Crack identification in thick beams has improved increasing considerations from the scientific and building areas since the unpredicted structural failure may cause disastrous, catastrophic and life trouble. The goal of the present examination is to predict the unknown crack location and its depth in thick beams from the information of frequency data obtained from experimental examination. The effectiveness of the proposed strategy is approved by numerical simulations in view of experimental data for a cantilever beam, free-free beam and simply supported beam. With the improvements in delicate figuring, optimization strategies are acknowledged to be an extremely proficient instrument to offer an answer for crack identification issue. In the simulation modeling, the parameters, for example, shift; modal assurance criterion (MAC) and stiffness, are predicted by utilizing optimized deep learning neural network (ODNN) approach in view of crack location and size. To improve the weight in DLNN, the opposition-based ant lion (OAL) is used by minimizing the mean square error (MSE) rate. The result shows that the proposed model achieves the optimal performance compared with existing techniques.

中文翻译：

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使用基于对抗的蚁狮方法优化深度学习以识别粗梁的裂纹

由于不可预测的结构失效可能导致灾难性、灾难性和生命问题，因此粗梁中的裂缝识别已经提高了科学和建筑领域的考虑。本次检查的目的是根据从实验检查中获得的频率数据信息来预测厚梁中未知的裂纹位置及其深度。鉴于悬臂梁、自由梁和简支梁的实验数据，数值模拟证实了所提出策略的有效性。随着精细计算的改进，优化策略被公认为是一种非常熟练的工具，可以为裂纹识别问题提供答案。在仿真建模中，参数，例如，偏移；模态置信度 (MAC) 和刚度，鉴于裂纹位置和大小，利用优化的深度学习神经网络 (ODNN) 方法进行预测。为了提高 DLNN 中的权重，通过最小化均方误差 (MSE) 率来使用基于对立的蚁狮 (OAL)。结果表明，与现有技术相比，所提出的模型实现了最佳性能。