Technical Papers
Dec 1, 2021

Global Time Optimization Method for Dredging Construction Cycles of Trailing Suction Hopper Dredger Based on Grey System Model

Publication: Journal of Construction Engineering and Management
Volume 148, Issue 2

Abstract

The efficient operation of trailing suction hopper dredgers can reduce equipment operation time and energy consumption, accelerate overall construction, reduce workload and material resource usage, and lower construction costs. Based on an in-depth analysis of the dredging construction optimization problem, the overflow loss prediction and a loading cycle optimization method based on the loaded earth curve are proposed in this study. The loading cycle optimization method is divided into three stages. In the first stage, statistical learning and machine learning methods are used to predict and analyze the loaded earth data by describing the time scale of the loaded earth data, and the grey system prediction model was selected for the subsequent developments. In the second stage, by analyzing the loaded earth curve prediction model, the concept of overflow loss rate and its predictive calculation method is proposed. In the third stage, combined with the loaded earth curve prediction model, a global time geometric coordinate optimization model of the loading cycle is proposed to avoid the power and time waste caused by excessive overflow loss. A channel dredging project in Tianjin Port was taken as an application example to verify the applicability of the proposed optimization method and optimize the construction work efficiency.

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Data Availability Statement

Data generated or analyzed during the study are available from the corresponding author upon reasonable request.

Acknowledgments

This research was jointly funded by the Tianjin Natural Science Foundation for Distinguished Young Scientists of China (Grant No. 17JCJQJC44000) and the National Natural Science Foundation of China (Grant No. 52179139).

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Go to Journal of Construction Engineering and Management
Journal of Construction Engineering and Management
Volume 148Issue 2February 2022

History

Received: Jul 12, 2021
Accepted: Oct 26, 2021
Published online: Dec 1, 2021
Published in print: Feb 1, 2022
Discussion open until: May 1, 2022

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Associate Professor, School of Civil Engineering and Architecture, Wuhan Univ. of Technology, Wuhan 430070, China; Sanya Science and Education Innovation Park, Wuhan Univ. of Technology, Sanya 572019, China. Email: [email protected]
Professor, State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin Univ., Tianjin 300350, China (corresponding author). ORCID: https://orcid.org/0000-0002-3010-0892. Email: [email protected]
Qiaorong Lu [email protected]
Master’s Student, State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin Univ., Tianjin 300350, China. Email: [email protected]
Huijing Tian [email protected]
Senior Engineer, Tianjin Dredging Company Limited, China Communications Construction Company Limited, Tianjin 300042, China. Email: [email protected]
Senior Engineer, Tianjin Dredging Company Limited, China Communications Construction Company Limited, Tianjin 300042, China. Email: [email protected]

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Cited by

  • Big Data-Based Performance Analysis of Tunnel Boring Machine Tunneling Using Deep Learning, Buildings, 10.3390/buildings12101567, 12, 10, (1567), (2022).
  • Productivity Prediction and Analysis Method of Large Trailing Suction Hopper Dredger Based on Construction Big Data, Buildings, 10.3390/buildings12101505, 12, 10, (1505), (2022).

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