Zhejiang-Belarus Joint Laboratory of Intelligent Equipment and System for Water Conservancy and Hydropower Safety Monitoring, College of Electrical Engineering, Zhejiang University of Water Resources and Electric Power, Hangzhou 310018, China.
College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China.
Sensors (Basel). 2022 May 19;22(10):3863. doi: 10.3390/s22103863.
This paper proposes a new intelligent recognition method for concrete ultrasonic detection based on wavelet packet transform and a convolutional neural network (CNN). To validate the proposed data-based method, a case study is presented where the K-fold cross-validation was adopted to produce the performance analysis and classification experiments. Moreover, three evaluation indicators, precision, recall, and F-score, are calculated for analyzing the classification performance of the trained models. As a result, the obtained four-classifying CNN reaches more than 99% detection accuracy while the lowest recognition accuracy is not less than 92.5% on the testing dataset for the six-classifying CNN model. Compared with the existing stochastic configuration network (SCN) models, the presented method achieves the design objective with better recognition performance. The calculation results of the six-classifying and five-classifying models and related research clearly indicate the remaining challenging tasks for intelligent recognition algorithms in extracting features and classifying mass data from various concrete defects precisely and efficiently.
本文提出了一种基于小波包变换和卷积神经网络（CNN）的混凝土超声检测智能识别新方法。为了验证所提出的基于数据的方法，进行了案例研究，采用 K 折交叉验证进行性能分析和分类实验。此外，还计算了三个评估指标，即精度、召回率和 F 分数，以分析训练模型的分类性能。结果表明，所获得的四分类 CNN 在测试数据集上的检测准确率超过 99%，而六分类 CNN 模型的最低识别准确率不低于 92.5%。与现有的随机配置网络（SCN）模型相比，该方法具有更好的识别性能，达到了设计目标。六分类和五分类模型的计算结果以及相关研究清楚地表明，智能识别算法在从各种混凝土缺陷中精确、高效地提取特征和分类海量数据方面仍存在挑战性任务。
