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Sensors and Sensing Technologies for Monitoring and Inspection during Construction
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Sensors and Sensing Technologies for Monitoring and Inspection during Construction

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

Deadline for manuscript submissions: 31 May 2025 | Viewed by 10877

Special Issue Editors

Dr. Simon Laflamme

E-Mail Website
Guest Editor
Department of Civil, Construction, and Environmental Engineering, Iowa State University, Ames, IA 50011, USA
Interests: smart systems; smart structures; sensors; structural health monitoring; real-time learning
Special Issues, Collections and Topics in MDPI journals
Dr. Roy Sturgill

E-Mail Website
Guest Editor
Department of Civil, Construction, and Environmental Engineering, Iowa State University, Ames, IA 50011, USA
Interests: construction inspection sensors; remote virtual inspection; digital inspection; visualization
Prof. Dr. Filippo Ubertini

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, University of Perugia, Perugia, Italy
Interests: structural health monitoring; smart materials; bridge engineering; earthquake engineering
Dr. Dan Tran

E-Mail Website
Guest Editor
Department of Civil, Environmental and Architectural Engineering, University of Kansas, Lawrence, KS 66045, USA
Interests: risk-based inspection; construction technology; design and construction interface; uncertainty quantification
Dr. Denvid Lau

E-Mail Website
Guest Editor
Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong, China
Interests: functionalized construction materials; multiscale modeling; moisture-induced debonding; durability; structural rehabilitation

Special Issue Information

Dear Colleagues,

We are organizing a Special Issue on sensors and sensing technologies targeting monitoring and inspection during the construction of building and infrastructure projects. Assessing the condition of structures, and their elements and materials, during the erection process is particularly difficult because of their constantly changing boundary conditions, high levels of uncertainty, and the lack of available prior knowledge. This Special Issue aims to synthesize the latest developments in emerging technologies enabling condition assessments and inspection in this challenging environment and to generate discussions on future research needs to empower the construction industry with real-time decision-making capabilities and ensure structural integrity and safe work sites.

Topics of interest include, but are not limited to:

  • Integrating building/bridge information models (BIMs) with sensor data.
  • Construction applications of digital twin models with integrated sensor data.
  • The co-design of structural systems of sensor networks for in-construction assessment.
  • Machine learning methods and other data-based methods that enable structural health monitoring during construction.
  • Real-time model updates using sensor data.
  • Human-in-the-loop techniques to interpret sensor information.
  • Sensing strategies for safe work sites.
  • Sensing technologies for remote virtual inspection.
  • Advanced photogrammetric methods for digital surveys in construction sites.
  • Digital technologies and sensors for automated defect identification in remote inspections.
  • AI methods for inspections and safe construction sites.
  • Decision-making science using in-construction measurements.
  • Sensing technologies and visualization for construction inspection.
  • Applications of emerging digital and sensing technologies for construction.

Dr. Simon Laflamme
Dr. Roy Sturgill
Prof. Dr. Filippo Ubertini
Dr. Dan Tran
Dr. Denvid Lau
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
  • inspection
  • sensing
  • structural ealth monitoring (SHM)
  • remote sensing
  • machine learning
  • physics-informed monitoring
  • digital twins
  • building information model (BIM)
  • bridge information model
  • bridge management system

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Published Papers (4 papers)

