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Machine learning models applied to a GNSS sensor network for automated bridge anomaly detection / Nicolas Manzini in Journal of structural engineering, Vol 148 n° 11 (November 2022)  

Public [article]  inJournal of structural engineering > Vol 148 n° 11 (November 2022) . - n° 3469 Titre : Machine learning models applied to a GNSS sensor network for automated bridge anomaly detection Type de document : Article/Communication Auteurs : Nicolas Manzini, Auteur ; André Orcesi, Auteur ; Christian Thom , Auteur ; Marc-Antoine Brossault, Auteur ; Serge Botton , Auteur ; Miguel Ortiz, Auteur ; John Dumoulin, Auteur Année de publication : 2022 Projets : 2-Pas d'info accessible - article non ouvert / Article en page(s) : n° 3469 Note générale : bibliographie EN ATTENTE DU DOCUMENT Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Topographie [Termes IGN] apprentissage automatique [Termes IGN] classification par réseau neuronal récurrent [Termes IGN] détection d'anomalie [Termes IGN] ouvrage d'art [Termes IGN] pont [Termes IGN] régression [Termes IGN] série temporelle [Termes IGN] surveillance d'ouvrage [Termes IGN] topométrie de précision Résumé : (auteur) Structural health monitoring (SHM) based on global navigation satellite systems (GNSS) is an interesting solution to provide absolute positions at different locations of a structure in a global reference frame. In particular, low-cost GNSS stations for large-scale bridge monitoring have gained increasing attention these last years because recent experiments showed the ability to achieve a subcentimeter accuracy for continuous monitoring with adequate combinations of antennas and receivers. Technical solutions now allow displacement monitoring of long bridges with a cost-effective deployment of GNSS sensing networks. In particular, the redundancy of observations within the GNSS network with various levels of correlations between the GNSS time series makes such monitoring solution a good candidate for anomaly detection based on machine learning models, using several predictive models for each sensor (based on environmental conditions, or other sensors as input data). This strategy is investigated in this paper based on GNSS time series, and an anomaly indicator is proposed to detect and locate anomalous structural behavior. The proposed concepts are applied to a cable-stayed bridge for illustration, and the comparison between multiple tools highlights recurrent neural networks (RNN) as an effective regression tool. Coupling this tool with the proposed anomaly detection strategy enables one to identify and localize both real and simulated anomalies in the considered data set. Numéro de notice : A2022-672 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1061/(ASCE)ST.1943-541X.0003469 En ligne : https://doi.org/10.1061/(ASCE)ST.1943-541X.0003469 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101615 [article]

Detecting preseismic signals in GRACE gravity solutions: Application to the 2011 Tohoku Mw 9.0 earthquake / Isabelle Panet in Journal of geophysical research : Solid Earth, vol 127 n° 8 (August 2022)  

Public [article]  inJournal of geophysical research : Solid Earth > vol 127 n° 8 (August 2022) . - n° e2022JB024542 Titre : Detecting preseismic signals in GRACE gravity solutions: Application to the 2011 Tohoku Mw 9.0 earthquake Type de document : Article/Communication Auteurs : Isabelle Panet , Auteur ; Clément Narteau, Auteur ; Jean-Michel Lemoine, Auteur ; Sylvain Bonvalot, Auteur ; Dominique Remy, Auteur Année de publication : 2022 Projets : 2-Pas d'info accessible - article non ouvert / Article en page(s) : n° e2022JB024542 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] déformation de la croute terrestre [Termes IGN] données GRACE [Termes IGN] gradient de gravitation [Termes IGN] Pacifique (océan) [Termes IGN] séisme [Termes IGN] sismicité [Termes IGN] subduction [Termes IGN] tectonique des plaques [Termes IGN] Tohoku (Japon) Résumé : (auteur) We conduct a global analysis of GRACE-reconstructed gravity gradients from July 2004 to February 2011, to test whether the deep signals preceding the March 2011 Tohoku earthquake can be detected before the event as a specific feature originating from solid Earth. First, we improve the angular resolution of the gravity gradients using two overlapping ranges of azimuthal sensitivity to investigate short-term signals of large amplitude aligned with the orientation of the Northwestern Pacific subduction. Then, we set-up a method to identify consistent solid Earth signals shared by different GRACE gravity models. Robust signals in a model are selected based on their spatial overlap and relative intensity with the signals of another model, so that their sensitivity to the GRACE data processing and ocean dealiasing product can be tested. We show that the dipolar gravity gradient anomaly before the Tohoku earthquake is nearly unique in space and time in the GRACE GRGS03 solutions. A well-resolved dipolar spatial pattern, typical of dislocations within the solid Earth and poorly sensitive to the ocean dealiasing model, is detected. In addition, the preseismic gravity gradient increase is highly consistent between the GRGS03 and CSR06 solutions, independently from their respective oceanic corrections, and can be clearly distinguished from rare anomalies of similar amplitudes all associated with the water cycle over continental areas. Our approach offers solutions for the continuous monitoring of the Pacific subduction belt to document transient slabs motions in real time from global satellite gravity fields, and their relation with shallower deformations and seismic events. Numéro de notice : A2022-605 Affiliation des auteurs : UMR IPGP-Géod+Ext (2020- ) Autre URL associée : vers HAL Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1029/2022JB024542 Date de publication en ligne : 06/08/2022 En ligne : https://doi.org/10.1029/2022JB024542 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101384 [article]

