Query Support for BIMs using Semantic and Spatial Conditions

Borrmann, André; Rank, Ernst

doi:10.4018/978-1-60566-928-1.ch018

Cited by 15 publications

(18 citation statements)

References 42 publications

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“…Querying BIM models for different applications using the physical aspects of a building has been investigated over the last decade [37][38][39][40][41]. The developed query languages have relied on various mechanisms.…”

Section: Data Query In Bimmentioning

confidence: 99%

Querying 3D Cadastral Information from BIM Models

Atazadeh

Rajabifard

Zhang

et al. 2019

IJGI

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There has been significant research on the intersection of 3D cadastre and building information modelling (BIM) over the recent years. BIM provides a multidimensional environment for capturing, curating and communicating the physical and functional aspects during a building’s lifecycle. A BIM-based solution for 3D cadastre provides a rich repository of legal and physical datasets in a common environment. The knowledge encapsulated inside a cadastral BIM model should be tapped to unlock the value of 3D cadastral information. Therefore, this article aims to develop BIM-based queries for interrogating questions about the legal ownership of properties inside multistorey buildings. These queries include identifying legal spaces that belong to a private or common property; querying physical elements that bound a legal space; and finding legal spaces that are adjacent to each other at a specific building element.

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Section: Data Query In Bimmentioning

confidence: 99%

Querying 3D Cadastral Information from BIM Models

Atazadeh

Rajabifard

Zhang

et al. 2019

IJGI

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“…The BIM authoring tools generate the data of building elements not only in the form of 3D solid objects but also associate useful semantic information with it (Goes and Santos, 2011). The objectoriented nature of BIM and its ability to capture information beyond 3D geometry makes it a unique facilitator for utilising the same information for downstream activities (Borrmann and Rank, 2010). The re-use of BIM data for downstream analysis and design activities -such as structural analysis (Shin et al, 2011), construction planning (Kumar and Cheng, 2015) or costestimation (Lee, Kim and Yu, 2014;Staub-French et al, 2003) -has the potential to minimise errors which are prevalent in traditional design processes (Christiansson, 2004).…”

Section: Context Settingmentioning

confidence: 99%

Utilising Building Component Data from BIM for Formwork Planning

Singh¹,

Sawhney

Sharma³

2017

CEB

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Advancements in the computing realm have assisted the Architecture, Engineering, and Construction (AEC) industry to progress significantly by automating several design tasks and activities. Building Information Modelling (BIM) authoring tools have played a significant role in automating design tasks and reducing the efforts required by the designer in redundant, repetitive or production-oriented activities. This paper explores one such approach that, with the help of BIM authoring tool and its Application Programming Interface (API), reduces the efforts expended on formwork design for concrete structures. The paper utilises the concept of using BIM data as input to compute the quantity of formwork, and generate visualisations and schedule of formwork. The developed approach first takes data input from semantic BIM to the API environment for computation and design of formwork systems, which is then placed within the BIM model, to generate visualisation and prepare schedules. The research work utilises a structural concrete wall as an example to demonstrate the presented approach. The approach will be influential in streamlining the formwork design process in the BIM environment and reducing efforts required by the designer and the planning engineer. Since the formwork elements are generated as 3-Dimensional (3D) solids and smart BIM elements, the generated model of formwork can be used for resolving clashes, scheduling, and resource planning.

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“…The major focus is on developing spatial filter strategies. Examples of these efforts include the Spatial query language [3], QL4BIM Spatio-semantic query language [10]; graph-based BIM retrieval [11], and topological querying [12]. The second group is to enhance the human-readability of data schema by utilizing an ontology approach to transform relations among data entities from implicit to explicit.…”

Section: Partial Digital Model Extractionmentioning

confidence: 99%

“…In order to enable digital data exchange between proprietary software applications, several neutral data standards, for instance, LandXML [1] and TransXML [2], have been developed. However, these schemas are presented in machine-readable format and so complicated that it is difficult for end users to extract the desired properties [3]. The end user is required to have considerable programming skills and properly understand the structure and the meaning of each entity or attribute included in the source data schema.…”

Section: Introductionmentioning

confidence: 99%

Technical Term Similarity Model for Natural Language Based Data Retrieval in Civil Infrastructure Projects

Le¹,

Jeong²

2016

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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-Recent advances in data and information technologies have enabled extensive digital datasets to be available to decision makers during the life cycle of a civil infrastructure project. However, much of the data is not yet fully reused due to the challenging and time consuming process of extracting the desired data for a specific purpose. Digital datasets are presented only in computer-readable formats and they are mostly complicated. In order to accurately extract a required subset of data, end users need to have deep understanding of the structure of the data schema, the meaning of each data entity and a query language. Thus, to truly facilitate the reuse of digital project data, a computational platform is needed to allow users to present their data needs in natural language. One of the critical requirements for a computer to perform this task is the ability to understand and interpret users' natural language inputs where keywords are a basic linguistic component. This research aims to collect technical terms commonly used in the civil infrastructure domain and develop a semantic similarity model that can measure the meaning relatedness/similarity between terms. Natural Language Processing (NLP) techniques and C-value method are used to automatically extract terms from text documents. A machine learning model called Skip-gram model is then employed to learn the semantic relatedness between technical terms using an unlabeled highway corpora as the input data. The input corpus includes 10 million words mainly collected from roadway design guidelines across the U.S. The model is evaluated by comparing the mapping results performed by a computer and a human.

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Query Support for BIMs using Semantic and Spatial Conditions

Cited by 15 publications

References 42 publications

Querying 3D Cadastral Information from BIM Models

Querying 3D Cadastral Information from BIM Models

Utilising Building Component Data from BIM for Formwork Planning

Technical Term Similarity Model for Natural Language Based Data Retrieval in Civil Infrastructure Projects

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