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1- Kharazmi University
Abstract: (381 Views)
| Selection of quarries and quality stones by design standards has always been one of the most important challenges in the location of breakwaters and the commencement of executive activities of rock mass breakwater construction projects. This research proposes using machine learning approaches to predict the result of the acceptance or rejection of quarry stones with the minimum possible tests based on the algorithm's output. The method used in this thesis is to provide a framework including complete and accurate data preprocessing and the use of machine learning algorithms such as decision tree, random forest, nearest neighbor, and the use of available data from the results of rock tests in the last ten years obtained in the construction of rock mass breakwaters in the coastal strip of the Sea of Oman. In this method, the available data is classified into two parts: main data and test data, and the algorithm is implemented on the main data and then the output of the algorithm is evaluated using the test data. The algorithm output is confirmed with an accuracy of 96%. The results obtained from this framework emphasize the importance of using existing data in the marine construction industry and the effectiveness of using machine learning algorithms in analyzing and interpreting existing data. The output of the results of this thesis reduces the time of experiments, reduces project costs, and reduces the duration of the project. The insight obtained from this research can help companies active in the field of marine construction and also specifically the Ports and Maritime Organization as the custodian of the construction and maintenance of marine structures in the country to locate breakwaters, optimize resource allocation, reduce the implementation time and operation of projects. |
Keywords: Rock mass breakwaters, decision tree algorithm, random forest algorithm, nearest neighbor algorithm, machine learning
Type of Study: Research Paper |
Subject:
Offshore Structure
Received: 2025/01/18 | Accepted: 2025/03/28
Received: 2025/01/18 | Accepted: 2025/03/28
فایل صوتی چکیده مقاله [MP3 2753 KB] (6 Download)
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International Journal of Maritime Technology is licensed under a
Creative Commons Attribution-NonCommercial 4.0 International License.