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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: closed (31 January 2023) | Viewed by 42146

Special Issue Editors

Department of Digital Engineering and Construction, Institute of Technology and Management in Construction, Karlsruhe Institute of Technology (KIT), Am Fasanengarten, Bldg. 50.31, 76131 Karlsruhe, Germany
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)
Special Issues, Collections and Topics in MDPI journals
Department of Geomatics Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
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, Collections and Topics in MDPI journals
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 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 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 (8 papers)

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Research

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19 pages, 5185 KiB  
Article
Identification, 3D-Reconstruction, and Classification of Dangerous Road Cracks
by Souhir Sghaier, Moez Krichen, Imed Ben Dhaou, Hela Elmannai and Reem Alkanhel
Sensors 2023, 23(7), 3578; https://0-doi-org.brum.beds.ac.uk/10.3390/s23073578 - 29 Mar 2023
Viewed by 1609
Abstract
Advances in semiconductor technology and wireless sensor networks have permitted the development of automated inspection at diverse scales (machine, human, infrastructure, environment, etc.). However, automated identification of road cracks is still in its early stages. This is largely owing to the difficulty obtaining [...] Read more.
Advances in semiconductor technology and wireless sensor networks have permitted the development of automated inspection at diverse scales (machine, human, infrastructure, environment, etc.). However, automated identification of road cracks is still in its early stages. This is largely owing to the difficulty obtaining pavement photographs and the tiny size of flaws (cracks). The existence of pavement cracks and potholes reduces the value of the infrastructure, thus the severity of the fracture must be estimated. Annually, operators in many nations must audit thousands of kilometers of road to locate this degradation. This procedure is costly, sluggish, and produces fairly subjective results. The goal of this work is to create an efficient automated system for crack identification, extraction, and 3D reconstruction. The creation of crack-free roads is critical to preventing traffic deaths and saving lives. The proposed method consists of five major stages: detection of flaws after processing the input picture with the Gaussian filter, contrast adjustment, and ultimately, threshold-based segmentation. We created a database of road cracks to assess the efficacy of our proposed method. The result obtained are commendable and outperform previous state-of-the-art studies. Full article
(This article belongs to the Special Issue Sensors for Construction Automation and Management)
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22 pages, 5532 KiB  
Article
Sensor Acquisition and Allocation for Real-Time Monitoring of Articulated Construction Equipment in Digital Twins
by Sanat A. Talmaki and Vineet R. Kamat
Sensors 2022, 22(19), 7635; https://0-doi-org.brum.beds.ac.uk/10.3390/s22197635 - 09 Oct 2022
Cited by 7 | Viewed by 2149
Abstract
The visibility available to an equipment operator on a dynamic construction site can often be blocked by various obstacles such as materials, temporary or permanent facilities, other equipment, and workers. Equipment monitoring in real-time digital twins can thus play a crucial role in [...] Read more.
The visibility available to an equipment operator on a dynamic construction site can often be blocked by various obstacles such as materials, temporary or permanent facilities, other equipment, and workers. Equipment monitoring in real-time digital twins can thus play a crucial role in accident prevention. This paper develops a scalable technical approach and presents a prototype application framework for transmitting real world sensor data to update 3D equipment models inside a graphical digital twin for concurrent visualization of a monitored construction operation. The developed framework and workflow can be extended to visualize any construction operation, as it occurs, inside a dynamic 3D world simply by outfitting the real equipment with appropriate sensors and connecting them to their virtual counterparts. The implemented proof-of-concept interface is described in the context of a real-time 3D digital twin for assisting excavator operators prevent unintended strikes with underground utilities. Experiments to validate the proposed technical approach by simulating the real-time motion of a backhoe loader’s articulated arm using orientation sensors installed on its boom, stick, and bucket are described. The experimental results characterize the scope and potential reasons for spatio-temporal discrepancies that can occur between a monitored real operation and its replicated digital twin. The effect of an operator warning mechanism based on preset safety thresholds is also investigated and described. Full article
(This article belongs to the Special Issue Sensors for Construction Automation and Management)
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22 pages, 3699 KiB  
Article
Automatic Real-Time Pose Estimation of Machinery from Images
by Marcel Bertels, Boris Jutzi and Markus Ulrich
Sensors 2022, 22(7), 2627; https://0-doi-org.brum.beds.ac.uk/10.3390/s22072627 - 29 Mar 2022
Cited by 5 | Viewed by 2842
Abstract
The automatic positioning of machines in a large number of application areas is an important aspect of automation. Today, this is often done using classic geodetic sensors such as Global Navigation Satellite Systems (GNSS) and robotic total stations. In this work, a stereo [...] Read more.
The automatic positioning of machines in a large number of application areas is an important aspect of automation. Today, this is often done using classic geodetic sensors such as Global Navigation Satellite Systems (GNSS) and robotic total stations. In this work, a stereo camera system was developed that localizes a machine at high frequency and serves as an alternative to the previously mentioned sensors. For this purpose, algorithms were developed that detect active markers on the machine in a stereo image pair, find stereo point correspondences, and estimate the pose of the machine from these. Theoretical influences and accuracies for different systems were estimated with a Monte Carlo simulation, on the basis of which the stereo camera system was designed. Field measurements were used to evaluate the actual achievable accuracies and the robustness of the prototype system. The comparison is present with reference measurements with a laser tracker. The estimated object pose achieved accuracies higher than 16 mm with the translation components and accuracies higher than 3 mrad with the rotation components. As a result, 3D point accuracies higher than 16 mm were achieved by the machine. For the first time, a prototype could be developed that represents an alternative, powerful image-based localization method for machines to the classical geodetic sensors. Full article
(This article belongs to the Special Issue Sensors for Construction Automation and Management)
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15 pages, 771 KiB  
Article
Hybrid Random Forest and Support Vector Machine Modeling for HVAC Fault Detection and Diagnosis
by Wunna Tun, Johnny Kwok-Wai Wong and Sai-Ho Ling
Sensors 2021, 21(24), 8163; https://0-doi-org.brum.beds.ac.uk/10.3390/s21248163 - 07 Dec 2021
Cited by 22 | Viewed by 3396
Abstract
The malfunctioning of the heating, ventilating, and air conditioning (HVAC) system is considered to be one of the main challenges in modern buildings. Due to the complexity of the building management system (BMS) with operational data input from a large number of sensors [...] Read more.
The malfunctioning of the heating, ventilating, and air conditioning (HVAC) system is considered to be one of the main challenges in modern buildings. Due to the complexity of the building management system (BMS) with operational data input from a large number of sensors used in HVAC system, the faults can be very difficult to detect in the early stage. While numerous fault detection and diagnosis (FDD) methods with the use of statistical modeling and machine learning have revealed prominent results in recent years, early detection remains a challenging task since many current approaches are unfeasible for diagnosing some HVAC faults and have accuracy performance issues. In view of this, this study presents a novel hybrid FDD approach by combining random forest (RF) and support vector machine (SVM) classifiers for the application of FDD for the HVAC system. Experimental results demonstrate that our proposed hybrid random forest–support vector machine (HRF–SVM) outperforms other methods with higher prediction accuracy (98%), despite that the fault symptoms were insignificant. Furthermore, the proposed framework can reduce the significant number of sensors required and work well with the small number of faulty training data samples available in real-world applications. Full article
(This article belongs to the Special Issue Sensors for Construction Automation and Management)
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24 pages, 9732 KiB  
Article
Evaluation of HoloLens Tracking and Depth Sensing for Indoor Mapping Applications
by Patrick Hübner, Kate Clintworth, Qingyi Liu, Martin Weinmann and Sven Wursthorn
Sensors 2020, 20(4), 1021; https://0-doi-org.brum.beds.ac.uk/10.3390/s20041021 - 14 Feb 2020
Cited by 86 | Viewed by 12858
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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19 pages, 3013 KiB  
Article
Incorporating Worker Awareness in the Generation of Hazard Proximity Warnings
by Kelsey Chan, Joseph Louis and Alex Albert
Sensors 2020, 20(3), 806; https://0-doi-org.brum.beds.ac.uk/10.3390/s20030806 - 02 Feb 2020
Cited by 24 | Viewed by 5242
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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21 pages, 5468 KiB  
Article
A Hybrid Kinematic-Acoustic System for Automated Activity Detection of Construction Equipment
by Behnam Sherafat, Abbas Rashidi, Yong-Cheol Lee and Changbum R. Ahn
Sensors 2019, 19(19), 4286; https://0-doi-org.brum.beds.ac.uk/10.3390/s19194286 - 03 Oct 2019
Cited by 32 | Viewed by 3859
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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Review

