1. Introduction
Due to the development of dredging processes worldwide, as well as the continuous progression of science and technology, and in order to meet the needs of different working conditions and the requirements of underwater dredging construction, there has been an increase in the different types of modern dredgers that are available. Dredging is no longer limited to general soil and sand, but can also include the digging of underwater rocks, minerals, and so on [
1]. With the large scale of dredging engineering nowadays, the depth of underwater construction areas, and the high efficiency of construction processes, more stringent requirements are being put forward for the future of dredging construction.
Dredging works mostly take place on the seabed or river beds, and the soil distribution in the area is often complex, with characteristics of uncertainty, anisotropy, dynamic change, etc. Without clear information about the soil, huge difficulties in the construction of dredging works can arise. Therefore, in order to improve the construction efficiency of a dredger, it is necessary to understand the soil distribution of the dredged area before construction.
At present, it is often the case that only the soil quality information of the dredged area is considered in the construction process, while the amount of the various types of soil in the dredged area is ignored. Since the soil recovered from dredging may have an impact on the environment [
2], and in order to facilitate people’s secondary use of the recovered soil, it is particularly important to calculate the amounts of the various types of soil.
In order to improve the construction efficiency of a dredger and to determine the amount of earthwork in all the different types of soil, this paper proposes a soil modeling and prediction method based on Python. This method is based on the extracted borehole location data and the soil layer data in the borehole, in combination with the geological point cloud generated using terrain files, and has the ability to identify each point of soil information. The distribution of soil in the construction area of a dredger can be predicted, and the amount of soil in the construction area can be calculated statistically.
Ghaderi et al. [
3] proposed a new and optimized multi-output generalized feedforward neural network (GFNN) structure to generate a digital map of the soil types of southwest Sweden using 58 piezoelectric cone penetration test points (CPTus). Moreover, Cao et al. [
4] established a Bayesian framework for probabilistic soil stratification to identify soil, while Wise et al. [
5] proposed the use of a combination of 3D points and acoustics to predict soil erosion, aimed at three-dimensional point clouds. For noisy points in point cloud models, Zeng et al. [
6] used a point cloud adaptive weighted guided filtering algorithm to smooth out noise based on its characteristics, which can effectively preserve the key points of the point cloud. In order to accurately predict the local details of point clouds, Hao et al. [
7] proposed a new adaptive point cloud growth grid, MapGNET, to generate higher-quality point clouds.
Urbancic et al. [
8] analyzed the influence of different surface interpolation methods and mesh element sizes on the calculation of earthwork volume. By comparing the volume of mesh and the triangulated irregular network (TIN) surface, they determined a good interpolation method and a suitable mesh element size and identified that the volume difference should not exceed 5%. Lee et al. [
9] used unmanned aerial vehicles (UAVs) and RGB cameras to perform earthwork calculations. Meanwhile, Slattery et al. [
10] used ground-based laser scanning (TLS) technology to develop an algorithm based on the finite element method to create a surface through the lowest scan point and to convert that surface into a TIN file; the amount of earthwork was calculated by comparing the TIN of the original terrain with the TIN of a completed project. Lee et al. [
11] used the point cloud data obtained using unmanned aerial vehicle (UAV) photogrammetry as the basis for creating a 3D model and calculated the volume of earthwork using the 3D model based on the cloud data of a construction site. Compared with the traditional measurement method, the amount of earth calculated using UAV photogrammetry was 2.36–2.51% larger than that calculated using TSM.
At present, research mainly focuses on the optimization calculation of earthwork quantity. However, relevant research related to the simultaneous calculation of soil quality and earthwork quantity is lacking, and there is a seriously insufficient amount of research in the dredging industry. At present, the prediction of earthwork quantity in the industry mainly focuses on its measurement and calculation using UAVs. Researchers have also discussed the calculation of the earthwork quantity of soil of various qualities by using the projection axis method, the similar section method [
12], and measurement software. However, for the projection axis and similar section methods, the soil layer order will be reversed, and the soil layer will be missing. The method of calculating soil mass using software usually requires a regular distribution of geological drilling points and an equal spacing of drilling holes.
The traditional soil type prediction methods mainly rely on the theoretical guidance of soil geography, through field investigations, as well as the observation and description of the morphology of soil profiles and their surrounding geographical environment. This includes analyzing, classifying, and evaluating both the physical and chemical properties of soil and then comparing and analyzing its occurrence, evolution, classification, distribution, and function. This method usually involves a large amount of fieldwork, with a long cycle, high cost, and complex process, especially in areas with complex terrain. With the development of technology, these traditional methods are gradually being supplemented or replaced by the digital soil mapping method. Also known as digital soil mapping (DSM) or predictive soil mapping, it is a modern method that utilizes geographic information systems (GISs), remote sensing technology, and statistical methods to predict and map soil types and their spatial distribution. This method can more efficiently process and analyze soil data, improving the accuracy and efficiency of soil mapping. Although DSM has significant advantages in obtaining and expressing soil spatial distribution information, it still has some shortcomings. The acquisition of high-quality soil data requires a lot of fieldwork and is time-consuming, especially in areas with complex geographical environments. Moreover, the generalization ability of this model is poor, and digital soil mapping models at different regions and scales need to be recalibrated; further research and exploration are needed to effectively display soil maps and apply them to practical soil management and environmental monitoring.
Therefore, this paper proposes a soil quality modeling and prediction method based on a three-dimensional point cloud that is determined using Python tools. This method can reconstruct a dredging construction area with a three-dimensional point cloud model. Compared with traditional modeling methods, the three-dimensional point cloud model proposed in this paper can effectively solve problems such as soil layer order reversal and soil layer loss. Additionally, because the point cloud model is composed of a series of discrete points, it can identify the soil quantity in the specified area, thus reducing the time spent on the calculation. Moreover, by using the visualization function in Python, the distribution of soil layers inside the model can be observed.
The soil quality identification and prediction method based on three-dimensional point clouds involves inserting holes into the point cloud and determining the soil quality status at each point through probabilistic methods that are based on borehole formation data. This method can accurately predict the soil type at each coordinate point of the three-dimensional point cloud model and can calculate the amount of various different types of soil. At the same time, it can effectively solve problems such as soil layer loss and the requirements for the location of the borehole. Not only can this method rapidly predict the whole dredging construction area, but it can also accurately predict the soil quantity.
The research objective of this paper is to put forward a new, innovative method of soil quantity identification. This method has a strong generalization ability and user-friendliness, and it can not only be applied to dredging engineering, but also identify and analyze statistics of land soil and its quantity.