Building Information Modeling Model Research Articles

Building Information Modeling and AI Algorithms for Optimizing Energy Performance in Hot Climates: A Comparative Study of Riyadh and Dubai

In response to increasing global temperatures and energy demands, optimizing buildings’ energy efficiency, particularly in hot climates, is an urgent challenge. While current research often relies on conventional energy estimation methods, there has been a decrease in the efforts dedicated to leveraging AI-based methodologies as technology advances. This implies a dearth of multiparameter examinations in AI-driven extreme case studies. For this reason, this study aimed to enhance the energy performance of residential buildings in the hot climates of Dubai and Riyadh by integrating Building Information Modeling (BIM) and Machine Learning (ML). Detailed BIM models of a typical residential villa in these regions were created using Revit, incorporating conventional, modern, and green building envelopes (BEs). These models served as the basis for energy simulations conducted with Green Building Studio (GBS) and Insight, focusing on crucial building features such as floor area, external and internal walls, windows, flooring, roofing, building orientation, infiltration, daylighting, and more. To predict Energy Use Intensity (EUI), four ML algorithms, namely, Gradient Boosting Machine (GBM), Random Forest (RF), Support Vector Machine (SVM), and Lasso Regression (LR), were employed. GBM consistently outperformed the others, demonstrating superior prediction accuracy with an R2 of 0.989. This indicates that the model explains 99% of the variance in EUI, highlighting its effectiveness in capturing the relationships between building features and energy consumption. Feature importance analysis (FIA) revealed that roofs (29% in Dubai scenarios (DS) and 40% in Riyadh scenarios (RS)), external walls (19% in DS and 29% in RS), and windows (15% in DS and 9% in RS) have the most impact on energy consumption. Additionally, the study explored the potential for energy optimization, such as cavity green walls and green roofs in RS and double brick walls with VIP insulation and green roofs in DS. The findings of the paper should be interpreted in light of certain limitations but they underscore the effectiveness of combining BIM and ML for sustainable building design, offering actionable insights for enhancing energy efficiency in hot climates.

Developing a BIM based digital twin system for structural health monitoring of civil infrastructure

Abstract The utilization of building information modeling (BIM) within digital technology facilitates the creation of three-dimensional representations for monitoring data in large-scale civil infrastructure. In response to the need for intelligent structural management, this study establishes a structural health monitoring (SHM) system and foundational framework based on digital twins. This framework integrates information from various sources and facilitates collaborative efforts for structural operation and maintenance. Additionally, the SHM system integrates actual monitoring measurements and early warning mechanisms to consolidate multi-source monitoring data with BIM. Through real-time analysis, the system provides insights into the operational status of bridges, capturing geometric, physical, and performance evolution characteristics. To construct the system, engineering challenges are initially digitized, with appropriate sensors deployed on real bridge structures to monitor dynamic (acceleration) and static (strain, displacement) physical information during bridge operation. Subsequently, through wireless communication and data storage technologies, the monitored physical data serves as input for mode identification and early warning algorithms, facilitating the acquisition of structural performance information. Finally, three-dimensional display technology enables real-time calculation and rendering of BIM models, fostering the exchange and interaction of monitoring and BIM information, thus enhancing the intelligence of SHM system.

IFC CLASS ASSIGNMENT ADD-ON: BIM MODELLING FOR ARCHITECTURAL AND ENGINEERING PROJECTS USING BLENDER

Building Information Modelling (BIM) is a working methodology for the design and management of construction projects that facilitates collaboration and communication between the different parties involved. It is based on the digital three-dimensional representation of infrastructures within the field of architecture and civil engineering. The generated models are true data containers, capable of storing the geometric and semantic information necessary to support the design, construction and maintenance operations of the assets. This methodology promotes data exchange between different software tools through interoperability and the use of standards. Usually in the development of projects throughout their life cycle, commercial, closed-source BIM software is used. This generally leads to limitations in interoperability and data reuse. This paper details advances in the use of Blender, a free and open-source software (FOSS), to support BIM workflows in construction projects. The paper addresses the automation of the process to export a 3D Blender model to a BIM model in IFC (Industry Foundation Classes). The IFC Class Assignment add-on is developed, which allows the automated assignment of IFC classes to the different elements of the 3D model in Blender to convert it into a BIM model, using scripts written in Python. The proposed extension is able to generate, in an agile and simple way, digital models in IFC4, adequately storing the geometric and semantic information of its components. The results provide a tool that contributes to automate the creation of BIM models in an open-source software such as Blender, simplifying modelling processes and supporting collaboration and standardisation in architectural and engineering projects. Keywords: BIM, IFC, Automation, Free and Open-Source Software, Blender, Python.

