基于贝叶斯理论的地下厂房开挖进度动态预测
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摘要
地下厂房的开挖过程受多种不确定因素的影响,导致实际开挖进度延误,初始进度计划无法合理指导实际施工。为此,基于贝叶斯理论构建地下厂房开挖进度动态预测模型,利用施工样本信息修正先验信息得到考虑不确定因素影响的后验信息,实现对地下厂房开挖剩余工期的动态预测。最后通过工程算例,验证了该预测方法的可行性,且根据计算得到的动态完工概率可为决策者评估现阶段进度执行情况,调整下一阶段进度目标提供一定依据。
The excavation process of underground powerhouse is affected by many uncertain factors,which leads to the delay of the actual excavation progress,and the initial schedule cannot guide the actual construction reasonably. Therefore,the dynamic prediction model of underground powerhouse excavation progress based on Bayesian theory was proposed. The construction sample information was used to modify the prior information to obtain the posterior information considering the influence of uncertain factors, and the dynamic prediction of the remaining construction period of the underground powerhouse excavation was realized. Finally,an engineering example was given to verify the feasibility of the method,moreover, the calculated dynamic completion probability could provide a certain basis for decision makers to evaluate the current progress implementation and adjust the progress target in the next stage.
The excavation process of underground powerhouse is affected by many uncertain factors,which leads to the delay of the actual excavation progress,and the initial schedule cannot guide the actual construction reasonably. Therefore,the dynamic prediction model of underground powerhouse excavation progress based on Bayesian theory was proposed. The construction sample information was used to modify the prior information to obtain the posterior information considering the influence of uncertain factors, and the dynamic prediction of the remaining construction period of the underground powerhouse excavation was realized. Finally,an engineering example was given to verify the feasibility of the method,moreover, the calculated dynamic completion probability could provide a certain basis for decision makers to evaluate the current progress implementation and adjust the progress target in the next stage.
引文
[1]毕磊,钟登华,赵梦琦.基于均匀设计的地下洞室群施工进度仿真多因素敏感性分析[J].水力发电学报,2016(1):118-124.
[2]陈瑞,黄建文.基于PERT的工程网络计划模糊完工概率分析[J].人民长江,2012(20):92-94.
[3]武菲菲,王学海,程铁信.基于PERT的多因素影响下的项目工期风险评价[J].工业工程,2015(6):89-92.
[4]朱庆杰,陈静,卢时林,等.贝叶斯预测模型在气温变化预测中的应用[J].河北理工学院学报,2002(4):91-98.
[5]MIGON H S,MONTERIO A B S.Rain-fall modeling:An Applicating of Bayesian forecasting[J].Stochastic Hydrology and Hydraulics,1997,11(2):115-127.
[6]宋华明,杨慧,罗建强.基于Bayes需求预测更新的供应链合作策略研究[J].管理工程学报,2011(2):220-227.
[7]冯云,朱亦丹,李将.基于贝叶斯理论的测量误差预报与修正方法[J].舰船电子对抗,2016(5):64-66.
[8]张社荣,杜成波,撒文奇,等.基于贝叶斯理论的地下洞室群时变施工进度风险预测方法[J].系统仿真学报,2014(5):1131-1137.
[9]楼灿洪,董峰辉,姚远,等.基于贝叶斯理论的建筑结构极值风速预测方法研究[J].建筑结构,2016(S1):986-988.
[10]茆诗松.贝叶斯统计[M].北京:中国统计出版社,2012.
[2]陈瑞,黄建文.基于PERT的工程网络计划模糊完工概率分析[J].人民长江,2012(20):92-94.
[3]武菲菲,王学海,程铁信.基于PERT的多因素影响下的项目工期风险评价[J].工业工程,2015(6):89-92.
[4]朱庆杰,陈静,卢时林,等.贝叶斯预测模型在气温变化预测中的应用[J].河北理工学院学报,2002(4):91-98.
[5]MIGON H S,MONTERIO A B S.Rain-fall modeling:An Applicating of Bayesian forecasting[J].Stochastic Hydrology and Hydraulics,1997,11(2):115-127.
[6]宋华明,杨慧,罗建强.基于Bayes需求预测更新的供应链合作策略研究[J].管理工程学报,2011(2):220-227.
[7]冯云,朱亦丹,李将.基于贝叶斯理论的测量误差预报与修正方法[J].舰船电子对抗,2016(5):64-66.
[8]张社荣,杜成波,撒文奇,等.基于贝叶斯理论的地下洞室群时变施工进度风险预测方法[J].系统仿真学报,2014(5):1131-1137.
[9]楼灿洪,董峰辉,姚远,等.基于贝叶斯理论的建筑结构极值风速预测方法研究[J].建筑结构,2016(S1):986-988.
[10]茆诗松.贝叶斯统计[M].北京:中国统计出版社,2012.