Performance of data-based models for early detection of damage in concrete dams

Hellgren, Rikard; Malm, Richard; Ansell, Anders

doi:10.1080/15732479.2020.1737146

Cited by 16 publications

(9 citation statements)

References 28 publications

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“…To be able to analyse this effect, detailed models are required that can capture the crack initiation and propagation in concrete such as non-linear finite element analyses based on constitutive material models. Other types of methods such as data-based (statistical) models or simplified models may capture the behaviour of an undamaged or an already damaged case, but can typically not capture the transition from undamaged to damaged or answer questions regarding the current state of a cracked dam [9]. However, the different methods have their benefits and the use of one type of model does not exclude the possibility of also using others.…”

Section: Discussionmentioning

confidence: 99%

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Lessons Learned Regarding Cracking of a Concrete Arch Dam Due to Seasonal Temperature Variations

2020

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Dams located in cold areas are subjected to large seasonal temperature variations and many concrete dams have cracked as a result. In the 14th International Commission on Large Dams (ICOLD) Benchmark Workshop, a case study was presented where contributors should predict the cracking and displacements due to seasonal variations. In this paper, the conclusions from this case study are presented. Overall, the results from the contributors are well in line with the observations that can be made on the dam and the measurements performed. This shows that using non-linear numerical models is a suitable tool to accurately predict cracking and estimate the displacements of cracked dams. This case study also highlighted important aspects that need special consideration in order to obtain realistic results that can be used to predict the crack pattern, these being: (1) the importance of performing transient thermal analyses based on robin boundary conditions; (2) the influence of contact formulation between the concrete dam and the foundation; and (3) the use of realistic non-linear material properties. The results and conclusions presented in this paper constitute one important step in achieving best practices to estimate dam safety and better understand the potential failure modes and ageing of concrete dams.

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Section: Discussionmentioning

confidence: 99%

“…These methods, however, need a sufficient amount of data, typically a few years of recordings, in order to train and evaluate the models. In addition, this type of model can have difficulties in detecting progressive damage evolution, as shown by [9].…”

Section: Introductionmentioning

confidence: 99%

Lessons Learned Regarding Cracking of a Concrete Arch Dam Due to Seasonal Temperature Variations

2020

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“…This method has been widely used. 1,19,26,[27][28][29][30][31] In addition, the typical small probability method is to make the probability distribution statistics of the monitoring data under different load combinations to determine the control limits of the target variable using the probability density function and the probability of dam failure. 32 Based on the idea of typical small probability, Prakash et al 33 calculated the distribution of the squared prediction error (SPE) and determined two control limits based on its cumulative distribution curve.…”

Section: Introductionmentioning

confidence: 99%

“…Anomaly detection is usually based on the hypothesis that the behavior of an undamaged dam follows an expected pattern, a deviation from which indicates damage. 1 Therefore, anomaly detection is comprised of two steps: establishment of a high-precision dam behavior model and determination of control limits.…”

Section: Introductionmentioning

confidence: 99%

An LSTM-based anomaly detection model for the deformation of concrete dams

Liu,

Pan,

Wang

2023

Structural Health Monitoring

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Anomaly detection in deformation is important for structural health monitoring and safety evaluation of dams. In this paper, an anomaly detection model for the deformation of arch dams is presented. It combines the long short-term memory network (LSTM)-based behavior model for dam deformation prediction and the small probability method for control limits determination, and thus is called an LSTM-based anomaly detection model. To demonstrate the advantages of the LSTM-based anomaly detection model, the traditional hydrostatic-seasonal-time behavior model and the confidence interval method are considered for comparison. The 178 m-high Longyangxia Arch Dam is taken as a case study. The results show that the LSTM-based model has sufficiently high accuracy for dam deformation prediction, especially can accurately predict displacement peaks and troughs. The LSTM-based anomaly detection model can significantly avoid false warnings and missing alarms and is able to send alarms in time when the occurrence of adverse conditions causes abnormal deformation of the dam.

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“…In addition, the model parameters don't have physical meaning and these models are not able to provide an essential explanation to the dam behaviours (e.g. long-term deformation) [7,8].…”

Section: Introductionmentioning

confidence: 99%