Search Results (136)

This paper presents a comprehensive review of research on Historic Building Information Modeling (HBIM), focusing on its role as a tool for managing knowledge and supporting conservation practices of Architectural Heritage. While previous review articles and most research works have predominantly addressed geometric modeling—given its significant challenges in the context of historic buildings—this study places greater emphasis on the integration of non-geometric data within the BIM environment. A systematic search was conducted in the Scopus database to extract the 451 relevant publications analyzed in this review, covering the period from 2008 to mid-2024. A bibliometric analysis was first performed to identify trends in publication types, geographic distribution, research focuses, and software usage. The main body of the review then explores three core themes in the development of the information system: the definition of model entities, both semantic and geometric; the data enrichment phase, incorporating historical, diagnostic, monitoring and conservation-related information; and finally, data use and sharing, including on-site applications and interoperability. For each topic, the review highlights and discusses the principal approaches documented in the literature, critically evaluating the advantages and limitations of different information management methods with respect to the distinctive features of the building under analysis and the specific objectives of the information model. Full article

This research introduces an automated 3D virtual reconstruction system tailored for architectural heritage (AH) applications, contributing to the ongoing paradigm shift from traditional CAD-based workflows to artificial intelligence-driven methodologies. It reviews recent advancements in machine learning and deep learning—particularly neural radiance fields (NeRFs) and its successor, Gaussian splatting (GS)—as state-of-the-art techniques in the domain. The study advocates for replacing point cloud data in heritage building information modeling workflows with image-based inputs, proposing a novel “photo-to-BIM” pipeline. A proof-of-concept system is presented, capable of processing photographs or video footage of ancient ruins—specifically, Romanesque–Mudéjar churches—to automatically generate 3D mesh reconstructions. The system’s performance is assessed using both objective metrics and subjective evaluations of mesh quality. The results confirm the feasibility and promise of image-based reconstruction as a viable alternative to conventional methods. The study successfully developed a system for automated 3D mesh reconstruction of AH from images. It applied GS and Mip-splatting for NeRFs, proving superior in noise reduction for subsequent mesh extraction via surface-aligned Gaussian splatting for efficient 3D mesh reconstruction. This photo-to-mesh pipeline signifies a viable step towards HBIM. Full article

Heritage Building Information Modeling (HBIM) has emerged as a key tool in advancing heritage conservation and sustainable management. Preceding reviews had typically concentrated on specific technical aspects but did not provide sufficient bibliometric analysis. This study aims to integrate existing HBIM research to identify key research patterns, emerging trends, and forecast future directions. A total of 1516 documents were initially retrieved from the Web of Science Core Collection using targeted search terms. Following a relevance screening, 1175 documents were related to the topic. CiteSpace 6.4.R1, VOSviewer 1.6.20, and Bibliometrix 4.1, three bibliometric tools, were employed to conduct both quantitative and qualitative assessments. The results show three historical phases of HBIM, identify core journals, influential authors, and leading regions, and extract six major keyword clusters: risk assessment, data acquisition, semantic annotation, digital twins, and energy and equipment management. Nine co-citation clusters further outline the foundational literature in the field. The results highlight growing scholarly interest in workflow integration and digital twin applications. Future projections emphasize the transformative potential of artificial intelligence in HBIM, while also recognizing critical implementation barriers, particularly in developing countries and resource-constrained contexts. This study provides a comprehensive and systematic framework for HBIM research, offering valuable insights for scholars, practitioners, and policymakers involved in heritage preservation and digital management. Full article

Heritage building conservation plays a special role in addressing modern sustainability challenges by preserving the cultural identity, retrofitting, restoring, and renovating these structures to improve energy performance, which is crucial for revitalisation. This research aims to use Heritage Building Information Modelling (HBIM) to increase energy efficiency and environmental sustainability in historic buildings. Retrofitting heritage buildings presents unique challenges and opportunities to simultaneously reduce energy consumption and carbon emissions while maintaining historical integrity. Traditional approaches are often insufficient to meet heritage structures’ energy needs. Modern technologies such as information building modelling and energy simulations can offer solutions. HBIM is a vigorous digital framework that facilitates interdisciplinary collaboration and offers detailed insights into building restoration and energy modelling. HBIM supports the integration of thermal and energy efficiency measures while maintaining the authenticity of heritage architecture by creating a comprehensive database. Using a case study heritage building, this research demonstrates how retrofitting the different aspects of heritage buildings can improve energy performance. Evaluating the preservation of heritage buildings’ cultural and architectural values and the effectiveness of using HBIM to model energy performance offers a viable framework for sustainable retrofitting of heritage buildings. Full article

