PDF(Pubmed)
Abstract：
This investigation introduces advanced predictive models for estimating axial strains in Carbon Fiber-Reinforced Polymer (CFRP) confined concrete cylinders, addressing critical aspects of structural integrity in seismic environments. By synthesizing insights from a substantial dataset comprising 708 experimental observations, we harness the power of Artificial Neural Networks (ANNs) and General Regression Analysis (GRA) to refine predictive accuracy and reliability. The enhanced models developed through this research demonstrate superior performance, evidenced by an impressive R-squared value of 0.85 and a Root Mean Square Error (RMSE) of 1.42, and significantly advance our understanding of the behavior of CFRP-confined structures under load. Detailed comparisons with existing predictive models reveal our approaches\' superior capacity to mimic and forecast axial strain behaviors accurately, offering essential benefits for designing and reinforcing concrete structures in earthquake-prone areas. This investigation sets a new benchmark in the field through meticulous analysis and innovative modeling, providing a robust framework for future engineering applications and research.
摘要：
这项研究引入了先进的预测模型，用于估算碳纤维增强聚合物（CFRP）约束混凝土圆柱体的轴向应变，解决地震环境中结构完整性的关键方面。通过从包含708个实验观察的大量数据集中综合见解，我们利用人工神经网络（ANN）和一般回归分析（GRA）的力量来提高预测准确性和可靠性。通过这项研究开发的增强模型展示了卓越的性能，令人印象深刻的R平方值为0.85，均方根误差（RMSE）为1.42，这大大增进了我们对CFRP约束结构在载荷下的行为的理解。与现有预测模型的详细比较揭示了我们的方法能够准确地模拟和预测轴向应变行为，为在地震多发地区设计和加固混凝土结构提供必要的好处。此次调查通过细致的分析和创新的建模，在该领域树立了新的标杆，为未来的工程应用和研究提供了一个强大的框架。
