{"type": "FeatureCollection", "features": [{"id": "10.1016/j.jsv.2021.116196", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:16:45Z", "type": "Journal Article", "created": "2021-05-10", "title": "Structural identification with physics-informed neural ordinary differential equations", "description": "Open AccessISSN:0022-460X", "keywords": ["Scientific machine learning", "Structural damage detection", "Neural ordinary differential equations", "Structural health monitoring", "0202 electrical engineering", " electronic engineering", " information engineering", "02 engineering and technology", "Discrepancy modeling", "Physics-informed machine learning", "Structural identification", "0201 civil engineering"]}, "links": [{"href": "https://doi.org/10.1016/j.jsv.2021.116196"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Sound%20and%20Vibration", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.jsv.2021.116196", "name": "item", "description": "10.1016/j.jsv.2021.116196", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.jsv.2021.116196"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-09-01T00:00:00Z"}}, {"id": "10.1016/j.probengmech.2020.103035", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:16:49Z", "type": "Journal Article", "created": "2020-02-12", "title": "Multivariate GP-VAR models for robust structural identification under operational variability", "description": "Open AccessISSN:0266-8920", "keywords": ["Gaussian Process (GPs) Vector AutoRegressive (GP-VAR) models", "Wind energy infrastructure", "0202 electrical engineering", " electronic engineering", " information engineering", "Environmental and Operational Variability (EOV)", "Structural Health Monitoring (SHM)", "02 engineering and technology", "Data-driven condition assessment", "0201 civil engineering"], "contacts": [{"organization": "Avenda\u00f1o-Valencia, Luis David, Chatzi, Eleni N.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.1016/j.probengmech.2020.103035"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Probabilistic%20Engineering%20Mechanics", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.probengmech.2020.103035", "name": "item", "description": "10.1016/j.probengmech.2020.103035", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.probengmech.2020.103035"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-04-01T00:00:00Z"}}, {"id": "10.1016/j.soildyn.2017.12.020", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:17:10Z", "type": "Journal Article", "created": "2018-01-08", "title": "Safety of buried steel natural gas pipelines under earthquake-induced ground shaking: A review", "description": "Abstract   Evidence from past earthquakes suggests that damage inflicted to buried natural gas (NG) pipelines can cause long service disruptions, leading to unpredictably high socioeconomic losses in unprepared communities. In this review paper, we aim to critically revisit recent progress in the demanding field of seismic analysis, design and resilience assessment of buried steel NG pipelines. For this purpose, the existing literature and code provisions are surveyed and discussed while challenges and gaps are identified from a research, industrial and legislative perspective. It is underscored that, in contrast to common belief, transient ground deformations in non-uniform sites are not necessarily negligible and can induce undesirable deformations in the pipe, overlooked in the present standards of practice. It is further highlighted that the current seismic fragility framework is rich in empirical fragility relations but lacks analytical and experimental foundations that would permit the reliable assessment of the different parameters affecting the expected pipe damage rates. Pipeline network resilience is still in a developing stage, thus only few assessment methodologies are available whereas absent is a holistic approach to support informed decision-making towards the necessary mitigation measures. Nevertheless, there is ground for improvement by adapting existing knowledge from research on other types of lifeline networks, such as transportation networks. All above aspects are discussed and directions for future research are provided.", "keywords": ["Gas networks", "Structural health monitoring", "330", "Seismic fragility", "0211 other engineering and technologies", "02 engineering and technology", "Natural gas", "Service disruption", "620", "Buried pipeline", "0201 civil engineering", "Seismic resilience", "Soil-pipe interaction"]}, "links": [{"href": "https://doi.org/10.1016/j.soildyn.2017.12.020"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Soil%20Dynamics%20and%20Earthquake%20Engineering", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.soildyn.2017.12.020", "name": "item", "description": "10.1016/j.soildyn.2017.12.020", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.soildyn.2017.12.020"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-03-01T00:00:00Z"}}, {"id": "10.1016/j.ymssp.2020.106905", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:17:25Z", "type": "Journal