Abstract
Construction simulation is an effective tool to provide schedule plans for construction schedule management. The simulation parameter is the foundation of construction simulation for high arch dams. However, the updating construction simulation parameters of the commonly used Bayesian algorithm are constant and inconsistent with the construction process. Due to the lack of construction data, the construction data are not sufficient for the Bayesian updating algorithm. Thus, the construction simulation of high arch dams based on fuzzy Bayesian updating algorithm is proposed. The construction parameters for a dynamic site construction situation are collected, and the original data are fuzzed by fuzzy set theory to provide the foundation for a variety of simulation parameters during the simulation process. Moreover, with the Bayesian updating algorithm, the fuzzed simulation parameters are updated and obtained via the selection of the membership degree. Finally, the construction simulation of high arch dams is conducted based on the updated simulation parameters. A case study shows that the updated simulation parameters are more in accordance with the construction parameters in situ than the original parameters, which can provide a foundation for the change of simulation parameters during the simulation process, and the simulation results are agreed with the actual construction situation.
中文概要
目的
针对当前高拱坝施工进度仿真研究中施工仿真参 数难以实现对现场施工状态的有效跟踪的现状, 研究施工仿真参数实时更新方法,以提高施工仿 真参数及仿真计算结果的准确度。
创新点
1. 通过贝叶斯更新方法,建立施工仿真参数实时 更新方法;2. 基于模糊集理论,并通过对隶属度 的取值,实现对仿真计算过程中施工仿真参数变 化的有效模拟。
方法
1. 通过对高拱坝施工过程的分析(图1),建立高 拱坝施工进度仿真的模型(图2 和3);2. 基于贝 叶斯更新方法,建立高拱坝施工仿真参数实时更 新方法(公式(4)~(8));3. 基于模糊集理论, 实现仿真过程中施工仿真参数实时更新;4. 将模 糊集理论与贝叶斯更新整合,建立模糊贝叶斯更 新方法,实现施工仿真参数实时更新(公式(17) 和(18));5. 将实时更新的施工仿真参数应用到 施工进度仿真中,实现施工进度的仿真分析。
结论
1. 采用贝叶斯更新方法,减小了施工仿真参数与 实际参数之间偏差程度;2. 采用模糊贝叶斯更新 方法,通过不断更新施工仿真参数,使得仿真计 算结果与实际施工状态更为接近。
Similar content being viewed by others
References
Alvanchi A, Lee SH, Abourizk SM, 2011. Modeling framework and architecture of hybrid system dynamics and discrete event simulation for construction. Computer-Aided Civil and Infrastructure Engineering, 26(2):77–91. https://doi.org/10.1111/j.1467-8667.2010.00650.x
Chang DY, 1986. RESQUE: a Resource Based Simulation for Construction Process Planning. PhD Thesis, University of Michigan, Ann Arbor, USA.
Chung TH, Mohamed Y, AbouRizk SM, 2006. Bayesian updating application into simulation in the North Edmonton Sanitary Trunk tunnel project. Journal of Construction Engineering & Management, 132(8):882–894. https://doi.org/10.1061/(asce)0733-9364(2006)132:8(882)
Gardoni P, Reinschmidt KF, Kumar R, 2007. A probabilistic framework for Bayesian adaptive forecasting of project progress. Computer-Aided Civil and Infrastructure Engineering, 22(3):182–196. https://doi.org/10.1111/j.1467-8667.2007.00478.x
Gholizadeh R, Shirazi AM, Gildeh BS, 2012. Fuzzy Bayesian system reliability assessment on the basis of prior twoparameter exponential distribution under different loss functions. Software Testing, Verification and Reliability, 22(3):203–217. https://doi.org/10.1002/stvr.436
Guan T, Zhong D, Ren B, et al., 2015. Construction schedule optimization for high arch dams on the basis of real-time interactive simulation. Journal of Industrial & Management Optimization, 11(4):1321–1342. https://doi.org/10.3934/jimo.2015.11.1321
Hajjar D, Abourizk SM, 2002. Unified modeling methodology for construction simulation. Journal of Construction Engineering & Management, 128(2):174–185. https://doi.org/10.1061/(asce)0733-9364(2002)128:2(174)
Halpin DW, 1973. An Investigation of the Use of Simulation Networks for Modeling Construction Operations. PhD Thesis, University of Illinois, Illinois, USA.
Jurecha W, Widmann R, 1973. Optimization of dam concreting by cable-cranes. 11th International Congress on Large Dams, III:43–49.
