Reasoning about designs through frequent patterns mining

Strug, Barbara; lusarczyk, G.

doi:10.1016/j.aei.2009.06.007

Cited by 20 publications

(7 citation statements)

References 11 publications

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“…The evolutionary approach introduces graph-based evolutionary operators, namely cross-over and mutation, in the floorplan generation process (Wong and Chan, 2009;Strug, Grabska and Ślusarczyk, 2014) The deep learning approach is achieved via a Generative Adversarial Networks (GAN), which takes a large dataset of pixel-based floorplans as inputs and generate novel ones by performing a generator and a discriminator on their graph representations (Nauata et al, 2020). Having graph-represented design solutions of floorplans, Strug and Ślusarczyk detected the frequent patterns via graph mining technique (Strug and Ślusarczyk, 2009). These patterns are further used as design features to evaluate the new floorplan design (Strug, 2013).…”

Section: Graph Modeling In Construction Projectsmentioning

confidence: 99%

“…An increasing number of unique modules might deteriorate the production efficiency. Last but not least, the previous studies end up with graph represented solutions, rather than BIMs (Strug and Ślusarczyk, 2009;Isaac, Bock and Stoliar, 2016;Samarasinghe et al, 2019). How could those graph patterns be managed as BIMs remains unsolved.…”

Section: Graph Modeling In Construction Projectsmentioning

confidence: 99%

“…In the construction domain, these frequent substructures represent a cluster of building elements that repeatedly occurred in the examined projects. In some sense, they might represent a standard design or "common knowledge" from previous works (Strug and Ślusarczyk, 2009). In this study, we apply "gSpan" algorithm (Yan and Han, 2002) to potential module identification.…”

Section: Frequent Pattern Miningmentioning

confidence: 99%

“…Besides, to facilitate library lifecycle management, BIM (Building Information Model) is used as product digital representations. Unlike early studies of knowledge reuse, such as drawings (Strug and Ślusarczyk, 2009) and simulations (Hiyama et al, 2014), BIM modules can be semantically enriched for their use in the design and production phases (Hamid, Tolba and El Antably, 2018). Meanwhile, the information stored in BIM supports the module analysis in terms of their performance, such as manufacturability.…”

Section: Introductionmentioning

confidence: 99%

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Module library development via graph mining

Cao¹,

Hall²

2021

Computing in Construction

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The use of prefabricated modules can benefit the construction industry with the economy of scales and production efficiency. However, the existing approach to develop module libraries is project-based, lacking the potential to reuse and manage in future projects. By taking the repeatability and manufacturability into account, this paper proposes a graph-based framework to identify possible modules automatically from multiple projects by frequent pattern mining. The results show that the repeated patterns share a degree of standardization and can be considered as module candidates. Finally, the framework is implemented as add-ons in the BIM environment to support module lifecycle management.

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Section: Graph Modeling In Construction Projectsmentioning

confidence: 99%

Section: Graph Modeling In Construction Projectsmentioning

confidence: 99%

Section: Frequent Pattern Miningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Module library development via graph mining

Cao¹,

Hall²

2021

Computing in Construction

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“…The Knowledge Discovery in Databases (KDD) process (Fayyad et al, 1996). (Strug & Ś lusarczyk, 2009). Sequential pattern mining algorithms allows product and quality engineers to extract hidden knowledge from a large industrial database, since significant patterns provide knowledge of one or more product/process failures that leads to future product/process fault(s) (Buddhakulsomsiri & Zakarian, 2009).…”

Section: Knowledge Discovery In Databases (Kdd) Processmentioning

confidence: 99%

Mining association rules for the quality improvement of the production process

Kamsu-Foguem

Rigal

Mauget

2013

Expert Systems with Applications

130

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To cite this version:Bernard Kamsu-Foguem, Fabien Rigal, Félix Mauget. Mining association rules for the quality improvement of the production process. Expert Systems with Applications, Elsevier, 2013, vol. 40, pp. 1034-1045 b s t r a c tAcademics and practitioners have a common interest in the continuing development of methods and computer applications that support or perform knowledge-intensive engineering tasks. Operations management dysfunctions and lost production time are problems of enormous magnitude that impact the performance and quality of industrial systems as well as their cost of production. Association rule mining is a data mining technique used to find out useful and invaluable information from huge databases. This work develops a better conceptual base for improving the application of association rule mining methods to extract knowledge on operations and information management. The emphasis of the paper is on the improvement of the operations processes. The application example details an industrial experiment in which association rule mining is used to analyze the manufacturing process of a fully integrated provider of drilling products. The study reports some new interesting results with data mining and knowledge discovery techniques applied to a drill production process. Experiment's results on real-life data sets show that the proposed approach is useful in finding effective knowledge associated to dysfunctions causes.

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