Multi-objective decision-making control of cutter suction dredger based on multi-scale graph representation with Bayesian optimization

Abstract

The operational parameters of cutter suction dredger (CSD) are frequently set based on empirical experience, leading to low productivity and high energy consumption. To address this issue, the present study proposes a Bayesian multi-objective optimization (MOO) framework integrating a multi-scale adaptive graph convolutional network (MAGCN) model and a multi-objective tree-structured Parzen estimator (MOTPE) algorithm. This framework aims to predict and optimize construction productivity and energy consumption performance while providing well-informed support for intelligent decision-making control. First, a hybrid feature selection method combining maximum information coefficient and Pearson correlation coefficient is applied to identify 6 key control parameters from 256 operational parameters. Second, an MAGCN model with multi-scale graph representation is proposed to establish the nonlinear mapping among geological conditions, operational parameters, construction productivity, and energy consumption. Third, the MOO framework with its mathematical formulation is constructed, and the Pareto optimal front for productivity and energy consumption is derived using the MOTPE-based Bayesian optimization algorithm. The optimal trade-off solution and corresponding operational parameters are determined by a weighted sum method. The proposed MOO framework is validated using operational data from the Tian Jing Hao CSD in the Pinglu Canal dredging project in China. The results show that the MAGCN model achieves high prediction accuracy for real-time productivity and energy consumption, with R² values of 0.942 and 0.979, respectively. The MOTPE-driven optimization reduces standard deviations of operational parameters by 21.24 % - 97.90 %, enhancing operational stability. The framework improves productivity by 3.79 % and reduces energy consumption by 3.53 %. This work provides real-time decision-making support for optimizing CSD operations.