An investigation into the applicability of building information models in geospatial environment in support of site selection and fire response management processes

Some tasks in the construction industry and urban management field such as site selection and fire response management are usually managed by using a Geographical Information System (GIS), as the tasks in these processes require a high level and amount of integrated geospatial information. Recently, a key element of this integrated geospatial information to emerge is detailed geometrical and semantic information about buildings. In parallel, Building Information Models (BIMs) of today have the capacity for storing and representing such detailed geometrical and semantic information. In this context, the research aimed to investigate the applicability of BIMs in geospatial environment by focusing specifically on these two domains; site selection and fire response management. In the first phase of the research two use case scenarios were developed in order to understand the processes in these domains in a more detailed manner and to establish the scope of a possible software development for transferring information from BIMs into the geospatial environment. In the following phase of the research two data models were developed - a Schema-Level Model View and a geospatial data model. The Schema-Level Model View was used in simplifying the information acquired from the BIM, while the geospatial data model acted as the template for creating physical files and databases in the geospatial environment. Following this, three software components to transfer building information into the geospatial environment were designed, developed, and validated. The first component served for acquiring the building information from the BIM, while the latter two served for transforming the information into the geospatial environment.The overall research demonstrated that it is possible to transfer (high level of geometric and semantic) information acquired from BIMs into the geospatial environment. The results also demonstrated that BIMs provide a sufficient level and amount of (geometric and semantic) information (about the building) for the seamless automation of data management tasks in the site selection and fire response management processes.     

Semantic enrichment of building and city information models: A ten-year review

Building Information Models (BIMs) and City Information Models (CIMs) have flourished in building and urban studies independently over the past decade. Semantic enrichment is an indispensable process that adds new semantics such as geometric, non-geometric, and topological information into existing BIMs or CIMs to enable multidisciplinary applications in fields such as construction management, geoinformatics, and urban planning. These two paths are now coming to a juncture for integration and juxtaposition. However, a critical review of the semantic enrichment of BIM and CIM is missing in the literature. This research aims to probe into semantic enrichment by comparing its similarities and differences between BIM and CIM over a ten-year time span. The research methods include establishing a uniform conceptual model, and sourcing and analyzing 44 pertinent cases in the literature. The findings plot the terminologies, methods, scopes, and trends for the semantic enrichment approaches in the two domains. With the increasing availability of data sources, algorithms, and computing power, they cross the border to enter each other’s domain. Future research will likely gain new momentums from the demands of value-added applications, development of remote sensing devices, intelligent data processing algorithms, interoperability between BIM and CIM software platforms, and emerging technologies such as big data analytics.     

基于IFC的室内地图模型构建研究

室内地图构建属于一项基础性研究,可为导航、疏散等建筑室内智能化应用提供 数据与技术支持。室内环境的复杂性,存在信息提取耗时且成本高,提取的室内信息不完整等 问题,而已有的室内地图模型通常体量庞大,数据复杂且冗余严重,实用性较低。为此,将 BIM 技术与室内地图研究相结合,以 BIM 通用交互格式工业基础类(IFC)文件为数据源,提取几何 与语义信息,提出一种室内地图模型的构建方法。内分类定义地图节点,完成对建筑室内信息 的抽象表达;通过设置阈值来简化地图中节点数量以达到模型的优化。该模型中可导入经典寻 路算法生成最短路径,并且设计路径优化方法。     

Information Quality Assessment for Facility Management

Assessing the quality of building information models (BIMs) is an important yet challenging task within the construction industry as projects are increasingly being delivered with BIM. This is particularly essential for facility management (FM) users as downstream information consumers that depend on the quality of models developed in the previous project phases. The research presented in this paper addresses this challenge by introducing a framework for information quality assessment (IQA) of BIMs for FM uses. The IQA framework is the outcome of an extensive study of two large owner organizations involving numerous BIM projects. The framework is structured based on the essential FM subjects: assets, spaces, and systems, and the model characteristics: objects, attributes, relationships, and spatial information. The framework is then operationalized through the development and evaluation of information quality (IQ) tests using BIM model checking tools across three projects with different levels of detail and complexity. The proposed IQA framework and associated tests advance the state of knowledge about BIM quality in terms of methods to represent and evaluate conformance to owner requirements.     

