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Smart Sensing in Building and Construction
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Smart Sensing in Building and Construction

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

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 21207

Special Issue Editors

Dr. Jia-Rui Lin

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Guest Editor
School of Civil Engineering, Tsinghua University, Beijing, China
Interests: intelligent design; construction process modeling; building information model (BIM); machine learning; digital twin
Special Issues, Collections and Topics in MDPI journals
Dr. Zhen-Zhong Hu

E-Mail Website
Guest Editor
Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
Interests: civil and marine engineering; digital twin; marine energy; digital disaster prevention and mitigation
Special Issues, Collections and Topics in MDPI journals
Dr. Jérôme Frisch

E-Mail Website
Guest Editor
Energy Efficient and Sustainable Building E3D, RWTH Aachen University, Mathieustr. 30, 52074 Aachen, Germany
Interests: building performance simulation; numerical analysis; algorithmic development; high-performance computing; fluid flow simulations
Special Issues, Collections and Topics in MDPI journals
Dr. Qian Wang

E-Mail
Guest Editor
Department of Building, School of Design and Environment, National University of Singapore, 4 Architecture Drive, Singapore 117566, Singapore
Interests: LiDAR; scan-to-BIM; computer vision; robotics
Special Issues, Collections and Topics in MDPI journals
Dr. Yichuan Deng

E-Mail Website
Guest Editor
School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510641, China
Interests: computer vision; knowledge management; construction safety
Dr. Yi Tan

E-Mail Website1 Website2
Guest Editor
Colledge of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518052, China
Interests: BIM/GIS integration; industry foundation classes; digital twin; VR/AR; smart construction and O&M
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, there has been a significant demand for sensing technology in the building and construction field. Timely collection and analysis of heterogeneous sensing data are essential to make wise decisions efficiently for safety management, health monitoring, performance management, remote operation, predictive maintenance, etc. However, built environment and construction projects are complex and dynamic with multiple stakeholders (users, maintainers, managers, engineers, etc.), equipment, and even robots involved, making it difficult to directly adopt existing methods and tools for collecting and mining sensing data. Therefore, enhanced data sensing and mining approaches are required to discover useful knowledge and patterns from multi-source sensing data with consideration of characteristics of the building and construction domain. Moreover, rich domain knowledge is embedded in physical and behavioral models and domain-specific knowledge graphs and it is worth exploring new methods to explore the value of sensing data with the assistance of model- and knowledge-driven approaches.

This Special Issue will collect state-of-the-art research findings on the latest developments and challenges of smart sensing in the building and construction field. High-quality reviews and original research papers that present current research gaps, theoretical frameworks, methodologies, and approaches are welcome.

Potential topics include but are not limited to the following:

  • Reviews or surveys on state-of-the-art of sensing-related topics in the building and construction field
  • Methods and tools for sensing data collection and processing, including internet of things, laser scanning, photogrammetry, thermal imaging, virtual sensing, crowdsourcing, multi-source data fusion, and design and development of new sensors
  • Technologies and approaches to extract complex patterns or knowledge from sensing data, i.e., computer vision, machine learning, and deep learning
  • Integration of sensing data mining and model- and knowledge-driven approaches to understand the complex and dynamic nature of buildings and construction projects
  • Real-world applications of remote sensing data mining, i.e., building performance monitoring, structural health monitoring, damage assessment, disaster monitoring, as-built model reconstruction, geometric quality inspection, construction process monitoring, etc.

Dr. Jia-Rui Lin
Dr. Zhen-Zhong Hu
Dr. Jérôme Frisch
Dr. Qian Wang
Dr. Yichuan Deng
Dr. Yi Tan
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 industry
  • internet of things
  • crowdsourcing
  • data sensing
  • machine learning
  • computer vision
  • data mining
  • digital twin

Published Papers (5 papers)