A framework of attributes for as-built BIM models: A systematic review

Accurate digital twins are crucial in improving facility management. However, facility owners often encounter inaccurate digital twins due to incorrect as-built Building Information Modeling (BIM) models. By understanding the attributes of correct as-built BIM models, it is possible to reduce the production of inaccurate digital twins. This study aims to establish a framework of attributes for developing as-built BIM models. To achieve this, a systematic review of published articles using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) technique was conducted to identify relevant attributes for developing as-built BIM models. Next, a comprehensive conceptual framework was established for the development of as-built BIM models. Through the analysis of 50 articles, nine attributes of as-built BIM models were identified and grouped into three categories: data quality, data interoperability, and data security. Data quality encompasses accuracy, completeness, ease of understanding, coordination, consistency, and up-to-date. Data interoperability includes accessibility and compatibility, and data security pertains to security. The study findings could provide valuable guidance to industry practitioners and policymakers in developing strategies for producing correct as-built BIM models, ultimately improving the production of accurate digital twins and enhancing overall facility management.

Geometric characterization and segmentation of historic buildings using classification algorithms and convolutional networks in HBIM

Building Information Models (BIM) are essential for managing information and creating 3D digital representations, especially in the study of historic buildings. However, generating BIM models from point clouds in these structures is challenging due to complex algorithms and architectural forms. Artificial Intelligence (AI) technologies are beginning to automate point cloud classification and segmentation, but fully effective methods for historic buildings are still lacking. This study compares Machine Learning (ML) methodologies and a Deep Learning (DL) classifier. It evaluates the effectiveness of a neighbourhood algorithm with commercial software used by geometers and surveyors, and the applicability of convolutional networks. The methods tested include the Random Forest algorithm in MATLAB, commercial geomatics software, and a variant of the PointNet architecture for DL. The results are evaluated by BIM experts, highlighting the high effectiveness of these approaches and their potential contributions to the field.

From BIM to thermal comfort: Leveraging BIM for rapid outdoor comfort assessments

This study introduces a novel system within a Building Information Modeling (BIM) environment to rapidly evaluate Outdoor Thermal Comfort (OTC). Traditionally, direct microclimate analysis in the design phase is hindered by cumbersome and resource-intensive processes. By utilizing information from BIM and integrating the COMFA thermal energy budget model, this approach facilitates immediate OTC evaluation. The system automates the Sky View Factor simulation using 3D modeling techniques and utilizes BIM model data to assess the thermal environment. Tested on a university campus, it effectively processes geospatial BIM data to compute microclimate parameters and estimate OTC using COMFA. This research represents a significant advancement in BIM-integrated OTC analysis, streamlining the design feedback process and aiding practitioners in improving outdoor thermal environments amidst climate change challenges.

Energy Efficient Design of Building Based on Building Information Modelling (BIM)

Building energy analysis is rarely carried out due to the complexity of building shape and materials. On the other hand, the urgency of environmentally friendly construction is increasing through the vision of the SDGs and Architecture 2030. The development of BIM technology is expected to provide accurate estimates of building energy consumption for projects undertaken, as well as recommendations for alternative designs and specifications to increase the energy efficiency of a building. The use of BIM technology for energy analysis during building design helps to implement green building sustainable design based on the analysis of BIM energy simulation software. Data of three-dimensional BIM model with the attributes of materials, project schedule and location are used in building energy consumption simulation analysis. Several scenarios of different materials and layout are carried out to determine the most efficient scenario for energy consumption and followed by its cost estimation. The results of this study are expected to be able to provide the energy value that can be saved through predetermined scenarios as well as the value of the costs required to run a more energy-friendly design scenario in a comprehensive manner.