An approach to combine progressively captured point clouds for BIM update

Building information models (BIMs) provide opportunities to serve as an information repository to store and deliver as-built information. Since a building is not always constructed exactly as the design information specifies, there will be discrepancies between a BIM created in the design phase (called as-designed BIM) and the as-built conditions. Point clouds captured by laser scans can be used as a reference to update an as-designed BIM into an as-built BIM (i.e., the BIM that captures the as-built information). Occlusions and construction progress prevent a laser scan performed at a single point in time to capture a complete view of building components. Progressively scanning a building during the construction phase and combining the progressively captured point cloud data together can provide the geometric information missing in the point cloud data captured previously. However, combining all point cloud data will result in large file sizes and might not always guarantee additional building component information. This paper provides the details of an approach developed to help engineers decide on which progressively captured point cloud data to combine in order to get more geometric information and eliminate large file sizes due to redundant point clouds.     

The IFC-based path planning for 3D indoor spaces

Path planning is a fundamental problem, especially for various AEC applications, such as architectural design, indoor and outdoor navigation, and emergency evacuation. However, the conventional approaches mainly operate path planning on 2D drawings or building layouts by simply considering geometric information, while losing abundant semantic information of building components. To address this issue, this paper introduces a new method to cope with path planning for 3D indoor space through an IFC (Industry Foundation Classes) file as input. As a major data exchange standard for Building Information Modeling (BIM), the IFC standard is capable of restoring both geometric information and rich semantic information of building components to support lifecycle data sharing. The method consists of three main steps: (1) extracting both geometric and semantic information of building components defined within the IFC file, (2) discretizing and mapping the extracted information into a planar grid, (3) and finally finding the shortest path based on the mapping for path planning using Fast Marching Method. The paper aims to process different kinds of building components and their corresponding properties to obtain rich semantic information that can enhance applications of path planning. In addition, the IFC-based distributed data sharing and management is implemented for path planning. The paper also presents some experiments to demonstrate the accuracy, efficiency and adaptability of the method. Video demonstration is available from http://cgcad.thss.tsinghua.edu.cn/liuyushen/ifcpath/.     

Querying a building information model for construction-specific spatial information

The design and construction community has shown increasing interest in adopting building information models (BIMs). The richness of information provided by BIMs has the potential to streamline the design and construction processes by enabling enhanced communication, coordination, automation and analysis. However, there are many challenges in extracting construction-specific information out of BIMs. In most cases, construction practitioners have to manually identify the required information, which is inefficient and prone to error, particularly for complex, large-scale projects. This paper describes the process and methods we have formalized to partially automate the extraction and querying of construction-specific information from a BIM. We describe methods for analyzing a BIM to query for spatial information that is relevant for construction practitioners, and that is typically represented implicitly in a BIM. Our approach integrates ifcXML data and other spatial data to develop a richer model for construction users. We employ custom 2D topological XQuery predicates to answer a variety of spatial queries. The validation results demonstrate that this approach provides a richer representation of construction-specific information compared to existing BIM tools.     

SpaRSE-BIM: Classification of IFC-based geometry via sparse convolutional neural networks

Information exchange between Building Information Modeling (BIM) tools is challenging, since many applications use their own native data formats. The Industry Foundation Classes (IFC) schema, an open data exchange format for BIM, does not capture the full semantic meaning needed for direct use by different BIM tools and can be prone to information loss due to reduction, simplification, translation and interpretation of the data. Current practice often treats the imported model as a reference and requires a user to remodel the building using the respective application’s native elements. Many BIM object properties are defined by its classification. Inconsistencies in the mapping between native BIM elements and IFC, e.g. due to unsupported export functionality or manual error, can lead to problems when using the model in a downstream application. Recent works demonstrate that neural networks offer a promising possibility to alleviate this issue via classification of the objects contained in a BIM model and suggesting those corrections to the user. However, the computational overhead of these deep learning models, either due to necessary pre-processing of the data or runtime performance of the model, makes it difficult for them to be used in plug-ins or middleware for BIM tools. This work proposes SpaRSE-BIM, a neural network model based on sparse convolutions for the classification of IFC-based geometry and semantic enrichment of BIM models. Experiments are performed on two IFC entity classification benchmark datasets. The results demonstrate that SpaRSE-BIM is significantly more efficient at inference time compared to previous approaches, while maintaining state-of-the-art accuracy. Further experiments explore the applicability of IFC entity classification datasets to the domain of Scan-to-BIM. It can be shown that the feature space of SpaRSE-BIM learns to discern objects in a semantically meaningful way, even in cases where fine-grained subtype information for IFC objects is not available during training.     