Energy retrofitting of historic buildings presents complex challenges, particularly when using internal insulation strategies. While such interventions can enhance thermal comfort and reduce energy demand, they can also pose risks of condensation and mold formation, thereby reducing usable space. This paper proposes an evaluation methodology for assessing the performance of internal insulating panels within a multicriteria framework to support decision-making during the design phase. The approach, scalable to various contexts, is grounded in a digital workflow that integrates heritage building information modeling (HBIM), visual programming (VP), and building energy modeling (BEM) to create a decision-support tool for renovation designers. The methodology, tested on a building located in Bologna (Italy), allows for assessing internal insulation systems with varying thermophysical properties and performance characteristics, and evaluating how they affect space- and wall-level key performance indicators, including condensation risk, energy efficiency improvement, and usable space reduction. The research was conducted under the Horizon Europe HERIT4AGES project, which aims to develop reversible, innovative insulation panels fabricated from local and recycled materials for historic building retrofitting. Full article

The concept of HBIM (Historic/Heritage Building Information Modeling) has attracted growing interest within research communities in recent years, as reflected in an expanding body of literature exploring its potential in data acquisition and modeling, historical evolution documentation, heritage management, and condition analysis. Yet, new challenges arise in extended HBIM capabilities by integration and interoperability with other technologies and environments for comprehensive heritage assessment. In this context, this paper presents a scoping review, based on the PRISMA protocol, of 60 publications from the Scopus database that document research frameworks and applications of IDPs (integrated digital platforms), where HBIM is combined with different systems to enhance data richness, functionality, and analytical evaluation, as well as to exchange, interpret, and use information effectively. The results show three major thematic areas, namely multi-scale analyses based on HBIM and GIS (geographic information systems); multi-source data repositories development; and sensor networks integration with advanced IoT (Internet of Things) systems. The overview outlines how these frameworks foster the development of interoperable, multi-layered, and data-driven ecosystems, advancing HBIM to an operational component in heritage management and enabling predictive diagnostics and real-time monitoring, while current limitations in semantic consistency, automation, and scalability still hinder full implementation. Full article

In architectural heritage conservation, fragmented data practices and heterogeneous formats hinder knowledge extraction, limiting the translation of raw data into actionable conservation insights. This study proposes a knowledge-centric framework integrating smart data methodologies to bridge this gap. The framework synergizes Heritage Building Information Modeling (HBIM), semantic knowledge graphs, and knowledge bases, prioritizing three interconnected dimensions: geometric digitization through 3D laser scanning and parametric HBIM reconstruction, semantic enrichment of historical texts via NLP and rule-based entity extraction, and knowledge graph-driven discovery of spatiotemporal patterns using Neo4j and ontology mapping. Validated through dual case studies—the Historical Educational Sites in South China (humanistic narratives) and the Dong ethnic drum towers (structural logic)—the framework demonstrates its capacity to automate knowledge generation, converting 20.5 GB of multi-source data into 2652 RDF triples that interconnect 1701 nodes across HBIM models and archival records. By enabling real-time visualization of semantic relationships (e.g., educator networks, mortise-and-tenon typologies) through graph queries, the system enhances interdisciplinary collaboration. Furthermore, the proposed smart data framework facilitated the generation of domain-specific knowledge through systematic data valorization, yielding actionable insights for architectural conservation practice. This research redefines conservation as a knowledge-to-action paradigm, where smart data methodologies unify tangible and intangible heritage values, fostering data-driven stewardship across cultural, historical, and technical domains. Full article

Vietnamese vernacular architecture (VVA), rich in cultural and historical significance, is increasingly endangered by modernization, the consequences of war, and environmental degradation. The preservation and revitalization of this architectural heritage demand the integration of advanced digital technologies. Building Information Modeling (BIM), known for its capabilities in digital documentation, data management, and design accuracy, offers significant potential. However, its adoption within the context of VVA remains underexplored, particularly due to a lack of specialized tools and methods that align with modern technical requirements. This study proposes an integrated BIM-based approach to automate the conceptual design of buildings inspired by VVA, utilizing Revit and Dynamo. The research follows a multi-stage methodology comprising data acquisition, architectural element analysis, and prototype model development. The outcomes aim to assist architects and engineers in efficiently generating design concepts that blend traditional aesthetics with contemporary building standards. Ultimately, this work contributes to sustainable architectural practices by bridging heritage preservation with modern construction imperatives. Full article

