Search Results (199)

Heritage Building Information Modelling (HBIM) requires the accurate classification of diverse building elements from 3D point clouds. This study presents a novel classification approach integrating a bespoke Uniclass-derived taxonomy with a hierarchical Random Forest model. It was applied to the 17th-century Queen’s House in Greenwich, a building rich in classical architectural elements whose geometric properties are often defined by principles of symmetry. The bespoke classification was implemented across three levels (50 mm, 20 mm, 5 mm point cloud resolutions) and evaluated against the prior experiment that used Uniclass classification. Results showed a substantial improvement in classification precision and overall accuracy at all levels. The Level 1 classifier’s accuracy increased by 15% of points (relative ~50% improvement) with the bespoke classification taxonomy, reducing the misclassifications and error propagation in subsequent levels. This research demonstrates that tailoring the Uniclass building classification for heritage-specific geometry significantly enhances machine learning performance, which, to date, has not been published in the academic domain. The findings underscore the importance of adaptive taxonomies and suggest pathways for integrating multi-scale features and advanced learning methods to support automated HBIM workflows. Full article

This study explores the transformative potential of cyber technologies in the preservation, representation, and restoration of architectural heritage. Bridging technical and humanistic dimensions, it examines how tools like Heritage Building Information Modeling (HBIM), As-Designed BIM, and Virtual Reality (VR) support deeper, multilevel, and multitemporal understandings of cultural sites. Central to the research is an experimental restoration project on the castles of Civitella in Val di Chiana (Arezzo), serving as a methodological testbed for a digitally integrated approach. Developed through a scan-to-BIM process, the project yields a high-fidelity immersive ecosystem—both a rigorous model for future restoration and a VR platform enabling access to previously unreachable spaces. Here, representation is not a secondary or illustrative phase but a central, operative component in historical interpretation and architectural design. This approach embraces cyber representation: a digitally mediated, interactive, and evolving form that extends heritage beyond its physical boundaries. The immersive model fosters renewed dialogue between past and present, encouraging critical reflection on material authenticity, spatial transformation, and conservation strategies within a dynamic, participatory, interactive webVR environment. Representation thus becomes a generative and narrative tool, shaping restoration scenarios while enhancing analytical depth and public engagement. The study ultimately proposes a shift in historical storytelling toward a polyphonic, experiential, cyber-mediated narrative—where technology, memory, and perception converge to create new forms of cultural continuity. Full article

Digital technologies significantly influence architectural heritage perception, preservation, and presentation, particularly in reconstructing fragmented archaeological sites. This study explores innovative applications of algorithmic design, Heritage Building Information Modelling (HBIM), and interactive visualisation through the virtual reconstruction of the Roman Forum Transitorium in Musti, Tunisia—a complex historical site influenced by Numidian, Roman, and Byzantine cultures. The research integrates algorithmic modelling, digital surveying, and cloud-based collaboration, employing software tools such as Archicad, Rhino, Grasshopper, and Virtual Tour platforms. Central to this approach is a parametric, hypothesis-driven methodology, enabling the iterative exploration of multiple reconstruction scenarios informed by historical sources, architectural analyses, and scanned archaeological fragments. Immersive technologies enhance user engagement, allowing for the transparent exploration and interpretation of the site’s historical uncertainties. The results highlight the effectiveness of algorithmic methods in managing interpretative variability, offering flexible, academically rigorous, and publicly accessible virtual reconstructions. By emphasising the hypothetical nature of digital reconstructions and interactive visualisations, this research contributes meaningfully to digital archaeology, demonstrating how innovative algorithmic approaches can bridge academic scholarship and broader heritage preservation practices. Full article

Historic earthen architecture—particularly rammed earth—is underrepresented in digital heritage initiatives despite its widespread historical use and vulnerability to degradation. This paper presents a novel methodology for integrating semantic, geometric, and geospatial information from earthen heritage into a unified digital environment, bridging Heritage Building Information Modeling (HBIM) and Geographic Information Systems (GIS) through an object-relational database. The proposed workflow enables automated and bidirectional data exchange between Revit (via Dynamo scripts) and open-source GIS tools (QGIS and PostgreSQL/PostGIS), supporting semantic alignment and spatial coherence. The method was tested on seven fortified rammed-earth sites in the southwestern Iberian Peninsula, chosen for their typological and territorial diversity. Results demonstrate the feasibility of multiscale documentation and analysis, supported by a structured database populated with geometric, semantic, diagnostic, and environmental information, enabling enriched interpretations of construction techniques, material variability, and conservation status. The approach also facilitates the integration of HBIM datasets into broader territorial management frameworks. This work contributes to the development of scalable, open-source digital tools tailored to vernacular heritage, offering a replicable strategy for bridging the gap between building-scale and landscape-scale documentation in cultural heritage management. Full article

