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New Insights on the Intelligent Preservation of Architectural Heritage
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New Insights on the Intelligent Preservation of Architectural Heritage

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Building Materials, and Repair & Renovation".

Deadline for manuscript submissions: 10 April 2026 | Viewed by 1549

Special Issue Editors

Prof. Dr. Ming Guo

E-Mail Website
Guest Editor
School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, China
Interests: architectural heritage; preventive protection; digital twins; artificial intelligence; quantum computing; augmented reality interactions; structural analysis; intelligent monitoring; restoration and defect repair
Dr. Guoli Wang

E-Mail Website
Guest Editor
School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, China
Interests: 3D modeling of historical buildings; point cloud data processing; 3D structural deformation monitoring system; LiDAR scanning; multi-source 3D reconstruction and integration; structural health monitoring platform for buildings

Special Issue Information

Dear Colleagues,

Cultural heritage, as a living testament to history, embodies the civilization of a nation, connecting us to past memories while deeply rooted in our identity and collective consciousness.

Cultural heritage holds irreplaceable value, and the work of preservation and restoration is receiving increasing attention. To address the complex demands of heritage conservation, interdisciplinary collaboration is essential, drawing on expertise and skills across different fields. With the continuous advancement of new artificial intelligence technologies, we now have innovative tools and methods for the structural analysis, monitoring, and conservation of cultural heritage. These interdisciplinary perspectives and innovations are opening up new pathways for the protection of cultural heritage, enabling us to more effectively tackle various challenges in this field.

We invite authors to contribute original research, theoretical and experimental studies, case studies, and comprehensive review papers. Topics for this Special Issue include, but are not limited to:

  • Applications of digital twins in the preventive conservation of architectural heritage;
  • Quantum computing-driven intelligent monitoring and analysis of architectural heritage;
  • Intelligent monitoring and structural analysis methods for industrial heritage;
  • Innovative applications of artificial intelligence and virtual reality in linear heritage conservation;
  • Liquid neural networks for behavioral prediction in architectural heritage.

Prof. Dr. Ming Guo
Dr. Guoli Wang
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 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

  • architectural heritage
  • preventive protection
  • digital twins
  • artificial intelligence
  • quantum computing
  • augmented reality interactions
  • intelligent monitoring
  • restoration and defect repair
  • structural analysis
  • liquid neural networks

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

Published Papers (4 papers)

