Abstract
The planning stages of dredging require comprehensive and detailed analyses. Identifying the dredging environment is one of the important points. A three‐dimensional (3D) geological modeling technology has been shown to be a robust tool for representing and analyzing the conditions of geology. From a general perspective, a 3D model is established by some spatial surfaces. Based on a dredging project, this study investigated the estimation capability of an interpolation method of triangulation combined with BP neural network, for modeling a rock layer surface. The performance of the proposed model is compared with some conventional methods in the literature. The results showed that this interpolation method is effective to be employed for surface modeling of the rock layer.
Highlights
A reasonable judgment about the location of all soil types in the dredging area is one of the most important elements for the planning of maritime dredging operations, as the material to be dredged determines the selection of dredging equipment and drives the productivity computations [1,2,3]
E interpolation method of triangulation combined with BP neural network, which is used for surface modeling in this study, contains two interpolation processes
Erefore, it can be inferred that the virtual drilling arrangement is an important part in the modeling using the interpolation method of triangulation combined with BP neural network. 4 steps of virtual drilling arrangements are made in this paper, aiming at obtaining a more accurate 3D model
Summary
A reasonable judgment about the location of all soil types in the dredging area is one of the most important elements for the planning of maritime dredging operations, as the material to be dredged determines the selection of dredging equipment and drives the productivity computations [1,2,3]. Surface modeling needs some necessary information, such as real data at sampled sites and predicted values at unsampled sites. For an unsampled point in the spatial position, the closer it is to the sampled point, the more likely the attribute value is similar, and this is the most basic assumption of spatial interpolation methods [11]. On this basis, various interpolation methods have been formed for surface estimation, such as triangulation interpolation, inverse distance weighting, nearest neighbor, and splines methods [12, 13]. A 3D model of rock stratum distribution in the construction area is established, and the amount of rock is estimated
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