基于GBRT模型的海洋平台结构裂纹扩展识别
Crack extension identification of ocean platform structure by gradient boosting regression tree
李阳 1苏馨 2代彤彤 3张崎 4黄一 4贾子光5
作者信息
- 1. 大连理工大学船舶工程学院,大连 116024;中国海洋石油集团有限公司,北京 100010
- 2. 大连理工大学船舶工程学院,大连 116024;大连理工大学化工海洋与生命学院,盘锦 124221
- 3. 华北电力大学(保定)机械工程系,保定 071003
- 4. 大连理工大学船舶工程学院,大连 116024
- 5. 大连理工大学化工海洋与生命学院,盘锦 124221
- 折叠
摘要
某海洋平台在多次维修中发现在生活楼与甲板连接的角隅处的裂纹有扩展现象,提出了根据裂纹周边多维应变进行裂纹长度识别的思想.搭建了含有初始裂纹的海洋平台有限元模型,以多维应变数据和对应裂纹长度分别作为机器学习模型特征输入与输出,通过梯度回归提升树(GBRT)模型对裂纹长度进行预测.测试结果表明,该模型对裂纹长度预测MSE(均方误差)值可达0.0006,R2可达0.9991,且该模型对噪声有良好的抗干扰性.
Abstract
After many times of maintenance of an offshore platform it is found that there is crack propagation at the corner which connects the living building to the deck.The idea of crack length identification based on the multi-dimensional strain around the crack is proposed in this paper.A finite element model of offshore platform with initial crack is built.Multi-scale strain data and corresponding crack length are used as feature input and output for the machine learning model respectively.The crack length is predicted by gradient boosting regression tree(GBRT)model.Test results show that the value of MSE and R2 can reach 0.0006 and 0.9991,respectively.At the same time,the model is proved to have good anti-interference to noise.
关键词
海洋平台/裂纹扩展/机器学习/GBRT算法Key words
ocean platform/crack extension/machine learning/gradient boosting regression tree引用本文复制引用
出版年
2024
NETL
NSTL
万方数据