Novel Method of Construction-Efficiency Evaluation of Cutter Suction Dredger Based on Real-Time Monitoring Data
Publication: Journal of Waterway, Port, Coastal, and Ocean Engineering
Volume 144, Issue 6
Abstract
A work condition monitoring system is widely used for recording the real-time states of cutter suction dredgers during the dredging process. However, the obtained data cannot provide enough actionable information directly for construction efficiency. This paper presents a novel method to evaluate the construction efficiency of cutter suction dredgers from the perspective of construction cycles. The construction cycle related to dredging operations was first introduced. A new algorithm of selecting cycle characteristic parameters (ASCCP) was proposed to determine the cycle characteristic parameters. Combined with three-dimensional (3D) visualization of the dredging track of the dredger, the method of construction cycle recognition was established. Then, the efficiency-evaluation methods based on construction cycles and the time-utilization ratio were adopted to evaluate the construction efficiency. Finally, a case study showed that the proposed approach was feasible to evaluate the construction efficiency of the cutter suction dredger. Moreover, the methods of the present work can be implemented in any cutter suction dredger and aid construction project managers in managing equipment-related work tasks on construction sites.
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Acknowledgments
This research was supported by the National Natural Science Foundation for Excellent Young Scientists of China (Grant 51622904), the Tianjin Science Foundation for Distinguished Young Scientists of China (Grant 17JCJQJC44000), and the National Natural Science Foundation for Innovative Research Groups of China (Grant 51621092).
References
Abbasian-Hosseini, S. A., M. L. Leming, and M. Liu. 2016. “Effects of idle time restrictions on excess pollution from construction equipment.” J. Manage. Eng. 32 (2): 04015046. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000408.
Ahn, C. R., S. Lee, and F. Peña-Mora. 2012. “Monitoring system for operational efficiency and environmental performance of construction operations using vibration signal analysis.” In Proc., Construction Research Congress 2012: Construction Challenges in a Flat World, 1879–1888. Reston, VA: ASCE.
Ahn, C. R., S. H. Lee, and F. Peña-Mora. 2013. “Acceleromter-based measurement of construction equipment operating efficiency for monitoring environmental performance.” Int. Workshop on Computing in Civil Engineering, 565–572. Reston, VA: ASCE.
Ahn, C. R., S. Lee, and F. Peña-Mora. 2015. “Application of low-cost accelerometers for measuring the operational efficiency of a construction equipment fleet.” J. Comput. Civ. Eng. 29 (2): 04014042. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000337.
Akhavian, R., and A. H. Behzadan. 2014. “Construction activity recognition for simulation input modeling using machine learning classifiers.” In Proc., 2014 Winter Simulation Conf., 3296–3307. New York: IEEE Press.
Chen, X., S. A. Miedema, and C. van Rhee. 2015. “Numerical modeling of excavation process in dredging engineering.” Procedia Eng. 102: 804–814. https://doi.org/10.1016/j.proeng.2015.01.194.
Cheng, C. F., A. Rashidi, M. A. Davenport, and D. V. Anderson. 2017. “Activity analysis of construction equipment using audio signals and support vector machines.” Autom. Constr. 81: 240–253. https://doi.org/10.1016/j.autcon.2017.06.005.
Fan, Q., and H. Fan. 2015. “Reliability analysis and failure prediction of construction equipment with time series models.” J. Adv. Manage. Sci. 3 (3): 203–210. https://doi.org/10.12720/joams.3.3.203-210.
Fang, Y. H., Y. K. Cho, S. J. Zhang, and E. Perez. 2016. “Case study of BIM and cloud–enabled real-time RFID indoor localization for construction management applications.” J. Constr. Eng. Manage. 142 (7): 05016003. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001125.
Golparvar-Fard, M., A. Heydarian, and J. C. Niebles. 2013. “Vision-based action recognition of earthmoving equipment using spatio-temporal features and support vector machine classifiers.” Adv. Eng. Inf. 27 (4): 652–663. https://doi.org/10.1016/j.aei.2013.09.001.
