AI SearchAI-driven predication of concrete beam size, design and capacity for gravity loads
Call Number: AIT Thesis no.ST-25-05 Material type:
SeriesSeries: Asian Institute of Technology. Thesis ; no. ST-25-05Publication details: Pathum Thani, Thailand : Asian Institute of Technology, 2025Description: 80 leaves : ill.+ 1 online resourceSubject(s): Online resources: Dissertation note: Thesis (M. Eng.) - Asian Institute of Technology, 2025 Summary: 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.
| Cover image | Item type | Current library | Home library | Collection | Shelving location | Call number | Materials specified | Vol info | URL | Copy number | Status | Notes | Date due | Barcode | Item holds | Item hold queue priority | Course reserves | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
67-Electronic Resource
|
Asian Institute of Technology Library Archives | AIT Thesis no.ST-25-05 (Browse shelf(Opens below)) | 1 | Not for loan |
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
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.
There are no comments on this title.