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Neural Sensors for Architecture, Engineering and Construction (AEC) Industry
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Neural Sensors for Architecture, Engineering and Construction (AEC) Industry

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

Deadline for manuscript submissions: closed (5 March 2024) | Viewed by 3637

Special Issue Editors

Prof. Dr. Bin Yang

E-Mail Website1 Website2
Guest Editor
1. School of Energy ＆ Safety Engineering, Tianjin Chengjian University, Tianjing 300384, China
2. Department of Applied Physics and Electronics, Umea Universitet, 90187 Umea, Sweden
Interests: human thermal comfort; building energy efficiency; low carbon and smart building
Special Issues, Collections and Topics in MDPI journals
Dr. Hanliang Fu

E-Mail Website1 Website2
Guest Editor
Laboratory of Neuromanagement in Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China
Interests: neural sensors; neuromanagement in engineering; engineering management; public acceptance
Special Issues, Collections and Topics in MDPI journals
Dr. Zhihan Lv

grade E-Mail Website1 Website2
Guest Editor
Department of Game Design, Uppsala University, Uppsala, Sweden
Interests: metaverse; digital twins, Internet of Things; cloud computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The architecture, engineering and construction (AEC) industry is the economic cornerstone for many nations worldwide. In the age of artificial intelligence, exploring the sustainable development of the industry through emerging technologies has become one of the future development directions. The incorporation of advanced sensing technologies that have been developed using neural engineering can endow sensors with biological elements of intelligence such as perception, recognition, and decision making. Thus, state-of-the-art neuroscience studies on perceptual decision-making could provide the necessary support for investigating the neural mechanisms behind construction workers’ behavior. This will lead to innovative solutions concerning construction safety, architectural environments, architectural cognition, intelligent building, and architectural design for the future AEC industry. Anecdotal evidence in the AEC industry already exists with respect to improvements of study methods made using neural sensing technologies such as eye movement, event-related potential (ERP), robotics, big data, artificial intelligence (Al), and functional near-infrared spectroscopy (fNIRS). Thus, neural sensing technologies used in the AEC industry are destined to fundamentally change research paradigms in the coming years.

This Special Issue aims to advance knowledge in the application of neural sensors in the AEC industry by encouraging contributions to new concepts, methodologies, and best practices, as well as experiments and case studies for current and future integration of technologies in AEC industry, including but are not limited to, the above mentioned technologies.

Prof. Dr. Bin Yang
Dr. Hanliang Fu
Dr. Zhihan Lv
Guest Editors

Manuscript Submission Information

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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

  • sensors
  • perception
  • advanced sensing technology
  • neural mechanisms
  • artificial intelligence
  • architecture, engineering, and construction
  • neuroscience
  • machine learning

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Published Papers (2 papers)

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19 pages, 1473 KiB  
Article
Magnetic Flux Sensor Based on Spiking Neurons with Josephson Junctions
by Timur Karimov, Valerii Ostrovskii, Vyacheslav Rybin, Olga Druzhina, Georgii Kolev and Denis Butusov
Sensors 2024, 24(7), 2367; https://doi.org/10.3390/s24072367 - 8 Apr 2024
Cited by 3 | Viewed by 1079
Abstract
Josephson junctions (JJs) are superconductor-based devices used to build highly sensitive magnetic flux sensors called superconducting quantum interference devices (SQUIDs). These sensors may vary in design, being the radio frequency (RF) SQUID, direct current (DC) SQUID, and hybrid, such as D-SQUID. In addition, [...] Read more.
Josephson junctions (JJs) are superconductor-based devices used to build highly sensitive magnetic flux sensors called superconducting quantum interference devices (SQUIDs). These sensors may vary in design, being the radio frequency (RF) SQUID, direct current (DC) SQUID, and hybrid, such as D-SQUID. In addition, recently many of JJ’s applications were found in spiking models of neurons exhibiting nearly biological behavior. In this study, we propose and investigate a new circuit model of a sensory neuron based on DC SQUID as part of the circuit. The dependence of the dynamics of the designed model on the external magnetic flux is demonstrated. The design of the circuit and derivation of the corresponding differential equations that describe the dynamics of the system are given. Numerical simulation is used for experimental evaluation. The experimental results confirm the applicability and good performance of the proposed magnetic-flux-sensitive neuron concept: the considered device can encode the magnetic flux in the form of neuronal dynamics with the linear section. Furthermore, some complex behavior was discovered in the model, namely the intermittent chaotic spiking and plateau bursting. The proposed design can be efficiently applied to developing the interfaces between circuitry and spiking neural networks. However, it should be noted that the proposed neuron design shares the main limitation of all the superconductor-based technologies, i.e., the need for a cryogenic and shielding system. Full article
(This article belongs to the Special Issue Neural Sensors for Architecture, Engineering and Construction (AEC) Industry)
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Review

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30 pages, 5675 KiB  
Review
A Bibliometric Analysis of Neuroscience Tools Use in Construction Health and Safety Management
by Zhikun Ding, Zhaoyang Xiong and Yewei Ouyang
Sensors 2023, 23(23), 9522; https://doi.org/10.3390/s23239522 - 30 Nov 2023
Cited by 1 | Viewed by 1851
Abstract
Despite longstanding traditional construction health and safety management (CHSM) methods, the construction industry continues to face persistent challenges in this field. Neuroscience tools offer potential advantages in addressing these safety and health issues by providing objective data to indicate subjects’ cognition and behavior. [...] Read more.
Despite longstanding traditional construction health and safety management (CHSM) methods, the construction industry continues to face persistent challenges in this field. Neuroscience tools offer potential advantages in addressing these safety and health issues by providing objective data to indicate subjects’ cognition and behavior. The application of neuroscience tools in the CHSM has received much attention in the construction research community, but comprehensive statistics on the application of neuroscience tools to CHSM is lacking to provide insights for the later scholars. Therefore, this study applied bibliometric analysis to examine the current state of neuroscience tools use in CHSM. The development phases; the most productive journals, regions, and institutions; influential scholars and articles; author collaboration; reference co-citation; and application domains of the tools were identified. It revealed four application domains: monitoring the safety status of construction workers, enhancing the construction hazard recognition ability, reducing work-related musculoskeletal disorders of construction workers, and integrating neuroscience tools with artificial intelligence techniques in enhancing occupational safety and health, where magnetoencephalography (EMG), electroencephalography (EEG), eye-tracking, and electrodermal activity (EDA) are four predominant neuroscience tools. It also shows a growing interest in integrating the neuroscience tools with artificial intelligence techniques to address the safety and health issues. In addition, future studies are suggested to facilitate the applications of these tools in construction workplaces by narrowing the gaps between experimental settings and real situations, enhancing the quality of data collected by neuroscience tools and performance of data processing algorithms, and overcoming user resistance in tools adoption. Full article
(This article belongs to the Special Issue Neural Sensors for Architecture, Engineering and Construction (AEC) Industry)
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