Instrumented Health Monitoring of an Earth Dam

Kolbadi, S. Mahdi S.; Hariri-Ardebili, Mohammad Amin; Mirtaheri, Masoud; Pourkamali-Anaraki, Farhad

doi:10.3390/infrastructures5030026

Cited by 14 publications

(5 citation statements)

References 22 publications

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“…Mata [29] conducted a comparative study on two statistical models named multiple linear regression (MLR) and neural network (NN) for monitoring of Alto Rabagão dam performance in the environmental condition. Seyed-Kolbadi et al [30] evaluated the stability of the Boostan earth dam by processing its longterm performance and interpreting the measured data.…”

Section: Structural Health Monitoring Of Dams Wang and Hementioning

confidence: 99%

A System Identification‐Based Damage‐Detection Method for Gravity Dams

Mirtaheri

Salkhordeh

Mohammadgholiha

2021

Shock and Vibration

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Dams are essential infrastructures as they provide a range of economic, environmental, and social benefits to the local populations. Damage in the body of these structures may lead to an irreparable disaster. This paper presents a cost-effective vibration-based framework to identify the dynamic properties and damage of the dams. To this end, four commonly occurred damage scenarios, including (1) damage in the neck of the dam, (2) damage in the toe of the structure, (3) simultaneous damage in the neck and the toe of the dam, and (4) damage in the lifting joints of the dam, are considered. The proposed method is based on processing the acceleration response of a gravity dam under ambient excitations. First, the random decrement technique (RDT) is applied to determine the free-vibration of the structure using the structural response. Then, a combined method based on Hilbert–Huang Transform (HHT) and Wavelet Transform (WT) is presented to obtain the dynamic properties of the structure. Next, the cubic-spline technique is used to make the mode shapes differentiable. Finally, Continuous Wavelet Transform (CWT) is applied to the residual values of mode shape curvatures between intact and damaged structures to estimate the damage location. In order to evaluate the efficiency of the proposed method in field condition, 10% noise is added to the structural response. Results show promising accuracy in estimating the location of damage even when the structure is subjected to simultaneous damage in different locations.

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Section: Structural Health Monitoring Of Dams Wang and Hementioning

confidence: 99%

A System Identification‐Based Damage‐Detection Method for Gravity Dams

Mirtaheri

Salkhordeh

Mohammadgholiha

2021

Shock and Vibration

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“…In the first step, a 60 g sample of Ardak dam sediment is added to the test tank. is sample was first mixed with a small amount of tank water using an electric mixer and then added to the water inside the tank [33]. After the particulate materials were well mixed, the reading was observed by using a spectrometer and an image was taken by using a camera [34].…”

Section: Preparation Of Samplesmentioning

confidence: 99%

Application of Remote Sensing Technology in Sediment Estimating Entering the Dam Reservoirs due to Floods

Hadian

Mosaedi

2021

Shock and Vibration

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The present study aimed to use remote sensing technology to estimate the concentration of particulate materials in the water entering the reservoirs of dams and consequently investigate the possibility of estimating the amount of sediment carried to the reservoir by flood during the life of the dam and its annual estimate. Using an advanced spectrometer device (ASD), the reflectance values of water containing different amounts of particulate sediments were measured in the range of 400–2500 nm; then, these reflectance values were represented for the Landsat 8 satellite OLI bands using their spectral response functions. In the study of interband correlation with the number of particulate materials, band 2 (blue) and band 5 (near-infrared) were identified to prepare a specific and appropriate model. The specificity of the reflectance difference in the two abovementioned bands was presented as an exponential relationship between the concentration of particulate materials and spectral reflectance. In this model, the RMSE parameter for the maximum possible sediment concentration was equal to 1.57 and the parameter R2 was equal to 0.91. In the second step, at the same time as the satellite passed, the area was visited and the sediments of the Ardak dam reservoir were sampled by recording their location. To complete this research, two measures were performed simultaneously, calculating the concentration of particulate materials sampled in the laboratory environment and their location on the image. Then, the number of particulate materials is estimated by taking into account the coordinates recorded from the images on which the relevant corrections have been made. According to the extracted exponential model, the results of estimating the concentration of particulate matter obtained from the model and Landsat satellite images with the concentration of particulate matter obtained from sampling showed its complete compatibility with field surveys to validate this research.

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“…Earth-rock dam is one of the critical facilities for hydropower development. In addition to water storage and power generation, it also undertakes flood control and drainage, water storage and power generation, comprehensive water supply, farmland irrigation, energy supply, ecological construction, and other essential tasks, and the safety problem is significant [1].…”

Section: Introductionmentioning

confidence: 99%

Seepage prediction model of the earth-rock dam based on TCN considering rainfall lag effect

2024

Meas. Sci. Technol.

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Renewable energy has the highest conversion efficiency, is the most flexible in regulating peak power in the grid, and has the potential to significantly reduce emissions. Hydropower is one of the main ways to optimize power energy structure by building earth-rock dams that block water and generate electricity. Seepage is a physical quantity that characterizes the safety of earth-rock dams. Studying the intelligent prediction model of earth-rock dams is an effective means of understanding the evolution of seepage behavior, and it is also crucial for the safe operation and energy efficiency of earth-rock dams. To create a rainfall factor expression reflecting the hysteresis effect of rain, actual monitoring data of different piezoelectric tubes on the upstream and downstream sides of the soil core wall of an earth-rock dam is considered. Based on the key influencing factors of the seepage behavior of earth-rock dams, the novel TCN algorithm in deep learning is introduced into the seepage behavior prediction of earth-rock dams, constructing the intelligent prediction model of seepage of earth-rock dams based on TCN. The engineering example shows that the seepage prediction model of the earth-rock dam based on TCN has better prediction performance than the seepage prediction model of the earth-rock dam based on SVR, ELM, and LSTM. The determination coefficient is more significant than 0.9, and the relative error of prediction is less than 1‰. The model's prediction accuracy is high, and the stability of the prediction performance is good. The model's prediction performance also improves after considering the rainfall lag effect.

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Instrumented Health Monitoring of an Earth Dam

Cited by 14 publications

References 22 publications

A System Identification‐Based Damage‐Detection Method for Gravity Dams

A System Identification‐Based Damage‐Detection Method for Gravity Dams

Application of Remote Sensing Technology in Sediment Estimating Entering the Dam Reservoirs due to Floods

Seepage prediction model of the earth-rock dam based on TCN considering rainfall lag effect

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