Architectural Flaw Detection in Civil Engineering Using GPT-4

TL;DR

This paper explores the use of GPT-4 Turbo vision model in civil engineering to detect architectural flaws during design, improving accuracy and safety while reducing costs and supporting sustainable practices.

Contribution

It introduces a novel application of GPT-4 Turbo vision model for architectural flaw detection in civil engineering design processes.

Findings

High precision and recall in flaw detection

Effective identification of missing doors and windows

Potential to reduce costly design revisions

Abstract

The application of artificial intelligence (AI) in civil engineering presents a transformative approach to enhancing design quality and safety. This paper investigates the potential of the advanced LLM GPT4 Turbo vision model in detecting architectural flaws during the design phase, with a specific focus on identifying missing doors and windows. The study evaluates the model's performance through metrics such as precision, recall, and F1 score, demonstrating AI's effectiveness in accurately detecting flaws compared to human-verified data. Additionally, the research explores AI's broader capabilities, including identifying load-bearing issues, material weaknesses, and ensuring compliance with building codes. The findings highlight how AI can significantly improve design accuracy, reduce costly revisions, and support sustainable practices, ultimately revolutionizing the civil engineering field by ensuring safer, more efficient, and aesthetically optimized structures.