ANN-Based Seabed Soil Type Classification Economical Impact for Subsea Trenching Process

Applications that involve image analysis frequently make use of an artificial neural network (ANN). It has been established that it is effective in classifying the types of soil that are found on the seabed, which would facilitate the process of trenching for underwater flow lines (pipelines and cables) as subsea trenchers are used to protecting the flow lines, but it still needs to be investigated from an economic point of view. This research is the economic study of utilizing ANN to classify the different types of seabed soil for use in the underwater trenching process. This economic analysis was carried out by computing the difference between the energy consumption levels of two jet trenchers before and after the implementation of this technology. The findings of this study indicated that the use of ANN image analysis successfully reduced trenchers’ energy consumption by 14–22%, which unquestionably cause a reduction in the overall cost of the trenching process.