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Research

23 pages, 1558 KB  
Article
Tilt Monitoring of Super High-Rise Industrial Heritage Chimneys Based on LiDAR Point Clouds
by Mingduan Zhou, Yuhan Qin, Qianlong Xie, Qiao Song, Shiqi Lin, Lu Qin, Zihan Zhou, Guanxiu Wu and Peng Yan
Buildings 2025, 15(17), 3046; https://doi.org/10.3390/buildings15173046 - 26 Aug 2025
Abstract
The structural safety monitoring of industrial heritage is of great significance for global urban renewal and the preservation of cultural heritage. However, traditional tilt monitoring methods suffer from limited accuracy, low efficiency, poor global perception, and a lack of intelligence, making them inadequate [...] Read more.
The structural safety monitoring of industrial heritage is of great significance for global urban renewal and the preservation of cultural heritage. However, traditional tilt monitoring methods suffer from limited accuracy, low efficiency, poor global perception, and a lack of intelligence, making them inadequate for meeting the tilt monitoring requirements of super-high-rise industrial heritage chimneys. To address these issues, this study proposes a tilt monitoring method for super-high-rise industrial heritage chimneys based on LiDAR point clouds. Firstly, LiDAR point cloud data were acquired using a ground-based LiDAR measurement system. This system captures high-density point clouds and precise spatial attitude data, synchronizes multi-source timestamps, and transmits data remotely in real time via 5G, where a data preprocessing program generates valid high-precision point cloud data. Secondly, multiple cross-section slicing segmentation strategies are designed, and an automated tilt monitoring algorithm framework with adaptive slicing and collaborative optimization is constructed. This algorithm framework can adaptively extract slice contours and fit the central axes. By integrating adaptive slicing, residual feedback adjustment, and dynamic weight updating mechanisms, the intelligent extraction of the unit direction vector of the central axis is enabled. Finally, the unit direction vector is operated with the x- and z-axes through vector calculations to obtain the tilt-azimuth, tilt-angle, verticality, and verticality deviation of the central axis, followed by an accuracy evaluation. On-site experimental validation was conducted on a super-high-rise industrial heritage chimney. The results show that, compared with the results from the traditional method, the relative errors of the tilt angle, verticality, and verticality deviation of the industrial heritage chimney obtained by the proposed method are only 9.45%, while the relative error of the corresponding tilt-azimuth is only 0.004%. The proposed method enables high-precision, non-contact, and globally perceptive tilt monitoring of super-high-rise industrial heritage chimneys, providing a feasible technical approach for structural safety assessment and preservation. Full article
(This article belongs to the Special Issue New Insights on the Intelligent Preservation of Architectural Heritage)
22 pages, 8947 KB  
Article
Research on Value-Chain-Driven Multi-Level Digital Twin Models for Architectural Heritage
by Guoli Wang, Yaofeng Wang, Ming Guo, Xuanshuo Liang, Yang Fu and Hongda Li
Buildings 2025, 15(17), 2984; https://doi.org/10.3390/buildings15172984 - 22 Aug 2025
Viewed by 215
Abstract
As a national treasure, architectural heritage carries multiple value dimensions such as history, technology, art, and culture. With the increasing demand for architectural heritage protection and utilization, the traditional static digital model of architectural heritage based on geometric expression can no longer meet [...] Read more.
As a national treasure, architectural heritage carries multiple value dimensions such as history, technology, art, and culture. With the increasing demand for architectural heritage protection and utilization, the traditional static digital model of architectural heritage based on geometric expression can no longer meet the practical application of multi-stage and multi-level scenarios. To this end, this paper proposes a value-chain-driven multi-level digital twin model of architectural heritage. Based on the three-stage logic of protection, management, and dissemination of value-chain classification, it integrates four types of models: geometry, physics, rules, and behavior. Combined with different hierarchical application levels, the digital model of architectural heritage is refined into a VCLOD (Value-Chain-Driven Level of Detail) detail hierarchy system to achieve a unified expression from spatial form restoration to intelligent response. Through the empirical application of three typical scenarios: the full-area guided tour of the Forbidden City, the exhibition curation of the central axis and the preventive protection of the Meridian Gate, the model shows the following specific results: (1) the efficiency of tourist guidance is improved through real-time personalized path planning; (2) the exhibition planning and visitor experience are improved through dynamic monitoring and interactive management of the exhibition environment; (3) the predictive analysis and preventive protection measures of structural safety are realized, effectively ensuring the structural safety of the Meridian Gate. The research results provide a theoretical basis and practical support for the systematic expression and intelligent evolution of digital twins of architectural heritage. Full article
(This article belongs to the Special Issue New Insights on the Intelligent Preservation of Architectural Heritage)
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32 pages, 19346 KB  
Article
Three-Dimensional Intelligent Understanding and Preventive Conservation Prediction for Linear Cultural Heritage
by Ruoxin Wang, Ming Guo, Yaru Zhang, Jiangjihong Chen, Yaxuan Wei and Li Zhu
Buildings 2025, 15(16), 2827; https://doi.org/10.3390/buildings15162827 - 8 Aug 2025
Viewed by 368
Abstract
This study proposes an innovative method that integrates multi-source remote sensing technologies and artificial intelligence to meet the urgent needs of deformation monitoring and ecohydrological environment analysis in Great Wall heritage protection. By integrating synthetic aperture radar (InSAR) technology, low-altitude oblique photogrammetry models, [...] Read more.
This study proposes an innovative method that integrates multi-source remote sensing technologies and artificial intelligence to meet the urgent needs of deformation monitoring and ecohydrological environment analysis in Great Wall heritage protection. By integrating synthetic aperture radar (InSAR) technology, low-altitude oblique photogrammetry models, and the three-dimensional Gaussian splatting model, an integrated air–space–ground system for monitoring and understanding the Great Wall is constructed. Low-altitude tilt photogrammetry combined with the Gaussian splatting model, through drone images and intelligent generation algorithms (e.g., generative adversarial networks), quickly constructs high-precision 3D models, significantly improving texture details and reconstruction efficiency. Based on the 3D Gaussian splatting model of the AHLLM-3D network, the integration of point cloud data and the large language model achieves multimodal semantic understanding and spatial analysis of the Great Wall’s architectural structure. The results show that the multi-source data fusion method can effectively identify high-risk deformation zones (with annual subsidence reaching −25 mm) and optimize modeling accuracy through intelligent algorithms (reducing detail error by 30%), providing accurate deformation warnings and repair bases for Great Wall protection. Future studies will further combine the concept of ecological water wisdom to explore heritage protection strategies under multi-hazard coupling, promoting the digital transformation of cultural heritage preservation. Full article
(This article belongs to the Special Issue New Insights on the Intelligent Preservation of Architectural Heritage)
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19 pages, 3117 KB  
Article
Research on Identification, Evaluation, and Digitization of Historical Buildings Based on Deep Learning Algorithms: A Case Study of Quanzhou World Cultural Heritage Site
by Siqi Wang, Jiahao Zhang, Aung Nyan Tun and Kyi Sein
Buildings 2025, 15(11), 1843; https://doi.org/10.3390/buildings15111843 - 27 May 2025
Cited by 1 | Viewed by 508
Abstract
Historical buildings have important historical and social value, but they are generally difficult to identify, complicated to evaluate, and insufficiently addressed in digitization research. On 25 July 2021, Quanzhou successfully applied for World Heritage status. In this case study, Qiaonan Village in the [...] Read more.
Historical buildings have important historical and social value, but they are generally difficult to identify, complicated to evaluate, and insufficiently addressed in digitization research. On 25 July 2021, Quanzhou successfully applied for World Heritage status. In this case study, Qiaonan Village in the Quanzhou World Heritage Area is selected, and a deep learning algorithm is proposed for the identification, evaluation, and digitization of historical buildings. By comparing multiple models, the optimal model is selected for intelligent identification and classification of building elevations. Combined with GIS, a distribution map of the village buildings is created for digitization research. An intelligent monitoring platform is built to enable dynamic monitoring and hierarchical protection of the buildings, aiding in the protection of historical structures and the sustainable development of the tourism industry. In the future, we will continue to optimize the integration of YOLO and GIS and explore a more universal model for the intelligent protection of historical buildings. Full article
(This article belongs to the Special Issue New Insights on the Intelligent Preservation of Architectural Heritage)
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