In: the 13th International Conference on Damage Assessment of Structures. Machine learning techniques for structural health monitoring. Damage detection of a cable-stayed bridge using feature extraction and selection methods. Structural Health Monitoring, 2019, 18(4): 1207–1218īabajanian Bisheh H, Ghodrati Amiri G, Nekooei M, Darvishan E. Investigation of damage detectability in composites using frequency-based classification of acoustic emission measurements. Time-frequency-based data-driven structural diagnosis and damage detection for cable-stayed bridges. Multi-classifier information fusion in risk analysis. Pan Y, Zhang L M, Wu X G, Skibniewski M J. Research and application of underwater full coverage unmanned detection technology for large diameter and long diversion tunnel. In: the 15th Annual IEEE International Systems Conference (Syscon 2021). VITA1: An unmanned underwater vehicle prototype for operation in underwater tunnels. Jorge V A M, Gava P D D, Silva J R B F, Mancilha T M, Vieira W, Adabo G J, Nascimento C L. Nashville: American Society of Civil Engineers, 2019 In: Conference on Pipeline Engineering-Concepts in Harmony (PIPELINES). Pardee tunnel inspection and condition assessment. Water Resources and Hydropower Engineering, 2010, 41(12): 78–81 A new defect detection technology for long-distance water conveyance tunnel. Furthermore, the proposed method can weigh the accuracy and model complexity on a platform with limited computing resources. The experimental results on the siltation dataset show that the classification accuracy of the proposed method reaches 97.2%, which is far better than the accuracy of other classifiers. Finally, several evaluation metrics are used to measure the performance of the proposed method. At the same time, the fully-connected network is applied as the meta-learner, and stacking ensemble learning is combined with the outputs of the primary classifiers to obtain satisfactory classification results.

Based on the VGG16 network, a compact convolutional neural network (CNN) is designed as a primary learner, called Silt-net, which is used to identify the siltation images. This paper introduces the idea of ensemble deep learning. However, it needs to improve its intelligent recognition of image data it obtains. The remotely operated vehicle (ROV) can detect such siltation. Accurate and efficient identification of such siltation can reduce risks and enhance safety and reliability of these projects. Siltation is an essential factor threatening the safety of water conveyance tunnels. The inspection of water conveyance tunnels plays an important role in water diversion projects.

Energy for water conveyance