摘要
由一名新闻记者-机器人与机器学习每日新闻编辑-研究人员详细介绍了人工智能的新数据。根据NewsRx记者从重庆发回的新闻报道，研究称：“本研究旨在利用机器学习模型评估FRP约束柱的抗压强度。通过系统地组织美国研究人员提出的规范和模型，识别出影响抗压强度的重要指标。”本报记者引用重庆交东大学的一篇研究报道:“建立了一个包含366个碳纤维复合材料和玻璃纤维复合材料试件的综合数据库,并在此基础上开发了一个机器学习模型来准确预测混凝土的抗压强度,对规范和研究人员提出的模型进行了全面的评估。此外,本文还对CFRP和GFRP试件的抗压强度进行了研究。”使用XGBoost模型进行了详细的参数分析。结果强调了基于规范和研究人员提出的模型S在增强我们对抗压强度的理解方面的重要性。然而，某些模型S显示出保守或高估预测的倾向，表明需要进一步提高精度。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in artific ial intelligence. According to news reporting originating from Chongqing, People 's Republic of China, by NewsRx correspondents, research stated, "This research aims to evaluate the compressive strength of FRP-confined columns using machine learning models. By systematically organizing codes and models proposed by vario us researchers, significant indicators influencing compressive strength have bee n identified." Our news correspondents obtained a quote from the research from Chongqing Jiaoto ng University: "A comprehensive database comprising 366 samples, including both CFRP and GFRP, has been assembled. Based on this database, a machine learning mo del was developed to accurately predict compressive strength. A thorough evaluat ion was conducted, comparing models proposed by codes and researchers. Additiona lly, a detailed parameter analysis was performed using the XGBoost model. The fi ndings highlight the importance of both code-based and researcher-proposed model s in enhancing our understanding of compressive strength. However, certain model s show tendencies towards conservative or overestimated predictions, indicating the need for further accuracy enhancement."
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