{"title":"Performance optimization of BP-DNN prediction model of suction caisson uplift bearing capacity employing modified Co-teaching method","authors":"","doi":"10.1016/j.compgeo.2024.106756","DOIUrl":null,"url":null,"abstract":"<div><p>In recent decades suction caisson has received increasing attention in offshore deepwater geotechnical foundation solutions. Accurately predicting the uplift bearing capacity of suction caissons is of significant importance in practical engineering. The objective of this research is to propose a more optimized prediction model using backpropagation deep neural network (BP-DNN) by improving the data quality using a modified Co-teaching denoising and fusion method. The database was built which contains a large number of results by experimental and numerical research from literature. Due to the variability of numerical results, the BP-DNN prediction model based on numerical data has greater error than that based on experimental data. Therefore, the Co-teaching denoising method was modified and then adopted to filter and obtain relatively high-quality numerical data. Then the optimal fusion model was developed using the data sampling plan with 2/3 experimental data and 1/3 experimental data + all clean numerical data. The overall performance of the fusion model was proved to be satisfactory.</p></div>","PeriodicalId":55217,"journal":{"name":"Computers and Geotechnics","volume":null,"pages":null},"PeriodicalIF":5.3000,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers and Geotechnics","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0266352X24006955","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Performance optimization of BP-DNN prediction model of suction caisson uplift bearing capacity employing modified Co-teaching method
In recent decades suction caisson has received increasing attention in offshore deepwater geotechnical foundation solutions. Accurately predicting the uplift bearing capacity of suction caissons is of significant importance in practical engineering. The objective of this research is to propose a more optimized prediction model using backpropagation deep neural network (BP-DNN) by improving the data quality using a modified Co-teaching denoising and fusion method. The database was built which contains a large number of results by experimental and numerical research from literature. Due to the variability of numerical results, the BP-DNN prediction model based on numerical data has greater error than that based on experimental data. Therefore, the Co-teaching denoising method was modified and then adopted to filter and obtain relatively high-quality numerical data. Then the optimal fusion model was developed using the data sampling plan with 2/3 experimental data and 1/3 experimental data + all clean numerical data. The overall performance of the fusion model was proved to be satisfactory.
期刊介绍:
The use of computers is firmly established in geotechnical engineering and continues to grow rapidly in both engineering practice and academe. The development of advanced numerical techniques and constitutive modeling, in conjunction with rapid developments in computer hardware, enables problems to be tackled that were unthinkable even a few years ago. Computers and Geotechnics provides an up-to-date reference for engineers and researchers engaged in computer aided analysis and research in geotechnical engineering. The journal is intended for an expeditious dissemination of advanced computer applications across a broad range of geotechnical topics. Contributions on advances in numerical algorithms, computer implementation of new constitutive models and probabilistic methods are especially encouraged.