The conservation and intervention of heritage structures require a flexible, interdisciplinary environment capable of managing data throughout the building’s life cycle. Historic building information modeling (HBIM) has emerged as an effective tool for supporting these processes. Originally conceived for parametric construction modeling, BIM can also integrate historical transformations, aiding in maintenance and preservation. Historic buildings often feature complex geometries and visible material traces of time, requiring detailed analysis. This research proposes a methodology for documenting and assessing the envelope of historic buildings by locating, classifying, and recording transformations, deterioration, and structural deformations. The approach is based on semantic segmentation and classification using data from terrestrial laser scanning (TLS) and unmanned aerial vehicles (UAVs), applied to the Palace of Miguel de Mañara—an iconic 17th-century building in Seville. Archival images were integrated into the HBIM model to identify previous restoration interventions and assess current deterioration. The methodology included geometric characterization, material mapping, semantic segmentation, diagnostic input, and temporal analysis. The results validated a process for detecting pathological cracks in masonry facades, providing a collaborative HBIM framework enriched with expert-validated data to support repair decisions and guide conservation efforts. Full article

This study integrates quantitative scientometric analysis with a qualitative systematic review to comprehensively examine the evolution, core research themes, and emerging trends of three-dimensional (3D) visualization technology in architectural heritage conservation from 2005 to 2024. A total of 813 relevant publications were retrieved from the Web of Science Core Collection and analyzed using CiteSpace to construct a detailed knowledge map of the field. The findings highlight that foundational technologies such as terrestrial laser scanning (TLS), photogrammetry, building information modeling (BIM), and heritage building information modeling (HBIM) have laid a solid technical foundation for accurate heritage documentation and semantic representation. At the same time, the integration of digital twins, the Internet of Things (IoT), artificial intelligence (AI), and immersive technologies has facilitated a shift from static documentation to dynamic perception, real-time analysis, and interactive engagement. The analysis identifies four major research domains: (1) 3D data acquisition and modeling techniques, (2) digital heritage documentation and information management, (3) virtual reconstruction and interactive visualization, and (4) digital transformation and cultural narrative integration. Based on these insights, this study proposes four key directions for future research: advancing intelligence and automation in 3D modeling workflows; enhancing cross-platform interoperability and semantic standardization; realizing the full lifecycle management of architectural heritage; and enhancing cultural narratives through digital expression. This study provides a systematic and in-depth understanding of the role of 3D visualization in architectural heritage conservation. It offers a solid theoretical foundation and strategic guidance for technological innovation, policy development, and interdisciplinary collaboration in the digital heritage field. Full article

Historical Building Information Modeling is a digital modeling technology applied to cultural heritage buildings. It has made remarkable progress in aspects such as data integration and management, digital protection of historical buildings, parametric and semantic modeling, multi-source data fusion, and interdisciplinary cooperation platforms. However, the sustainability of this technology has not been explored yet. This paper analyzes nearly a hundred related research achievements between 2010 and 2024 and finds that there is not only a lack of a review of the development and application of Historical Building Information Modeling (HBIM) technology in China, but also a serious shortage of discussions and explorations regarding its sustainability. Therefore, taking the development and application of Historical Building Information Modeling technology in China as the research scope, using relevant practical projects and research achievements in China, and combining a small number of the latest foreign achievements as cases, centering around the research question of the sustainability of Historical Building Information Modeling, this paper adopts the methods of review research and comparative research. It sorts out four development directions and the faced dilemmas in the development process of Historical Building Information Modeling in China and puts forward constructive suggestions for sustainable development, as well as a set of theoretical paths for the sustainability of HBIM technology based on Revit (version 23.0.1.318), Dynamo (version 2.17), Python (version 3.12.1) (Open 3D v0.18 and PointNet++), Network Attached Storage, and cloud-based collaboration platforms. The purpose is to provide a referable path for the sustainable development of Historical Building Information Modeling technology in China. Full article