A novel Data-Driven framework based on BIM and knowledge graph for automatic model auditing and Quantity Take-off

Model auditing is a critical step before conducting Building Information Modeling (BIM)-based Quantity Take-off (QTO) because these models may contain various human errors and mistakes, leading to insufficient semantic information and inconsistent modeling style in BIM models. The traditional object-oriented approach has difficulties in representing unstructured BIM data (e.g., interrelationships), while rule-based methods involve tremendous human efforts to develop rule sets, lacking flexibility for different requirements. Therefore, this study aims to establish a novel data-driven framework based on BIM and knowledge graph (KG) to represent unstructured BIM data for automatic inferences of auditing results of BIM model mistakes. It starts by establishing a BIM-KG data model via identifying required information for auditing purposes. Subsequently, BIM data is automatically transformed into the BIM-KG representations, the embeddings of which are trained using a knowledge graph embedding model. Automatic mechanisms are then developed to utilize the computable embeddings to effectively identify mistake BIM elements. The framework is validated using illustrative examples and the results show that 100% mistake elements can be identified successfully without human intervention.     

A meta-model approach for formal specification and consistent management of multi-LOD building models

The design of a building is a collaborative process among experts from multiple disciplines. Using Building Information Modeling (BIM), a model is developed through multiple refinement stages to satisfy various design and engineering requirements. Such refinements of geometric and semantic information are described as levels of development (LOD). Thus far, there is no method to explicitly define an LOD’s requirements nor to precisely specify the uncertainties involved. Furthermore, despite the insufficient information in the early design stages, a BIM model appears precise and certain, which can lead to false assumptions and model evaluations, for example, in the case of energy efficiency calculations or structural analyses. Hence, this paper presents a multi-LOD meta-model to explicitly describe an LOD’s requirements, incorporating the potential fuzziness of both, geometric and semantic information of individual elements. The explicitly defined fuzziness can be taken into account when applying simulations or analyses for assessing the performance of different building design variants. To support the continuous elaboration of a building from the conceptual to the detailed design stages, the multi-LOD model makes it possible to ensure the consistency of the geometric and semantic information as well as the topological coherence across the different LODs. The feasibility of the approach is demonstrated by its prototypical implementation as a web-server and user-interface, providing a means for managing and checking the exchange requirements both at the meta-level and for concrete building model instances. The paper is concluded with a case study of a real-world construction project that demonstrates the use of the meta-model to support model analysis and the decision-making process.     

Construction-specific spatial information reasoning in Building Information Models

In recent years, there have been significant advances in modeling technology for object-oriented building products. However, the building models are still lacking of providing construction-specific spatial information required for construction planning. Consequently, construction planners visually analyze building product models and derive geometric characteristics such as bounded spaces and exterior perimeter to develop detailed construction plans. Such a process presents fragmented information flows, from building product information to construction planning, that rely on subjective decisions of construction planners. In order to overcome these drawbacks, this research proposes a geometric reasoning system that analyzes geometric information in building designs, derives the construction-specific spatial information, and uses the information to assist in construction planning. The scope of presented work includes detecting work packages formed by faces during construction, such as large work faces and bounded spaces, and using information in the work packages directly to support planning of selected indoor construction activities. The main features of the proposed system named Construction Spatial Information Reasoner (CSIR) include a set of relationship acquisition algorithms, building component relationship data structure, and interpretation of the relationship to support detailed construction activity planning. The relationship acquisition algorithms identify adjacency between building components that is stored in the relational data structure. Then, acquired adjacency relationships are transformed into a set of graphs that represent work packages. To implement the proposed approach, CSIR utilized a commercially-available Building Information Modeling (BIM) platform and the algorithms were imbedded to the BIM platform. For validation, CSIR was tested on a real commercial building. For interior ceiling grid installation activities, CSIR successfully detected existing work packages and analyzed the spatial characteristics impacting construction productivity. The major contribution of the presented research would be to enable a realistic analysis of building geometric condition that is not possible in current BIM and a seamless information flow from building product information to construction process plans. These can potentially reduce current manual and error-prone construction planning processes. Limitations and future research suggestions are also presented.     

