate the integration of worker field-of-view in alarm generation. Based on these findings, an improved hazard proximity warning system incorporating worker field-of-view was developed for field applications that utilizes wearable inertial measurement units and localization sensors. The system’s effectiveness is illustrated through several case studies. This research provides a fresh perspective to the growing adoption of wearable sensors by incorporating the awareness of workers into the generation of hazard alarms. The proposed system is anticipated to reduce unnecessary and distracting alarms which can potentially lead to superior safety performance in construction. Full article
(This article belongs to the Special Issue Sensors for Construction Automation and Management)
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Open AccessArticle
A Hybrid Kinematic-Acoustic System for Automated Activity Detection of Construction Equipment
Sensors 2019, 19(19), 4286; https://doi.org/10.3390/s19194286 - 03 Oct 2019
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Sensors for Construction Automation and Management)
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