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Journal of ZheJiang University (Engineering Science)  2020, Vol. 54 Issue (2): 241-247    DOI: 10.3785/j.issn.1008-973X.2020.02.004
Civil and Transportation Engineering     
Parallel study of seismic reliability analysis of water supply pipe network based on quasi-Monte Carlo method
Li LONG1,2(),Shan-suo ZHENG1,2,*(),Yan ZHOU1,2,Jin-chuan HE3,Hong-li MENG1,2,Yong-long CAI1,2
1. School of Civil Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China
2. Key Laboratory of Structural Engineering and Earthquake Resistance, Ministry of Education, Xi’an University of Architecture and Technology, Xi’an 710055, China
3. Institute of Architectural Design and Research, Xi’an University of Architecture and Technology, Xi’an 710055, China
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Abstract  

In order to improve the seismic reliability analysis efficiency of water supply pipe network based on Monte Carlo simulation, the failure probabilities of water supply pipe network nodes and pipes were sampled by using low discrepancy Sobol sequence instead of pseudo-random number sequence. Combined with the breadth-first search algorithm, a parallel algorithm for seismic reliability analysis of water supply pipe network based on quasi-Monte Carlo method and compute unified device architecture (CUDA) was proposed. The parallel algorithm was optimized from the aspects of memory, execution configuration and instructions. A city water supply pipe network was taken as the computational example, the accuracy and efficiency of serial and parallel computing methods were compared, and the influence of Sobol sequence and pseudo-random number sequence on the reliability analysis of pipe network was analyzed. Results show that the maximum error of the parallel and serial methods is 0.52%. The maximum acceleration ratio of the parallel method is 96 times that of the serial method, and the parallel method significantly improves the computational efficiency while ensuring the accuracy of results. 1 000 parallel simulations were performed based on Sobol sequences and 5 000 parallel simulations were performed based on pseudo-random number sequences, and the maximum errors between the two simulation results and the analytical value based on fuzzy mathematics were 0.2% and 0.4%, respectively. It indicates that the parallel method based on quasi-Monte Carlo has higher accuracy and faster convergence speed.



Key wordswater supply pipe network      compute unified device architecture (CUDA)      breadth-first search      parallel computing      network reliability analysis      quasi-Monte Carlo method     
Received: 30 July 2019      Published: 10 March 2020
CLC:  TU 990  
Corresponding Authors: Shan-suo ZHENG     E-mail: longliforever@163.com;zhengshansuo@263.net
Cite this article:

Li LONG,Shan-suo ZHENG,Yan ZHOU,Jin-chuan HE,Hong-li MENG,Yong-long CAI. Parallel study of seismic reliability analysis of water supply pipe network based on quasi-Monte Carlo method. Journal of ZheJiang University (Engineering Science), 2020, 54(2): 241-247.

URL:

http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2020.02.004     OR     http://www.zjujournals.com/eng/Y2020/V54/I2/241


基于拟蒙特卡罗方法的供水管网抗震可靠性分析并行化研究

为了提高基于蒙特卡罗(Monte Carlo)方法的供水管网抗震可靠性分析效率,以低偏差Sobol点列替代伪随机数序列对供水管网节点和管段破坏概率进行抽样,结合宽度优先搜索算法,提出基于拟Monte Carlo方法和统一计算设备架构(CUDA)的供水管网抗震可靠性分析并行算法,并从内存、执行配置和指令等方面优化并行算法. 以某城市供水管网系统为例,对比串行和并行计算方法的精度及效率,分析Sobol点列和伪随机数序列对管网可靠性分析的影响. 结果表明,并行和串行方法计算结果的误差最大为0.52%,并行方法最高加速比为串行算法的96倍,在保证结果精度的同时大幅度提高计算效率. 基于Sobol点列进行1 000次并行模拟及基于伪随机数序列进行5 000次并行模拟,2种模拟结果与基于模糊数学法的解析值的最大误差分别为0.2%、0.4%,表明基于拟Monte Carlo的并行方法具有更高的精确度,更快的收敛速度.


关键词: 供水管网,  统一计算设备架构(CUDA),  宽度优先搜索,  并行计算,  网络可靠性分析,  拟Monte Carlo方法 
Fig.1 Memory space on CUDA device
Fig.2 Parallel computing flow diagram of seismic reliability analysis of pipe network based on CUDA
Fig.3 Layout plan of pipe network in an urban area
Fig.4 Connection probabilities of water supply pipe network in an urban area under different earthquake actions
k Ts/s Tp/s Rs
1 000 9.260 0.116 80
3 000 28.351 0.313 91
5 000 47.333 0.498 95
10 000 93.723 0.972 96
Tab.1 Comparison of serial and parallel computing time
序列 T/s
伪随机数 0.258
Sobol序列 0.256
Tab.2 Computational time to generate sampling points
Fig.5 Comparison of Monte Carlo and quasi-Monte Carlo simulation results under action of seven degree earthquake
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