Advances in AI, Digitization, Robotics, IoT, BIM, and Spatial Modeling in Building Sciences

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Construction Management, and Computers & Digitization".

Deadline for manuscript submissions: 31 January 2025 | Viewed by 1136

Special Issue Editors


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Guest Editor
Center for Space and Remote Sensing Research & Department of Civil Engineering, National Central University, Taoyuan 320317, Taiwan
Interests: remote sensing; spatial analysis; image analysis; 3D metrology and reconstruction, geovisualization
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Civil Engineering, National Yang Ming Chiao Tung University, Hsinchu 300093, Taiwan
Interests: geomatics; GeoBIM; GeoAI; photogrammetry; remote sensing

Special Issue Information

Dear Colleagues,

The developments and advances in computer and information sciences, geospatial information, sensors and Internet of Things devices, artificial intelligence, and other related sectors have significant impacts on the building and construction sciences, engineering, and management. These advanced technologies provide new opportunities for researchers and engineers to formulate innovative solutions in order to address complicated issues more effectively, efficiently, or economically. Nevertheless, the effective incorporation of assorted new technologies and their implementation in different domains for sophisticated applications is also a great challenge. This Special Issue aims to provide insights into how advanced technologies can be effectively adopted and successfully implemented in various kinds of applications in buildings and built environments. Topics of interest include, but are not limited to:

  • Artificial intelligence applications;
  • Robotics and autonomous technology;
  • Digitization, visualization, and 3D printing;
  • Multi-dimensional/multi-LOD building models;
  • High-definition building models;
  • SLAM (simultaneous localization and mapping/modelling);
  • Automation in construction;
  • Computer-aided design and engineering;
  • Building information modeling;
  • Spatial analysis in built environments;
  • Sensors and the Internet of Things;
  • Indoor/outdoor positioning, navigation, and location-based services;
  • Ontology and societal impacts.

Prof. Dr. Fuan Tsai
Prof. Dr. Tee-Ann Teo
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. Buildings is an international peer-reviewed open access monthly 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

  • digital building models
  • multi-LOD building models
  • high-definition 3D models
  • building information modelling
  • construction management
  • automation in construction
  • building spatial information
  • indoor location services
  • CAD/CAE
  • artifical intelligence/machine learning/deep learning

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Published Papers (2 papers)

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Research

19 pages, 3525 KiB  
Article
Hyperparameter Tuning Technique to Improve the Accuracy of Bridge Damage Identification Model
by Su-Wan Chung, Sung-Sam Hong and Byung-Kon Kim
Buildings 2024, 14(10), 3146; https://doi.org/10.3390/buildings14103146 - 2 Oct 2024
Viewed by 445
Abstract
In recent years, active research has been conducted using deep learning to evaluate damage to aging bridges. However, this method is inappropriate for practical use because its performance deteriorates owing to numerous classifications, and it does not use photos of actual sites. To [...] Read more.
In recent years, active research has been conducted using deep learning to evaluate damage to aging bridges. However, this method is inappropriate for practical use because its performance deteriorates owing to numerous classifications, and it does not use photos of actual sites. To this end, this study used image data from an actual bridge management system as training data and employed a combined learning model for each member among various instance segmentation models, including YOLO, Mask R-CNN, and BlendMask. Meanwhile, techniques such as hyperparameter tuning are widely used to improve the accuracy of deep learning, and this study aimed to improve the accuracy of the existing model through this. The hyperparameters optimized in this study are DEPTH, learning rate (LR), and iterations (ITER) of the neural network. This technique can improve the accuracy by tuning only the hyperparameters while using the existing model for bridge damage identification as it is. As a result of the experiment, when DEPTH, LR, and ITER were set to the optimal values, mAP was improved by approximately 2.9% compared to the existing model. Full article
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17 pages, 3423 KiB  
Article
Spatial Analysis with Detailed Indoor Building Models for Emergency Services
by Min-Lung Cheng, Fuan Tsai and Tee-Ann Teo
Buildings 2024, 14(9), 2798; https://doi.org/10.3390/buildings14092798 - 5 Sep 2024
Viewed by 473
Abstract
This paper presents a systematic approach to perform spatial analysis with detailed indoor building models for emergency service decision supports. To achieve a more realistic spatial application, this research integrates three-dimensional (3D) indoor building models and their attributes to simulate an emergency evacuation [...] Read more.
This paper presents a systematic approach to perform spatial analysis with detailed indoor building models for emergency service decision supports. To achieve a more realistic spatial application, this research integrates three-dimensional (3D) indoor building models and their attributes to simulate an emergency evacuation scenario. Indoor building models of a complicated train station with different levels of detail are generated from two-dimensional (2D) floor plans and Building Information Model (BIM) datasets. In addition to the 3D building models, spatial and non-spatial attributes are also associated with the created building models and the objects within them. The ant colony optimization (ACO) algorithm is modified to analyze the indoor building models for emergency service decision support applications. The detailed indoor models and the proposed spatial analysis algorithms are tested in simulated emergency evacuation scenarios to select the best routes during emergency services. The experimental results demonstrate that the proposed system is helpful for selecting the optimal route with the least cost at varying time stamps. Together with the developed spatial analysis framework, they have a great potential for effective decision support during emergency situations. Full article
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