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scientifique

Semantic segmentation of bridge components and road infrastructure from mobile LiDAR data / Yi-Chun Lin in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 6 (December 2022)  

Public [article]  inISPRS Open Journal of Photogrammetry and Remote Sensing > vol 6 (December 2022) . - n° 100023 Titre : Semantic segmentation of bridge components and road infrastructure from mobile LiDAR data Type de document : Article/Communication Auteurs : Yi-Chun Lin, Auteur ; Ayman Habib, Auteur Année de publication : 2022 Article en page(s) : n° 100023 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] apprentissage profond [Termes IGN] autoroute [Termes IGN] couplage GNSS-INS [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] lidar mobile [Termes IGN] pont [Termes IGN] réseau neuronal de graphes [Termes IGN] réseau routier [Termes IGN] segmentation sémantique [Termes IGN] semis de points Résumé : (auteur) Emerging mobile LiDAR mapping systems exhibit great potential as an alternative for mapping urban environments. Such systems can acquire high-quality, dense point clouds that capture detailed information over an area of interest through efficient field surveys. However, automatically recognizing and semantically segmenting different components from the point clouds with efficiency and high accuracy remains a challenge. Towards this end, this study proposes a semantic segmentation framework to simultaneously classify bridge components and road infrastructure using mobile LiDAR point clouds while providing the following contributions: 1) a deep learning approach exploiting graph convolutions is adopted for point cloud semantic segmentation; 2) cross-labeling and transfer learning techniques are developed to reduce the need for manual annotation; and 3) geometric quality control strategies are proposed to refine the semantic segmentation results. The proposed framework is evaluated using data from two mobile mapping systems along an interstate highway with 27 highway bridges. With the help of the proposed cross-labeling and transfer learning strategies, the deep learning model achieves an overall accuracy of 84% using limited training data. Moreover, the effectiveness of the proposed framework is verified through test covering approximately 42 miles along the interstate highway, where substantial improvement after quality control can be observed. Numéro de notice : A2022-814 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1016/j.ophoto.2022.100023 Date de publication en ligne : 24/10/2022 En ligne : https://doi.org/10.1016/j.ophoto.2022.100023 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101975 [article]

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]

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]

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)  

Public Titre de série : Proceedings of the 1st Conference of the European Association on Quality Control of Bridges and Structures : EUROSTRUCT 2021 Titre : An automated machine learning-based approach for structural novelty detection based on SHM Type de document : Article/Communication Auteurs : Nicolas Manzini, Auteur ; Ndeye Mar, Auteur ; Franziska Schmidt, Auteur ; Jean-François Bercher, Auteur ; André Orcesi, Auteur ; Pierre Marchand, Auteur ; Julien Gazeaux , Auteur ; Christian Thom , Auteur Editeur : Springer Nature Année de publication : 2022 Collection : Lecture Notes in Civil Engineering num. 200 Projets : 2-Pas d'info accessible - article non ouvert / Importance : pp 1180 - 1189 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] apprentissage automatique [Termes IGN] détection d'anomalie [Termes IGN] ouvrage d'art [Termes IGN] pont [Termes IGN] régression multiple [Termes IGN] réseau de capteurs [Termes IGN] résidu [Termes IGN] surveillance d'ouvrage Résumé : (auteur) One major goal of structural health monitoring (SHM) is to detect, and possibly locate, quantify or predict damage on structures. Without detailed knowledge of structural mechanical behavior, data analysis is a complex task and operational monitoring is often limited to the use of more or less arbitrary thresholds. Data-driven techniques, which rely on a statistical analysis of data, have encountered a growing interest over the past two decades. In parallel, SHM is now increasingly considered for several types of structures with the development of low-cost sensors and IoT. In this context, this paper proposes an approach based on multiple automated machine learning-based models for novelty detection and location in monitoring data. This study focuses on the monitoring of large structures with multiple sensors. For each sensor, multiple regression models (based on neural networks) are generated using the same training set, with various input data: internal temperature, environmental conditions, or data from other sensors deployed on the structure. Anomalies are then identified in the dataset based on residuals between model outputs and in situ data. For a given sensor, residuals of all models are then compiled to produce an anomaly indicator. This paper presents some of the results obtained on data acquired from the monitoring of a large concrete bridge. Some anomalies are simulated and added to the dataset to demonstrate the detection performance of the proposed approach. Numéro de notice : C2022-001 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : POSITIONNEMENT Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1007/978-3-030-91877-4_134 Date de publication en ligne : 12/12/2021 En ligne : https://doi.org/10.1007/978-3-030-91877-4_134 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99378

