Parameter Identification, Verification and Simulation of the CSD Transport Process

Li, Wei; Wang, Feixin; Jiang, Jian

doi:10.1109/icaci.2019.8778465

Cited by 1 publication

(2 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Excavation productivity refers to the average volume of slurry excavated by cutter head per unit time, which is mainly affected by factors such as cutter size, soil type, and motor power of the dredging ship. Pipeline transportation productivity refers to the average volume of slurry transported through the pipeline per unit time, determined primarily by the characteristics of the pipeline and the slurry (Li et al , 2019a, b). Tam et al (2002) used artificial neural networks model to predict the excavator productivity and got a promising result.…”

Section: Productivity Influencing Factors and Machine-learning Methodsmentioning

confidence: 99%

“…A non-linear productivity optimization model based on Lagrange algorithm was also proposed and verified. Li et al (2019a, b) used Kalman Filter to decompose monitoring data, simulated the pipeline transportation of CSD, and established a mathematical model of pump and pipeline for improving dredging productivity. Yang et al (2015) established a backpropagation neural network (BPNN) model to predict CSD productivity based on three influencing factors: swing speed, the velocity of the hydraulic pipeline transportation, and the work-pressure of the cutter.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Productivity estimation of cutter suction dredger operation through data mining and learning from real-time big data

Tian

Song

et al. 2021

ECAM

View full text Add to dashboard Cite

PurposeThis paper presents a new approach of productivity estimation of cutter suction dredger operation through data mining and learning from real-time big data.Design/methodology/approachThe paper used big data, data mining and machine learning techniques to extract features of cutter suction dredgers (CSD) for predicting its productivity. ElasticNet-SVR (Elastic Net-Support Vector Machine) method is used to filter the original monitoring data. Along with the actual working conditions of CSD, 15 features were selected. Then, a box plot was used to clean the corresponding data by filtering out outliers. Finally, four algorithms, namely SVR (Support Vector Regression), XGBoost (Extreme Gradient Boosting), LSTM (Long-Short Term Memory Network) and BP (Back Propagation) Neural Network, were used for modeling and testing.FindingsThe paper provided a comprehensive forecasting framework for productivity estimation including feature selection, data processing and model evaluation. The optimal coefficient of determination (R2) of four algorithms were all above 80.0%, indicating that the features selected were representative. Finally, the BP neural network model coupled with the SVR model was selected as the final model.Originality/valueMachine-learning algorithm incorporating domain expert judgments was used to select predictive features. The final optimal coefficient of determination (R2) of the coupled model of BP neural network and SVR is 87.6%, indicating that the method proposed in this paper is effective for CSD productivity estimation.

show abstract

Section: Productivity Influencing Factors and Machine-learning Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Productivity estimation of cutter suction dredger operation through data mining and learning from real-time big data

Tian

Song

et al. 2021

ECAM

View full text Add to dashboard Cite

show abstract

Parameter Identification, Verification and Simulation of the CSD Transport Process

Cited by 1 publication

References 1 publication

Productivity estimation of cutter suction dredger operation through data mining and learning from real-time big data

Productivity estimation of cutter suction dredger operation through data mining and learning from real-time big data

Contact Info

Product

Resources

About