Over the past 15 years, Building Information Modelling (BIM), Historic BIM (HBIM), Digital Twins, and Internet of Things (IoT) have gained prominence in architecture, construction, and building technology. This study presents a comprehensive bibliometric analysis of 5568 publications indexed in the Web of Science Core Collection between 1993 and 2024, using VOSviewer and Biblioshiny. The analysis investigates publication trends, research hotspots, citation structures, and collaborative networks, revealing evolving patterns across countries, institutions, and disciplines. The peak year was 2023 (905 papers, 2226 citations), with Automation in Construction, Buildings, and Journal of Building Engineering as the leading journals. Cheng JCP emerged as the most cited author (2059 citations, 56 papers), while Hong Kong Polytechnic University ranked highest in institutional output. China, the USA, and the UK were the top publishing countries. This study uniquely integrates BIM, HBIM, Digital Twins, and IoT as interconnected technological domains, analysing their convergence in shaping intelligent, data-driven infrastructure within the AEC sector. Unlike previous bibliometric reviews that treat these domains in isolation, this paper offers a unified framework and highlights underexplored research intersections—such as the integration of IoT in heritage documentation. The results show clear thematic clusters, a strong shift toward sustainability and interoperability, and gaps in geographic and methodological diversity. This bibliometric mapping not only synthesizes the state of research but also formulates future research directions and proposes original research questions that can guide scholars and practitioners alike. Full article

This work concentrates on an experimental project for the integration of Large Language Models (LLMs) inside a Historic Building Information Modeling (HBIM) workflow. In particular, this evaluation was carried out by using open source solutions as concerns parametric modeling of BIM elements. This experimental test focuses on how Python scripts, generated by AI agents, can create parametric models for HBIM purposes and archaeology: starting from the archaeological plan, the parametric modeling of the Parthenon temple was carried out via a text-to-BIM workflow based on OpenAI and open source tools. The use of AI in generating these scripts can potentially automate and streamline the modeling process, making it more efficient and less prone to human error (or almost). FreeCAD, being a Python-based software, is identified as the perfect fieldwork for this test. Its open source nature allows extensive customization and experimentation, making it an ideal platform for integrating AI-generated Python scripts. In addition to proving a flexible and operative BIM platform, this approach could achieve the same results by parametric modeling via Python scripts generated by LLMs. By harnessing the power of LLMs, FreeCAD could serve not only as a robust BIM tool but also as a testbed for pushing the boundaries of what AI can achieve in the realm of parametric modeling and HBIM. This project opens new possibilities for automating the creation of detailed, accurate BIM models, ultimately contributing to the preservation and management of heritage buildings. Full article

Since the early 21st century, BIM technology has enhanced building design, construction and management, while continuously evolving to create new specializations. Despite this, its full potential remains untapped. Today, BIM offers diverse applications in construction and related industries, incorporating advanced techniques such as laser scanning and photogrammetry. A specialized approach, HBIM (Heritage Building Information Modeling), enables the digital mapping, documentation, analysis and management of historic architecture. This study focuses on the Koprowski Family Villa in Celestynów, known as “Koprówka”, demolished twenty years ago. Despite its cultural significance, the property disappeared from the village. Using LiDAR survey data, preserved window frames, archival photographs and documents, this engineering study reconstructs “Koprówka” as an HBIM model, integrated into the existing landscape. The resulting 3D model can aid municipal authorities and potential investors in rebuilding “Koprówka”, while emphasizing the importance of cultural heritage in shaping local identity and raising awareness of historical structures’ value. Full article

This paper presents an advanced hierarchical classification framework using the Random Forest (RF) algorithm to segment and classify large-scale point clouds of heritage buildings. By integrating the Uniclass classification system into a multi-resolution workflow, the research addresses key challenges in point cloud classification, including class imbalance, computational constraints, and semantic overlap at coarse resolutions. It adopts an experimental research design using the heritage case study from Royal Greenwich Museum in the UK. The findings demonstrate that industry classification systems and data taxonomies can be aligned with machine learning workflows. This study contributes to Heritage-Building Information Modelling (HBIM) by proposing optimised hierarchical structures and scalable machine learning techniques. The research concludes with recommendations for future research, based on the performance of the Random Forest technique, particularly in further developing AI applications within HBIM. Full article