Search Results (364)

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

The process of designing reinforced concrete (RC) buildings has traditionally relied on manually evaluating a limited number of layout alternatives—a time-intensive process that may not always yield the most functionally efficient solution. This research introduces a parametric algorithmic model for the automated optimization of RC buildings with solid slab systems. The model automates and optimizes the layout process, yielding measurable improvements in spatial efficiency while maintaining compliance with structural performance criteria. Unlike prior models that address structural or architectural parameters separately, the proposed framework integrates both domains through a unified generative design approach within a BIM environment, enabling automated evaluation of structurally viable and architecturally coherent slab layouts. Developed within the parametric visual programming environment in Dynamo for Revit, the model employs a generative design (GD) engine to explore and refine various design alternatives while adhering to structural constraints. By leveraging a BIM-based framework, this method enhances efficiency, optimizes resource utilization, and systematically balances structural and architectural requirements. The model was validated through three case studies, demonstrating cost reductions between 2.7% and 17%, with material savings of up to 13.38% in concrete and 20.87% in reinforcement, achieved within computational times ranging from 120 to 930 s. Despite the current development being limited to vertical load scenarios and being most suitable for regular slab-based configurations, the results demonstrated the model’s effectiveness in optimizing grid dimensions and reducing material quantities and costs, and highlighted its ability to streamline early-stage design processes. Full article

This study conducts a comprehensive systematic review of the economic impact of Digital Twin (DT) technology within the Architecture, Engineering, and Construction (AEC) industry, following the PRISMA methodology. While DT adoption has been accelerated by advancements in Building Information Modelling (BIM), the Internet of Things (IoT), and data analytics, significant challenges persist—most notably, high initial investment costs and integration complexities. Synthesising the literature from 2016 onwards, this review identifies sector-specific barriers, regulatory burdens, and a lack of standardisation as key factors constituting DT implementation costs. Despite these hurdles, DTs demonstrate strong potential for enhancing construction productivity, optimising lifecycle asset management, and enabling predictive maintenance, ultimately reducing operational expenditures and improving long-term financial performance. Case studies reveal cost efficiencies achieved through DTs in modular construction, energy optimisation, and infrastructure management. However, limited financial resources and digital skills continue to constrain the uptake across the sector, with various extents of impact. This paper calls for the development of unified standards, innovative public–private funding mechanisms, and strategic collaborations to unlock and utilise DTs’ full economic value. It also recommends that future research explore theoretical frameworks addressing governance, data infrastructure, and digital equity—particularly through conceptualising DT-related data as public assets or collective goods in the context of smart cities and networked infrastructure systems. Full article

The digital transformation of the Architecture, Engineering and Construction sector is accelerating the adoption of digital platforms as critical enablers of data integration, stakeholder collaboration and process optimization. This paper presents a systematic review of 125 peer-reviewed journal articles (2015–2025), selected through a PRISMA-guided search using the Scopus database, with inclusion criteria focused on English-language academic literature on platform-enabled digitalization in the built environment. Studies were grouped into six thematic domains, i.e., artificial intelligence in construction, digital twin integration, lifecycle cost management, BIM-GIS for underground utilities, energy systems and public administration, based on a combination of literature precedent and domain relevance. Unlike existing reviews focused on single technologies or sectors, this work offers a cross-sectoral synthesis, highlighting shared challenges and opportunities across disciplines and lifecycle stages. It identifies the functional roles, enabling technologies and systemic barriers affecting digital platform adoption, such as fragmented data sources, limited interoperability between systems and siloed organizational processes. These barriers hinder the development of integrated and adaptive digital ecosystems capable of supporting real-time decision-making, participatory planning and sustainable infrastructure management. The study advocates for modular, human-centered platforms underpinned by standardized ontologies, explainable AI and participatory governance models. It also highlights the importance of emerging technologies, including large language models and federated learning, as well as context-specific platform strategies, especially for applications in the Global South. Full article

Despite growing interest in integrating Lean Construction (LC) and Building Information Modeling (BIM) to advance sustainability in the Architecture, Engineering, and Construction (AEC) industry, research remains fragmented and lacks a unified implementation framework. This study bridges this gap by conducting a systematic literature review (2010–2024) of 96 journal articles to (1) analyze research trends in BIM-LC integration; (2) evaluate its benefits for sustainable built environments; and (3) identify barriers to adoption. A key contribution is the development of a novel four-dimensional BIM-LC integration framework, encompassing information integration, supply chain management, waste management, and life cycle management, which synergizes LC principles with BIM’s technical capabilities to reduce waste, enhance resource efficiency, and support carbon neutrality goals. The findings reveal that while BIM-LC integration significantly improves construction productivity and reduces environmental impacts, technical challenges in data interoperability and fragmented lifecycle management persist. Actionable solutions are further proposed, including semantic model standardization, AI-driven supply chain resilience, and circular economy integration. This framework provides both scholars and practitioners with a roadmap to advance BIM-LC adoption for sustainable construction. Full article