Guo, H. L., Y. T. Yu, and M. Skitmore. 2017. “Visualization technology-based construction safety management: A review.” Autom. Constr. 73: 135–144. https://doi.org/10.1016/j.autcon.2016.10.004.
Heikkilä, R., T. Leinonen, H. Paukkeri, and H. Virtanen. 2014. “Development of the BIM based process for dredging works.” In Proc., 31st Int. Symp. on Automation and Robotics in Construction and Mining, 171–175. Sydney, Australia: Univ. of Technology.
Heydarian, A., M. Golparvar-Fard, and J. C. Niebles. 2012. “Automated visual recognition of construction equipment actions using spatio-temporal features and multiple binary support vector machines.” In Proc., Construction Research Congress 2012: Construction Challenges in a Flat World, 889–898. Reston, VA: ASCE.
Huang, P., C. J. Liu, X. Yang, L. Xiao, and J. Chen. 2014. “Wireless spectrum occupancy prediction based on partial periodic pattern mining.” IEEE Trans. Parallel Distrib. Syst. 25 (7): 1925–1934. https://doi.org/10.1109/TPDS.2013.283.
IADC (International Association of Dredging Companies). 2011. “Dredging in figures 2010 review of the global dredging market.” Accessed June 6, 2018. www.iadc-dredging.com/ul/cms/fck-uploaded/documents/PDF%20Dredging%20in%20Figures/dredging-in-figures-2010.pdf.
Jeong, A., S. Kim, M. Kim, and K. Jung. 2016. “Development of optimization model for river dredging management using MCDA.” Procedia Eng. 154: 369–373. https://doi.org/10.1016/j.proeng.2016.07.494.
Li, M. C., S. Han, and J. Shi. 2017. “An enhanced ISODATA algorithm for recognizing multiple electric appliances from the aggregated power consumption dataset.” Energy Build. 140: 305–316. https://doi.org/10.1016/j.enbuild.2017.02.006.
Loginov, A., A. Proskurnikov, E. Ambrosovskaya, and D. Romaev. 2012. “DP systems for track control of dredging vessels.” IFAC Proc. Volumes 45 (27): 453–458. https://doi.org/10.3182/20120919-3-IT-2046.00077.
Ministry of Communications of the People’s Republic of China. 1999. The technical code of dredging engineering, 141–143. Beijing: Jiao Tong Ji Chu (JTJ).
Mohsenijam, A., M. F. F. Siu, and M. Lu. 2017. “Modified stepwise regression approach to streamlining predictive analytics for construction engineering applications.” J. Comput. Civ. Eng. 31 (3): 04016066. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000636.
Ni, F. S., L. J. Zhao, L. Gu, S. Jiang, L. N. Qian, L. Q. Xu, K. J. He, R. X. Liu, and Q. S. Zhou. 2010. “Simulation of dredging processes of a cutter suction dredger.” In Proc., 2010 Int. Conf. on Audio Language and Image Processing, 628–632. New York: IEEE.
Nyrkov, A., S. Sokolov, S. Chernyi, and D. Mamunts. 2015. “Using information technologies in dredging.” Metall. Min. Ind. 7 (6): 521–524.
Omar, T., and M. L. Nehdi. 2016. “Data acquisition technologies for construction progress tracking.” Autom. Constr. 70: 143–155. https://doi.org/10.1016/j.autcon.2016.06.016.
Park, J. W., K. Kim, and Y. K. Cho. 2017. “Framework of automated construction-safety monitoring using cloud-enabled BIM and BLE mobile tracking sensors.” J. Constr. Eng. Manage. 143 (2): 05016019. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001223.
Park, M. W., and I. Brilakis. 2012. “Enhancement of construction equipment detection in video frames by combining with tracking.” Int. Conf. on Computing in Civil Engineering, 421–428. Reston, VA: ASCE.
