Gradient tree boosting machine learning on predicting the failure modes of the RC panels under impact loads
Gradient tree boosting machine learning on predicting the failure modes of the RC panels under impact loads
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DOI：
10.1007/s00366-019-00842-w
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发表时间：
2019-08
影响因子：
8.7
通讯作者：
Duc‐Kien Thai;T. M. Tu;T. Bui;T.-T. Bui-T.
中科院分区：
文献类型：
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作者：
Duc‐Kien Thai;T. M. Tu;T. Bui;T.-T. Bui-T.
提出了一种基于梯度推进机器学习（Gradient Boosting Machine Learning，GBML）的钢筋混凝土（RC）板冲击损伤预测方法。收集了大量的RC板冲击试验数据，用于训练和测试所提出的模型。由于板在冲击载荷下的结构行为的复杂性和高成本，缺乏试验数据，准确预测失效模式是一个挑战。为了克服这一挑战，本研究提出了一种机器学习模型，该模型使用一种强大的技术来解决问题，并使用最少的资源。虽然由于数据的缺乏和试验输出不平衡的特点，预测结果的准确性不如预期，但本文提供了一种新的方法，可以替代传统的方法来预测RC板在冲击载荷下的破坏模式。该方法也有望广泛应用于预测复杂和极端载荷下构件和结构的结构行为。
This paper proposed a new approach in predicting the local damage of reinforced concrete (RC) panels under impact loading using gradient boosting machine learning (GBML), one of the most powerful techniques in machine learning. A number of experimental data on the impact test of RC panels were collected for training and testing of the proposed model. With the lack of test data due to the high cost and complexity of the structural behavior of the panel under impact loading, it was a challenge to predict the failure mode accurately. To overcome this challenge, this study proposed a machine-learning model that uses a robust technique to solve the problem with a minimal amount of resources. Although the accuracy of the prediction result was not as high as expected due to the lack of data and the unbalance experimental output features, this paper provided a new approach that may alternatively replace the conventional method in predicting the failure mode of RC panel under impact loading. This approach is also expected to be widely used for predicting the structural behavior of component and structures under complex and extreme loads.