Fast evaluation of crack growth path using time series forecasting

This paper aims a forecasting the crack propagation in risk assessment of engineering structures based on time series algorithms named long short-term memory and mull-layer neural network. The core idea is how to predict precisely the accuracy of solution of crack growth in engineering fracture structures without requiring re-modeling and re-computational attempts. The underlying method only requires a small amount of data from numerical analysis or experimental processes. Based on optimal parameters learned from information of a given dataset, the machine learning methods are used to quickly forecast the crack growth without any analysis tools. In addition, the advanced techniques such as dropout and mini-batch are also utilized to enhance computational performance. The effectiveness and accuracy of the current method are verified by comparing gained results with those from numerical approaches or experimental data.