A new paradigm for construction safety management in China: Introducing knowledge graph and accident database into the early-stage of BIM

Significant progress has been made in managing construction safety risks based on Building Information Modelling (BIM) technology. BIM is active in all stages of construction, including5 planning, design, pre-construction and construction. However, the causes of risks and accidents are complex and can be related to humans, the environment, data and project portraits. The current construction safety risk management process lacks scientific reference and efficiency in conducting comprehensive inspections for specific projects. The uneven and substandard data quality in BIM models significantly undermines their effectiveness. This article introduces a knowledge graph and accident database through data crawling and structuring, with the use and analysis of an accident causation model, providing key risk elements and management directions for safety managers who require a high level of attention during the early-stage of BIM. The obtained knowledge graph was subjected to data mining analysis to explore the causes of accidents, considering the combined effect of multiple factors, such as “case-date-cause” and “case-address-cause”. The paradigm can be recognized by the BIM, and through the secondary development on the development side, a data interface is constructed to transfer the knowledge graph into the BIM. The approach mentioned in the article can provide standard and structured data for the BIM model, analysing high-risk points of projects, which is valuable for engineering applications.

Enhancing Open BIM Interoperability: Automated Generation of a Structural Model from an Architectural Model

Building information modelling (BIM) is an appreciated technology in the field of architecture and construction management. Collaboration of information in BIM has not been fully utilized in the structural engineering stream as many engineers keep on working with previous prevailing design approaches. Failure to adequately facilitate automation in design could lead to structural defects, construction rework, or even structural clashes, with major financial implications. Given the inherent complexity of large-scale construction projects, the ‘manual design and detailing’ of structure is a challenging task and prone to human errors. Against this backdrop, this study developed a 4D building information management approach to facilitate automated structural models for professionals designing all the elements required in reinforced concrete (RC) structures like slabs, beams, and columns. The main contribution of this study is to obtain structural models directly from architecture models automatically, which reduces effort and possible errors in the previous prevailing approaches. The framework enables execution of all the model design works automatically through coding. This is achieved by executing a script which is beneficial for integrated project delivery (IPD). The 3D structural model in BIM software presented in this study extracts and transfers the geometrical data and links these data in Industry Foundation Classes (IFC) files using integration facilitated by Python 3.6 and IFCopenshell. The developed automated programme framework offers a cost-effective and accurate methodology to address the limitations and inefficiencies of traditional methods of structural modelling, which had been carried out manually. The authors have developed a novel tool to extract structural models from architectural models without proprietary software, greatly benefiting BIM managers by enhancing 3D BIM models. This advancement toward Open BIM, crucial for the architecture, engineering, and construction (AEC) industry’s future, is accessible to educators and beginners and highlights BIM’s effectiveness in improving structural analysis and productivity. The core finding of this study is to generate a structural model from an architecture model by automating the script with Python integration of IFC and IFCopenshell. The merits of the developed framework are reduced clashes, more economical structural modelling, and fully automated smart work as functions of the IPD.

BIM-Based Strategies for the Revitalization and Automated Management of Buildings: A Case Study

This study explores the transformative potential of integrating Building Information Modeling (BIM) and Generative Design methodologies in heritage conservation and building management. By utilizing BIM, detailed architectural, structural, and MEP models were created, facilitating precise design and effective stakeholder collaboration. Generative Design enabled the exploration of multiple design solutions, optimizing spatial layouts and structural integrity. The project also integrated automated management systems and IoT sensors to enhance real-time monitoring, energy efficiency, and user comfort through the development of a digital twin. Despite encountering challenges such as technical complexities and budget constraints, the project successfully preserved the cinema’s historical essence while incorporating modern functionalities. The findings highlight the contributions of BIM and Generative Design to the AEC industry, emphasizing their role in improving design accuracy, operational efficiency, and sustainability. This research provides valuable insights for future projects in heritage conservation, offering a blueprint for balancing historical preservation with contemporary needs. The revitalization of the “Ex Cinema Santa Barbara” in Paternò exemplifies these advancements, demonstrating how these technologies can restore and modernize culturally significant historical buildings effectively.

