Zwe Yan Naing creator Krishna, Chaitanya Chairperson Anwar, Naveed (Co-chairperson) Pennung Warnitchai Examination Committee Punchet Thammarak Examination Committee Panon Latcharote Examination Committee AIT Fellowship Scholarship Donor text series technical report th Pathum Thani, Thailand Asian Institute of Technology 2025 continuing eng

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80 leaves : ill.+ 1 online resource Artificial Intelligence (AI) has emerged as a transformative technology, driving innovation across numerous industries through its ability to process information and data, learn patterns, and make predictions.This research addresses the need for efficiency and innovation in structural engineering, where traditional design methods involving iterative calculations can be time-consuming and complex. The study develops an AI-driven framework, utilizing Artificial Neural Networks (ANNs), to predict cross-section size, rebar area, and loading capacity for continuous rectangular shaped reinforced concrete beam. The ultimate goal is to deploy these models as a cloud-based web application, integrating Large Language Models (LLMs) to enhance accessibility and interaction for civil and structural engineers, as well as students. This application will provide a practical, code-compliant tool for efficient structural design,streamlining the traditional process especially in preliminary design stage and offering an intuitive, automated solution in innovative structural design workflow. A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Structural Engineering Thesis (M. Eng.) - Asian Institute of Technology, 2025 Structural design Data processing Sturctural Engineering Artificial intelligence Deep learning (Machine learning) Asian Institute of Technology. http://203.159.5.9/ait-thesis/Viewer/viewer.php?id=B23406 http://203.159.5.9/ait-thesis/Viewer/viewer.php?id=B23406 260202 20260817162000.0