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dc.contributor.authorRamirez, German Solorzano
dc.contributor.authorPlevris, Vagelis
dc.date.accessioned2022-04-07T09:08:44Z
dc.date.available2022-04-07T09:08:44Z
dc.date.created2021-10-28T15:33:30Z
dc.date.issued2021
dc.identifier.isbn978-618-85072-7-2
dc.identifier.urihttps://hdl.handle.net/11250/2990441
dc.description.abstractIn engineering practice, the design of structural elements is a repetitive task that has proven to be difficult to fully automate. This is mainly because of the complex relations of the design variables and the multiple strength and other requirements that must be fulfilled based on code provisions to ensure safety and endurance, usually under extreme loading conditions or harsh environments. An optimal design can be defined as a set of values for the design variables that correspond to the optimal performance of the structural element in terms of a given criterion, usually related to the minimization of cost, while also satisfying all constraints related to strength, serviceability, functionality and safety. Such a design problem can be formally written as a function that maps a structural element, under given loading conditions, into a unique optimal design. In recent years, Artificial Neural Networks (ANN) have been adopted as a powerful strategy to solve complicated regression and classification problems where the underlying mapping function is generally unknown and difficult to formulate analytically. The ANN learns patterns contained in large databases through an automated process called training and uses that information to make highly accurate predictions. In the present study, a methodology that uses ANNs for the optimal design of structural elements is developed and applied to the design of reinforced concrete (RC) isolated footings under axial loading. First, a Genetic Algorithm is employed for the generation of the training dataset for the ANN, which includes RC footing designs that are optimized in terms of the material cost. Then, the ANN is trained and finally asked to produce new optimal designs for new sets of input parameters. Parametric tests are performed to determine the required size of the dataset and the most suitable network architecture. The results show that the accuracy of the prediction is very good, especially when larger datasets are used. It is shown that training an ANN to design structural elements is a viable option that gives acceptable solutions quickly, requiring extremely low computational cost. Furthermore, it is highlighted that good results can be obtained using a simple ANN architecture and a relatively small training dataset.en_US
dc.language.isoengen_US
dc.publisherECCOMASen_US
dc.relation.ispartofProceedings of the 14th International Conference on Evolutionary and Deterministic Methods For Design, Optimization and Control
dc.relation.ispartofseriesEUROGEN;Proceedings of the 14th International Conference on Evolutionary and Deterministic Methods For Design, Optimization and Control
dc.subjectMachine learningen_US
dc.subjectStructural designen_US
dc.subjectStructural optimizationen_US
dc.subjectArtificial neural networksen_US
dc.subjectReinforced concreteen_US
dc.subjectIsolated footingsen_US
dc.titleDesign of Reinforced Concrete Isolated Footings Under Axial Loading with Artificial Neural Networksen_US
dc.typeConference objecten_US
dc.description.versionpublishedVersionen_US
dc.rights.holder© 2021 The Authorsen_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1
dc.identifier.doihttps://www.eccomasproceedia.org/conferences/thematic-conferences/eurogen-2021/7955
dc.identifier.cristin1949364
dc.source.volume14en_US
dc.source.issue14en_US
dc.source.pagenumber118-131en_US


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