Experimental data-centric prediction of penetration depth and holding capacity of dynamically installed anchors using machine learning

Fu，Yong1,3; Ding，Kailin1; Han，Congcong2

2024-06-01

10.1016/j.compgeo.2024.106249

Computers and Geotechnics   影响因子和分区

0266-352X

1873-7633

The visualization of experimental research phenomena and the reliability of experimental data enable their utilization in validating theoretical and numerical methods. However, the existing literature on dynamically installed anchors (DIAs) lacks cohesion, resulting in a reliance on onshore driven pile design theory, as well as empirical or semi-empirical calculation methods for DIA design. Therefore, this study aims to establish an experimental database and propose an data-centric design method for DIAs. The established experimental database comprises 503 sets of experimental data from various sources, including 254 sets of field tests, 210 sets of centrifuge tests and 39 sets of 1g-model tests of seven representative DIAs (i.e., torpedo anchor, DPA, OMNI-Max anchor, DEPLA, L-GIPLA, DPAIII, and fish anchor). The geometric characteristics as well as in-soil installation and loading performance of these DIAs are systematically summarized and explored. Based on this comprehensive experimental database, a novel machine learning (ML) algorithm-based approach is proposed for predicting the penetration depth and holding capacity of DIAs. This research provides a new perspective centered around existing experimental data for the design methodology of DIAs.

Dynamically installed anchor (DIA) Experimental database Holding capacity Machine learning (ML) Penetration depth

SCI ; EI

英语

第一 ; 通讯

COMPUTER SCIENCE

2-s2.0-85189105335

Scopus

1.Department of Ocean Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China
2.State Key Laboratory of Coastal and Offshore Engineering,Dalian University of Technology,Dalian,116024,China
3.Zhejiang Engineering Research Center of Intelligent Urban Infrastructure,Hangzhou City University,Hangzhou,310015,China

Fu，Yong,Ding，Kailin,Han，Congcong. Experimental data-centric prediction of penetration depth and holding capacity of dynamically installed anchors using machine learning[J]. Computers and Geotechnics,2024,170.

Fu，Yong,Ding，Kailin,&Han，Congcong.(2024).Experimental data-centric prediction of penetration depth and holding capacity of dynamically installed anchors using machine learning.Computers and Geotechnics,170.

Fu，Yong,et al."Experimental data-centric prediction of penetration depth and holding capacity of dynamically installed anchors using machine learning".Computers and Geotechnics 170(2024).