Article", "created": "2020-04-30", "title": "Full-field structural monitoring using event cameras and physics-informed sparse identification", "description": "Closed AccessMechanical Systems and Signal Processing, 145", "keywords": ["Physics-informed data science", "Structural health monitoring", "Strain estimation", "Boundary condition learning", "0202 electrical engineering", " electronic engineering", " information engineering", "Vision-based monitoring", "02 engineering and technology", "Event camera", "0201 civil engineering"]}, "links": [{"href": "https://doi.org/10.1016/j.ymssp.2020.106905"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Mechanical%20Systems%20and%20Signal%20Processing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.ymssp.2020.106905", "name": "item", "description": "10.1016/j.ymssp.2020.106905", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.ymssp.2020.106905"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-11-01T00:00:00Z"}}, {"id": "10.5281/zenodo.4896835", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:23:29Z", "type": "Report", "title": "Virtual loads predictions of wake-affected wind turbines: Gaussian process regression and deep neural networks", "description": "Load analysis of wind turbines may be performed either via physics-based models or via direct measurement. On the first case, loads are calculated with aero-elastic models, based on significant assumptions on the mechanical and aeroelastic properties of the structure and the acting forces (wind, wave and control). Otherwise, loads can be directly measured based on a sensor network, which entails increased costs due to installation, maintenance and calibration of sensors and IT infrastructure. These costs can be manageable for a single wind turbine but become substantial on densely instrumented wind farms. In turn, the increased costs negatively affect the levelized cost of energy. This is, in fact, the main reason why stakeholders shy away from applying monitoring technologies in wind farms. To overcome the above challenges, we need an alternative way of estimating the loads which would involve a reduced number of sensors while replicating the actual load measurement scenario. To this end, we propose data-driven models to predict the loads acting on different components of a wind turbine. These models use SCADA, wind inflow and other variables to predict loads in components of interest of a wind turbine. We have already successfully demonstrated this concept in the past on simulated wind turbine Damage Equivalent Loads (DELs) based on Gaussian Process regression [1,2], and on real wind turbine data [3]. In this work, we validate this approach on actual wind turbine data from the Alpha Ventus Wind Farm obtained within the framework of the Research Alpha Ventus (RAVE) project. Two Senvion turbines are selected for this study. One of the wind turbines is used to train and validate a regression model to predict the tower base DELs based on SCADA, wind inflow and other environmental variables. Afterwards, the trained model is used to predict the loads in the second wind turbine. Load prediction is attained with two machine learning methods, the first one based on Gaussian Process Regression (GPR) and the second one based on Artificial Neural Networks (ANN). For the first one, a decision tree is used to separate the different operating modes of the wind turbine (idling, operating and transitioning). The decision tree is built on simple heuristics on a subset of SCADA variables (mean and standard deviation of the rotor RPM and blade pitch angle). Subsequently, a GPR is built for each one of the operating modes. In the second method, the SCADA variables are fed to the ANN after undergoing an initial transformation for data compression and collinearity reduction.", "keywords": ["machine learning", "structural health monitoring", "13. Climate action", "wind turbines", "virtual sensing", "7. Clean energy"], "contacts": [{"organization": "Avenda\u00f1o-Valencia, Luis David, Abdallah, Imad, Venu, Anish, Chatzi, Eleni,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4896835"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4896835", "name": "item", "description": "10.5281/zenodo.4896835", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4896835"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-01-01T00:00:00Z"}}, {"id": "10.1109/ssci.2017.8280947", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:18:36Z", "type": "Journal Article", "created": "2018-02-07", "title": "Applying design knowledge and machine learning to scada data for classification of wind turbine operating regimes", "description": "Open AccessISBN:978-1-5386-2727-3", "keywords": ["supervised classi\ufb01cation", "data dimensionality reduction", "data clustering", "structural health monitoring", "13. Climate action", "11. Sustainability", "0202 electrical engineering", " electronic engineering", " information engineering", "unsupervised classi\ufb01cation", "02 engineering and technology", "7. Clean