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Research

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22 pages, 11325 KiB  
Article
BIM and Data-Driven Predictive Analysis of Optimum Thermal Comfort for Indoor Environment
by Vincent J.L. Gan, Han Luo, Yi Tan, Min Deng and H.L. Kwok
Sensors 2021, 21(13), 4401; https://doi.org/10.3390/s21134401 - 27 Jun 2021
Cited by 25 | Viewed by 4148
Abstract
Mechanical ventilation comprises a significant proportion of the total energy consumed in buildings. Sufficient natural ventilation in buildings is critical in reducing the energy consumption of mechanical ventilation while maintaining a comfortable indoor environment for occupants. In this paper, a new computerized framework [...] Read more.
Mechanical ventilation comprises a significant proportion of the total energy consumed in buildings. Sufficient natural ventilation in buildings is critical in reducing the energy consumption of mechanical ventilation while maintaining a comfortable indoor environment for occupants. In this paper, a new computerized framework based on building information modelling (BIM) and machine learning data-driven models is presented to analyze the optimum thermal comfort for indoor environments with the effect of natural ventilation. BIM provides geometrical and semantic information of the built environment, which are leveraged for setting the computational domain and boundary conditions of computational fluid dynamics (CFD) simulation. CFD modelling is conducted to obtain the flow field and temperature distribution, the results of which determine the thermal comfort index in a ventilated environment. BIM–CFD provides spatial data, boundary conditions, indoor environmental parameters, and the thermal comfort index for machine learning to construct robust data-driven models to empower the predictive analysis. In the neural network, the adjacency matrix in the field of graph theory is used to represent the spatial features (such as zone adjacency and connectivity) and incorporate the potential impact of interzonal airflow in thermal comfort analysis. The results of a case study indicate that utilizing natural ventilation can save cooling power consumption, but it may not be sufficient to fulfil all the thermal comfort criteria. The performance of natural ventilation at different seasons should be considered to identify the period when both air conditioning energy use and indoor thermal comfort are achieved. With the proposed new framework, thermal comfort prediction can be examined more efficiently to study different design options, operating scenarios, and changeover strategies between various ventilation modes, such as better spatial HVAC system designs, specific room-based real-time HVAC control, and other potential applications to maximize indoor thermal comfort. Full article
(This article belongs to the Special Issue Smart Sensing in Building and Construction)
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25 pages, 6004 KiB  
Article
BIM and Computer Vision-Based Framework for Fire Emergency Evacuation Considering Local Safety Performance
by Hui Deng, Zhibin Ou, Genjie Zhang, Yichuan Deng and Mao Tian
Sensors 2021, 21(11), 3851; https://doi.org/10.3390/s21113851 - 02 Jun 2021
Cited by 26 | Viewed by 4239
Abstract
Fire hazard in public buildings may result in serious casualties due to the difficulty of evacuation caused by intricate interior space and unpredictable development of fire situations. It is essential to provide safe and reliable indoor navigation for people trapped in the fire. [...] Read more.
Fire hazard in public buildings may result in serious casualties due to the difficulty of evacuation caused by intricate interior space and unpredictable development of fire situations. It is essential to provide safe and reliable indoor navigation for people trapped in the fire. Distinguished from the global shortest rescue route planning, a framework focusing on the local safety performance is proposed for emergency evacuation navigation. Sufficiently utilizing the information from Building Information Modeling (BIM), this framework automatically constructs geometry network model (GNM) through Industry Foundation Classes (IFC) and integrates computer vision for indoor positioning. Considering the available local egress time (ALET), a back propagation (BP) neural network is applied for adjusting the rescue route according to the fire situation, improving the local safety performance of evacuation. A campus building is taken as an example for proving the feasibility of the framework proposed. The result indicates that the rescue route generated by proposed framework is secure and reasonable. The proposed framework provides an idea for using real-time images only to implement the automatic generation of rescue route when a fire hazard occurs, which is passive, cheap, and convenient. Full article
(This article belongs to the Special Issue Smart Sensing in Building and Construction)
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20 pages, 14188 KiB  
Article
Digital Twin-Based Safety Risk Coupling of Prefabricated Building Hoisting
by Zhansheng Liu, Xintong Meng, Zezhong Xing and Antong Jiang
Sensors 2021, 21(11), 3583; https://doi.org/10.3390/s21113583 - 21 May 2021
Cited by 54 | Viewed by 4685
Abstract
Safety management in hoisting is the key issue to determine the development of prefabricated building construction. However, the security management in the hoisting stage lacks a truly effective method of information physical fusion, and the safety risk analysis of hoisting does not consider [...] Read more.
Safety management in hoisting is the key issue to determine the development of prefabricated building construction. However, the security management in the hoisting stage lacks a truly effective method of information physical fusion, and the safety risk analysis of hoisting does not consider the interaction of risk factors. In this paper, a hoisting safety risk management framework based on digital twin (DT) is presented. The digital twin hoisting safety risk coupling model is built. The proposed model integrates the Internet of Things (IoT), Building Information Modeling (BIM), and a security risk analysis method combining the Apriori algorithm and complex network. The real-time perception and virtual–real interaction of multi-source information in the hoisting process are realized, the association rules and coupling relationship among hoisting safety risk factors are mined, and the time-varying data information is visualized. Demonstration in the construction of a large-scale prefabricated building shows that with the proposed framework, it is possible to complete the information fusion between the hoisting site and the virtual model and realize the visual management. The correlative relationship among hoisting construction safety risk factors is analyzed, and the key control factors are found. Moreover, the efficiency of information integration and sharing is improved, the gap of coupling analysis of security risk factors is filled, and effective security management and decision-making are achieved with the proposed approach. Full article
(This article belongs to the Special Issue Smart Sensing in Building and Construction)
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19 pages, 5016 KiB  
Article
A Framework for the Automatic Integration and Diagnosis of Building Energy Consumption Data
by Shuang Yuan, Zhen-Zhong Hu, Jia-Rui Lin and Yun-Yi Zhang
Sensors 2021, 21(4), 1395; https://doi.org/10.3390/s21041395 - 17 Feb 2021
Cited by 7 | Viewed by 2885
Abstract
Buildings account for a majority of the primary energy consumption of the human society, therefore, analyses of building energy consumption monitoring data are of significance to the discovery of anomalous energy usage patterns, saving of building utility expenditures, and contribution to the greater [...] Read more.
Buildings account for a majority of the primary energy consumption of the human society, therefore, analyses of building energy consumption monitoring data are of significance to the discovery of anomalous energy usage patterns, saving of building utility expenditures, and contribution to the greater environmental protection effort. This paper presents a unified framework for the automatic extraction and integration of building energy consumption data from heterogeneous building management systems, along with building static data from building information models to serve analysis applications. This paper also proposes a diagnosis framework based on density-based clustering and artificial neural network regression using the integrated data to identify anomalous energy usages. The framework and the methods have been implemented and validated from data collected from a multitude of large-scale public buildings across China. Full article
(This article belongs to the Special Issue Smart Sensing in Building and Construction)
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Review

