Data mining approach to construction productivity prediction for cutter suction dredgers

Bai, Shuo; Li, Mingchao; Kong, Rui; Han, Shuai; Li, Heng; Qin, Liang

doi:10.1016/j.autcon.2019.102833

Cited by 64 publications

(30 citation statements)

References 18 publications

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“…Figure 4 shows that Q 1 , Q 2 , Q 3 , Q 4 , and Q 10 are most relative to SC, and in fact they are independent to each other [ 18 ]. Each of them can be used to evaluate the working state of the γ -ray sensor in dredging engineering.…”

Section: The Proposed Methodsmentioning

confidence: 99%

Solid Concentration Estimation by Kalman Filter

Tan

Yue

2020

Sensors

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One of the major tasks in process industry is solid concentration (SC) estimation in solid–liquid two-phase flow in any pipeline. The γ-ray sensor provides the most used and direct measurement to SC, but it may be inaccurate due to very local measurements and inaccurate density baseline. Alternatively, under various conditions there are a tremendous amount of indirect measurements from other sensors that can be used to adjust the accuracy of SC estimation. Consequently, there is complementarity between them, and integrating direct and indirect measurements is helpful to improve the accuracy of SC estimation. In this paper, after recovering the interrelation of these measurements, we proposed a new SC estimation method according to Kalman filter fusion. Focusing on dredging engineering fields, SCs of representative flow pattern were tested. The results show that our proposed methods outperform the fused two types of measurements in real solid–liquid two-phase flow conditions. Additionally, the proposed method has potential to be applied to other fields as well as dredging engineering.

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Section: The Proposed Methodsmentioning

confidence: 99%

Solid Concentration Estimation by Kalman Filter

Tan

Yue

2020

Sensors

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“…During the construction, mud formation is influenced by many factors such as soil type, mechanical parameters and rotation speed of the cutter, traverse speed of the dredger, dredge pump parameters, and so on. To monitor and control the dredging process, up to 255 specific real-time sensors were arranged to collect the operational data [27]. Figure 4 shows some of the related monitoring parameters and relationship in automatic control system.…”

Section: Principal Components Analysis Based On Mechanism and Knowledgementioning

confidence: 99%

A Novel Principal Component Analysis Integrating Long Short-Term Memory Network and Its Application in Productivity Prediction of Cutter Suction Dredgers

et al. 2021

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Dredging is a basic construction for waterway improvement, harbor basin maintenance, land reclamation, environmental protection dredging, and deep-sea mining. The dredging process of cutter suction dredgers is so complex that the operational data show strong characteristics of dynamic, nonlinearity, and time delay, which make it difficult to predict the productivity accurately via basic principles models. In this paper, we propose a novel integrating PCA-LSTM model to improve the productivity prediction of cutter suction dredger. Firstly, multiple variables are reduced in dimension and selected by PCA method based on the working mechanism of cutter suction dredger. Then the productivity is predicted via mud concentration in long short-term memory network with relevant operational time-series data. Finally, the proposed method is successfully applied to an actual case study in China. Also, it performs well in the cross-validation and comparative study for several important characteristics: (i) it involves the operational parameters based on the mechanism analysis; and (ii) it is a deep-learning-based approach that can deal with operation series data with a special memory mechanism. This study provides a heuristic idea for integrating the data-driven method and supervision of human knowledge for application in practical engineering.

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“…MIC is also used in the fault diagnosis [32], [33] and prediction [34] of complex systems and equipment. Besides, MIC plays a role in engineering [35], astronomy [36], chemistry [37], sociology [38], optics [39], and agronomy [40]. Overall, MIC is an effective association detection method for feature selection [41], classification [18], and prediction [42].…”

Section: Introductionmentioning

confidence: 99%

Detecting Associations Based on the Multi-Variable Maximum Information Coefficient

Guo

et al. 2021

IEEE Access

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The maximum information coefficient (MIC) is a novel and widely-using measure of association detection in large datasets. The most outstanding feature of MIC is that it has both generality and equability. However, MIC can only deal with two variables and cannot precisely estimate coupling associations of multiple variables. In this paper, we propose an extension of MIC to deal with multi-variable datasets, called the multi-variable maximum information coefficient (MMIC). Some inherited and novel properties of MMIC are proved, including generality, equability, monotonicity, and subadditivity. We design an algorithm based on greedy stepwise strategy and upper confidence bound (UCB) for an approximate calculation of MMIC. The tests of MMIC on generated datasets and examples on real datasets are carried out to detect known and novel associations.

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Data mining approach to construction productivity prediction for cutter suction dredgers

Cited by 64 publications

References 18 publications

Solid Concentration Estimation by Kalman Filter

Solid Concentration Estimation by Kalman Filter

A Novel Principal Component Analysis Integrating Long Short-Term Memory Network and Its Application in Productivity Prediction of Cutter Suction Dredgers

Detecting Associations Based on the Multi-Variable Maximum Information Coefficient

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