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Abstract: 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.

The paper is written mostly in a well-structured and well-organized fashion. The graphics are of a good quality and the paper is easy to read.The authors have proposed a new method for RCC dam construction simulations based on real-time monitoring.

基于实时监控的碾压混凝土坝施工仿真

[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.

[2]Akhavian R, Behzadan AH, 2013. Knowledge-based simulation modeling of construction fleet operations using multi-model-process data mining. American Society of Civil Engineers, 139(11):755-768.

[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.

[4]Banks J, 2005. Discrete Event System Simulation, 4th Edition. Pearson Education India, NJ, USA.

[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).

[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).

[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.

[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).

[9]Gelman A, Carlin J, Stern H, et al., 2004. Bayesian Data Analysis, 2nd Edition. Chapman & Hall/CRC, USA, p.283-310.

[10]Han S, Lee S, Halpin D, 2005. Productivity evaluation of the conventional and GPS-based earthmoving systems using construction simulation. Construction Research Congress.

[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.

[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.

[13]Jurecha W, Widmann R, 1973. Optimization of dam concreting by cable-cranes. 11th International Congress on Large Dams, p.43-49.

[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.

[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.

[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.

[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).

[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.

[19]Navon S, 2005a. Field experiments in automated monitoring of road construction. Journal of Construction Engineering and Management, 131(4):487-493.

[20]Navon S, 2005b. A model for automated monitoring of road construction. Construction Management and Economics, 23(9):941-951.

[21]Reclus F, Drouard K, 2010. Geofencing for fleet & freight management. International Conference on Intelligent Transport Systems Telecommunications, p.353-356.

[22]Song L, Eldin NN, 2012. Adaptive real-time tracking and simulation of heavy construction operations for look-ahead scheduling. Automation in Construction, 27:32-39.

[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.

[24]Straub D, Papaioannou I, 2015. Bayesian updating with structural reliability methods. Journal of Engineering Mechanics, 141(3):04014134.

[25]Vahdatikhaki F, Hammad A, 2014. Framework for near real-time simulation of earthmoving projects using location tracking technologies. Automation in Construction, 42: 50-67.

[26]Vahdatikhaki F, Hammad A, 2015. Optimization-based excavator pose estimation using real-time location systems. Automation in Construction, 56:76-92.

[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).

[28]White DJ, Thompson MJ, Jovaag K, et al., 2006. Filed Evaluation of Compaction Monitoring Technology: Phase II. Lowa State University, USA.

[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.

[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.

[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.

[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).

[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).

[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).

[35]Zhong DH, Hu W, Wu BP, et al., 2017. Dynamic time-cost-quality tradeoff of rockfill dam construction based on real-time monitoring. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 18(1):1-19.

[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).

[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.