Megahed Khaled
Department of Structural Engineering, Mansoura University, PO BOX 35516, Mansoura, Egypt.
Sci Rep. 2025 Jan 7;15(1):1202. doi: 10.1038/s41598-024-83666-z.
A novel type of concrete-encased steel (CES) composite column implementing Engineered Cementitious Composites (ECC) confinement (ECC-CES) has recently been introduced, offering significantly enhanced failure behavior, ductility, and toughness when compared to conventional CES columns. This study presents an innovative method for predicting the eccentric compressive capacity of ECC-CES columns, utilizing adaptive sampling and machine learning (ML) techniques. Initially, the research introduces a finite element (FE) model for ECC-CES columns, incorporating material and geometric nonlinearities to capture the inelastic behavior of both ECC and steel through appropriate constitutive material laws. The FE model was validated against experimental data, demonstrating strong predictive accuracy. An adaptive sampling process was employed to efficiently explore the design space, resulting in a database of 2,908 FE models. Subsequently, six machine learning models were used to predict the eccentric compressive capacity based on the generated FE database. These models were thoroughly evaluated and demonstrated superior prediction accuracy compared to established design standards like EC4 and AISC360. Based on evaluation metrics, the Gaussian Process Regression (GPR), CatBoost (CATB), and LightGBM (LGBM) models emerged as the most accurate and reliable, with over 97% of the finite element (FE) samples falling within a 10% error range. While the ML models demonstrate impressive performance, their black-box nature restricts their practical use in design applications. Consequently, this study introduces a proposed design that offers competitive performance metrics. The novelty of this work lies in integrating adaptive sampling through Bayesian Optimization (BO) with the power of machine learning (ML) to generate training data that effectively covers a large input space while minimizing error. SVR, CatBoost, and GPR models demonstrated mean μ, R, and a20-index values near 1.0, with CoV and MAPE% values consistently low, indicating highly accurate predictions across testing subsets.
最近引入了一种新型的采用工程水泥基复合材料（ECC）约束的外包钢（CES）组合柱（ECC-CES），与传统的CES柱相比，其破坏行为、延性和韧性都有显著提高。本研究提出了一种利用自适应采样和机器学习（ML）技术预测ECC-CES柱偏心抗压能力的创新方法。首先，该研究引入了ECC-CES柱的有限元（FE）模型，通过适当的本构材料定律纳入材料和几何非线性，以捕捉ECC和钢材的非弹性行为。该FE模型通过实验数据进行了验证，显示出很强的预测准确性。采用自适应采样过程有效地探索设计空间，得到了一个包含2908个FE模型的数据库。随后，使用六个机器学习模型基于生成的FE数据库预测偏心抗压能力。这些模型经过了全面评估，与诸如欧洲规范4（EC4）和美国钢结构协会标准360（AISC360）等既定设计标准相比，显示出卓越的预测准确性。基于评估指标，高斯过程回归（GPR）、CatBoost（CATB）和LightGBM（LGBM）模型表现最为准确可靠，超过97%的有限元（FE）样本误差范围在10%以内。虽然ML模型表现出令人印象深刻的性能，但其黑箱性质限制了它们在设计应用中的实际使用。因此，本研究提出了一种具有竞争力性能指标的设计方案。这项工作的新颖之处在于将通过贝叶斯优化（BO）的自适应采样与机器学习（ML）的能力相结合，生成能有效覆盖大输入空间同时最小化误差的训练数据。支持向量回归（SVR）、CatBoost和GPR模型的平均μ、R和a20指数值接近1.0，变异系数（CoV）和平均绝对百分比误差（MAPE%）值一直很低，表明在测试子集中预测高度准确。
