Productivity Prediction and Analysis Method of Large Trailing Suction Hopper Dredger Based on Construction Big Data

Cheng, Tao; Lu, Qiaorong; Kang, Hengrui; Fan, Ziyuan; Bai, Shuo

doi:10.3390/buildings12101505

Open AccessArticle

Productivity Prediction and Analysis Method of Large Trailing Suction Hopper Dredger Based on Construction Big Data

by

Tao Cheng

¹,

Qiaorong Lu

²,

Hengrui Kang

¹,

Ziyuan Fan

¹ and

Shuo Bai

^1,3,*

¹

School of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan 430070, China

²

State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300350, China

³

Sanya Science and Education Innovation Park, Wuhan University of Technology, Sanya 572019, China

^*

Author to whom correspondence should be addressed.

Buildings 2022, 12(10), 1505; https://doi.org/10.3390/buildings12101505

Submission received: 8 August 2022 / Revised: 29 August 2022 / Accepted: 19 September 2022 / Published: 22 September 2022

(This article belongs to the Section Construction Management, and Computers & Digitization)

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Abstract

Trailing suction hopper dredgers (TSHD) are the most widely used type of dredgers in dredging engineering construction. Accurate and efficient productivity prediction of dredgers is of great significance for controlling dredging costs and optimizing dredging operations. Based on machine learning and artificial intelligence, this paper proposes a feature selection method based on the Lasso-Maximum Information Coefficient (MIC), uses methods such as Savitzky-Golay (S-G) filtering for data preprocessing, and then selects different models for prediction. To avoid the limitations of a single model, we assign weights according to the predicted goodness of fit of each model and obtain a weight combination model (WCM) with better generalization performance. By comparing multiple error metrics, we find that the optimization effect is obvious. The method effectively predicts the construction productivity of the TSHD and can provide meaningful guidance for the construction control of the TSHD, which has important engineering significance.

Keywords: trailing suction hopper dredger; feature selection; S-G filtering; weight combination model

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MDPI and ACS Style

Cheng, T.; Lu, Q.; Kang, H.; Fan, Z.; Bai, S. Productivity Prediction and Analysis Method of Large Trailing Suction Hopper Dredger Based on Construction Big Data. Buildings 2022, 12, 1505. https://doi.org/10.3390/buildings12101505

AMA Style

Cheng T, Lu Q, Kang H, Fan Z, Bai S. Productivity Prediction and Analysis Method of Large Trailing Suction Hopper Dredger Based on Construction Big Data. Buildings. 2022; 12(10):1505. https://doi.org/10.3390/buildings12101505

Chicago/Turabian Style

Cheng, Tao, Qiaorong Lu, Hengrui Kang, Ziyuan Fan, and Shuo Bai. 2022. "Productivity Prediction and Analysis Method of Large Trailing Suction Hopper Dredger Based on Construction Big Data" Buildings 12, no. 10: 1505. https://doi.org/10.3390/buildings12101505

APA Style

Cheng, T., Lu, Q., Kang, H., Fan, Z., & Bai, S. (2022). Productivity Prediction and Analysis Method of Large Trailing Suction Hopper Dredger Based on Construction Big Data. Buildings, 12(10), 1505. https://doi.org/10.3390/buildings12101505

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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Productivity Prediction and Analysis Method of Large Trailing Suction Hopper Dredger Based on Construction Big Data

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