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Sensors, Real-Time Applications and Data Fusion in Smart City, Building and Construction

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

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 11875

Special Issue Editors

Dr. Sara Shirowzhan

E-Mail Website
Guest Editor
City Futures Research Centre, School of Built Environment, University of New South Wales, Kensington, Sydney, NSW 2052, Australia
Interests: sensing technologies; AI; machine learning; advanced GIS; BIM; digital twins; city analytics methods; digital construction; smart cities; smart construction
Special Issues, Collections and Topics in MDPI journals
Dr. Botao Zhong

E-Mail Website
Guest Editor
1. Associate Professor, School of Civil Engineering and Mechanics, Huazhong University of Science and Technology, Wuhan 430074, China
2. Hubei Engineering Research Center for Virtual, Safe, and Automated Construction (ViSAC), Huazhong University of Science and Technology, Wuhan 430074, China
Interests: engineering informatics; BIM; knowledge-based systems for construction management; blockchain applications in construction; automation in construction
Dr. Samad Sepasgozar

E-Mail Website
Guest Editor
School of Built Environment, UNSW Sydney, Sydney, NSW 1466, Australia
Interests: sustainability; energy efficiency; artificial intelligence; smart city; digital twin; applications of the internet of things; advanced GIS; LiDAR; BIM; digital technology in infrastructure; mixed reality applications; information and communication technology; spatial analysis and visualization; authentic education
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advances in digital technologies have made interoperability and a certain “smartness” possible. For example, many devices and software packages at work and home can be networked to give users many unexpected possibilities; increase the efficiencies, quality, and safety; and offer a higher level of productivity. From a community perspective, the high penetration rate of real-time applications, Internet of Things (IoT) in cities, transportation systems, and construction projects allow for introducing full versions of the smart home, smart building, smart city, smart construction, and smart transportation. Workers are equipped with innovative and digital technological solutions and services for improving efficiently, sustainability, quality, and productivity. Smart technologies comprise a variety of software and hardware, including sensors, monitors, interfaces, online appliances, and devices networked together to enable full automation at different levels of organisations, projects, and cities.

Recent Advances in the following will be considered:

  • New trends in smart buildings and homes
  • Sensor networks and smart computing
  • Lidar remote sensing
  • 3D point clouds for intelligent cities and transportation systems
  • Low-cost sensors in construction
  • Intelligent sensors for smart building and city with AI, IoT, and big data
  • Robot and sensor networks for environmental monitoring
  • Advanced machine learning and deep networks for processing, modelling, and classification
  • IoT-enabled smart construction and transportation
  • Sensors and sensing technologies for indoor and outdoor positioning and navigation
  • Smart cloud computing technologies and applications in the built environment
  • Robotic sensing for smart cities, transportation, and construction
  • Sensor fusion and visualization in IoT applications for cities and construction
  • Text mining and NLP in construction
  • Blockchain and building supply chain management
  • Automated point clouds and images processing, and scan-to-BIM applications
  • Sensors, 3D printing, and robot control
  • Radio frequency-based real-time location systems (RTLS)
  • Smart built environment (city, building, transportation, and construction).
Dr. Sara Shirowzhan
Dr. Botao Zhong
Dr. Samad M. E. Sepasgozar
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
  • Building information modelling (BIM)
  • Smart home technologies and automation
  • Deep and machine learning
  • Laser scanning technology
  • Internet of Things
  • Interoperability
  • Smart monitoring
  • Smart living
  • Information and communication technologies (ICT)
  • Artificial intelligence
  • Energy management
  • Smart sensors
  • Wireless sensor networks
  • Intelligent and autonomous systems
  • Sensor fusion/integration
  • Construction and transportation infrastructures
  • Life-cycle assessment
  • Image processing

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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Research

19 pages, 1651 KiB  
Article
Practical Judgment of Workload Based on Physical Activity, Work Conditions, and Worker’s Age in Construction Site
by Nobuki Hashiguchi, Kota Kodama, Yeongjoo Lim, Chang Che, Shinichi Kuroishi, Yasuhiro Miyazaki, Taizo Kobayashi, Shigeo Kitahara and Kazuyoshi Tateyama
Sensors 2020, 20(13), 3786; https://doi.org/10.3390/s20133786 - 6 Jul 2020
Cited by 18 | Viewed by 6597
Abstract
It is important for construction companies to sustain a productive workforce without sacrificing its health and safety. This study aims to develop a practical judgement method to estimate the workload risk of individual construction workers. Based on studies, we developed a workload model [...] Read more.
It is important for construction companies to sustain a productive workforce without sacrificing its health and safety. This study aims to develop a practical judgement method to estimate the workload risk of individual construction workers. Based on studies, we developed a workload model comprising a hygrothermal environment, behavioral information, and the physical characteristics of workers). The construction workers’ heart rate and physical activity were measured using the data collected from a wearable device equipped with a biosensor and an acceleration sensor. This study is the first report to use worker physical activity, age, and the wet bulb globe temperature (WBGT) to determine a worker’s physical workload. The accuracy of this health risk judgment result was 89.2%, indicating that it is possible to easily judge the health risk of workers even in an environment where it is difficult to measure the subject in advance. The proposed model and its findings can aid in monitoring the health impacts of working conditions during construction activities, and thereby contribute toward determining workers’ health damage. However, the sampled construction workers are 12 workers, further studies in other working conditions are required to accumulate more evidence and assure the accuracy of the models. Full article
(This article belongs to the Special Issue Sensors, Real-Time Applications and Data Fusion in Smart City, Building and Construction)
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33 pages, 2293 KiB  
Article
Seismic Model Parameter Optimization for Building Structures
by Lengyel Károly, Ovidiu Stan and Liviu Miclea
Sensors 2020, 20(7), 1980; https://doi.org/10.3390/s20071980 - 1 Apr 2020
Cited by 3 | Viewed by 3790
Abstract
Structural dynamic modeling is a key element in the analysis of building behavior for different environmental factors. Having this in mind, the authors propose a simple nonlinear model for studying the behavior of buildings in the case of earthquakes. Structural analysis is a [...] Read more.
Structural dynamic modeling is a key element in the analysis of building behavior for different environmental factors. Having this in mind, the authors propose a simple nonlinear model for studying the behavior of buildings in the case of earthquakes. Structural analysis is a key component of seismic design and evaluation. It began more than 100 years ago when seismic regulations adopted static analyzes with lateral loads of about 10% of the weight of the structure. Due to the dynamics and non-linear response of the structures, advanced analytical procedures were implemented over time. The authors’ approach is the following: having a nonlinear dynamic model (in this case, a multi-segment inverted pendulum on a cart with mass-spring-damper rotational joints) and at least two datasets of a building, the parameters of the building’s model are estimated using optimization algorithms: Particle Swarm Optimization (PSO) and Differential Evolution (DE). Not having much expertise on structural modeling, the present paper is focused on two aspects: the proposed model’s performance and the optimization algorithms performance. Results show that among these algorithms, the DE algorithm outperformed its counterpart in most situations. As for the model, the results show us that it performs well in prediction scenarios. Full article
(This article belongs to the Special Issue Sensors, Real-Time Applications and Data Fusion in Smart City, Building and Construction)
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