Building Information Modeling (BIM) has excellent potential to enhance building operation and maintenance. However, as a standardized data format in the architecture, engineering, and construction (AEC) industry, the retrieval of BIM information generally requires specialized software. Cumbersome software operations prevent its effective application in the actual operation and management of buildings. This paper presents BIMCoder, a model designed to translate natural language queries into structured query statements compatible with professional BIM software (e.g., BIMserver v1.5). It serves as an intermediary component between users and various BIM platforms, facilitating access for users without specialized BIM knowledge. A dedicated BIM information query dataset was constructed, comprising 1680 natural language query and structured BIM query string pairs, categorized into 12 groups. Three classical pre-trained large language models (LLMs) (ERNIE 3.0, Llama-13B, and SQLCoder) were evaluated on this dataset. A fine-tuned model based on SQLCoder was then trained. Subsequently, a fusion model (BIMCoder) integrating ERNIE and SQLCoder was designed. Test results demonstrate that the proposed BIMCoder model achieves an outstanding accurate matching rate of 87.16% and an Execution Accuracy rate of 88.75% for natural language-based BIM information retrieval. This study confirms the feasibility of natural language-based BIM information retrieval and offers a novel solution to reduce the complexity of BIM system interaction. Full article

The built environment plays a critical role in achieving climate neutrality, yet the construction sector continues to contribute significantly to carbon emissions and resource depletion. This study evaluates the experimental phase of the New European Bauhaus Academy (NEBA) Alliance pilot project, which aims to support sustainable transformation in the built environment through the integration of circular economy principles, adaptive reuse, and nature-based solutions. Conducted at the Lodz University of Technology, the pilot study involved interdisciplinary modules combining Building Information Modeling (BIM), urban regeneration strategies, and sustainable material use. A mixed-methods approach was employed, including structured surveys and qualitative analysis of student projects, to assess the effectiveness of these interventions. The results indicate that the pilot project successfully enhanced the participants’ understanding of sustainable design practices and their application in real-world architectural and urban contexts. Participants demonstrated increased competence in using digital tools for low-carbon design and in proposing regenerative solutions for existing urban fabric. The findings suggest that targeted, design-led initiatives can contribute meaningfully to the transformation of the built environment, aligning with the goals of the European Green Deal and the New European Bauhaus. This study offers a replicable model for embedding sustainability into professional practice through applied, context-sensitive strategies. Full article

Building Information Modeling (BIM) concepts are permeating the approach to the design of buildings and landscapes for the architectural, engineering, and construction sectors. Recent regulations require that even medium–small-size public works are managed through BIM-driven design. These circumstances have led to an increase in research on the topic. The expansion of the demand of BIM-skilled professionals urges higher education institutions to re-engineer their design programs. The aim of this paper is to evaluate this academic education transition in the Department of Agricultural Sciences at the University of Sassari, Italy. The method consists of a BIM academic education assessment framework based on ten criteria clustered into three macro-issues. The application of this framework to the assessment of three diploma final theses signals that some actions have been undertaken (i.e., introducing BIM basic concepts in rural building and landscape design, stimulating interest in students, clarifying the dimensions of BIM, and promoting the concept of 3D object design and management), but still, much work must be carried out. The work confirms typical barriers to the implementation of BIM concepts in the core curriculum and the need to mobilize the whole educational ecosystem to achieve satisfactory progress toward effective innovation in contemporary BIM-led design teaching. This work represents the first attempt to evaluate the progress of the Department of Agricultural Sciences, University of Sassari, toward the integration of BIM concepts in its courses and to position this transition in an international panorama. 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

In response to the growing demand for sustainable and performance-driven building design, this study proposes an integrated digital methodology that combines Building Information Modeling (BIM), parametric scripting, and multi-criteria decision-making (MCDM) to optimize external wall assemblies. The approach leverages Autodesk Revit and Dynamo to automate the parametrization of insulation thickness while ensuring compliance with regulatory thresholds for thermal transmittance and surface mass. Acoustic performance is estimated using ECHO software, and a Weighted Sum Model (WSM) is applied to evaluate and rank configurations based on four criteria: economic cost, Global Warming Potential (GWP), embodied energy, and acoustic insulation. A case study involving 24 wall assemblies—generated from eight base stratigraphies and three insulation materials—demonstrates the method’s ability to balance environmental impact, occupant comfort, and construction feasibility. The results indicate that natural and bio-based materials, such as rammed earth and cork, offer the best overall performance, while conventional systems remain competitive in terms of cost. The proposed workflow reduces design time, increases transparency, and supports informed decision-making during early design stages. This research contributes to the digitalization of sustainability assessment in architecture by promoting integrative, replicable, and regulation-aligned practices for low-impact building envelopes. Full article