Live semantic data from building digital twins for robot navigation: Overview of data transfer methods

Increasing reliance on automation and robotization presents great opportunities to improve the management of construction sites as well as existing buildings. Crucial in the use of robots in a built environment is their capacity to locate themselves and navigate as autonomously as possible. Robots often rely on planar and 3D laser scanners for that purpose, and building information models (BIM) are seldom used, for a number of reasons, namely their unreliability, unavailability, and mismatch with localization algorithms used in robots. However, while BIM models are becoming increasingly reliable and more commonly available in more standard data formats (JSON, XML, RDF), they become more promising and reliable resources for localization and indoor navigation, in particular in the more static types of existing infrastructure (existing buildings). In this article, we specifically investigate to what extent and how such building data can be used for such robot navigation. Data flows are built from BIM model to local repository and further to the robot, making use of graph data models (RDF) and JSON data formats. The local repository can hereby be considered to be a digital twin of the real-world building. Navigation on the basis of a BIM model is tested in a real world environment (university building) using a standard robot navigation technology stack. We conclude that it is possible to rely on BIM data and we outline different data flows from BIM model to digital twin and to robot. Future work can focus on (1) making building data models more reliable and standard (modelling guidelines and robot world model), (2) improving the ways in which building features in the digital building model can be recognized in 3D point clouds observed by the robots, and (3) investigating possibilities to update the BIM model based on robot feedback.     

An iterative reference mapping approach for BIM IFCXML classified content compression

Industry Foundation Classes (IFC) and Industry Foundation Classes XML (IFCXML) are widely accepted data frameworks and formats for information storage and exchange among building information models (BIM). IFCXML are comprehensive and compatible with the major BIM platforms; however, many studies suggest their complex structure often results in redundancy and inflexibility. Researchers have proposed various IFC compression methods to reduce file size and restructure data organization, such as partial model extraction, Solibri IFC Optimizer, and ACC4IFC. However, simplification often results in missing information or data leakage, and there has never been a compressor specific to IFCXML. To overcome these issues, this study proposed a conservative compression method that removes duplicated information while maintaining the data structure of IFCXML through an iterative reference mapping method. Based on the data structure and geometry of IFCXML, the algorithm identified three kinds of duplicate information: independent entity duplication (IED), cross-reference entity duplication (CED), and property-set entity duplication (PED). To validate the proposed compressing method, this study conducted a validation test on six typical BIM models and benchmarked with other existing compressors (Solibri IFC Optimizer and ACC4IFC). The outcomes suggested that the proposed model could compress IFCXML files by 18%-59% without losing information.     

An ontology-based analysis of the industry foundation class schema for building information model exchanges

Robust knowledge sharing frameworks between different stakeholders in a building project is of high priority. Industry Foundation Classes (IFC) provides a rich schema for interoperability through object-based transactions. However, IFC lacks semantic clarity in mapping entities and relationships, resulting in multiple definitions to map the same information between different federated models. The objective of this research is to examine IFC from a perspective of an ontological framework, which can make the IFC definitions more formal, consistent and unambiguous. Different methods of ontological approaches to engineering knowledge are reviewed. Various issues such as the need for a logical framework, the current semantic approaches in the AEC/FM industry, and advantages of building an ontology structure are addressed. A comparative study of the ontology and segments of the existing IFC schema definition are performed. This exercise reveals the ambiguous nature of current IFC definitions and proposes reforms such that data exchanges would be more semantically robust. An ontology would structure the overall interoperability of BIM tools by providing a formal and consistent taxonomy and classification structure for extending IFC and for defining subsets as model view definitions (MVD).     

