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Analysis of Concrete Air Voids: Comparing OpenAI-Generated Python Code with MATLAB Scripts and Enhancing 2D Image Processing Using 3D CT Scan Data

Iman Asadi

^1,2,*,

Andrei Shpak

³ and

Stefan Jacobsen

⁴

Td-Lab Sustainable Mineral Resources, Department for Knowledge and Communication Management, Faculty of Business and Globalization, University for Continuing Education Krems, 3500 Krems an der Donau, Austria

Built Environment and Engineering Program (BEE), College of Sport, Health, and Engineering (CoSHE), Victoria University, Melbourne, VIC 8001, Australia

Department of Architecture, Building Materials and Structures, SINTEF Community, 7034 Trondheim, Norway

⁴

Department of Structural Engineering, Norwegian University of Science and Technology, 7491 Trondheim, Norway

Author to whom correspondence should be addressed.

Buildings 2024, 14(12), 3712; https://doi.org/10.3390/buildings14123712

Submission received: 15 October 2024 / Revised: 10 November 2024 / Accepted: 14 November 2024 / Published: 21 November 2024

(This article belongs to the Section Building Materials, and Repair & Renovation)

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Abstract

The air void system in concrete significantly affects its mechanical, thermal, and frost durability properties. This study explored the use of ChatGPT, an AI tool, to generate Python code for analyzing air void parameters in hardened concrete, such as total air void content (A), specific surface (α), and air void spacing factor (L). Initially, Python scripts were created by requesting ChatGPT-3.5 to convert MATLAB scripts developed by Fonseca and Scherer in 2015. The results from Python closely matched those from MATLAB when applied to polished sections of seven different concrete mixes, demonstrating ChatGPT’s effectiveness in code conversion. However, generating accurate code without referencing the original MATLAB scripts required detailed prompts, highlighting the need for a strong understanding of the test method. Finally, a Python script was applied to modify void reconstruction in 2D images into 3D by stereology, and comparing this with (3D) CT scanner results, showing comparable results.

Keywords: fly ash; air-entrained concrete; python; cumulative air voids; MATLAB

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MDPI and ACS Style

Asadi, I.; Shpak, A.; Jacobsen, S. Analysis of Concrete Air Voids: Comparing OpenAI-Generated Python Code with MATLAB Scripts and Enhancing 2D Image Processing Using 3D CT Scan Data. Buildings 2024, 14, 3712. https://doi.org/10.3390/buildings14123712

AMA Style

Asadi I, Shpak A, Jacobsen S. Analysis of Concrete Air Voids: Comparing OpenAI-Generated Python Code with MATLAB Scripts and Enhancing 2D Image Processing Using 3D CT Scan Data. Buildings. 2024; 14(12):3712. https://doi.org/10.3390/buildings14123712

Chicago/Turabian Style

Asadi, Iman, Andrei Shpak, and Stefan Jacobsen. 2024. "Analysis of Concrete Air Voids: Comparing OpenAI-Generated Python Code with MATLAB Scripts and Enhancing 2D Image Processing Using 3D CT Scan Data" Buildings 14, no. 12: 3712. https://doi.org/10.3390/buildings14123712

APA Style

Asadi, I., Shpak, A., & Jacobsen, S. (2024). Analysis of Concrete Air Voids: Comparing OpenAI-Generated Python Code with MATLAB Scripts and Enhancing 2D Image Processing Using 3D CT Scan Data. Buildings, 14(12), 3712. https://doi.org/10.3390/buildings14123712

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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Analysis of Concrete Air Voids: Comparing OpenAI-Generated Python Code with MATLAB Scripts and Enhancing 2D Image Processing Using 3D CT Scan Data

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