Pfeifer, J., K. Barker, J. E. Ramirez-Marquez, and N. Morshedlou. 2015. “Quantifying the risk of project delays with a genetic algorithm.” Int. J. Prod. Econ. 170: 34–44. https://doi.org/10.1016/j.ijpe.2015.09.007.
Pradhananga, N., and J. Teizer. 2013. “Automatic spatio-temporal analysis of construction site equipment operations using GPS data.” Autom. Constr. 29: 107–122. https://doi.org/10.1016/j.autcon.2012.09.004.
Ray, S. J., and J. Teizer. 2016. “Dynamic blindspots measurement for construction equipment operators.” Saf. Sci. 85: 139–151. https://doi.org/10.1016/j.ssci.2016.01.011.
Rebolj, D., N. Č. Babič, A. Magdič, P. Podbreznik, and M. Pšunder. 2008. “Automated construction activity monitoring system.” Adv. Eng. Inf. 22 (4): 493–503. https://doi.org/10.1016/j.aei.2008.06.002.
Ren, X. N., Z. H. Zhu, C. Germain, and R. Belair. 2017. “Automated monitoring of the utilization rate of onsite construction equipment.” Int. Workshop on Computing in Civil Engineering 2017, 74–81. Reston, VA: ASCE.
Sacks, R., R. Navon, I. Brodetskaia, and A. Shapira. 2005. “Feasibility of automated monitoring of lifting equipment in support of project control.” J. Constr. Eng. Manage. 131 (5): 604–614. https://doi.org/10.1061/(ASCE)0733-9364(2005)131:5(604).
Setiawan, R. 2015. “Parametric analysis on off-shore dredging process using cutter suction dredgers.” ACEAN Eng. J. 6 (1): 37–46.
Tang, J. Z., Q. F. Wang, and Z. Y. Bi. 2008. “Expert system for operation optimization and control of cutter suction dredger.” Expert Syst. Appl. 34 (3): 2180–2192. https://doi.org/10.1016/j.eswa.2007.02.025.
Tang, J. Z., Q. F. Wang, and T. Y. Zhong. 2009. “Automatic monitoring and control of cutter suction dredger.” Autom. Constr. 18 (2): 194–203. https://doi.org/10.1016/j.autcon.2008.07.006.
Teizer, J., and T. Cheng. 2015. “Proximity hazard indicator for workers-on-foot near miss interactions with construction equipment and geo-referenced hazard areas.” Autom. Constr. 60: 58–73. https://doi.org/10.1016/j.autcon.2015.09.003.
Turkan, Y., F. Bosche, C. T. Haas, and R. Haas. 2012. “Automated progress tracking using 4D schedule and 3D sensing technologies.” Autom. Constr. 22: 414–421. https://doi.org/10.1016/j.autcon.2011.10.003.
Wu, Z., X. H. Xia, and B. Zhu. 2015. “Model predictive control for improving operational efficiency of overhead cranes.” Nonlinear Dyn. 79 (4): 2639–2657. https://doi.org/10.1007/s11071-014-1837-8.
Yue, P., D. H. Zhong, Z. J. Miao, and J. Yu. 2015. “Prediction of dredging productivity using a rock and soil classification model.” J. Waterw. Port Coastal Ocean Eng. 141 (4): 06015001. https://doi.org/10.1061/(ASCE)WW.1943-5460.0000303.
Zhang, C., and A. Hammad. 2012. “Improving lifting motion planning and re-planning of cranes with consideration for safety and efficiency.” Adv. Eng. Inf. 26 (2): 396–410. https://doi.org/10.1016/j.aei.2012.01.003.
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© 2018 American Society of Civil Engineers.
History
Received: Mar 27, 2018
Accepted: Jun 20, 2018
Published online: Aug 7, 2018
Published in print: Nov 1, 2018
Discussion open until: Jan 7, 2019
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