Jump to: Research

25 pages, 40140 KiB  
Review
Computer Vision-Based Construction Process Sensing for Cyber–Physical Systems: A Review
by Binghan Zhang, Bin Yang, Congjun Wang, Zhichen Wang, Boda Liu and Tengwei Fang
Sensors 2021, 21(16), 5468; https://doi.org/10.3390/s21165468 - 13 Aug 2021
Cited by 6 | Viewed by 3593
Abstract
Cyber–physical systems (CPSs) are generally considered to be the next generation of engineered systems. However, the actual application of CPSs in the Architecture, Engineering and Construction (AEC) industry is still at a low level. The sensing method in the construction process plays a [...] Read more.
Cyber–physical systems (CPSs) are generally considered to be the next generation of engineered systems. However, the actual application of CPSs in the Architecture, Engineering and Construction (AEC) industry is still at a low level. The sensing method in the construction process plays a very important role in the establishment of CPSs. Therefore, the purpose of this paper is to discuss the application potential of computer vision-based sensing methods and provide practical suggestions through a literature review. This paper provides a review of the current application of CPSs in the AEC industry, summarizes the current knowledge gaps, and discusses the problems with the current construction site sensing approach. Considering the unique advantages of the computer vision (CV) method at the construction site, the application of CV for different construction entities was reviewed and summarized to achieve a CV-based construction site sensing approach for construction process CPSs. The potential of CPS can be further stimulated by providing rich information from on-site sensing using CV methods. According to the review, this approach has unique advantages in the specific environment of the construction site. Based on the current knowledge gap identified in the literature review, this paper proposes a novel concept of visual-based construction site sensing method for CPS application, and an architecture for CV-based CPS is proposed as an implementation of this concept. The main contribution of this paper is to propose a CPS architecture using computer vision as the main information acquisition method based on the literature review. This architecture innovatively introduces computer vision as a sensing method of construction sites, and realizes low-cost and non-invasive information acquisition in complex construction scenarios. This method can be used as an important supplement to on-site sensing to further promote the automation and intelligence of the construction process. Full article
(This article belongs to the Special Issue Smart Sensing in Building and Construction)
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