Constraint-based evaluation of map images generalized by deep learning / Azelle Courtial in Journal of Geovisualization and Spatial Analysis, vol 6 n° 1 (June 2022)  

Public [article]  inJournal of Geovisualization and Spatial Analysis > vol 6 n° 1 (June 2022) . - n° 13 Titre : Constraint-based evaluation of map images generalized by deep learning Type de document : Article/Communication Auteurs : Azelle Courtial , Auteur ; Guillaume Touya , Auteur ; Xiang Zhang, Auteur Année de publication : 2022 Projets : 2-Pas d'info accessible - article non ouvert / Article en page(s) : n° 13 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] apprentissage profond [Termes IGN] connexité (graphes) [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] montagne [Termes IGN] programmation par contraintes [Termes IGN] qualité des données [Termes IGN] rendu réaliste [Termes IGN] route [Vedettes matières IGN] Généralisation Résumé : (auteur) Deep learning techniques have recently been experimented for map generalization. Although promising, these experiments raise new problems regarding the evaluation of the output images. Traditional map generalization evaluation cannot directly be applied to the results in a raster format. Additionally, the internal evaluation used by deep learning models is mostly based on the realism of images and the accuracy of pixels, and none of these criteria is sufficient to evaluate a generalization process. Finally, deep learning processes tend to hide the causal mechanisms and do not always guarantee a result that follows cartographic principles. In this article, we propose a method to adapt constraint-based evaluation to the images generated by deep learning models. We focus on the use case of mountain road generalization, and detail seven raster-based constraints, namely, clutter, coalescence reduction, smoothness, position preservation, road connectivity preservation, noise absence, and color realism constraints. These constraints can contribute to current studies on deep learning-based map generalization, as they can help guide the learning process, compare different models, validate these models, and identify remaining problems in the output images. They can also be used to assess the quality of training examples. Numéro de notice : A2022-449 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s41651-022-00104-2 Date de publication en ligne : 07/05/2022 En ligne : http://dx.doi.org/10.1007/s41651-022-00104-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100646 [article]

Completeness assessment and improvement in mobile crowd-sensing environments / Souheir Mehanna in SN Computer Science, vol 3 n° 3 (May 2022)  

Public [article]  inSN Computer Science > vol 3 n° 3 (May 2022) . - n° 216 Titre : Completeness assessment and improvement in mobile crowd-sensing environments Type de document : Article/Communication Auteurs : Souheir Mehanna, Auteur ; Zoubida Kedad, Auteur ; Mohamed Chachoua , Auteur Année de publication : 2022 Projets : 2-Pas d'info accessible - article non ouvert / Article en page(s) : n° 216 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] analyse multidimensionnelle [Termes IGN] exhaustivité des données [Termes IGN] indicateur de qualité [Termes IGN] pollution atmosphérique [Termes IGN] qualité de l'air [Termes IGN] qualité des données [Termes IGN] téléphonie mobile [Termes IGN] traitement de données localisées Résumé : (auteur) Mobile sensors are increasingly used to monitor air quality to accurately quantify human exposure to air pollution. These sensors are subject to various issues (misuse, malfunctions, battery problems, etc) that are likely to cause data quality problems. These quality problems may have a considerable impact on the reliability of analytical studies. In this work, we address the problem of data quality evaluation and improvement in mobile crowd-sensing environments. Our work is focused on the data completeness quality dimension. We introduce a multi-dimensional model to represent the data coming from the sensors in this context, and then present the different facets of data completeness inspired by the model. We propose quality indicators capturing different facets of completeness along with their corresponding quality metrics. We also propose an approach to improve data completeness by extending two existing data imputation techniques, SVDImpute and KNNImpute, with information about the sensor quality. Our experiments show that our quality-aware imputation approach improves the accuracy of the imputation achieved by the original techniques. Numéro de notice : A2022-454 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE/INFORMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s42979-022-01104-1 Date de publication en ligne : 10/04/2022 En ligne : http://dx.doi.org/10.1007/s42979-022-01104-1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101084 [article]

