灌浆施工质量预测作为施工过程控制的重要抓手,为寻求具有高精度及低耗时性的灌浆施工质量预测方法,建立了基于粗糙集理论和樽海鞘群算法优化的随机森林的灌浆施工质量预测模型。通过工程应用,对帷幕灌浆施工质量进行了预测分析,并与支持向量机、BP神经网络对比,结果显示本文所提出的方法耗时仅为219.313 s,预测值与实测值的Pearson相关系数为0.936、平均绝对误差为0.140、均方误差为0.037、平均绝对百分比误差为0.059,与实际值具有一致性。研究表明,所建立的模型可为灌浆施工质量控制提供参考。
Abstract
To develop a grouting construction quality prediction model that is both highly accurate and efficient, we established a curtain grouting construction quality model based on an integration of the Rough Set Theory, Salp Swarm Algorithm, and Random Forests. The model is specifically designed for practical application in engineering projects. Comparisons were made with the SVM and BP neural network models, revealing that the proposed model achieved superior performance. Specifically, the proposed model required a mere 219.313 s for computation, and exhibited a Pearson correlation coefficient of 0.936 between predicted and measured values. Furthermore, the average absolute error, mean square error, and average absolute percentage error were measured at 0.140, 0.037, and 0.059, respectively. These findings highlight the potential of the proposed model to serve as a valuable reference for grouting construction quality control.
关键词
帷幕灌浆 /
粗糙集理论 /
樽海鞘群算法 /
随机森林 /
施工质量 /
回归预测
Key words
curtain grouting /
rough set theory /
salp swarm algorithm /
random forest /
construction quality /
regression prediction
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基金
雅砻江流域水电开发有限公司科研项目(LHKG2022KY116YS)
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