Kavanagh DP, 1985. SIREN: a repetitive construction simulation model. Journal of Construction Engineering & Management, 111(3):308–323. https://doi.org/10.1061/(asce)0733-9364(1985)111:3(308)
Kim B, Reinschmidt KF, 2009. Probabilistic forecasting of project duration using Bayesian inference and the beta distribution. Journal of Construction Engineering & Management, 135(3):178–186. https://doi.org/10.1061/(asce)0733-9364(2009)135:3(178)
Kim C, Kim H, Kim MK, 2011. Applicability of 4D CAD in civil engineering construction: case study of a cablestayed bridge project. Journal of Construction Engineering & Management, 25(1):98–107. https://doi.org/10.1061/(asce)CP.1943-5487.0000074
Liu LY, 1991. COOPS: Construction Object-oriented Simulation System. PhD Thesis, University of Michigan, Ann Arbor, USA.
Liu X, Wu ZR, Yang Y, et al., 2012. Information fusion diagnosis and early-warning method for monitoring the long-term service safety of high dams. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 13(9):687–699. https://doi.org/10.1631/jzus.A1200122
Osoba O, Mitaim S, Kosko B, 2011. Bayesian inference with adaptive fuzzy priors and likelihoods. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 41(5):1183–1197. https://doi.org/10.1109/TSMCB.2011.2114879
Ponz-Tienda JL, Pellicer E, Yepes V, 2012. Complete fuzzy scheduling and fuzzy earned value management in construction projects. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 13(1): 56–68. https://doi.org/10.1631/jzus.A1100160
Razavi SN, Haas CT, 2012. Reliability-based hybrid data fusion method for adaptive location estimation in construction. Journal of Computing in Civil Engineering, 26(1):1–10. https://doi.org/10.1061/(asce)CP.1943-5487.0000101
Ruan L, Xiong Y, Wang Y, 2001. Construction cost integrated control on the basis of computer simulation. Journal of Zhejiang University-SCIENCE, 2(1):28–33. https://doi.org/10.1631/jzus.2001.0028
Vahdatikhaki F, Hammad A, 2014. Framework for near realtime simulation of earthmoving projects using location tracking technologies. Automation in Construction, 42(2): 50–67. https://doi.org/10.1016/j.autcon.2014.02.018
Wu HC, 2004. Fuzzy reliability estimation using Bayesian approach. Computers & Industrial Engineering, 46(3): 467–493. https://doi.org/10.1016/j.cie.2004.01.009
Wu HC, 2006. Fuzzy Bayesian system reliability assessment on the basis of exponential distribution. Applied Mathematical Modelling, 30(6):509–530. https://doi.org/10.1016/j.apm.2005.05.014
Yin ZR, Li JS, 2011. Bayesian reliability assessment of electronic equipment under fuzzy sample information. Proceedings 2011 International Conference on Transportation, Mechanical, and Electrical Engineering (TMEE), p.1050–1053. https://doi.org/10.1109/TMEE.2011.6199384
Zhang H, Tam CM, Li H, 2005. Activity object-oriented simulation strategy for modeling construction operations. Journal of Computing in Civil Engineering, 19(3):313–322. https://doi.org/10.1061/(asce)0887-3801(2005)19:3(313)
Zhang S, Du C, Sa W, et al., 2013. Bayesian-based hybrid simulation approach to project completion forecasting for underground construction. Journal of Construction Engineering & Management, 140(1):685–696. https://doi.org/10.1061/(asce)CO.1943-7862.0000764
Zhang Y, AbouRizk SM, Xie H, et al., 2012. Design and implementation of loose-coupling visualization components in a distributed construction simulation environment with HLA. Journal of Computing in Civil Engineering, 26(2):248–258. https://doi.org/10.1061/(asce)CP.1943-5487.0000131
Zhong DH, Li JR, Zhu HR, et al., 2004. Geographic information system-based visual simulation methodology and its application in concrete dam construction processes. Journal of Construction Engineering & Management, 130(5):742–750. https://doi.org/10.1061/(asce)0733-9364(2004)130:5(742)
Zhong DH, Ren BY, Li MC, et al., 2010. Theory on real-time control of construction quality and progress and its application to high arc dam. Science China Technological Sciences, 53(10):2611–2618. https://doi.org/10.1007/s11431-010-4078-1
Zhong DH, Hu W, Wu BP, et al., 2017. Dynamic time-costquality tradeoff of rockfill dam construction based on real-time monitoring. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 18(1): 1–19. https://doi.org/10.1631/jzus.A1600564
Author information
Authors and Affiliations
Corresponding author
Additional information
Project supported by the Foundation for Key Program of Natural Science Foundation of High Arch Dam (No. 51339003), the National Natural Science Foundation of China (No. 51439005), and the National Science Foundation of China (No. 51409186)
Rights and permissions
About this article
Cite this article
Guan, T., Zhong, DH., Ren, BY. et al. Construction simulation of high arch dams based on fuzzy Bayesian updating algorithm. J. Zhejiang Univ. Sci. A 19, 505–520 (2018). https://doi.org/10.1631/jzus.A1700372
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1631/jzus.A1700372
Key words
- High arch dams
- Construction simulation
- Bayesian updating algorithm; Fuzzy set theory
- Simulation parameters