Robustness analysis of underground powerhouse construction simulation based on Markov Chain Monte Carlo method

作     者：ZHONG DengHua BI Lei YU Jia ZHAO MengQi 

作者机构：State Key Laboratory of Hydraulic Engineering Simulation and Safety Tianjin University 

出 版 物：《Science China(Technological Sciences)》 (中国科学（技术科学英文版）)

年 卷 期：2016年第59卷第2期

页      面：252-264页

核心收录：

学科分类：08[工学] 0802[工学-机械工程] 

基　　金：supported by the Innovative Research Groups of the National Natural Science Foundation of China(Grant No.51321065) the National Natural Science Foundation of China(Grant Nos.91215301 51439005) 

主　　题：underground powerhouse construction schedule simulation model MCMC method robustness 

摘      要：Scheduling is a major concern in construction planning and management, and current construction simulation research typically targets the shortest total duration. However, uncertainties are inevitable in actual construction, which may lead to discrepancies between the actual and planned schedules and increase the risk of total duration delay. Therefore, developing a robust construction scheduling technique is of vital importance for mitigating disturbance and improving completion probability. In the present study, the authors propose a robustness analysis method that involves underground powerhouse construction simulation based on the Markov Chain Monte Carlo(MCMC) method. Specifically, the MCMC method samples construction disturbances by considering the interrelationship between the states of parameters through a Markov state transition probability matrix, which is more robust and efficient than traditional sampling methods such as the Monte Carlo(MC) method. Additionally, a hierarchical simulation model coupling critical path method(CPM) and a cycle operation network(CYCLONE) is built, using which construction duration and robustness criteria can be calculated. Furthermore, a detailed measurement method is presented to quantize the robustness of underground powerhouse construction, and the setting model of the time buffer is proposed based on the MCMC method. The application of this methodology not only considers duration but also robustness, providing scientific guidance for engineering decision making. We analyzed a case study project to demonstrate the effectiveness and superiority of the proposed methodology.