Crack Detection in Earth Dam and Levee Passive Seismic Data Using Support Vector Machines

Fisher, Wendy; Camp, Tracy; Krzhizhanovskaya, Valeria V.

doi:10.1016/j.procs.2016.05.339

Cited by 44 publications

(20 citation statements)

References 8 publications

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“…The former type of approaches require a set of labeled training data in which each data point/instance must be labeled as normal or anomalous. Examples of using this approach include [4] and [5]. However, it is expensive and difficult to obtain sufficient and accurately labeled training data since labeling is often done by human experts manually.…”

Section: Related Workmentioning

confidence: 99%

RePAD: Real-Time Proactive Anomaly Detection for Time Series

Lee

Lin

Gran

2020

Advanced Information Networking and Applications

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During the past decade, many anomaly detection approaches have been introduced in different fields such as network monitoring, fraud detection, and intrusion detection. However, they require understanding of data pattern and often need a long off-line period to build a model or network for the target data. Providing real-time and proactive anomaly detection for streaming time series without human intervention and domain knowledge is highly valuable since it greatly reduces human effort and enables appropriate countermeasures to be undertaken before a disastrous damage, failure, or other harmful event occurs. However, this issue has not been well studied yet. To address it, this paper proposes RePAD, which is a Real-time Proactive Anomaly Detection algorithm for streaming time series based on Long Short-Term Memory (LSTM). RePAD utilizes short-term historical data points to predict and determine whether or not the upcoming data point is a sign that an anomaly is likely to happen in the near future. By dynamically adjusting the detection threshold over time, RePAD is able to tolerate minor pattern change in time series and detect anomalies either proactively or on time. Experiments based on two time series datasets collected from the Numenta Anomaly Benchmark demonstrate that RePAD is able to proactively detect anomalies and provide early warnings in real time without human intervention and domain knowledge.

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Section: Related Workmentioning

confidence: 99%

RePAD: Real-Time Proactive Anomaly Detection for Time Series

Lee

Lin

Gran

2020

Advanced Information Networking and Applications

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“…It has a great advantage in solving nonlinear problems with a small sample. SVM has been widely used in hydrological prediction [33,34] and anomaly detection [23,35]. Figure 12 shows the structure of support vector machine.…”

Section: Support Vector Machinementioning

confidence: 99%

A Machine Learning Approach to Evaluating the Damage Level of Tooth-Shape Spur Dikes

Wei

Wang

et al. 2018

Water

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Little research has been done on the application of machine learning approaches to evaluating the damage level of river training structures on the Yangtze River. In this paper, two machine learning approaches to evaluating the damage level of spur dikes with tooth-shaped structures are proposed: a supervised support vector machine (SVM) model and an unsupervised model combining a Kohonen neural network with an SVM model (KNN-SVM). It was found that the supervised SVM model predicted the damage level of the validation samples with high accuracy, and the unsupervised data-mining KNN-SVM model agreed well with the empirical evaluation result. It is shown that both machine learning approaches could become effective tools to evaluate the damage level of spur dikes and other river training structures.

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“…When detecting defects under uneven illumination conditions, if the same threshold detection is used for strong and weak illumination, it will inevitably lead to missed detection and false detection. Moreover, this phenomenon also occurs when cracks are detected in images captured in the areas of underwater dams [ 1 , 2 , 3 ], highway pavements [ 4 , 5 , 6 ], and bridges [ 7 , 8 , 9 ]. When the width and number of cracks exceed the allowable range, it will lead to structural decay and affect compressive strength variation [ 10 ], structural response, and seismic fragility [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Partition Gaussian Crack Detection Algorithm Based on Projection Curve Distribution

Xue

Yuan

2020

Sensors

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When detecting the cracks in the tunnel lining image, due to uneven illumination, there are generally differences in brightness and contrast between the cracked pixels and the surrounding background pixels as well as differences in the widths of the cracked pixels, which bring difficulty in detecting and extracting cracks. Therefore, this paper proposes a dynamic partitioned Gaussian crack detection algorithm based on the projection curve distribution. First, according to the distribution of the image projection curve, the background pixels are dynamically partitioned. Second, a new dynamic partitioned Gaussian (DPG) model was established, and the set rules of partition boundary conditions, partition number, and partition corresponding threshold were defined. Then, the threshold and multi-scale Gaussian factors corresponding to different crack widths were substituted into the Gaussian model to detect cracks. Finally, crack morphology and the breakpoint connection algorithm were combined to complete the crack extraction. The algorithm was tested on the lining gallery captured on the site of the Tang-Ling-Shan Tunnel in Liaoning Province, China. The optimal parameters in the algorithm were estimated through the Recall, Precision, and Time curves. From two aspects of qualitative and quantitative analysis, the experimental results demonstrate that this algorithm could effectively eliminate the effect of uneven illumination on crack detection. After detection, Recall could reach more than 96%, and after extraction, Precision was increased by more than 70%.

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Crack Detection in Earth Dam and Levee Passive Seismic Data Using Support Vector Machines

Cited by 44 publications

References 8 publications

RePAD: Real-Time Proactive Anomaly Detection for Time Series

RePAD: Real-Time Proactive Anomaly Detection for Time Series

A Machine Learning Approach to Evaluating the Damage Level of Tooth-Shape Spur Dikes

Dynamic Partition Gaussian Crack Detection Algorithm Based on Projection Curve Distribution

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