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Intelligent Sensors and Artificial Intelligence in Building
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Intelligent Sensors and Artificial Intelligence in Building

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

Deadline for manuscript submissions: 20 November 2025 | Viewed by 6094

Special Issue Editors

Dr. Ke Yan

E-Mail Website
Guest Editor
Department of Mechanical and Electrical Engineering, Hunan University, Changsha 410082, China
Interests: AI; IoT; smart building; smart environment
Dr. Xin Li

E-Mail Website
Guest Editor
School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China
Interests: artificial-intelligence-based testing and verification
Special Issues, Collections and Topics in MDPI journals
Dr. Vincent Gan

E-Mail Website
Guest Editor
Department of the Built Environment, National University of Singapore, Singapore 119077, Singapore
Interests: AI; data-driven design; parametric structural optimization Social Media Account
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smart building is an important component in the progress of constructing smart city and smart city 4.0. Intelligent sensors with the abilities of precise perception, automatic detection/diagnosis, data mining, information interaction, etc., providing the underlying support of big data and industrial Internet of Things (IoT) for smart building. Based on the remote sensing information supported by intelligent sensors, various artificial intelligence (AI) technologies such as deep learning, edge computing, smart blockchain, have been deeply involved in the full life-cycle management of intelligent building, greatly improving the comfort, safety and humanization of modern building.

This Special Issue therefore aims to put together original research and review articles on recent advances, technologies, solutions, applications, and new challenges for intelligent sensors and artificial intelligence in building. Potential topics include but are not limited to:

  1. Non-contact, non-intrusive building environment perception technology.
  2. Intelligent devices and instruments for smart building.
  3. IoT applications with intelligent sensors in building.
  4. AI-assisted building decision making.
  5. AI-based energy consumption prediction of building.
  6. Intelligent security monitoring and building facility fault diagnosis.
  7. Building information modeling (BIM) with AI.
  8. Cutting-edge AI technologies for full life-cycle management of building

Dr. Ke Yan
Dr. Xin Li
Dr. Vincent Gan
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.

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

Published Papers (5 papers)

