Journal of Zhejiang University-SCIENCE A

, Volume 19, Issue 5, pp 367–383 | Cite as

Construction simulation approach of roller-compacted concrete dam based on real-time monitoring

  • Qian-wei Wang
  • Deng-hua Zhong
  • Bin-ping Wu
  • Jia Yu
  • Hao-tian Chang


The parameters of existing roller-compacted concrete (RCC) dam construction simulation are usually fixed based on experience while the actual construction conditions of an RCC dam change during the process of the project. The simulation accuracy of an RCC dam is therefore reduced because the change has not been considered. A new method for RCC dam construction simulations based on real-time monitoring is presented in this paper. First, real-time monitoring technology is used to collect and analyze the actual construction information. Second, meteorological data obtained from the real-time monitoring system are analyzed using the fuzzy average function method, and the weather conditions of the next stage are forecasted. Then the construction schedule simulation model is updated via the Bayesian update method. Results of the analysis are used as the input to the construction simulation parameters, and the construction simulation is performed. A real-world engineering example is presented to compare the simulation results with the actual construction schedule. The results demonstrate that the method can effectively improve the accuracy and real-time performance of construction simulations.

Key words

Roller-compacted concrete (RCC) dam Construction simulation Real-time monitoring Bayesian update Fuzzy mean generating function 






1. 通过碾压混凝土坝施工信息实时获取技术,分析计算碾压混凝土坝施工仿真参数;2. 利用贝叶斯更新技术对施工仿真参数进行更新;3. 利用模糊均生函数对坝区短期降雨量进行预测;4. 建立基于实时监控的碾压混凝土坝施工仿真模型,对碾压混凝土坝施工过程进行仿真并与实际施工进度对比。


1. 通过实地采集,获取碾压混凝土坝施工过程中实时施工信息(图2);2. 通过理论推导,构建施工仿真参数先验分布均值和方差与后验分布均值和方差之间的关系,得到施工仿真参数更新方案(公式(16)和(17));3. 通过理论推导,利用已知坝区降雨量数据预测未来短期内的降雨情况(图5);4. 通过仿真模拟,得到施工仿真参数更新后的仿真成果并将其与实际施工进行对比,验证本方法的有效性和准确性。


1. 施工仿真参数的准确性对碾压混凝土坝施工仿真结果准确性有很大影响;2. 可以利用贝叶斯更新技术对施工仿真中的仿真参数进行更新,利用模糊均生函数对坝区短时期内降雨量进行预测;3. 运用基于实时监控的碾压混凝土坝施工仿真方法对碾压混凝土坝施工过程进行仿真,仿真结果与实际施工进度之间的偏差明显减少,仿真准确性明显提高。


