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AIoT for Building Construction and Maintenance Engineering
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AIoT for Building Construction and Maintenance Engineering

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

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 2020

Special Issue Editors

Dr. Xincong Yang

E-Mail Website
Guest Editor
School of Civil and Environment Engineering, Harbin Institute of Technology, Shenzhen 518055, China
Interests: reality capture and understanding for advanced engineering intelligence
Prof. Dr. Xiaochun Luo

E-Mail Website
Guest Editor
School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, China
Interests: reality capture and understanding for advanced engineering intelligence
Dr. Zezhou Wu

E-Mail Website
Guest Editor
Department of Construction Management and Real Estate, Shenzhen University, Shenzhen, China
Interests: sustainable construction; carbon emission reduction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

During the past decade, the integration of Artificial Intelligence (AI) and Internet of Things (IoT) has brought significant improvements to the construction industry, allowing for more efficient and effective management of various aspects along the lifecycle of buildings and infrastructures. The use of AIoT systems has enabled real-time and analysis locally at each node, increasing the quality of services and reducing the communication payloads.

This Special Issue on AIoT for building construction and maintenance engineering is timely and important, as it will provide a platform for researchers and practitioners to share their latest findings, technologies, and solutions in this field. The issue will cover a wide range of topics related to AIoT in architecture, engineering, construction, operation, and maintenance industry. Potential research topics include but are not limited to:

  • AIoT-enabled supply chain management in the construction industry;
  • AIoT-based safety management and quality management at construction sites;
  • AIoT solutions for energy-efficient building management;
  • Smart sensors and actuators for building automation and control;
  • AIoT-powered maintenance for building systems;
  • AIoT-driven data analysis for construction and maintenance optimization;
  • Distributed AIoT systems in the construction industry;
  • Advanced AI algorithms for smart construction and intelligence maintenance;
  • BIM/CIM and AIoT data fusion;
  • Digital twins and AIoT integration.

Dr. Xincong Yang
Prof. Dr. Xiaochun Luo
Dr. Zezhou Wu
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.

Published Papers (2 papers)

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Research

19 pages, 6762 KiB  
Article
Skeleton-Based Activity Recognition for Process-Based Quality Control of Concealed Work via Spatial–Temporal Graph Convolutional Networks
by Lei Xiao, Xincong Yang, Tian Peng, Heng Li and Runhao Guo
Sensors 2024, 24(4), 1220; https://doi.org/10.3390/s24041220 - 14 Feb 2024
Viewed by 735
Abstract
Computer vision (CV)-based recognition approaches have accelerated the automation of safety and progress monitoring on construction sites. However, limited studies have explored its application in process-based quality control of construction works, especially for concealed work. In this study, a framework is developed to [...] Read more.
Computer vision (CV)-based recognition approaches have accelerated the automation of safety and progress monitoring on construction sites. However, limited studies have explored its application in process-based quality control of construction works, especially for concealed work. In this study, a framework is developed to facilitate process-based quality control utilizing Spatial–Temporal Graph Convolutional Networks (ST-GCNs). To test this model experimentally, we used an on-site collected plastering work video dataset to recognize construction activities. An ST-GCN model was constructed to identify the four primary activities in plastering works, which attained 99.48% accuracy on the validation set. Then, the ST-GCN model was employed to recognize the activities of three extra videos, which represented a process with four activities in the correct order, a process without the activity of fiberglass mesh covering, and a process with four activities but in the wrong order, respectively. The results indicated that activity order could be clearly withdrawn from the activity recognition result of the model. Hence, it was convenient to judge whether key activities were missing or in the wrong order. This study has identified a promising framework that has the potential to the development of active, real-time, process-based quality control at construction sites. Full article
(This article belongs to the Special Issue AIoT for Building Construction and Maintenance Engineering)
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14 pages, 10117 KiB  
Article
Study on Improvement of Radio Propagation Characteristics of Cast Iron Boxes for Water Smart Meters
by Eiichi Tateishi, Yuantong Yi, Nobuhiro Kai, Takaya Kumagae, Tatsuya Yamaguchi and Haruichi Kanaya
Sensors 2023, 23(24), 9716; https://doi.org/10.3390/s23249716 - 8 Dec 2023
Viewed by 806
Abstract
Water utilities in Japan face a number of challenges, including declining water demand due to a shrinking population, shrinking workforce, and aging water supply facilities. Widespread use of smart water meters is crucial for solving these problems. The widespread use of smart water [...] Read more.
Water utilities in Japan face a number of challenges, including declining water demand due to a shrinking population, shrinking workforce, and aging water supply facilities. Widespread use of smart water meters is crucial for solving these problems. The widespread use of smart water meters is expected to bring many benefits such as reduced labor by automating meter reading, early identification of leaks, and visualization of pipeline data to strengthen the infrastructure of water services, business continuity, and customer service, as detailed data can be obtained using wireless communication. Demonstration tests are actively conducted in Japan; however, many problems have been reported with cast iron meter boxes blocking radio waves. To address the issue, a low-cost slit structure for cast iron meter boxes is investigated in this study. The results confirm that the L-shaped tapered slit array structure with a cavity, which can be fabricated in a cast iron integral structure, satisfies the design loads required for road installation. The proposed slit structure achieved gain characteristics from −3.32 to more than 9.54 dBi in the 800 to 920 MHz band. The gain characteristics of conventional cast iron meter boxes range from −15 to −20 dBi, and the gain has been significantly improved. Antennas with a gain of −2.0 to +1.5 dB (0.8 to 2.5 GHz) were used for the transmitter antenna, which was found to have a higher gain than the transmit antenna in the 800 to 880 MHz frequency band. In the 1.5 to 2.0 GHz band, a high peak gain of 4.25 dBi was achieved at 1660 MHz, with no null and the lowest gain confirmed that this is an improvement of more than 10 dBi over conventional products. Full article
(This article belongs to the Special Issue AIoT for Building Construction and Maintenance Engineering)
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