Integrating building information modelling (BIM) and extended reality (XR) in the transportation infrastructure industry

Although building information modelling (BIM) has been widely used in the building industry, its usage in infrastructure projects such as bridges has been very challenging. Extended Reality (XR) that simulates a construction project is still considered a new technology in the Architecture, Engineering, and Construction (AEC) industry. This paper investigates the viability of integrating both BIM and XR technologies into transportation infrastructure projects. A fully integrated workflow for introducing different XR, Augmented Reality (AR), and Virtual Reality (VR), and BIM technologies using different software for a case study of El-Merghani bridge, a reinforced concrete girder type bridge, was developed. The models for the bridge included GIS integration and geometric road design according to AASHTO, documentation, shop drawings and quantification were developed for the bridge. A hypothetical time schedule was generated in Oracle Primavera P6 elevating the BIM model to 4-D and developing VR and AR virtual experiences with the capability to investigate, visualize, and present the model in a virtual environment, at a high level of immersion. Additionally, the associated risk reduction for the considered XR technology was assessed using Monte Carlo simulation. The workflow and the detailed 3D models developed for the bridge along with the highly immersive VR and AR experiences have provided an interactive platform for engineers and different stakeholders to monitor the project during the design and construction phases. The risk analysis showed that significant cost savings can be achieved with the utilization of VR and AR technology in bridges construction.

Semantic enrichment for BIM-based building energy performance simulations using semantic textual similarity and fine-tuning multilingual LLM

To achieve the global targets of the Paris Agreement of limiting global warming, it is necessary to reduce the operational energy of buildings, which are responsible for around 30% of the global greenhouse gas emissions. Building Energy Performance Simulation (BEPS) is an established method to estimate the building’s energy demand in early design stages. Building Information Models (BIM) provides geometric and semantic information to create precise Building Energy Models (BEM) in early design stages. However, manual enrichment of missing semantic information is still a time-consuming and laborious process. Therefore, we propose a novel methodology to automatically enrich missing information to BIM using Semantic Textual Similarity (STS) and fine-tuned Large Language Models (LLM). For every IfcSpace, we match room-specific space types and constructions with missing thermal properties using the semantic most similar pairs of the BIM model and the according databases. We use three real-world case studies to fine-tune LLMs, and two case studies evaluate the whole methodology. Different fine-tuning strategies, such as using different loss functions, adding opposing word pairs or domain-specific abbreviations, significantly improve the accuracy of the matching. At the same time, however, findings show that semantic matching based on multilingual fine-tuned LLM performs worse than translated, monolingually fine-tuned LLM. Finally, BEPS results from automatically enriched BEM only slightly deviate from manually enriched BEM.

AI-Driven BIM Integration for Optimizing Healthcare Facility Design

Efficient healthcare facility design is crucial for providing high-quality healthcare services. This study introduces an innovative approach that integrates artificial intelligence (AI) algorithms, specifically particle swarm optimization (PSO), with building information modeling (BIM) and digital twin technologies to enhance facility layout optimization. The methodology seamlessly integrates AI-driven layout optimization with the robust visualization, analysis, and real-time capabilities of BIM and digital twins. Through the convergence of AI algorithms, BIM, and digital twins, this framework empowers stakeholders to establish a virtual environment for the streamlined exploration and evaluation of diverse design options, significantly reducing the time and manual effort required for layout design. The PSO algorithm generates optimized 2D layouts, which are seamlessly transformed into 3D BIM models through visual programming in Dynamo. This transition enables stakeholders to visualize, analyze, and monitor designs comprehensively, facilitating well-informed decision-making and collaborative discussions. The study presents a comprehensive methodology that underscores the potential of AI, BIM, and digital twin integration, offering a path toward more efficient and effective facility design.

Developing the hybrid BIM-BEM and jellyfish search optimization system for optimizing energy consumption and building installation costs

In recent years, the use of Building Information Modeling (BIM) with Building Energy Modeling (BEM) has become the primary research focus for reducing the energy consumption of buildings in the planning and operational phases. The combination of BIM and BEM offers advantages for the various phases of a construction project. However, there are currently very few studies that can integrate multi-objective optimization algorithms into the BIM-BEM process to achieve automatic optimization and effectively manage many aspects of building development. In this study, an EnergyPlus integrated multi-objective jellyfish search (EP-MOJSO) system was developed, utilizing an optimization algorithm to find the best thermal insulation layers for an Aluminum composite material (ACM) wall. The goal is to reduce the energy consumption and total cost in a BIM-BEM environment. In the case study, the authors successfully applied the system to a real building, resulting in a 10.7% reduction in total cost and a 65 kWh/m2/year reduction in EUI. It is expected that the results of the study will open up new ways of using algorithms for multi-criteria optimization in BIM models to optimize various project factors such as energy and total cost and thus make an important contribution to sustainable building design.

