Stacking Operation Planning for Replenishing Wave-Dissipating Blocks based on Deep Learning and Physics Engine

Abstract

Wave-dissipating blocks made of large concrete slabs are essential components of the armor layer of breakwaters that protect their core from direct wave attacks. However, the blocks will gradually sink or ablate as the corrosion. Therefore, regular block stacking is required to maintain the height or shape of the breakwater. This study aims to develop a system that can simulate the replenishment works utilizing a physics engine and deep learning, which can predict the additional block amounts and their stacking poses and provide pre-visualization of their stacking operations. Deep learning was used to estimate the additional block poses that better fit the currently stacked blocks. The simulation was applied to actual block stacking operation in a local port at Hokkaido. The accuracy and practicality of the simulation were verified with the final construction results.