energy", "vibration data", "supervised classi\ufb01cation; unsupervised classi\ufb01cation; data clustering; data dimensionality reduction; vibration data; structural health monitoring"]}, "links": [{"href": "http://xplorestaging.ieee.org/ielx7/8267146/8280782/08280947.pdf?arnumber=8280947"}, {"href": "https://doi.org/10.1109/ssci.2017.8280947"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2017%20IEEE%20Symposium%20Series%20on%20Computational%20Intelligence%20%28SSCI%29", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1109/ssci.2017.8280947", "name": "item", "description": "10.1109/ssci.2017.8280947", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1109/ssci.2017.8280947"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-11-01T00:00:00Z"}}, {"id": "10.3389/fbuil.2017.00069", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:20:45Z", "type": "Journal Article", "created": "2017-12-07", "title": "Gaussian Process Time-Series Models for Structures under Operational Variability", "description": "Open AccessISSN:2297-3362", "keywords": ["metamodels", "random coefficient", "02 engineering and technology", "Engineering (General). Civil engineering (General)", "0201 civil engineering", "time-series models", "HT165.5-169.9", "Structural Health Monitoring", "Structural Health Monitoring; Gaussian Process Time-Series Models", "gaussian process", "TA1-2040", "Gaussian Process Time-Series Models", "uncertainty", "City planning"]}, "links": [{"href": "https://doi.org/10.3389/fbuil.2017.00069"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Frontiers%20in%20Built%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3389/fbuil.2017.00069", "name": "item", "description": "10.3389/fbuil.2017.00069", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3389/fbuil.2017.00069"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-12-08T00:00:00Z"}}, {"id": "10.3390/s21010142", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:21:10Z", "type": "Journal Article", "created": "2020-12-28", "title": "Comparing Direct Observation of Torsion with Array-Derived Rotation in Civil Engineering Structures", "description": "<p>In this article, we analyze the rotation rates in a building derived from a network of translation sensors and recorded by a rotation sensor. The building is Grenoble city hall, a reinforced concrete structure with permanent accelerometric translation sensors at the top and bottom of the building. A temporary experiment was conducted, consisting in installing a BlueSeis-3A rotation sensor for more than 24 h at the top of the structure. The ambient vibrations were analyzed. The amplitudes of translation accelerations and rotation rates at the top and bottom of the building, along with their variations over time, were analyzed. The acceleration/rotation ratios were then compared with the impulse wave velocities derived from seismic interferometry by deconvolution between the top and bottom. Perspectives with regard to building imaging, time monitoring of structural integrity and understanding the contribution of rotations to the structure\uffe2\uff80\uff99s response are discussed, offering new suggestions for research projects.</p>", "keywords": ["570", "structural health monitoring (SHM)", "Chemical technology", "Communication", "[SDU.STU]Sciences of the Universe [physics]/Earth Sciences", "TP1-1185", "02 engineering and technology", "530", "rotation", "buildings", "City-Hall Grenoble", "0201 civil engineering", "13. Climate action", "11. Sustainability", "[SDU.STU] Sciences of the Universe [physics]/Earth Sciences", "array-derived", "civil engineering"], "contacts": [{"organization": "Gu\u00e9guen, Philippe, Guattari, Fr\u00e9d\u00e9ric, Aubert, Coralie, Laudat, Theo,", "roles": ["creator"]}]}, "links": [{"href": "https://www.mdpi.com/1424-8220/21/1/142/pdf"}, {"href": "https://doi.org/10.3390/s21010142"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Sensors", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/s21010142", "name": "item", "description": "10.3390/s21010142", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/s21010142"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-12-28T00:00:00Z"}}, {"id": "10.3929/ethz-b-000278733", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:21:19Z", "type": "Journal Article", "created": "2018-07-06", "title": "Cost\u2013benefit optimization of structural health monitoring sensor networks", "description": "<p>Structural health monitoring (SHM) allows the acquisition of information on the structural integrity of any mechanical system by processing data, measured through a set of sensors, in order to estimate relevant mechanical parameters and indicators of performance. Herein we present a method to perform the cost\uffe2\uff80\uff93benefit optimization of a sensor network by defining the density, type, and positioning of the sensors to be deployed. The effectiveness (benefit) of an SHM system may be quantified by means of information theory, namely through the expected Shannon information gain provided by the measured