Behavior Prediction of Connections in Eco-Designed Thin-Walled Steel–Ply–Bamboo Structures Based on Machine Learning for Mechanical Properties

Wanwan Xia, Yujie Gao (), Zhenkai Zhang, Yuhan Jie, Jingwen Zhang, Yueying Cao, Qiuyue Wu, Tao Li (), Wentao Ji and Yaoyuan Gao
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Wanwan Xia: School of Physical and Mathematical Sciences, Nanjing Tech University, Nanjing 210037, China
Yujie Gao: School of Physical and Mathematical Sciences, Nanjing Tech University, Nanjing 210037, China
Zhenkai Zhang: School of Physical and Mathematical Sciences, Nanjing Tech University, Nanjing 210037, China
Yuhan Jie: College of Art and Design, Nanjing Tech University, Nanjing 210037, China
Jingwen Zhang: College of Energy Science and Engineering, Nanjing Tech University, Nanjing 210037, China
Yueying Cao: Arizona College of Technology, Hebei University of Technology, Tianjin 300130, China
Qiuyue Wu: College of Art and Design, Nanjing Tech University, Nanjing 210037, China
Tao Li: College of Art and Design, Nanjing Tech University, Nanjing 210037, China
Wentao Ji: College of Civil Engineering, Nanjing Tech University, Nanjing 210037, China
Yaoyuan Gao: College of Civil Engineering, Nanjing Tech University, Nanjing 210037, China

Sustainability, 2025, vol. 17, issue 15, 1-36

Abstract: This study employed multiple machine learning and hyperparameter optimization techniques to analyze and predict the mechanical properties of self-drilling screw connections in thin-walled steel–ply–bamboo shear walls, leveraging the renewable and eco-friendly nature of bamboo to enhance structural sustainability and reduce environmental impact. The dataset, which included 249 sets of measurement data, was derived from 51 disparate connection specimens fabricated with engineered bamboo—a renewable and low-carbon construction material. Utilizing factor analysis, a ranking table recording the comprehensive score of each connection specimen was established to select the optimal connection type. Eight machine learning models were employed to analyze and predict the mechanical performance of these connection specimens. Through comparison, the most efficient model was selected, and five hyperparameter optimization algorithms were implemented to further enhance its prediction accuracy. The analysis results revealed that the Random Forest (RF) model demonstrated superior classification performance, prediction accuracy, and generalization ability, achieving approximately 61% accuracy on the test set (the highest among all models). In hyperparameter optimization, the RF model processed through Bayesian Optimization (BO) further improved its predictive accuracy to about 67%, outperforming both its non-optimized version and models optimized using the other algorithms. Considering the mechanical performance of connections within TWS composite structures, applying the BO algorithm to the RF model significantly improved the predictive accuracy. This approach enables the identification of the most suitable specimen type based on newly provided mechanical performance parameter sets, providing a data-driven pathway for sustainable bamboo–steel composite structure design.

Keywords: thin-walled steel–ply–bamboo; self-drilling screw connection; factor analysis; machine learning; hyperparameter optimization (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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