碾压混凝土坝 施工仿真 实时监控 贝叶斯更新 模糊均生函数 

CLC number



Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Akhavian R, Behzadan AH, 2012. An integrated data collection and analysis framework for remote monitoring and planning of construction operations. Advanced Engineering Informatics, 26(4):749–761. CrossRefGoogle Scholar
  2. Akhavian R, Behzadan AH, 2013. Knowledge-based simulation modeling of construction fleet operations using multimodel-process data mining. American Society of Civil Engineers, 139(11):755–768.Google Scholar
  3. Anderegg R, von Felten D, Kaufmann K, 2006. Compaction monitoring using intelligent soil compactors. Proceedings of GeoCongress: Geotechnical Engineering in the Information Technology Age, p.41–46. Google Scholar
  4. Banks J, 2005. Discrete Event System Simulation, 4th Edition. Pearson Education India, NJ, USA.zbMATHGoogle Scholar
  5. Chang HT, Zhong DH, Wang SQ, 2013. The whole process simulation of the construction progress of the roller compacted concrete dam with the dynamic combined warehouse. Journal of Tianjin University, 46(1):29–39 (in Chinese).Google Scholar
  6. Cui B, 2010. Integrated Theory and Application of Construction Quality Monitoring System for Core Rock Fill Dam. PhD Thesis, Tianjin University, Tianjin, China (in Chinese).Google Scholar
  7. Cui B, Zhong DH, Zhang P, et al., 2009. The application of computer graphic technology on monitoring roller compaction quality of rock-fill dam. 6th International Conference on Computer Graphics, Imaging and Visualization, p.520–524. Google Scholar
  8. Dong H, Zhao C, 2013. Simulation and optimization research on the operating system of the construction of the RCC dam. Water Conservancy and Hydropower Technology, 44(1):79–82 (in Chinese).Google Scholar
  9. Gelman A, Carlin J, Stern H, et al., 2004. Bayesian Data Analysis, 2nd Edition. Chapman & Hall/CRC, USA, p.283–310.zbMATHGoogle Scholar
  10. Han S, Lee S, Halpin D, 2005. Productivity evaluation of the conventional and GPS-based earthmoving systems using construction simulation. Construction Research Congress. Google Scholar
  11. Han S, Lee S, Hong T, et al., 2006. Simulation analysis of productivity variation by global positioning system (GPS) implementation in earth moving operations. Canadian Journal of Civil Engineering, 33(9):1105–1114. CrossRefGoogle Scholar
  12. Hossain M, Mulandi J, Keach L, 2006. Intelligent compaction control, airfield and highway pavements: meeting today’s challenges with emerging technologies. Processing of the Airfield and Highway Pavement Specialty Conference, p.304–316.CrossRefGoogle Scholar
  13. Jurecha W, Widmann R, 1973. Optimization of dam concreting by cable-cranes. 11th International Congress on Large Dams, p.43–49.Google Scholar
  14. Liu DH, Cui B, Liu YG, et al., 2013. Automatic control and real-time monitoring system for earth-rock dam material truck watering. Automation in Construction, 30:70–80. CrossRefGoogle Scholar
  15. Liu YX, Zhong DH, Cui B, et al., 2015. Study on real-time construction quality monitoring of storehouse surfaces for RCC dams. Automation in Construction, 49:100–112. CrossRefGoogle Scholar
  16. Lu M, Dai F, Chen W, 2007. Real-time decision support for planning concrete plant operations enabled by integrating vehicle tracking technology, simulation, and optimization algorithms. Canadian Journal of Civil Engineering, 34(8):912–922. CrossRefGoogle Scholar
  17. Luo W, 2009. RCC dam simulation of petri network coupling model based on process queue. Journal System Simulation, 21(19):6280–6283 (in Chinese).Google Scholar
  18. Mooney MA, Rinehart RV, 2007. Field monitoring of roller vibration during compaction of subgrade soil. Journal of Geotechnical and Geoenvironmental Engineering, 133(3):2571265.CrossRefGoogle Scholar
  19. Navon S, 2005a. Field experiments in automated monitoring of road construction. Journal of Construction Engineering and Management, 131(4):487–493. CrossRefGoogle Scholar
  20. Navon S, 2005b. A model for automated monitoring of road construction. Construction Management and Economics, 23(9):941–951. CrossRefGoogle Scholar
  21. Reclus F, Drouard K, 2010. Geofencing for fleet & freight management. International Conference on Intelligent Transport Systems Telecommunications, p.353–356. Google Scholar
  22. Song L, Eldin NN, 2012. Adaptive real-time tracking and simulation of heavy construction operations for lookahead scheduling. Automation in Construction, 27:32–39. CrossRefGoogle Scholar
  23. Song S, Zhang A, Wang J, et al., 2015. Screen: stream data cleaning under speed constraints. ACM SIGMOD International Conference on Management of Data, p.827–841. Google Scholar
  24. Straub D, Papaioannou I, 2015. Bayesian updating with structural reliability methods. Journal of Engineering Mechanics, 141(3):04014134. CrossRefGoogle Scholar
  25. Vahdatikhaki F, Hammad A, 2014. Framework for near realtime simulation of earthmoving projects using location tracking technologies. Automation in Construction, 42: 50–67. CrossRefGoogle Scholar
  26. Vahdatikhaki F, Hammad A, 2015. Optimization-based excavator pose estimation using real-time location systems. Automation in Construction, 56:76–92. CrossRefGoogle Scholar
  27. Wei FY, Cao HX, 1993. A fuzzy mean generating function (FMGF) model and its application. Chinese Academy of Meteorological Sciences, 19(2):7–11 (in Chinese).Google Scholar
  28. White DJ, Thompson MJ, Jovaag K, et al., 2006. Filed Evaluation of Compaction Monitoring Technology: Phase II. Lowa State University, USA.Google Scholar
  29. Zhang C, Hammad A, Rodriguez S, 2012. Crane pose estimation using UWB real-time location system. Journal of Computing in Civil Engineering, 26(5):625–637. CrossRefGoogle Scholar
  30. Zhang SR, Du CB, Sa WQ, et al., 2014. Bayesian-based hybrid simulation approach to project completion forecasting for underground construction. Journal of Construction Engineering and Management, 140(1):04013031.CrossRefGoogle Scholar
  31. Zhong DH, Cui B, Liu DH, 2011. Real-time compaction quality monitoring of high core rockfill dam. Science China Technological Sciences, 54(7):1906–1913. CrossRefGoogle Scholar
  32. Zhong DH, Chang H, Li MC, 2012a. Dynamic simulation of high RCC dam construction. International Journal of Hydropower Dams, 19(5):70–74 (in Chinese).Google Scholar
  33. Zhong DH, Zhong GL, Cui B, 2012b. The theory and application of microclimate information real-time monitoring and control RCC dam. Water Conservancy and Hydropower Technology, 43(1):84–87 (in Chinese).Google Scholar
  34. Zhong DH, Chang HT, Wang SQ, et al., 2013. The whole process simulation of the construction progress of the roller compacted concrete dam with the dynamic combined warehouse. Journal of Tianjin University, 46(1): 29–37 (in Chinese).Google Scholar
  35. 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. CrossRefGoogle Scholar
  36. Zhong GL, 2012. The Theory and Application of Construction Quality Real Time Monitoring of the Construction Quality of RCC Dam. PhD Thesis, Tianjin University, Tianjin, China (in Chinese).Google Scholar
  37. Zhou YH, Zhao CJ, 2008. Optimizing resource allocation based visual construction simulation system for an RCC dam. Progressing World Hydro Development, p.767–775.Google Scholar

Copyright information

© Zhejiang University and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.State Key Laboratory of Hydraulic Engineering Simulation and SafetyTianjin UniversityTianjinChina

Personalised recommendations