长沙理工大学学报(自然科学版)
基于RF-PSO-LSSVM的高层建筑项目工期风险预测
作者:
作者单位:

(长沙理工大学交通运输工程学院,湖南长沙 410114)

作者简介:

刘伟军(1975-),男,副教授,主要从事公路工程造价管理与项目管理方面的研究。

通讯作者:

刘伟军(1975-),男,副教授,主要从事公路工程造价管理与项目管理方面的研究。

中图分类号:

TU974;TP399

基金项目:

河南省交通运输厅科技项目(2014G25);湖南省自然科学基金资助项目(2020JJ4629)


Risk prediction of high-rise building project duration based on RF-PSO-LSSVM
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Affiliation:

(School of Traffic and Transportation Engineering,Changsha University of Science & Technology,Changsha 410114,China)

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    摘要:

    针对高层建筑项目在工期风险预测时样本数据少且特征维度高的特点,建立了利用随机森林(ran- domforest,RF)算法和粒子群(particleswarm optimization,PSO)算法优化最小二乘支持向量机 (least squares support vector machine,LSSVM)的高层建筑项目工期风险预测模型。采用在特征选择方面具有显著优势的RF算法筛选出最佳特征子集;利用 PSO 算法对 LSSVM 的正则化参数和核函数参数进行优化;采用精确率、召回率以及F1m值对所建立模型的预测性能进行验证与评估。研究结果表明:用所建立的模型对高层建筑项目进行工期风险预测,平均精确率达到了93.71%,平均召回率达到了94.04%。该模型能够准确预测高层建筑项目工期的风险等级,进一步完善了高层建筑项目工期风险的预测方法,其预测结果可为高层建筑项目控制工期风险提供一定的参考。

    Abstract:

    Aiming at the characteristics of small sample data and high feature dimension in risk prediction of high-rise building project duration,a risk prediction model of high-rise building project duration was proposed based on least squares support vector machine(LSSVM) optimized by random forest(RF)algorithm and particle swarm optimization(PSO)algorithm.RF algorithm with obvious advantages in feature selection was used to select the best feature subset.PSO algorithm was used to optimize the regularization parameters and kernel function parameters of LSSVM.The precision rate,recall rate and F1m value were used to verify and evaluate the predictive performance of the proposed model.The research results show that the average precision rate reaches 93.71%,and the averagere call rate reaches 94.04% to predict the risk of high-rise building project duration using the proposed model. The proposed model can accurately predict the risk level of high-rise building project duration,improves the risk prediction method of high-rise building project duration,and the predicted results would provide a reference for risk control of high-rise building project duration.

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引用本文

刘伟军,赵 威.基于RF-PSO-LSSVM的高层建筑项目工期风险预测[J].长沙理工大学学报(自然科学版),2021,18(2):49-56.
LIU Wei-jun, ZHAO Wei. Risk prediction of high-rise building project duration based on RF-PSO-LSSVM[J]. Journal of Changsha University of Science & Technology (Natural Science),2021,18(2):49-56.

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  • 在线发布日期: 2022-03-02
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