The integration of Building Information Modeling (BIM) and Artificial Intelligence (AI) is reshaping the architecture, engineering, and construction (AEC) industry, particularly by bridging two critical phases: the early design stage and architect–client communication. This paper proposes a new AI-BIM framework that aims to enhance architect–client communication and streamline feedback during early design stages. Traditional methods often fail to convey complex design ideas clearly, leading to misinterpretations and inefficiencies. The proposed framework combines real-time BIM visualization with AI-driven generative design and large language models (LLMs) to facilitate better understanding and collaboration. Observations from case studies suggest improvements in iterative feedback processes, greater alignment between client needs and design outputs, and enhanced early decision-making. In addition to exploring the challenges of AI-BIM integration, including technical and ethical considerations, this research highlights strategies to support its practical application. The framework contributes to advancing design practices in the evolving AEC industry. Full article

The integration of computer vision and deep learning into Building Information Modeling (BIM) workflows has created a growing need for structured datasets that enable the semantic segmentation of indoor building elements. This paper presents StructScan3D v1, the first version of an RGB-D dataset specifically designed to facilitate the automated segmentation and modeling of architectural and structural components. Captured using the Kinect Azure sensor, StructScan3D v1 comprises 2594 annotated frames from diverse indoor environments, including residential and office spaces. The dataset focuses on six key building elements: walls, floors, ceilings, windows, doors, and miscellaneous objects. To establish a benchmark for indoor RGB-D semantic segmentation, we evaluate D-Former, a transformer-based model that leverages self-attention mechanisms for enhanced spatial understanding. Additionally, we compare its performance against state-of-the-art models such as Gemini and TokenFusion, providing a comprehensive analysis of segmentation accuracy. Experimental results show that D-Former achieves a mean Intersection over Union (mIoU) of 67.5%, demonstrating strong segmentation capabilities despite challenges like occlusions and depth variations. As an evolving dataset, StructScan3D v1 lays the foundation for future expansions, including increased scene diversity and refined annotations. By bridging the gap between deep learning-driven segmentation and real-world BIM applications, this dataset provides researchers and practitioners with a valuable resource for advancing indoor scene reconstruction, robotics, and augmented reality. Full article

This paper explores the evolving relationship between Digital Twins (DT) and the Metaverse, two foundational yet often conflated digital paradigms in digital architecture. While DTs function as mirrored models of real-world systems—integrating IoT, BIM, and real-time analytics to support decision-making—Metaverses are typically fictional, immersive, multi-user environments shaped by social, cultural, and speculative narratives. Through several research projects, the team investigate the divergence between DTs and Metaverses through the lens of their purpose, data structure, immersion, and interactivity, while highlighting areas of convergence driven by emerging technologies in Artificial Intelligence (AI) and Extended Reality (XR).This study aims to investigate the convergence of DTs and the Metaverse in digital architecture, examining how emerging technologies—such as AI, XR, and Large Language Models (LLMs)—are blurring their traditional boundaries. By analyzing their divergent purposes, data structures, and interactivity modes, as well as hybrid applications (e.g., data-integrated virtual environments and AI-driven collaboration), this study seeks to define the opportunities and challenges of this integration for architectural design, decision-making, and immersive user experiences. Our research spans multiple projects utilizing XR and AI to develop DT and the Metaverse. The team assess the capabilities of AI in DT environments, such as reality capture and smart building management. Concurrently, the team evaluates metaverse platforms for online collaboration and architectural education, focusing on features facilitating multi-user engagement. The paper presents evaluations of various virtual environment development pipelines, comparing traditional BIM+IoT workflows with novel approaches such as Gaussian Splatting and generative AI for content creation. The team further explores the integration of Large Language Models (LLMs) in both domains, such as virtual agents or LLM-powered Non-Player-Controlled Characters (NPC), enabling autonomous interaction and enhancing user engagement within spatial environments. Finally, the paper argues that DTs and Metaverse’s once-distinct boundaries are becoming increasingly porous. Hybrid digital spaces—such as virtual buildings with data-integrated twins and immersive, social metaverses—demonstrate this convergence. As digital environments mature, architects are uniquely positioned to shape these dual-purpose ecosystems, leveraging AI, XR, and spatial computing to fuse data-driven models with immersive and user-centered experiences. Full article