Developing final as-built BIM model management system for owners during project closeout: A case study

To apply final as-built BIM models to facility management (FM) during the operation phase, it is important for owners to obtain an accurate, final as-built model from the general contractors (GCs) following project closeout. Confirming the accuracy of the final as-built BIM model is one of the most important works executed by owners to meet the accuracy requirement of final as-built models for FM. However, many practical problems arise relating to the management of final as-built models such as final as-built model mismatch, the lack of available final as-built models, and the entry of incorrect non-geometric information into the final as-built models. To solve these practical problems, this study develops a Final As-built BIM Model Management (FABMM) system for owners to handle final as-built BIM model inspection, modification, and confirmation (BMIMC) work beyond project closeout. The proposed approach and system can be used to manage the status and results of BMIMC management work for the final as-built BIM model to be performed. The proposed approach and system were applied in a case study in a selected building in Taiwan to verify and demonstrate its practical effectiveness. This study identifies the benefits, limitations, and conclusions of the FABMM system, and presents suggestions for its further application.     

State of research in automatic as-built modelling

Building Information Models (BIMs) are becoming the official standard in the construction industry for encoding, reusing, and exchanging information about structural assets. Automatically generating such representations for existing assets stirs up the interest of various industrial, academic, and governmental parties, as it is expected to have a high economic impact. The purpose of this paper is to provide a general overview of the as-built modelling process, with focus on the geometric modelling side. Relevant works from the Computer Vision, Geometry Processing, and Civil Engineering communities are presented and compared in terms of their potential to lead to automatic as-built modelling.     

Cloud-to-BIM-to-FEM: Structural simulation with accurate historic BIM from laser scans

The complexity of historic constructions, with irregular geometry, inhomogeneous materials, variable morphology, alterations and damages, poses numerous challenges in the digital modeling and simulation of structural performances under different types of actions. Although recent developments in Building Information Modeling have introduced advanced simulation capabilities, the numerical characterization of historic buildings is still a challenging task for the lack of reliable procedures for structural simulation.This paper presents an innovative two-step methodology (Cloud-to-BIM-to-FEM) able to convert a historic BIM into a finite element model for structural simulation. The generation of the BIM (Cloud-to-BIM) is carried out with an accurate survey that integrates geometrical aspects, diagnostic analysis based on destructive and non-destructive inspections, material information, element interconnections, and architectural and structural considerations. The BIM is then turned into a finite element model (BIM-to-FEM) with a geometric rationalization which preserves irregularities and anomalies, such as verticality deviation and variable thickness. After setting material properties, loads, and boundary conditions, the structural simulation is run with a detailed model that respects the uniqueness and authenticity of the historic building, without the typical excessive geometric simplifications of the shape.A real case study is illustrated and discussed to prove that a rigorous Cloud-to-BIM-to-FEM workflow allows the generation of an accurate historic BIM from a set of laser scanning point clouds. Structural simulation was carried out with a 3D mesh derived from the BIM in order to take into consideration the geometrical irregularity of a castle. Here, the advantages and disadvantages of the proposed approach are illustrated and discussed.     

面向 Web 的建筑三维模型可视化方法研究

针对当前工业基础类(IFC)标准文件与 WebGL 框架集成方案存在模型语义完整性差及数据互操 性弱等问题,提出一套建筑信息模型(BIM)数据网络可视化实施方案。该方案以 IFC 标准文件作为输入数据源, 首先对 BIM 模型进行重构,并提出基于 IFC 结构树的构件实例层次拆分策略将原始 IFC 文件实施数模分离; 然后,将重构后的 IFC 模型文件向 glTF 格式文件进行转换,并在转换过程中完成几何空间和语义属性的双重 关系映射;最后,提出基于层次包围体(BVH)结构的 BS-AB 场景构件可视性检测算法对建筑构件进行可见性剔 除。实验结果表明：该方法能够显著减少 BIM 模型数据冗余,且高效完成了对复杂 BIM 场景不可见构件的剔 除工作,降低了渲染管线的开销,为基于 IFC 标准与 WebGL 框架集成方案的 BIM 模型数据网络可视化提供了 一种可行的参考方案。