Industrial Pipelines Data Generator, Mendeley Data. The properties of each branch are investigated and the correlation between different pipelines components are identified (refer to Al-Alawi, M., Bouferguene, A., and Mohamed, Y. The pipelines components are tube, elbow, flange, tee, valve, fblind, ftube, reducer, closure, cap, instrument, coupling, and pcomponent.Ī recursive function is used to breakdown the pipelines data into two-sub pipelines, the main branch and the secondary branches extending from the main branch. Each node in this tree is a data structure consisting of an array of values like the type of component, length, material etc. The generated data is organized in a tree structure whose nodes describe the pipeline components.
#Raw data generator generator
The total number of pipelines used to build the proposed generator is 1052 with a total of 33324 components. The data from a real industrial project was used to build the properties of the industrial pipelines data. Since this generator allows researchers to have access to similar pipeline datasets, it becomes possible to conduct statistical comparisons of the efficiency of optimization algorithms including those used to design 3D pipeline structures that are constrained to fit in a pre-defined volume. This allows statistical analyses to be conducted with respect to fabrication, transportation, on-site installation processes, etc. With this generator, researchers will be able to generate replicates of a simulated (yet realistic) reference project, which in practice is impossible to do. Thanks to the ease of generating simulated (yet realistic) industrial pipeline datasets, researchers can rapidly start exploring and testing their ideas in order to gain better understanding about the amount and the type of real data that need to be collected for validation. As a result, this generator can alleviate the need for real-life project data which generally are not publically available. The proposed industrial pipelines data generator allows simulating realistic industrial pipelines data structures. The data generator can be used to develop benchmark problem instances for optimization problems and for simulation studies of construction operations in industrial projects.
2020 Ĭrossref | Scopus (4) | Google Scholar See all References]. Application of industrial pipelines data generator in the experimental analysis: Pipe Spooling Optimization Problem definition, formulation, and testing. Its application in construction engineering and management research, more specifically in the experimental analysis of an optimization algorithm, was described by the authors in previous work Al Alawi, M., Yasser, M., and Bouferguene, A. This generator can simulate the properties (the type of component, length, diameter, running direction, and the connectivity relationships between components) of real industrial pipelines. This article describes an industrial pipelines data generator that was developed using topological and physical properties of pipelines from real industrial projects. As a result, an industrial pipelines data generator able to realistically simulate pipelines structures will lessen the dependence on real-life data. The design data of pipelines is complex, usually unique for each project and not easily available to researchers and the public for confidentiality reasons. The construction of industrial projects involves fabricating and installing massive quantities of pipelines. The generated data was validated by comparing it against real industrial pipelines data. All pipelines formation components properties were studied and the relationships among them were identified. Real industrial pipelines data was used to build the industrial pipelines data generatorĪn industrial pipelines project database was analysed. The generator produces industrial pipelines data similar to real pipelines.
Real industrial pipelines data are used in developing the industrial pipelines data generator. Industrial pipelines data generator is developed using Markov Chain model. Construction Engineering and Operational Researchĭataset generation for construction engineering optimization problems
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