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Research

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20 pages, 10183 KiB  
Article
Low-Cost Efficient Wireless Intelligent Sensor (LEWIS) for Research and Education
by Mahsa Sanei, Solomon Atcitty and Fernando Moreu
Sensors 2024, 24(16), 5308; https://doi.org/10.3390/s24165308 - 16 Aug 2024
Viewed by 4944
Abstract
Sensors have recently become valuable tools in engineering, providing real-time data for monitoring structures and the environment. They are also emerging as new tools in education and training, offering learners real-time information to reinforce their understanding of engineering concepts. However, sensing technology’s complexity, [...] Read more.
Sensors have recently become valuable tools in engineering, providing real-time data for monitoring structures and the environment. They are also emerging as new tools in education and training, offering learners real-time information to reinforce their understanding of engineering concepts. However, sensing technology’s complexity, costs, fabrication and implementation challenges often hinder engineers’ exploration. Simplifying these aspects could make sensors more accessible to engineering students. In this study, the researcher developed, fabricated, and tested an efficient low-cost wireless intelligent sensor aimed at education and research, named LEWIS1. This paper describes the hardware and software architecture of the first prototype and their use, as well as the proposed new versions, LEWIS1-β and LEWIS1-γ, which simplify both hardware and software. The capabilities of the proposed sensor are compared with those of an accurate commercial PCB sensor. This paper also demonstrates examples of outreach efforts and suggests the adoption of the newer versions of LEWIS1 as tools for education and research. The authors also investigated the number of activities and sensor-building workshops that have been conducted since 2015 using the LEWIS sensor, showing an increasing trend in the excitement of people from various professions to participate and learn sensor fabrication. Full article
(This article belongs to the Special Issue Sensors and Sensing Technologies for Monitoring and Inspection during Construction)
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14 pages, 3425 KiB  
Article
Probabilistic Method to Fuse Artificial Intelligence-Generated Underground Utility Mapping
by Kunle Sunday Oguntoye, Simon Laflamme, Roy Sturgill, Daniel A. Salazar Martinez, David J. Eisenmann and Anne Kimber
Sensors 2024, 24(11), 3559; https://doi.org/10.3390/s24113559 - 31 May 2024
Viewed by 1263
Abstract
Utility as-built plans, which typically provide information about underground utilities’ position and spatial locations, are known to comprise inaccuracies. Over the years, the reliance on utility investigations using an array of sensing equipment has increased in an attempt to resolve utility as-built inaccuracies [...] Read more.
Utility as-built plans, which typically provide information about underground utilities’ position and spatial locations, are known to comprise inaccuracies. Over the years, the reliance on utility investigations using an array of sensing equipment has increased in an attempt to resolve utility as-built inaccuracies and mitigate the high rate of accidental underground utility strikes during excavation activities. Adapting data fusion into utility engineering and investigation practices has been shown to be effective in generating information with improved accuracy. However, the complexities in data interpretation and associated prohibitive costs, especially for large-scale projects, are limiting factors. This paper addresses the problem of data interpretation, costs, and large-scale utility mapping with a novel framework that generates probabilistic inferences by fusing data from an automatically generated initial map with as-built data. The probabilistic inferences expose regions of high uncertainty, highlighting them as prime targets for further investigations. The proposed model is a collection of three main processes. First, the automatic initial map creation is a novel contribution supporting rapid utility mapping by subjecting identified utility appurtenances to utility inference rules. The second and third processes encompass the fusion of the created initial utility map with available knowledge from utility as-builts or historical satellite imagery data and then evaluating the uncertainties using confidence value estimators. The proposed framework transcends the point estimation of buried utility locations in previous works by producing a final probabilistic utility map, revealing a confidence level attributed to each segment linking aboveground features. In this approach, the utility infrastructure is rapidly mapped at a low cost, limiting the extent of more detailed utility investigations to low-confidence regions. In resisting obsolescence, another unique advantage of this framework is the dynamic nature of the mapping to automatically update information upon the arrival of new knowledge. This ultimately minimizes the problem of utility as-built accuracies dwindling over time. Full article
(This article belongs to the Special Issue Sensors and Sensing Technologies for Monitoring and Inspection during Construction)
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18 pages, 4448 KiB  
Article
Enhancing Safety on Construction Sites: A UWB-Based Proximity Warning System Ensuring GDPR Compliance to Prevent Collision Hazards
by Silvia Mastrolembo Ventura, Paolo Bellagente, Stefano Rinaldi, Alessandra Flammini and Angelo L. C. Ciribini
Sensors 2023, 23(24), 9770; https://doi.org/10.3390/s23249770 - 12 Dec 2023
Cited by 3 | Viewed by 2367
Abstract
Construction is known as one of the most dangerous industries in terms of worker safety. Collisions due the excessive proximity of workers to moving construction vehicles are one of the leading causes of fatal and non-fatal accidents on construction sites internationally. Proximity warning [...] Read more.
Construction is known as one of the most dangerous industries in terms of worker safety. Collisions due the excessive proximity of workers to moving construction vehicles are one of the leading causes of fatal and non-fatal accidents on construction sites internationally. Proximity warning systems (PWS) have been proposed in the literature as a solution to detect the risk for collision and to alert workers and equipment operators in time to prevent collisions. Although the role of sensing technologies for situational awareness has been recognised in previous studies, several factors still need to be considered. This paper describes the design of a prototype sensor-based PWS, aimed mainly at small and medium-sized construction companies, to collect real-time data directly from construction sites and to warn workers of a potential risk of collision accidents. It considers, in an integrated manner, factors such as cost of deployment, the actual nature of a construction site as an operating environment and data protection. A low-cost, ultra-wideband (UWB)-based proximity detection system has been developed that can operate with or without fixed anchors. In addition, the PWS is compliant with the General Data Protection Regulation (GDPR) of the European Union. A privacy-by-design approach has been adopted and privacy mechanisms have been used for data protection. Future work could evaluate the PWS in real operational conditions and incorporate additional factors for its further development, such as studies on the timely interpretation of data. Full article
(This article belongs to the Special Issue Sensors and Sensing Technologies for Monitoring and Inspection during Construction)
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Review

Jump to: Research

22 pages, 830 KiB  
Review
Content Analysis of Three-Dimensional Model Technologies and Applications for Construction: Current Trends and Future Directions
by Nhien Le, Daniel Tran and Roy Sturgill
Sensors 2024, 24(12), 3838; https://doi.org/10.3390/s24123838 - 13 Jun 2024
Viewed by 1443
Abstract
The proliferation of digital technologies is substantially transforming inspection methodologies for construction activities. Although the implementation of a three-dimensional (3D) model has emerged as an advantageous, feasible inspection application, the selection of the most suitable 3D models is challenging due to multiple technology [...] Read more.
The proliferation of digital technologies is substantially transforming inspection methodologies for construction activities. Although the implementation of a three-dimensional (3D) model has emerged as an advantageous, feasible inspection application, the selection of the most suitable 3D models is challenging due to multiple technology options. The primary objectives of this study were to investigate current trends and identify future technologies for 3D models in the construction industry. This study utilized systematic reviews by identifying and selecting quality journals, analyzing selected articles, and conducting content analysis and meta-analysis to identify dominant themes in 3D models. Results showed that the top technologies used to model construction projects are building information models, remote sensing, stereo vision system/photo processing programs, and augmented reality/virtual reality. The main benefits and challenges of these technologies for modeling were also determined. This study identified three areas with significant knowledge gaps for future research: (1) the amalgamation of two or more technologies to overcome project obstacles; (2) solution optimization for inspections in remote areas; and (3) the development of algorithm-based technologies. This research contributes to the body of knowledge by exploring current trends and future directions of 3D model technologies in the construction industry. Full article
(This article belongs to the Special Issue Sensors and Sensing Technologies for Monitoring and Inspection during Construction)
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