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Research

33 pages, 18034 KiB  
Article
Clustering and Interpretability of Residential Electricity Demand Profiles
by Sarra Kallel, Manar Amayri and Nizar Bouguila
Sensors 2025, 25(7), 2026; https://doi.org/10.3390/s25072026 - 24 Mar 2025
Viewed by 629
Abstract
Efficient energy management relies on uncovering meaningful consumption patterns from large-scale electricity load demand profiles. With the widespread adoption of sensor technologies such as smart meters and IoT-based monitoring systems, granular and real-time electricity usage data have become available, enabling deeper insights into [...] Read more.
Efficient energy management relies on uncovering meaningful consumption patterns from large-scale electricity load demand profiles. With the widespread adoption of sensor technologies such as smart meters and IoT-based monitoring systems, granular and real-time electricity usage data have become available, enabling deeper insights into consumption behaviors. Clustering is a widely used technique for this purpose, but previous studies have primarily focused on a limited set of algorithms, often treating clustering as a black-box approach without addressing interpretability. This study explores a wide number of clustering algorithms by comparing hard clustering algorithms (K-Means, K-Medoids) versus soft clustering techniques (Fuzzy C-Means, Gaussian Mixture Models) in segmenting electricity consumption profiles. The clustering performance is evaluated using five different clustering validation indices (CVIs), assessing intra-cluster cohesion and inter-cluster separation. The results show that soft clustering methods effectively capture inter-cluster characteristics, leading to better cluster separation, whereas intra-cluster characteristics exhibit similar behavior across all clustering approaches. This study assesses which CVIs provide reliable evaluations independent of clustering algorithm sensitivity. It provides a comprehensive analysis of the different CVIs’ responses to changes in data characteristics, highlighting which indices remain robust and which are more susceptible to variations in cluster structures. Beyond evaluating clustering effectiveness, this study enhances interpretability by introducing two decision tree models, axis-aligned and sparse oblique decision trees, to generate human-readable rules for cluster assignments. While the axis-aligned tree provides a complete explanation of all clusters, the sparse oblique tree offers simpler, more interpretable rules, emphasizing a trade-off between full interpretability and rule complexity. This structured evaluation provides a framework for balancing transparency and complexity in clustering explanations, offering valuable insights for utility providers, policymakers, and researchers aiming to optimize both clustering performance and explainability in sensor-driven energy demand analysis. Full article
(This article belongs to the Special Issue Intelligent Sensors and Artificial Intelligence in Building)
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27 pages, 5600 KiB  
Article
Integration of Accelerometers and Machine Learning with BIM for Railway Tight- and Wide-Gauge Detection
by Jessada Sresakoolchai, Chayutpong Manakul and Ni-Asri Cheputeh
Sensors 2025, 25(7), 1998; https://doi.org/10.3390/s25071998 - 22 Mar 2025
Viewed by 424
Abstract
Railway tight and wide gauges are critical factors affecting the safety and reliability of railway systems. Undetected tight and wide gauges can lead to derailments, posing significant risks to operations and passenger safety. This study explores a novel approach to detecting railway tight [...] Read more.
Railway tight and wide gauges are critical factors affecting the safety and reliability of railway systems. Undetected tight and wide gauges can lead to derailments, posing significant risks to operations and passenger safety. This study explores a novel approach to detecting railway tight and wide gauges by integrating accelerometer data, machine-learning techniques, and building information modeling (BIM). Accelerometers installed on axle boxes provide real-time dynamic data, capturing anomalies indicative of tight and wide gauges. These data are processed and analyzed using supervised machine-learning algorithms to classify and predict potential tight- and wide-gauge events. The integration with BIM offers a spatial and temporal framework, enhancing the visualization and contextualization of detected issues. BIM’s capabilities allow for the precise mapping of tight- and wide-gauge locations, streamlining maintenance workflows and resource allocation. Results demonstrate high accuracy in detecting and predicting tight and wide gauges, emphasizing the reliability of machine-learning models when coupled with accelerometer data. This research contributes to railway maintenance practices by providing an automated, data-driven methodology that enhances the proactive identification of tight and wide gauges, reducing the risk of derailments and maintenance costs. Additionally, the integration of machine learning and BIM highlights the potential for comprehensive digital solutions in railway asset management. Full article
(This article belongs to the Special Issue Intelligent Sensors and Artificial Intelligence in Building)
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23 pages, 9369 KiB  
Article
A YOLO-Based Method for Head Detection in Complex Scenes
by Ming Xie, Xiaobing Yang, Boxu Li and Yingjie Fan
Sensors 2024, 24(22), 7367; https://doi.org/10.3390/s24227367 - 19 Nov 2024
Cited by 1 | Viewed by 1252
Abstract
Detecting objects in intricate scenes has always presented a significant challenge in the field of machine vision. Complex scenes typically refer to situations in images or videos where there are numerous densely distributed and mutually occluded objects, making the object detection task even [...] Read more.
