Structural Engineering and Mechanics

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A novel method for generation and prediction of crack propagation in gravity dams

  • Zhang, Kefan (College of Liberal Art and Science, National University of Defense Technology) ;
  • Lu, Fangyun (College of Liberal Art and Science, National University of Defense Technology) ;
  • Peng, Yong (College of Liberal Art and Science, National University of Defense Technology) ;
  • Li, Xiangyu (College of Liberal Art and Science, National University of Defense Technology)
  • Received : 2021.06.30
  • Accepted : 2021.12.13
  • Published : 2022.03.25
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Abstract

The safety problems of giant hydraulic structures such as dams caused by terrorist attacks, earthquakes, and wars often have an important impact on a country's economy and people's livelihood. For the national defense department, timely and effective assessment of damage to or impending damage to dams and other structures is an important issue related to the safety of people's lives and property. In the field of damage assessment and vulnerability analysis, it is usually necessary to give the damage assessment results within a few minutes to determine the physical damage (crack length, crater size, etc.) and functional damage (decreased power generation capacity, dam stability descent, etc.), so that other defense and security departments can take corresponding measures to control potential other hazards. Although traditional numerical calculation methods can accurately calculate the crack length and crater size under certain combat conditions, it usually takes a long time and is not suitable for rapid damage assessment. In order to solve similar problems, this article combines simulation calculation methods with machine learning technology interdisciplinary. First, the common concrete gravity dam shape was selected as the simulation calculation object, and XFEM (Extended Finite Element Method) was used to simulate and calculate 19 cracks with different initial positions. Then, an LSTM (Long-Short Term Memory) machine learning model was established. 15 crack paths were selected as the training set and others were set for test. At last, the LSTM model was trained by the training set, and the prediction results on the crack path were compared with the test set. The results show that this method can be used to predict the crack propagation path rapidly and accurately. In general, this article explores the application of machine learning related technologies in the field of mechanics. It has broad application prospects in the fields of damage assessment and vulnerability analysis.

Keywords

Acknowledgement

The project was supported by the National Natural Science Foundations of China (11902355).

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