An interpretable machine learning-based model for shear resistance prediction of CFRP-strengthened RC beams using experimental and synthetic dataset

IF 6.3 2区 材料科学 Q1 MATERIALS SCIENCE, COMPOSITES
Amirhossein Mohammadi , Joaquim A.O. Barros , José Sena-Cruz
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Abstract

Existing analytical models for predicting the shear resistance of RC beams strengthened with externally bonded CFRP reinforcements exhibit deficient performance due to their inability to accurately capture the complex resisting mechanisms. Combined with significant statistical uncertainties in shear failure, driven by its brittle nature, this further undermines the reliability of these models. To address these limitations, this study leverages Machine Learning (ML) to develop more robust and reliable predictive tool. A rigorous feature-selection process identified eight predictors as the most influential. Subsequently, nine ML-algorithms were trained on a refined experimental dataset comprising 239 beams, with XGBoost emerging as the top performer. This model also outperformed established models like fib Bulletin-90 and ACI 2023 models. However, the limited scope of the experimental dataset constrained the model’s predictive performance especially when separately evaluated on beams strengthened with U-wraps, full wraps or side-bonded FRP configurations. Therefore, to achieve a more reliable model a synthetic dataset was generated using Tabular Variational Auto-Encoder. The XGBoost model trained with the synthetic dataset significantly improved the performance of the former model and exhibited better predictions for all strengthening configurations. Finally, to ensure the physical consistency of predictions, values obtained from the SHapley Additive exPlanations method were analysed.
基于机器学习的可解释模型,利用实验和合成数据集预测 CFRP 加固 RC 梁的抗剪性能
由于无法准确捕捉复杂的抗剪机理,现有用于预测使用外部粘结 CFRP 加固材料的 RC 梁抗剪性能的分析模型存在缺陷。由于剪切破坏具有脆性,再加上剪切破坏在统计上存在很大的不确定性,这进一步削弱了这些模型的可靠性。为了解决这些局限性,本研究利用机器学习(ML)技术开发了更强大、更可靠的预测工具。通过严格的特征选择过程,确定了八个最有影响力的预测因子。随后,九种 ML 算法在由 239 根横梁组成的精炼实验数据集上进行了训练,其中 XGBoost 表现最佳。该模型的表现也优于 fib Bulletin-90 和 ACI 2023 模型等成熟模型。然而,实验数据集的范围有限,限制了模型的预测性能,尤其是在对采用 U 型缠绕、全缠绕或侧粘接 FRP 配置加固的梁进行单独评估时。因此,为了获得更可靠的模型,我们使用表格变异自动编码器生成了一个合成数据集。使用合成数据集训练的 XGBoost 模型显著提高了前一模型的性能,并对所有加固配置进行了更好的预测。最后,为了确保预测结果的物理一致性,对 SHapley Additive exPlanations 方法获得的值进行了分析。
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来源期刊
Composite Structures
Composite Structures 工程技术-材料科学:复合
CiteScore
12.00
自引率
12.70%
发文量
1246
审稿时长
78 days
期刊介绍: The past few decades have seen outstanding advances in the use of composite materials in structural applications. There can be little doubt that, within engineering circles, composites have revolutionised traditional design concepts and made possible an unparalleled range of new and exciting possibilities as viable materials for construction. Composite Structures, an International Journal, disseminates knowledge between users, manufacturers, designers and researchers involved in structures or structural components manufactured using composite materials. The journal publishes papers which contribute to knowledge in the use of composite materials in engineering structures. Papers deal with design, research and development studies, experimental investigations, theoretical analysis and fabrication techniques relevant to the application of composites in load-bearing components for assemblies, ranging from individual components such as plates and shells to complete composite structures.
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