基于机器学习的塑料光纤嵌入透明混凝土抗压强度预测

Q2 Engineering
Manish Pratap Singh, Anish Kumar, Sanjeev Sinha
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引用次数: 0

摘要

研究了塑料光纤集成对M20混凝土抗压强度的影响。对基于SVM-RBF、SVM-Linear和XGBoost的机器学习预测模型进行训练,并与传统线性回归模型进行比较。抗压强度分析证实,POF夹杂物由于界面薄弱和空隙形成而降低了强度,特别是在纤维间距较小的情况下。然而,将纤维间距增加到20mm可以最大限度地减少强度损失,为实际应用展示了更可行的配置。性能指标、回归误差特征(REC)曲线、泰勒图和面积曲线(AOC)结果表明,XGBoost是最准确的预测模型,优于SVM-RBF、SVM-Linear和线性回归模型。XGBoost模型在训练和检验中的R2值分别为0.999和0.997。XGBoost模型在训练和测试中的RMSE值分别为0.151和0.259。单调性分析表明,纤维间距和养护天数对抗压强度有正向影响,而其他混合变量在试验范围内保持相对不变。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine learning-based prediction of compressive strength of plastic optical fiber embedded transparent concrete

This study investigates the effect of plastic optical fiber integration on the compressive strength of M20 concrete. SVM-RBF, SVM-Linear, and XGBoost based machine learning prediction models were also trained and compared with conventional linear regression model. The compressive strength analysis confirms that POF inclusion reduces strength due to weak interfaces and void formation, particularly at smaller fiber spacings. However, increasing fiber spacing to 20 mm minimizes strength loss, demonstrating a more viable configuration for practical applications. The performance metrics, regression error characteristic (REC) curves, taylor diagram, and area over curve (AOC) results highlight XGBoost as the most accurate predictive model, outperforming SVM-RBF, SVM-Linear, and linear regression models. The R2 values in training and testing for the XGBoost model are 0.999 and 0.997 respectively. The RMSE values in training and testing for the XGBoost model are 0.151 and 0.259 respectively. The monotonicity analysis reveals that fiber spacing and curing days positively affect compressive strength, while other mix variables remain relatively unchanged within the tested range.

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来源期刊
Asian Journal of Civil Engineering
Asian Journal of Civil Engineering Engineering-Civil and Structural Engineering
CiteScore
2.70
自引率
0.00%
发文量
121
期刊介绍: The Asian Journal of Civil Engineering (Building and Housing) welcomes articles and research contributions on topics such as:- Structural analysis and design - Earthquake and structural engineering - New building materials and concrete technology - Sustainable building and energy conservation - Housing and planning - Construction management - Optimal design of structuresPlease note that the journal will not accept papers in the area of hydraulic or geotechnical engineering, traffic/transportation or road making engineering, and on materials relevant to non-structural buildings, e.g. materials for road making and asphalt.  Although the journal will publish authoritative papers on theoretical and experimental research works and advanced applications, it may also feature, when appropriate:  a) tutorial survey type papers reviewing some fields of civil engineering; b) short communications and research notes; c) book reviews and conference announcements.
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