Proceedings of the Thirty-second (2022) International Ocean and Polar Engineering ConferenceShanghai, China, June 5–10, 2022
Copyright © 2022 by the International Society of Offshore and Polar Engineers (ISOPE)
ISBN 978-1-880653-81-4; ISSN 1098-6189

www.isope.org

Research on Modeling of Cutter Suction Dredger Excavating and Conveying System
Jiajun Lu, Feixin Wang*, Bo Yang, Zhongwei Zhou
National Engineering Research Center of Dredging Technology and Equipment, Shanghai, China

them, the cutter suction dredger adopts the continuous operation modeof digging, transporting and unloading. It uses a rotating reamer to cutthe underwater soil layer, and then uses a centrifugal pump to transport
the muddy water mixture over a long distance. It is widely used in theconstruction and maintenance of coastal and inland port waterways,
dredging of rivers and lakes, and environmental protection dredging
construction (Chen et al., 2018; Wang et al., 2021). During theconstruction process, the flow velocity, concentration and production of
the dredger are related to many factors, such as the speed of the mud
pump, the speed of the reamer, the traverse speed of the bridge, thestepping displacement of the trolley, the depth of the bridge, etc (Zhanget al., 2015). The influencing factors are numerous and complex, withlarge randomness and difficulty in predicting. To this end, Wang et al.
(2021) of the National Engineering Research Center of Dredging
adopted a data-driven approach and established a construction output
model of cutter suction dredger based on PCA and RBF neural network,
which solved the black box problem between control variables andinstantaneous output in the control system of cutter suction dredger.
Similar researches on the modeling of cutter suction dredger based ondata-driven method include the prediction model of cutter suction
dredger output based on data mining method (Bai et al., 2019), the cutter
suction dredger model established by double hidden layer BP neuralnetwork (Yang et al., 2016), and the cutter suction dredger model
considering the influence of time delay (Chen et al., 2019), etc. However,
the above-mentioned models all have defects such as the prediction
results are only valid within the scope of the sample data, thegeneralization of the extrapolation is questionable, and the physicalmeaning is unclear. While the traditional mathematical and physicalmodels established by the working characteristics of the key equipmentof the cutter suction dredger, such as the modeling of the driving systemof the cutter suction dredger (Hu et al., 2019) and the dynamic theoreticalmodel of the mud transportation of the long-distance pipeline of thecutter suction dredger (Zhou et al., 2021), are faced with computational
bias and computational limitations caused by using empirical formulas
to simplify the mechanism model.
The dredging operation process is a complex dynamic change process,which has the characteristics of strong coupling, nonlinearity, largeinertia, and time-varying parameters, and is easily affected by various

ABSTRACTThe simulation of the operating performance and operating parametersof the excavating and conveying system of a cutter suction dredger has
always been a research hotspot and difficulty. The single mechanism
model has problems such as weak timeliness and large calculation
deviations, while the data-driven model faces defects such as poor
generalization ability and difficult interpretation of the model. In
response to this problem, this paper combines the advantages of themechanism model reflecting the physical rules and the flexibility of thedata-driven method, establishing a dredging operation model of a cutter
suction dredger with strong extrapolation generalization ability and highaccuracy to realize the numerical simulation of the excavation systemand conveying system of the cutter suction dredger. It first processes andanalyzes the historical construction data of the cutter suction dredger,
make use of the method of information gain rate to select the control
variables, and builds the production prediction model of the excavation
system through the machine learning method based on improved KNN.
Then the excavation production calculated by the excavation system isused as the input of the transportation system, and the improved Euler
method in fluid mechanics is used to calculate the segmentation anddensity of the pipeline, and the mechanism model of the pipelinetransportation system of the cutter suction dredger is constructed.
Finally, adjust the control parameters in real-time in the excavation andconveying system model, and observe their influence on the results. The
analysis and comparison between the numerical results and the actual
ship construction data show that the simulation results are accurate andreliable, and can achieve an accurate simulation of the performance andoperating conditions of the excavating and conveying system of thecutter suction dredger.

KEY WORDS: Cutter suction dredger; machine learning; data mining;mathematical and physical modeling.

INTRODUCTION
In the process of dredging rivers and filling embankments, engineering
equipment such as dredgers plays a key role (Hu et al., 2011). Among1541