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Buildings
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Big Data and Machine/Deep Learning in Construction
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Big Data and Machine/Deep Learning in Construction

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

Deadline for manuscript submissions: 30 November 2024 | Viewed by 500

Special Issue Editor

Dr. Fan Lei

E-Mail Website
Guest Editor
Department of Civil Engineering, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China
Interests: point cloud and remotely sensed imagery; machine and deep learning; semantic segmentations; computer vision; monitoring of structures and geohazards

Special Issue Information

Dear Colleagues,

The use of big data and machine/deep learning in the field of construction has rapidly gained momentum in recent years, offering unprecedented opportunities to improve efficiency, safety and sustainability. This Special Issue aims to bring together cutting-edge research and innovative applications that harness the power of big data and machine/deep learning in the construction domain.

We invite researchers, practitioners and industry experts to submit high-quality original research and review articles that address, but are not limited to, the following topics:

  • Detection of objects, hazards and defects at construction sites;
  • Semantic segmentation of construction scenes;
  • Building information modeling (BIM);
  • Sustainable construction practices;
  • Energy efficiency in construction;
  • Data-driven decision making in construction;
  • Monitoring and quality control of construction processes;
  • Predictive construction-related maintenance;
  • Smart construction equipment;
  • Construction project management and resource optimization;
  • Construction risk management;
  • AI-enabled wearable technology in construction.

Submissions should present original research, case studies or comprehensive reviews that contribute to the theoretical and practical understanding of leveraging big data and machine/deep learning to advance the construction industry. We welcome interdisciplinary contributions that combine expertise from construction engineering, computer science, data analytics and related fields.

Dr. Fan Lei
Guest Editor

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

  • detection of objects, hazards and defects at construction sites
  • semantic segmentation of construction scenes
  • building information modeling (BIM)
  • sustainable construction practices
  • energy efficiency in construction
  • data-driven decision making in construction
  • monitoring and quality control of construction processes
  • predictive construction-related maintenance
  • construction project management and resource optimization
  • construction risk management
  • AI-enabled wearable technology in construction

Published Papers (1 paper)

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Research

19 pages, 8335 KiB  
Article
MSFA-Net: A Multiscale Feature Aggregation Network for Semantic Segmentation of Historical Building Point Clouds
by Ruiju Zhang, Yaqian Xue, Jian Wang, Daixue Song, Jianghong Zhao and Lei Pang
Buildings 2024, 14(5), 1285; https://doi.org/10.3390/buildings14051285 - 1 May 2024
Viewed by 367
Abstract
In recent years, research on the preservation of historical architecture has gained significant attention, where the effectiveness of semantic segmentation is particularly crucial for subsequent repair, protection, and 3D reconstruction. Given the sparse and uneven nature of large-scale historical building point cloud scenes, [...] Read more.
In recent years, research on the preservation of historical architecture has gained significant attention, where the effectiveness of semantic segmentation is particularly crucial for subsequent repair, protection, and 3D reconstruction. Given the sparse and uneven nature of large-scale historical building point cloud scenes, most semantic segmentation methods opt to sample representative subsets of points, often leading to the loss of key features and insufficient segmentation accuracy of architectural components. Moreover, the geometric feature information at the junctions of components is cluttered and dense, resulting in poor edge segmentation. Based on this, this paper proposes a unique semantic segmentation network design called MSFA-Net. To obtain multiscale features and suppress irrelevant information, a double attention aggregation module is first introduced. Then, to enhance the model’s robustness and generalization capabilities, a contextual feature enhancement and edge interactive classifier module are proposed to train edge features and fuse the context data. Finally, to evaluate the performance of the proposed model, experiments were conducted on a self-curated ancient building dataset and the S3DIS dataset, achieving OA values of 95.2% and 88.7%, as well as mIoU values of 86.2% and 71.6%, respectively, further confirming the effectiveness and superiority of the proposed method. Full article
(This article belongs to the Special Issue Big Data and Machine/Deep Learning in Construction)
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