Detecting objects in intricate scenes has always presented a significant challenge in the field of machine vision. Complex scenes typically refer to situations in images or videos where there are numerous densely distributed and mutually occluded objects, making the object detection task even more difficult. This paper introduces a novel head detection algorithm, YOLO-Based Head Detection in Complex Scenes (YOLO-HDCS). Firstly, in complex scenes, head detection typically involves a large number of small objects that are randomly distributed. Traditional object detection algorithms struggle to address the challenge of small object detection. For this purpose, two new modules have been constructed: one is a feature fusion module based on context enhancement with scale adjustment, and the other is an attention-based convolutional module. These modules are characterized by high detection efficiency and high accuracy. They significantly improve the model’s multi-scale detection capabilities, thus enhancing the detection ability of the system. Secondly, it was found in practical operations that the original IoU function has a serious problem with overlapping detection in complex scenes. There is an IoU function that can ensure that the final selection boxes cover the object as accurately as possible without overlapping. This not only improves the detection performance but also greatly aids in enhancing the detection efficiency and accuracy. Our method achieves impressive results for head detection in complex scenarios, with average accuracy of 82.2%, and has the advantage of rapid loss convergence during training. Full article
(This article belongs to the Special Issue Intelligent Sensors and Artificial Intelligence in Building)
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15 pages, 2945 KiB  
Article
Massive Point Cloud Processing for Efficient Construction Quality Inspection and Control
by Zhansheng Liu, Zehong Liu and Zhe Sun
Sensors 2024, 24(21), 6806; https://doi.org/10.3390/s24216806 - 23 Oct 2024
Viewed by 1569
Abstract
The construction of large-scale civil infrastructures requires massive spatiotemporal data to support the management and control of scheduling, quality control, and safety monitoring. Existing artificial-intelligence-based data processing algorithms rely heavily on experienced engineers to adjust the parameters of data processing, which is inefficient [...] Read more.
The construction of large-scale civil infrastructures requires massive spatiotemporal data to support the management and control of scheduling, quality control, and safety monitoring. Existing artificial-intelligence-based data processing algorithms rely heavily on experienced engineers to adjust the parameters of data processing, which is inefficient and time-consuming when dealing with huge datasets. Limited studies have compared the performance of different algorithms on a unified dataset. This study proposes a framework and evaluation system for comparing different data processing policies for processing huge spatiotemporal data in construction quality control. The proposed method compares the combination of multiple types of algorithms involved in the processing of massive point cloud data. The performance of data processing strategies is evaluated through this framework, and the optimal point cloud processing strategies are explored based on registration accuracy and data fidelity. Results show that a reasonable choice of combinations of point cloud sampling, filtering, and registration algorithms can significantly improve the efficiency of point cloud data processing and satisfy engineering demands for data accuracy and completeness. The proposed method can be applied to the civil engineering problem of processing a large amount of point cloud data and selecting the optimal processing method. Full article
(This article belongs to the Special Issue Intelligent Sensors and Artificial Intelligence in Building)
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19 pages, 43558 KiB  
Article
Intelligent Monitoring and Visualization System for High Building Nighttime Utilization Based on Image Processing
by Yuanrong He, Xianhui Yu, Qihao Liang, Peng Yu, Zhiying Xie, Le Xia and Minghang Qin
Sensors 2024, 24(21), 6793; https://doi.org/10.3390/s24216793 - 22 Oct 2024
Cited by 2 | Viewed by 1255
Abstract
The rise of complex high-rise buildings has made building management increasingly challenging, especially the nighttime supervision of university laboratories. Idle occupation increases the risk of accidents and undermines campus sustainability. Effective occupancy detection is essential for optimizing campus building safety and energy efficiency. [...] Read more.
The rise of complex high-rise buildings has made building management increasingly challenging, especially the nighttime supervision of university laboratories. Idle occupation increases the risk of accidents and undermines campus sustainability. Effective occupancy detection is essential for optimizing campus building safety and energy efficiency. Environmental sensors for occupancy detection offer limited coverage and are costly, making them unsuitable for campuses. Surveillance cameras, as part of campus infrastructure, provide wide coverage. On this basis, we designed a detection algorithm that uses light brightness to assess nighttime building use. Experimental results showed that the algorithm achieves an average accuracy of 98.67%, enabling large-scale nighttime occupancy detection without the need for installing additional sensors, significantly improving the efficiency of campus building management. In addition, to address the limitations of indoor space representation in geographic information system (GIS) management models, this paper developed a comprehensive 3D GIS model based on a “building–floor–room” hierarchical structure, utilizing oblique photogrammetry and laser scanning technology. This study combined the detection results with real-world 3D data for visualization, providing a new perspective for the 3D spatiotemporal refinement of complex high-rise buildings, and providing a reference framework for the detection and analysis of other types of building environments. Full article
(This article belongs to the Special Issue Intelligent Sensors and Artificial Intelligence in Building)
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