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Whale optimization algorithm-based point cloud data processing method for sewer pipeline inspection

delete2022-09-01
delete19
PRE
AI
刘文黎 cover
刘文黎 (Wenli Liu)
Y
Yixiao Shao
陈
陈珂 (Ke Chen)
C
Chen Li *
H
Hanbin Luo
DOI：10.1016/j.autcon.2022.104423delete
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Abstract

Abstract

En 中文
Sewerages are critical infrastructure assets for wastewater carriage in urban lifelines, but their function can be seriously affected by blockages or deterioration. Existing sewer pipeline inspection methods, such as closed-circuit television and sonar detection, have been blamed for low efficiency and considerable noise in the collected data. Therefore, this paper attempts to enhance the blockage and deterioration assessment inside the sewer pipeline by proposing a whale optimization algorithm-based point cloud data (WOAPCD) processing method. The method consists of an improved WOA for data clustering and fitting and a reverse slicing method for modeling the as-is conditions. The applicability of this proposed approach is validated in an actual sewerage system, and the results show that the WOAPCD can accurately and effectively reconstruct the 3D model of the sewer, providing valuable information for quantifying siltation conditions. The proposed method has better performance than PSO and GA in terms of the fitting error and modeling speed.
Keywords:
Sewer pipeline inspection
Point cloud data
Whale optimization algorithm
3D model reconstruction

Journal

Automation in Construction cover
Automation in Construction
IF:
11.5
Papers:
6.2K
Citations:
4.2W

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No organization information available