Department of Civil and Environmental Engineering, University of Brasilia, Brasilia 70910-900, Brazil.
Sensors (Basel). 2022 Mar 24;22(7):2482. doi: 10.3390/s22072482.
Internal erosion is the most important failure mechanism of earth and rockfill dams. Since this type of erosion develops internally and silently, methodologies of data acquisition and processing for dam monitoring are crucial to guarantee a safe operation during the lifespan of these structures. In this context, artificial intelligence techniques show up as tools that can simplify the analysis and verification process not of the internal erosion itself, but of the effects that this pathology causes in the response of the dam to external stimuli. Therefore, within the scope of this paper, a methodological framework for monitoring internal erosion in the body of earth and rockfill dams will be proposed. For that, artificial intelligence methods, especially deep neural autoencoders, will be used to treat the acoustic data collected by geophones installed on a dam. The sensor data is processed to identify patterns and anomalies as well as to classify the dam's structural health status. In short, the acoustic dataset is preprocessed to reduce its dimensionality. In this process, for each second of acquired data, three parameters are calculated (Hjorth parameters). For each parameter, the data from all the available sensors are used to calibrate an autoencoder. Then, the reconstruction error of each autoencoder is used to monitor how far from the original (normal) state the acoustic signature of the dam is. The time series of reconstruction errors are combined with a cumulative sum (CUSUM) algorithm, which indicates changes in the sequential data collected. Additionally, the outputs of the CUSUM algorithms are treated by a fuzzy logic framework to predict the status of the structure. A scale model is built and monitored to check the effectiveness of the methodology hereby developed, showing that the existence of anomalies is promptly detected by the algorithm. The framework introduced in the present paper aims to detect internal erosion inside dams by combining different techniques in a novel context and methodological workflow. Therefore, this paper seeks to close gaps in prior studies, which mostly treated just parts of the data acquisition-processing workflow.
内部侵蚀是土石坝最重要的破坏机制。由于这种侵蚀是在内部发生的且无声无息，因此大坝监测的数据采集和处理方法对于保证这些结构在使用寿命期间的安全运行至关重要。在这种情况下，人工智能技术作为工具出现，可以简化分析和验证过程，而不是内部侵蚀本身，而是这种病理学对大坝对外界刺激的反应造成的影响。因此，在本文的范围内，将提出一种土石坝体内部侵蚀监测的方法框架。为此，将使用人工智能方法，特别是深度神经网络自动编码器，来处理安装在大坝上的地震检波器收集的声学数据。处理传感器数据以识别模式和异常，并对大坝的结构健康状况进行分类。简而言之，对声数据集进行预处理以降低其维度。在此过程中，对于采集到的每一秒数据，计算三个参数（Hjorth 参数）。对于每个参数，使用所有可用传感器的数据来校准自动编码器。然后，使用每个自动编码器的重建误差来监测大坝的声学特征与原始（正常）状态的偏离程度。重建误差的时间序列与累积和（CUSUM）算法相结合，该算法指示所收集的顺序数据的变化。此外，CUSUM 算法的输出由模糊逻辑框架进行处理，以预测结构的状态。建立和监测比例模型以检查所开发方法的有效性，表明算法可以及时检测到异常的存在。本文介绍的框架旨在通过在新的背景和方法工作流程中结合不同的技术来检测大坝内部的侵蚀。因此，本文旨在弥补先前研究的空白，这些研究大多只处理数据采集处理工作流程的部分内容。
