Date of Award
8-2025
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Civil Engineering
Committee Chair/Advisor
M.Z. Naser
Committee Member
Miguel Cid Montoya
Committee Member
Laura Redmond
Committee Member
Brandon Ross
Abstract
Structural design, by nature, is a complex process that requires a considerable amount of time, expertise, and knowledge. In such a process, the structural designer must navigate various codal provisions to crystallize a proper and adequate design. As the role of automation continues to shape the domain of structural engineering over the past few decades, a new front leverages artificial intelligence (AI). Currently, AI acts as a "copilot," collaborating to advance workflow speed and accelerate the design process. Among all the different collaborations between humans and AI, GANs (Generative Adversarial Networks) are seen to complement human creativity by automating specific facets of the design process. GANs can assist in creating structural designs and exploring the possibilities of different design scenarios. More specifically, conditional GANs usually neglect important parameters such as material properties and allow structural designs to be aligned with the engineer’s notion without compromising the design standards. To fully understand the advantages of using such copilots, it is necessary to identify, generate, and validate the accuracy of the models. As a result, this thesis aims to help examine the feasibility of having generative models, verify and assess this model's performance, and deploy a companion software for structural drawing generation from architectural drawings.
Recommended Citation
Teymori Gharah Tapeh, Arash, "Structural Design Using Conditional-Gan Fused With Property Text Information: A Case for Masonry Structures" (2025). All Dissertations. 4051.
https://open.clemson.edu/all_dissertations/4051
Author ORCID Identifier
https://orcid.org/0000-0001-9174-079X