Internal characterization of embankment dams using ground penetrating radar (GPR) and thermographic analysis: A case study of the Medau Zirimilis Dam (Sardinia, Italy)

Anchuela, Óscar Pueyo; Frongia, Paolo; Gregorio, Felice Di; Sáinz, Antonio María Casas; Juan, Andrés Pocoví

doi:10.1016/j.enggeo.2018.02.015

Cited by 32 publications

(11 citation statements)

References 15 publications

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“…In contrast to other common geophysical field techniques used to map already existing pipes (e.g., electrical resistivity tomography and ground penetrating radar) (Anchuela et al, 2018;Bernatek-Jakiel & Kondracka, 2016;Shin et al, 2019) the seismic anisotropy measurements could indicate potentially endangered areas by finding zones prone to piping-favourable conditions (severe jointing and stiff layers).…”

Section: Factors Controlling Ground Fissuresmentioning

confidence: 99%

Ground fissures within the Main Ethiopian Rift: Tectonic, lithological and piping controls

Valenta

Verner

Martínek

et al. 2021

Earth Surf Processes Landf

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Ground fissures, especially if they open due to a sudden collapse of the surface, is a serious risk for populated areas. Their common occurrence in unconsolidated sediments of the Main Ethiopian Rift was found to be mostly a result of piping.The fissures start by piping in linear sub-horizontal underground voids, which often propagate upwards resulting in ceiling collapse and formation of deep and long ground fissures with vertical walls. In the southern and central Main Ethiopian Rift the fissures pose a serious risk to infrastructure and settlements. The ground fissures are often linear (up to several kilometres long and often tens of metres deep) and accompanied by sinkholes (along the length). A detailed field mapping of the geological (rock composition, orientation and character of lithological boundaries, primary fabrics and brittle structures) and geomorphological features (especially a length, width and depth of fissures, sinkholes and gullies) followed by in situ seismic anisotropy measurements and a laboratory determination of the geomechanical properties of volcanoclastic deposits was carried out to investigate the ground fissures' origin. The conditions and factors leading to the formation of the ground fissures have been linked to: (a) the presence of regional normal faults and the associated extensional joints and (b) the alternation of lithological units with contrasting hydraulic permeability. The latter corresponds to a sequence of less permeable hard rocks (e.g., rhyolitic ignimbrites) overlain by heterogeneous, soft and permeable, unconsolidated volcaniclastic deposits with a low amount of clay (less than 10%). The ground fissures' occurrence has shown affiliation to areas which have a significantly high seismic anisotropy (more than 20% at the study sites), which can be used as a proxy to map out high risk areas prone to piping and ground fissure formation.

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Section: Factors Controlling Ground Fissuresmentioning

confidence: 99%

Ground fissures within the Main Ethiopian Rift: Tectonic, lithological and piping controls

Valenta

Verner

Martínek

et al. 2021

Earth Surf Processes Landf

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“…The GPR method in research on flood embankments has been extensively described in the scientific literature Gołębiowski and Małysa 2018;Anchuela and Pueyo 2018;Łój et al 2018;Perri et al 2014;Gołębiowski et al 2012;Słowik 2011;Xu et al 2010;Di Prinzio 2010;Mori 2009;Marcak et al 2005); however, there are still many unresolved problems that can be solved by scientists improving further on the existing approaches which is the focus of this paper.…”

Section: Introductionmentioning

confidence: 99%

Ground penetrating radar use in flood prevention

Tomecka‐Suchoń

2019

Acta Geophys.

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The main goal of the work is to create an automatic method of locating weak zones within flood embankments structure based on ground penetrating radar (GPR) measurements. The presented research shows the possibilities of using advanced methods of GPR signal processing and its analysis with the help of signal attributes for detecting zones threatening the stability of the structure of flood embankments. Obtained results may help in quick detection of potential weak zones of the embankments and consequently give means to ameliorate them, which may prevent damage to the embankments during rise in the level of river water. The presented analyses were carried out on GPR data obtained for the flood banks of the Rudawa River (Kraków, Poland) in the area of their visible degradation. The use of signal attributes, such as Energy, instantaneous frequency, similarity, curvature gradient, dominant frequency, allowed initial indication of anomalous zones threatening the stability of embankment. Advanced processing supported by the use of advanced filters such as GLCM, Grubbs filter threshold and Convolve Prewitt helped in the analysis of the structure of the embankments. Artificial neural networks (ANNs) in the supervised and unsupervised variants were used to perform the automatic classification of weakened zones within the embankments. The results demonstrated the usefulness of GPR geophysical method through integration of ANN in the analysis of the data.

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“…Pueyo Anchuela et al [ 10 ], on the other hand, studied the internal characterization of embankment dams using ground-penetrating radar (GPR) and thermographic analysis. Those authors indicate that GPR analyses at the embankments usually have the handicap of high clay content that precludes electromagnetic wave penetration.…”

Section: Introductionmentioning

confidence: 99%

Structural Health Monitoring of Dams Based on Acoustic Monitoring, Deep Neural Networks, Fuzzy Logic and a CUSUM Control Algorithm

Ozelim

Borges

Cavalcante

et al. 2022

Sensors

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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.

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Internal characterization of embankment dams using ground penetrating radar (GPR) and thermographic analysis: A case study of the Medau Zirimilis Dam (Sardinia, Italy)

Cited by 32 publications

References 15 publications

Ground fissures within the Main Ethiopian Rift: Tectonic, lithological and piping controls

Ground fissures within the Main Ethiopian Rift: Tectonic, lithological and piping controls

Ground penetrating radar use in flood prevention

Structural Health Monitoring of Dams Based on Acoustic Monitoring, Deep Neural Networks, Fuzzy Logic and a CUSUM Control Algorithm

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