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26 pages, 1231 KiB  
Review
Application of Building Information Modelling (BIM) in the Health Monitoring and Maintenance Process: A Systematic Review
by Reihane Shafie Panah and Mahdi Kioumarsi
Sensors 2021, 21(3), 837; https://0-doi-org.brum.beds.ac.uk/10.3390/s21030837 - 27 Jan 2021
Cited by 36 | Viewed by 8421
Abstract
Improvements in the science of health monitoring and maintenance have facilitated the observation of damage and defects in existing structures and infrastructures, such as bridges and railways. The need to extend sensing technology through the use of wireless sensors as well as the [...] Read more.
Improvements in the science of health monitoring and maintenance have facilitated the observation of damage and defects in existing structures and infrastructures, such as bridges and railways. The need to extend sensing technology through the use of wireless sensors as well as the lack of description tools for understanding, visualizing, and documenting sensor outputs has encouraged researchers to use powerful tools such as Building Information Modelling (BIM) systems. BIM has become important because of conducting tools widely used in the Architecture, Engineering, and Construction (AEC) industry to present and manage information on structural systems and situations. Since combining health monitoring and maintenance results with BIM models is a new field of study, and most projects utilize various aspects of it, we have conducted a review of important work related to this subject published from 2010 to November of 2020. After reviewing 278 journal articles, research trends, approaches, methods, gaps, and future agenda related to BIM in monitoring and maintenance were highlighted. This paper, through a bibliometric and content analysis, concludes that besides main improvements, some limitations now exist which affect the modeling and maintenance process. These limitations are related to extending the IFC schema, optimizing sensor data, interoperability among various BIM platforms, optimization of various sensing technologies for fault detection and management of huge amounts of data, besides consideration of environmental effects on monitoring hazards and underground objects. Finally, this paper aims to help to solve the mentioned limitation through a comprehensive review of existing research. Full article
(This article belongs to the Special Issue Sensors for Construction Automation and Management)
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