Ontology for BIM-Based Robotic Navigation and Inspection Tasks

The availability of inspection robots in the construction and operation phases of buildings has led to expanding the scope of applications and increasing technological challenges. Furthermore, the building information modeling (BIM)-based approach for robotic inspection is expected to improve the inspection process as the BIM models contain accurate geometry and relevant information at different phases of the lifecycle of a building. Several studies have used BIM for navigation purposes. Also, some studies focused on developing a knowledge-based ontology to perform activities in a robotic environment (e.g., CRAM). However, the research in this area is still limited and fragmented, and there is a need to develop an integrated ontology to be used as a first step towards logic-based inspection. This paper aims to develop an ontology for BIM-based robotic navigation and inspection tasks (OBRNIT). This ontology can help system engineers involved in developing robotic inspection systems by identifying the different concepts and relationships between robotic inspection and navigation tasks based on BIM information. The developed ontology covers four main types of concepts: (1) robot concepts, (2) building concepts, (3) navigation task concepts, and (4) inspection task concepts. The ontology is developed using Protégé. The following steps are taken to reach the objectives: (1) the available literature is reviewed to identify the concepts, (2) the steps for developing OBRNIT are identified, (3) the basic components of the ontology are developed, and (4) the evaluation process is performed for the developed ontology. The semantic representation of OBRNIT was evaluated through a case study and a survey. The evaluation confirms that OBRNIT covers the domain’s concepts and relationships, and can be applied to develop robotic inspection systems. In a case study conducted in a building at Concordia University, OBRNIT was used to support an inspection robot in navigating to identify a ceiling leakage. Survey results from 33 experts indicate that 28.13% strongly agreed and 65.63% agreed on the usage of OBRNIT for the development of robotic navigation and inspection systems. This highlights its potential in enhancing inspection reliability and repeatability, addressing the complexity of interactions within the inspection environment, and supporting the development of more autonomous and efficient robotic inspection systems.

New Frontiers in Water Distribution System Management and Monitoring: First Development of a Water Safety Plan Based on Heritage Building Information Modeling (HBIM) in Neptune Fountain, Bologna, Italy

The World Health Organization (WHO) recommends the introduction of a water safety plan (WSP) approach on drinking water, in all types of settings. This study represents the first WSP developed on the Neptune Fountain, in Bologna (Italy), based on an interdisciplinary approach, integrating hydraulic and microbiological features, in a Building Information Modeling (BIM). The aim was to develop a dynamic and digital platform to update and share the maintenance program, promoting collaboration among microbiologists, engineers, and municipal staff. Water samples were collected along fountain water distribution systems (WDS) from 2016 to 2021 to monitor water quality through the heterotrophic bacteria at 22 °C and 37 °C, as well as to conduct an Enterococci, Coliform bacteria, Escherichia coli, Pseudomonas aeruginosa, Clostridium perfringens, and Staphylococcus aureus assessment. Simultaneously, hydraulic measures were performed, and advanced geomatics techniques were used to detect the WDS structural components, with a focus on the water treatment system (WTS). The WTS consisted of 10 modules corresponding to specific treatments: descaling, carbon–sand filtration, reverse osmosis, and ultraviolet disinfection. Fecal indicators, heterotrophic bacteria, and P. aeruginosa exceeded the reference limits in most of the modules. Several disinfections and washing treatments, other than changing the maintenance procedure scheduling, were performed, improving the WTS and controlling the contamination. The developed microbiological results, hydraulic measurements, and maintenance procedures were integrated in the BIM model to optimize the data storage, updating procedures and the real-time data sharing. This approach improved the fountain management, operation, and material conservation, ultimately preserving the health of daily visitors.

Enriching Building Information Modeling Models through Information Delivery Specification

The efficient acquisition and dissemination of information are crucial in building information modeling (BIM). Current BIM models face significant challenges, including inadequate modeling techniques, poorly defined information requirements, and low interoperability. These issues result in poor information quality and complicate the transition from information acquisition to model processing. Public authorities often provide documentation in various formats, requiring manual transfer to software, which is error-prone and burdensome. This process is particularly difficult for small and medium enterprises lacking resources and knowledge. To address these issues, the IDS (Information Delivery Specification) Collab Tool is under development. This tool aims to automate the import of requirements into authoring software, perform automated compliance checks, and enhance interoperability among stakeholders. It will assist designers in providing accurate information according to requirements through the IDS standard, improving model quality and efficiency from early design stages. Adapting BIM models to specific project requirements and aligning new IDS capabilities with traditional industry practices remain significant challenges. Preliminary evaluations indicate the tool’s potential to significantly improve workflow efficiency and compliance in BIM modeling. However, broader awareness and adoption of the IDS standard are needed. Further research and refinement are essential to fully realize the benefits of digital tools in revolutionizing design and construction practices.