The reconstruction of historic buildings requires the integration of diverse data sources, both geometric and non-geometric. This study presents a multi-source data analysis methodology for heritage reconstruction using 3D modeling and Historic Building Information Modeling (HBIM). The proposed approach combines geometric data, including point clouds acquired via Terrestrial Laser Scanning (TLS), with architectural documentation and non-geometric information such as photographs, historical records, and technical descriptions. The case study focuses on a wooden Orthodox church in Żmijowiska, Poland, analyzing geometric changes in the structure over multiple decades. The reconstruction process integrates modern surveys with archival sources and, in the absence of complete geometric data, utilizes semantic, topological, and structural information. Geometric datasets from the 1990s, 1930s, and the turn of the 20th century were analyzed, supplemented by intermediate archival photographs and technical documentation. This integrated method enabled the identification of transformation phases and verification of discrepancies between historical records and the building’s actual condition. The findings confirm that the use of HBIM and multi-source data fusion facilitates accurate reconstruction of historical geometry and supports visualization of spatial changes across decades. Full article

The conservation of architectural heritage (AH) in regions threatened by natural and human-induced factors requires interdisciplinary approaches that integrate physical documentation with semantic modeling. This study introduces a comprehensive framework combining Historic Building Information Modeling (HBIM) with ontology-based modeling aligned with the CIDOC Conceptual Reference Model (CIDOC CRM). Focusing on the Big Gate and adjacent curtain walls in Ibb, Yemen, where the gate is entirely lost, the study reconstructs the structure using historical photographs, eyewitness accounts, and analogical references. The methodology incorporates UAV and terrestrial photogrammetry surveys, point cloud generation, and semantic enrichment using Autodesk Revit V. 2024 and Protégé V. 5.5. Decay phenomena such as cracks, efflorescence, and disintegration were ontologically classified and spatially linked to the HBIM model, revealing deterioration patterns concerning historical phases and environmental exposure. The resulting system enables dynamic documentation, facilitates strategic conservation planning, and enhances data interoperability across heritage platforms. The proposed framework is transferable to other heritage sites, supporting both the conservation of extant structures and the reconstruction of lost ones. Full article

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

The Building Information Modelling (BIM) methodology has been applied in distinct sectors of the construction industry with a growing demonstration of benefits, supporting the elaboration of integrated and collaborative projects. The main foundation of the methodology is the generation of a three-dimensional (3D) digital representation, the BIM model, concerning the different disciplines that make up a complete project. The BIM model includes a database referring to all the information regarding the geometric and physical aspects of the project. The procedure related to the generation of BIM models presents a significant difference depending on whether the project refers to new or old buildings. Current BIM systems contain libraries with various types of parametric objects that are effortlessly adaptable to new constructions. However, the generation of models of old buildings, supported by the definition of detailed new parametric objects, is required. The present study explores the distinct modelling procedures applied in the generation of specific parametric objects for new and old constructions, with the objective of evaluating the comparative complexity that the designer faces in modelling specific components. For a correct representation of new buildings in the design phase or for the reproduction of the accurate architectural configuration of heritage buildings, the modelling process presents significant differences identified in the study. 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

The rapid urbanization and technological advancements in the United Arab Emirates (UAE) have placed its modern architectural heritage from the 1970s and 1980s at increasing risk of being unrecognized and lost, particularly in Dubai following the discovery of oil. This research addresses the critical need for the documentation and heritage representation of Dubai’s modern heritage, a city undergoing rapid transformation within a globalized urban landscape. Focusing on the Nasser Rashid Lootah Building (Toyota Building), an iconic early 1970s residential high-rise representing the modern architecture of Dubai and a significant milestone in its architectural history, this study explores a replicable and cost-effective approach to digitally document and conserve urban heritage under threat. The existing building was meticulously documented and analyzed to highlight its enduring value within the fast-changing urban fabric. Through the innovative combination of drone photography, ground-based photography, and HBIM, a high-resolution 3D model and a semantically organized HBIM prototype were generated. This research demonstrates a replicable measure for identifying architectural values, understanding modernist design typologies, and raising local community awareness about Dubai’s modern heritage. Ultimately, this study contributes toward developing recognition criteria and guiding efforts in documenting modern high-rise buildings as vital heritage worthy of recognition, documentation, and future conservation in the UAE. 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

Sustainable risk and disaster management in the built environment has become a critical research focus amid escalating environmental challenges. Building Information Modeling (BIM) is recognized as a key digital tool for enhancing disaster resilience through simulation, data integration, and collaborative management. This study systematically reviews BIM applications in sustainable risk and disaster management from 2014 to 2024, employing the PRISMA framework, literature coding, and network analysis. Five primary research clusters are identified: (a) sustainable construction and life cycle assessment, (b) performance evaluation and implementation, (c) technology integration and digital innovation, (d) Historic Building Modeling (HBIM) and post-disaster reconstruction, and (e) project management and technology adoption. Despite increasing scholarly attention, the field remains dominated by conceptual studies, with limited empirical exploration of emerging technologies such as artificial intelligence (AI). Four key challenges are highlighted: weak foundational integration with structural risk research, technological bottlenecks in AI and digital applications, limited practical implementation, and insufficient linkage between sustainability and risk management. Future trends are expected to focus on achieving Industry 4.0 interoperability, advancing AI-driven intelligent disaster response, and adopting multi-objective optimization strategies balancing resilience, sustainability, and cost-effectiveness. This study provides a comprehensive overview of the field’s evolution and offers insights into strategic directions for future research and practical innovation. 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