3D reconstruction of bridges from airborne laser scanning data and cadastral footprints / Steffen Goebbels in Journal of Geovisualization and Spatial Analysis, vol 5 n° 1 (June 2021)  

Public [article]  inJournal of Geovisualization and Spatial Analysis > vol 5 n° 1 (June 2021) . - n° 10 Titre : 3D reconstruction of bridges from airborne laser scanning data and cadastral footprints Type de document : Article/Communication Auteurs : Steffen Goebbels, Auteur Année de publication : 2021 Article en page(s) : n° 10 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] Allemagne [Termes IGN] axe médian [Termes IGN] CityGML [Termes IGN] données cadastrales [Termes IGN] données lidar [Termes IGN] empreinte [Termes IGN] pont [Termes IGN] Ransac (algorithme) [Termes IGN] reconstruction 3D [Termes IGN] semis de points Résumé : (auteur) The given paper describes a method for automatic 3D reconstruction of bridges from cadastral footprints and airborne laser scanning point clouds. The reconstructed bridges are used to enrich 3D city models. Unlike roofs, decks of bridges are typically smooth without ridge lines or step edges. Therefore, established methods for roof reconstruction are not suitable for bridges. The standard description language for semantic city models is CityGML. This specification of the Open Geospatial Consortium assumes that surfaces are composed of planar polygons. The approximation of smooth decks by planar polygons is achieved by using a medial axis tree. Instead of the medial axis of the footprint, a modified medial axis is computed that does not consider counter bearing edges. The resulting tree represents centerline connections between all counter bearing edges and, in conjunction with filtered height values of a point cloud, serves as the basis for approximation with polygons. In addition to modeling decks, superstructures such as pylons and cables are also derived from the point cloud. For this purpose, planes carrying many superstructure points are detected using the Random Sampling Consensus Algorithm (RANSAC). Images are generated by projecting points onto these planes. Then, image processing methods are used to find connected contours that are extruded to form 3D objects. The presented method was successfully applied to all bridges of two German cities as well as to large bridges built over the Rhine River. Numéro de notice : A2021-359 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s41651-021-00076-9 Date de publication en ligne : 12/04/2021 En ligne : https://doi.org/10.1007/s41651-021-00076-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97623 [article]

Récepteurs GNSS bas coût pour la surveillance des grands ponts / Nicolas Manzini in XYZ, n° 167 (juin 2021)

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Comprehensive time-series analysis of bridge deformation using differential satellite radar interferometry based on Sentinel-1 / Matthias Schlögl in ISPRS Journal of photogrammetry and remote sensing, vol 172 (February 2021)  

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BIM/GIS integration for web GIS-based bridge management / Junxiang Zhu in Annals of GIS, vol 27 n° 1 (January 2021)  

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A decade of modern bridge monitoring using terrestrial laser scanning: Review and future directions / Maria Rashidi in Remote sensing, vol 12 n° 22 (December-1 2020)  

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Combined InSAR and terrestrial structural monitoring of bridges / Sivasakthy Selvakumaran in IEEE Transactions on geoscience and remote sensing, vol 58 n° 10 (October 2020)  

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Predicting displacement of bridge based on CEEMDAN-KELM model using GNSS monitoring data / Qian Fan in Journal of applied geodesy, vol 14 n° 3 (July 2020)  

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Robust deformation monitoring of bridge structures using MEMS accelerometers and image-assisted total stations / Mohammad Omidalizarandi (2020)  

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Surveillance de santé structurale des ouvrages d'art incluant les systèmes de positionnement par satellites / Nicolas Manzini (2020)  

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The possibility of measuring the dynamic response of structures using non-contact geodetic method / Bostjan Kovacic in Geodetski vestnik, vol 63 n° 1 (March - May 2019)  

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Structural health monitoring using a GPS sensor network / Nicolas Manzini (2018)  

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