Artificial intelligence for BIM content management and delivery: Case study of association rule mining for construction detailing

Abdirad, Hamid; Mathur, Pegah

doi:10.1016/j.aei.2021.101414

Cited by 23 publications

(16 citation statements)

References 30 publications

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“…e accuracy and scope of information extraction can be improved using an entity-relationship-based framework [42][43][44][45][46][47]. Few research works employed the term-frequency methodology for ranking the webpages [48][49][50][51][52][53][54]. us, there is a demand for a practical ontological framework for managing documents and retrieving information based on the user query.…”

Section: Literature Reviewmentioning

confidence: 99%

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Managing and Retrieving Bilingual Documents Using Artificial Intelligence-Based Ontological Framework

Alothman

Sait

2022

Computational Intelligence and Neuroscience

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In recent times, artificial intelligence (AI) methods have been applied in document and content management to make decisions and improve the organization’s functionalities. However, the lack of semantics and restricted metadata hinders the current document management technique from achieving a better outcome. E-Government activities demand a sophisticated approach to handle a large corpus of data and produce valuable insights. There is a lack of methods to manage and retrieve bilingual (Arabic and English) documents. Therefore, the study aims to develop an ontology-based AI framework for managing documents. A testbed is employed to simulate the existing and proposed framework for the performance evaluation. Initially, a data extraction methodology is utilized to extract Arabic and English content from 77 documents. Researchers developed a bilingual dictionary to teach the proposed information retrieval technique. A classifier based on the Naïve Bayes approach is designed to identify the documents’ relations. Finally, a ranking approach based on link analysis is used for ranking the documents according to the users’ queries. The benchmark evaluation metrics are applied to measure the performance of the proposed ontological framework. The findings suggest that the proposed framework offers supreme results and outperforms the existing framework.

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Section: Literature Reviewmentioning

confidence: 99%

“…us, organizations cannot access the document's content without its metadata [48][49][50][51]. e KSA's Vision 2030 motivates researchers to apply innovative techniques to the current functionalities of the organization.…”

Section: Introductionmentioning

confidence: 99%

Managing and Retrieving Bilingual Documents Using Artificial Intelligence-Based Ontological Framework

Alothman

Sait

2022

Computational Intelligence and Neuroscience

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“…As a result, a large number of objects is rarely used again (Iyer et al, 2005). (Abdirad and Mathur, 2021) reports that searching for the correct BIM object in libraries approximately takes 2-4% of annual working hours. To combat this issue, BIM content management and delivery (BCMD) techniques are investigated to standardize naming, quering and organisation.…”

Section: Window and Door Detection And Modellingmentioning

confidence: 99%

“…To combat this issue, BIM content management and delivery (BCMD) techniques are investigated to standardize naming, quering and organisation. Additionally, ML quering is proposed to retrieve BIM object models more effectively during design (Abdirad and Mathur, 2021). This is supported by , who discuss how to more efficiently build 3D models from scratch and enable the reuse of already existing object models.…”

Section: Window and Door Detection And Modellingmentioning

confidence: 99%

Review of Window and Door Type Detection Approaches

Geyter

Bassier²,

Winter³

et al. 2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The use of as-built Building Information Models (BIM) has become increasingly commonplace. This process of creating a BIM model from point cloud data, also referred to as Scan-to-BIM, is a mostly manual task. Due to the large amount of manual work, the entire Scan-to-BIM process is time-consuming and error prone. Current research focuses on the automation of the Scan-to-BIM pipeline by applying state-of-the-art techniques on its consecutive steps including the data acquisition, data processing, data interpretation and modelling. By automating the matching and modelling of window and door objects, a considerable amount of time can be saved in the Scan-to-BIM process. This is so because each window and door instance needs to be examined by the modeller and must be adapted to the actual on-site situation. Large object libraries containing predefined window and door objects exists but the matching to the best-fit predefined object remains time consuming. The aim of this research is to examine the possibilities to speed up the modelling of window and door objects. First, a literature review discussing existing methods for window and door detection and matching is presented. Second, the acquired data is examined to explore the capabilities of capturing window and door information for different remote sensing devices. Followed by tests of some commonplace features in the use for window and door occurrence matching and clustering.

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“…Una idea similar tiene (63,64) ya que sugiere integrar la metodología (BIM) para realizar modelos digitales y simulaciones de los proyectos, para que de esta manera se pueda ejecutarlos con mayor seguridad y calidad, (65) le llaman uso de herramientas computacionales, lo cual (66,67) mencionan que usar esta metodología es eficiente, ya que se puede obtener datos y detalles precisos que pueden mejorar la gestión y productividad.…”

Section: Calidad Y Seguridadunclassified

Una revisión sobre el rol de la inteligencia artificial en la industria de la construcción

Pérez

Peña

Vílchez

2022

inycomp

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En los últimos años las estadísticas sobre la industria de la construcción muestran un crecimiento importante que debemos tener en cuenta, de la misma manera la tecnología hace lo suyo con la creación de inteligencia artificial, es decir la creación de sistemas y máquinas que cumplen un rol importante mejorando el rendimiento, optimizando las labores y el tiempo a diferentes áreas de trabajo en la construcción, sin dejar de lado la capacitación debida para el uso de dicha tecnología. En el presente documento el objetivo es investigar y obtener una comprensión sistemática de la importancia, desafíos y soluciones que ofrece la inteligencia artificial en el mundo de la construcción, para ello se realizó una revisión de 86 artículos debidamente indexados de los últimos 7 años entre 2015 y 2021, los cuales se dividen de la siguiente manera: 39 de ScienceDirect, 24 de ESCOPUS y 23 de EBSCO, documentos en los que señalan la importancia de funcionar la inteligencia artificial y la industria de la construcción, dando buenos resultados en cuanto avance, seguridad, calidad y rendimiento en los trabajos, por lo que se puede concluir que la implementación de tecnología en la construcción es una alternativa potencialmente viable.

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Artificial intelligence for BIM content management and delivery: Case study of association rule mining for construction detailing

Cited by 23 publications

References 30 publications

Managing and Retrieving Bilingual Documents Using Artificial Intelligence-Based Ontological Framework

Managing and Retrieving Bilingual Documents Using Artificial Intelligence-Based Ontological Framework

Review of Window and Door Type Detection Approaches

Una revisión sobre el rol de la inteligencia artificial en la industria de la construcción

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