Design Optimization of Truss Structures Using Artificial Neural Networks

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dc.contributor.advisorEl-Badry, Mamdouh
dc.contributor.authorNourian, Navid
dc.contributor.committeememberDann, Markus
dc.contributor.committeememberBillah, Muntasir
dc.contributor.committeememberXue, Deyi
dc.date2023-11
dc.date.accessioned2023-09-27T17:44:18Z
dc.date.available2023-09-27T17:44:18Z
dc.date.issued2023-09-22
dc.description.abstractOne of the primary objectives of structural design optimization is to achieve a design possessing the lowest possible weight, while it can safely withstand the effects of external loads. In the case of a truss of a specific topology, the role of an optimization algorithm is to determine the configuration and number of the truss elements as well as their cross-sectional areas. In this study, a novel model is proposed, by which the main optimization problem is decomposed into two more manageable problems: a size optimization within a shape optimization problem. A Deep Neural Network (DNN) is trained to approximate the optimal cross-sectional areas of the elements of a truss with a given shape and support positions. Furthermore, truss structures are characterized by pin joints connected by truss members, a concept that can be analogized to vertices and edges in a mathematical graph. Leveraging this analogy, a Graph Neural Network (GNN) is utilized to exploit the advantages of representing trusses as graphs. Specifically, a graph neural network-based surrogate model integrated with Particle Swarm Optimization (PSO) algorithm is developed to approximate nodal displacements of trusses during the design optimization process. Several truss examples are used to investigate the validity and effectiveness of the proposed optimization techniques in comparison with conventional FEM-based models.
dc.identifier.citationNourian, N. (2023). Design optimization of truss structures using artificial neural networks (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.
dc.identifier.urihttps://hdl.handle.net/1880/117284
dc.identifier.urihttps://doi.org/10.11575/PRISM/42126
dc.language.isoen
dc.publisher.facultySchulich School of Engineering
dc.publisher.institutionUniversity of Calgary
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subjectdesign optimization
dc.subjectartificial neural network
dc.subjectgraph neural network
dc.subjectdeep neural network
dc.subjectgenetic algorithm
dc.subjectparticle swarm optimization
dc.subjectshape optimization
dc.subjectsize optimization
dc.subjectsurrogate model
dc.subjecttruss structures
dc.subject.classificationEngineering--Civil
dc.subject.classificationArtificial Intelligence
dc.subject.classificationComputer Science
dc.titleDesign Optimization of Truss Structures Using Artificial Neural Networks
dc.typemaster thesis
thesis.degree.disciplineEngineering – Civil
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameMaster of Science (MSc)
ucalgary.thesis.accesssetbystudentI do not require a thesis withhold – my thesis will have open access and can be viewed and downloaded publicly as soon as possible.
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