A Simplified Framework to Integrate Databases with Building Information Modeling for Building Energy Assessment in Multi-Climate Zones

BIM models are seldom used for the energy certification of buildings. This paper discusses the advantages of linking two important fields: building information modeling (BIM) and building environmental assessment methods (BEAM), presented as a rating system and a proposal for the Chilean context. The state of the art in both fields around the world is discussed, with an in-depth examination of current BIM software and related applications, followed by a discussion about previous research on integrating them. A lack of interoperability and data losses between BIM and BEM were found. A new tool is presented that addresses these challenges to ensure accurate rating system data, and this new framework is based on database exchange and takes crucial information from BIM to BEAM platforms. The development of the method includes BIM programming (API), database links, and spreadsheets for a Chilean building energy certification through a new tool, also applicable to multiclimactic zones. This new semi-automatic tool allows architects to model their design in a BIM platform and use this information as input for the energy certification process. The potential and risks of this method are discussed. Several improvements and enhancements of the energy certification process were found when incorporating this new framework in comparison to current methodologies.

Innovative BIM technology application in the construction management of highway

Within the global architecture, engineering, and construction industry, the use of Building Information Modeling (BIM) technology has significantly expanded. However, given the unique characteristics of road infrastructure, the application of BIM technology is still being explored. This article focuses on the Yuanchen Expressway, exploring innovative applications of BIM technology in comprehensive construction management. The project employs advanced technologies, including BIM, Geographic Information Systems (GIS), and the Internet of Things (IoT), to precisely identify critical nodes and breakthroughs. Supported by a detailed BIM model and a multi-level, diversified digital management platform, the project effectively addresses construction challenges in multiple tunnels, bridges, and complex interchanges, achieving intelligent construction innovation throughout the Yuanchen Expressway with BIM technology. By guiding construction through BIM models, utilizing a BIM+GIS-based management cloud platform system, and employing VR safety briefings, the project effectively reduces the difficulty of communication and coordination in project management, shortens the project measurement cycle, improves on-site work efficiency, and ensures comprehensive control and safety management. This article provides an exemplary case for the application of full-line construction management using BIM technology in the highway sector both in China and globally, offering new perspectives and strategies for highway construction management.

HBIM for Conservation of Built Heritage

Building information modeling (BIM) has recently become more popular in historical buildings as a method to rebuild their geometry and collect relevant information. Heritage BIM (HBIM), which combines high-level data about surface conditions, is a valuable tool for conservation decision-making. However, implementing BIM in heritage has its challenges because BIM libraries are designed for new constructions and are incapable of accommodating the morphological irregularities found in historical structures. This article discusses an architecture survey workflow that uses TLS, imagery, and deep learning algorithms to optimize HBIM for the conservation of the Nabatean built heritage. In addition to creating new resourceful Nabatean libraries with high details, the proposed approach enhanced HBIM by including two data outputs. The first dataset contained the TLS 3D dense mesh model, which was enhanced with high-quality textures extracted from independent imagery captured at the optimal time and location for accurate depictions of surface features. These images were also used to create true orthophotos using accurate and reliable 2.5D DSM derived from TLS, which eliminated all image distortion. The true orthophoto was then used in HBIM texturing to create a realistic decay map and combined with a deep learning algorithm to automatically detect and draw the outline of surface features and cracks in the BIM model, along with their statistical parameters. The use of deep learning on a structured 2D true orthophoto produced segmentation results in the metric units required for damage quantifications and helped overcome the limitations of using deep learning for 2D non-metric imagery, which typically uses pixels to measure crack widths and areas. The results show that the scanner and imagery integration allows for the efficient collection of data for informative HBIM models and provide stakeholders with an efficient tool for investigating and analyzing buildings to ensure proper conservation.