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

• Published in: Science China. Technological sciences Vol. 59; no. 2; pp. 252 - 264
• Main Authors: Zhong, DengHua, Bi, Lei, Yu, Jia, Zhao, MengQi
• Format: Journal Article
• Language: English
• Published: Beijing Science China Press 01.02.2016
• Subjects: Engineering; MCMC方法; 地下厂房; 施工仿真; 状态转移概率矩阵; 稳健性; 蒙特卡罗法; 马尔可夫链蒙特卡罗方法; 鲁棒性分析; robustness; underground powerhouse; construction schedule; MCMC method; simulation model
• ISSN: 1674-7321; 1869-1900
• DOI: 10.1007/s11431-015-5859-3

Scheduling is a major concern in construction planning and management, and current construction simulation research typical- ly targets the shortest total duration. However, uncertainties are inevitable in actual construction, which may lead to discrepan- cies 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 simu- lation based on the Markov Chain Monte Carlo （MCMC） method. Specifically, the MCMC method samples construction dis- turbances 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. Addition- ally, 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 pro- posed 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 effective- ness and superiority of the proposed methodology.