A new measuring method of dredging concentration based on hybrid ensemble deep learning technique. (January 2022)

• Record Type: Journal Article
• Authors: Bai, Shuo; Li, Mingchao; Lu, Qiaorong; Fu, Jiake; Li, Jinfeng; Qin, Liang
• Is Part Of: Measurement. Volume 188(2022)
• Publication Date: 2022-01
• Subjects: Dredger -- Radioactive source densitometer -- Hybrid ensemble deep learning (HEDL) -- Virtual sensor of slurry concentration; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2021.110423
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: A virtual sensor that can assist or replace the radioactive concentration meter is proposed. A hybrid ensemble deep learning (HEDL) algorithm framework based on multi differentiation model is put forward. Eliminating the problem of time delay of physical sensor monitoring data by integrating method. The effective monitoring parameters with potential relationship with concentration were selected. Abstract: Aiming at the problems of safety, management, environmental protection, virtual sensor technology of dredger slurry concentration based on a hybrid ensemble deep learning (HEDL) framework is proposed. The purpose of this paper is to use the dredging construction big data, through the method of artificial intelligence, to deeply excavate the hidden relationship between the slurry concentration and the construction monitoring parameters in the construction process to generate virtual sensors, and then overcome some limitations of physical sensors. Firstly, this method removes the time lag effect of the monitoring data of physical sensors and then selects variables potentially related to the mud concentration. It makes full use of the advantages of each model to build a HEDL dredger slurry concentration prediction and measurement model embedded with multiple intelligent algorithms. The base learner of the model includes Deep Belief Network (DBN), Muti-Layer Perception (MLP), Convolutional Neural Networks (CNN), Gated Recurrent Neural Networks (GRU), Long Short-Term Memory (LSTM), Support Vector Regression (SVR). Finally, taking the Tianjin Port Channel Deepening Project as an applied research case, the accuracy and applicability of slurry concentration virtual sensors are verified.