Performance analysis of low-cost GNSS stations for structural health monitoring of civil engineering structures / Nicolas Manzini in Structure and Infrastructure Engineering, vol 18 n° 5 ([01/05/2022])  

Public [article]  inStructure and Infrastructure Engineering > vol 18 n° 5 [01/05/2022] . - pp 595 - 611 Titre : Performance analysis of low-cost GNSS stations for structural health monitoring of civil engineering structures Type de document : Article/Communication Auteurs : Nicolas Manzini, Auteur ; André Orcesi, Auteur ; Christian Thom , Auteur ; Marc-Antoine Brossault, Auteur ; Serge Botton , Auteur ; Miguel Ortiz, Auteur ; John Dumoulin, Auteur Année de publication : 2022 Projets : 2-Pas d'info accessible - article non ouvert / Article en page(s) : pp 595 - 611 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] auscultation d'ouvrage [Termes IGN] déformation d'édifice [Termes IGN] effet thermique [Termes IGN] pont [Termes IGN] RTKLIB [Termes IGN] surveillance d'ouvrage [Termes IGN] test de performance [Termes IGN] topométrie de précision Résumé : (auteur) Global Navigation Satellite Systems (GNSS) have been used in various monitoring applications for the past two decades, as one of the very few options to provide absolute positions in a global reference frame. However, high performance GNSS stations are expensive, and sometimes may be impractical because of their size, power consumption or software requirements. Thus, the use of low-cost GNSS stations for structural health monitoring (SHM) has gained increasing attention. This paper presents a detailed experimental assessment of multiple combinations of GNSS receivers and antennas, and highlights an optimal cost-efficient solution for monitoring applications. Several sets of processing parameters and constraints are also evaluated using open source RTKLib software. The performance of the proposed solution is evaluated through two experimental dynamic scenarios, proving its ability to track quick displacements down to 4 mm and oscillations of 1 cm with a frequency up to 0.25 Hz with a 1 Hz receiver. Finally, a two-week dataset acquired from on a network of low-cost GNSS stations deployed on a suspended bridge is used to validate on-site performance. Results show good agreement between GNSS time series, traditional displacement sensors, and numerical simulations made using an operational mechanical model of the bridge, highlighting the potential of such low-cost solutions for structural health monitoring applications. Numéro de notice : A2021-170 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15732479.2020.1849320 Date de publication en ligne : 30/11/2020 En ligne : https://doi.org/10.1080/15732479.2020.1849320 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97105 [article]

Comparison of atmospheric mass density models using a new data source: COSMIC satellite ephemerides / Yang Yang in IEEE Aerospace and Electronic Systems Magazine, vol 37 n° 2 (February 2022)  

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In situ C-band data for wheat physiological functioning monitoring in the South Mediterranean region / Nadia Ouaadi (2022)  

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Predicting AIS reception using tropospheric propagation forecast and machine learning / Zackary Vanche (2022)  

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Proceedings of the 1st Conference of the European Association on Quality Control of Bridges and Structures : EUROSTRUCT 2021. An automated machine learning-based approach for structural novelty detection based on SHM / Nicolas Manzini (2022)  

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The Loop Effect: how climate change impacts the mitigation potential of the French forest sector / Philippe Delacote in Journal of Forest Economics, vol 36 n° 3 ([01/06/2021])  

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A data fusion-based framework to integrate multi-source VGI in an authoritative land use database / Lanfa Liu in International Journal of Digital Earth, vol 14 n° 4 (April 2021)  

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Search for top‐down and bottom‐up drivers of latitudinal trends in insect herbivory in oak trees in Europe / Elena Valdés-Correcher in Global ecology and biogeography, vol 30 n° 3 (March 2021)  

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Assessing the interest of a multi-modal gap-filling strategy for monitoring changes in grassland parcels / Anatol Garioud (2021)  

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Assessment of sky diffuse irradiance and building reflected irradiance in cast shadows / Manchun Lei (2021)  

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Diurnal cycles of C-band temporal coherence and backscattering coefficient over an olive orchard in a semi-arid area: Comparison of in situ and Sentinel-1 radar observations / Adnane Chakir (2021)  

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