Version 1
: Received: 26 March 2024 / Approved: 27 March 2024 / Online: 27 March 2024 (11:42:20 CET)
How to cite:
Elsayed, K.; Mutalib, A.A.; Elsayed, M.; Azmi, M.R. Optimising Plate Thickness in Interlocking Inter-Module Connections for Modular Steel Buildings: A Finite Element and Machine Learning Approach. Preprints2024, 2024031676. https://doi.org/10.20944/preprints202403.1676.v1
Elsayed, K.; Mutalib, A.A.; Elsayed, M.; Azmi, M.R. Optimising Plate Thickness in Interlocking Inter-Module Connections for Modular Steel Buildings: A Finite Element and Machine Learning Approach. Preprints 2024, 2024031676. https://doi.org/10.20944/preprints202403.1676.v1
Elsayed, K.; Mutalib, A.A.; Elsayed, M.; Azmi, M.R. Optimising Plate Thickness in Interlocking Inter-Module Connections for Modular Steel Buildings: A Finite Element and Machine Learning Approach. Preprints2024, 2024031676. https://doi.org/10.20944/preprints202403.1676.v1
APA Style
Elsayed, K., Mutalib, A.A., Elsayed, M., & Azmi, M.R. (2024). Optimising Plate Thickness in Interlocking Inter-Module Connections for Modular Steel Buildings: A Finite Element and Machine Learning Approach. Preprints. https://doi.org/10.20944/preprints202403.1676.v1
Chicago/Turabian Style
Elsayed, K., Mohamed Elsayed and Mohd Reza Azmi. 2024 "Optimising Plate Thickness in Interlocking Inter-Module Connections for Modular Steel Buildings: A Finite Element and Machine Learning Approach" Preprints. https://doi.org/10.20944/preprints202403.1676.v1
Abstract
Modular steel buildings (MSBs) interlocking inter-module connections (IMCs) have piqued researchers’ interest. However, the existing literature has not yet studied the optimisation of its plate thicknesses. This paper, therefore, focuses on optimising interlocking (IMCs) plate thickness in (MSBs), leveraging previous experimental and numerical simulation methodologies. Various numerical models for four MSBs interlocking (IMCs) with plate thicknesses (4 mm, 6 mm, 10 mm, and 12 mm) were developed under compression loading conditions. The study’s novelty lay in its comprehensive parametric analysis, which evaluated the slip phenomenon in (MSBs) connections and introduced a slip prediction model validated against empirical data. Further innovative machine learning approach was utilised to predict slip values based on applied force through a random forest regression model trained using the 'Treebagger' function. Sensitivity analysis and comparisons with alternative methods were utilised to underscore the reliability and applicability of the findings. The results showed that 11.03 mm was the optimal plate thickness for interlocking (IMCs) in modular steel buildings, with up to 8.08% reduced material costs. Increasing plate thickness boosts its deformation resistance by up to 50.75%. ‘TreeBagger’ random forest for anomaly detection and machine learning improves slip prediction up to 7% at higher force levels.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