data, which allows the inherent uncertainties of the experimental process (i.e., those associated with the prediction error and the parameters to be estimated) to be accounted for. In order to evaluate the computationally expensive Monte Carlo estimator of the objective function, a framework comprising surrogate models (polynomial chaos expansion), model order reduction methods (principal component analysis), and stochastic optimization methods is introduced. Two optimization strategies are proposed: the maximization of the information provided by the measured data, given the technological, identifiability, and budgetary constraints; and the maximization of the information\uffe2\uff80\uff93cost ratio. The application of the framework to a large-scale structural problem, the Pirelli tower in Milan, is presented, and the two comprehensive optimization methods are compared.</p>", "keywords": ["Stochastic Processes", "structural health monitoring", "structural health monitoring; Bayesian inference; cost\u2013benefit analysis; stochastic optimization; information theory; Bayesian experimental design; surrogate modeling; model order reduction", "Chemical technology", "Cost-Benefit Analysis", "Bayesian inference", "Bayesian experimental design", "Uncertainty", "Bayes Theorem", "TP1-1185", "02 engineering and technology", "stochastic optimization", "Bayesian experimental design; Bayesian inference; Benefit analysis; Cost; Information theory; Model order reduction; Stochastic optimization; Structural health monitoring; Surrogate modeling; Algorithms; Monte Carlo Method; Nonlinear Dynamics; Stochastic Processes; Uncertainty; Bayes Theorem; Cost-Benefit Analysis; Analytical Chemistry; Atomic and Molecular Physics", " and Optics; Biochemistry; Instrumentation; Electrical and Electronic Engineering", "Article", "surrogate modeling", "0201 civil engineering", "Nonlinear Dynamics", "model order reduction", "cost\u2013benefit analysis", "Monte Carlo Method", "Algorithms", "information theory"]}, "links": [{"href": "http://www.mdpi.com/1424-8220/18/7/2174/pdf"}, {"href": "https://re.public.polimi.it/bitstream/11311/1085132/1/Sensors_2018b.pdf"}, {"href": "https://doi.org/10.3929/ethz-b-000278733"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Sensors", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3929/ethz-b-000278733", "name": "item", "description": "10.3929/ethz-b-000278733", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3929/ethz-b-000278733"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-07-06T00:00:00Z"}}, {"id": "10.3929/ethz-b-000445427", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:21:19Z", "type": "Journal Article", "created": "2020-09-08", "title": "GP-ARX-Based Structural Damage Detection and Localization under Varying Environmental Conditions", "description": "<p>The representation of structural dynamics in the absence of physics-based models, is often accomplished through the identification of parametric models, such as the autoregressive with exogenous inputs, e.g. ARX models. When the structure is amenable to environmental variations, parameter-varying extensions of the original ARX model can be implemented, allowing for tracking of the operational variability. Yet, the latter occurs in sufficiently longer time-scales (days, weeks, months), as compared to system dynamics. For inferring a \uffe2\uff80\uff9cglobal\uffe2\uff80\uff9d, long time-scale varying ARX model, data from a full operational cycle has to typically become available. In addition, when the sensor network comprises multiple nodes, the identification of long time-scale varying, vector ARX models grow in complexity. We address these issues by proposing a distributed framework for structural identification, damage detection and localization. Its main features are: (i) the individual estimation of local, single-input-single-output ARX models at every operational point; (ii) the long time-scale representation of each individual ARX coefficient via a Gaussian process regression, which captures dependency on varying Environmental and Operational Conditions (EOCs); (iii) the establishment of a distributed residual generation algorithm for damage detection, which produces time-series of well-defined stationary statistics, with detected discrepancies used for damage diagnosis; and, (iv) exploitation of ARX-inferred mode shape curvatures, obtained via ARX-inferred global state-space models, of the healthy and damaged states, for damage localization. The method is assessed via application on two numerical case studies of different complexity, with the results confirming its efficacy for diagnostics under varying EOCs.</p>", "keywords": ["Technology", "0209 industrial biotechnology", "varying environmental and operational conditions", "Structural health monitoring", "structural health monitoring", "Damage detection and localization", "T", "mode shape curvatures", "distributed sensor network", "Autoregressive with exogenous inputs", "02 engineering and technology", "0201 civil engineering", "autoregressive with exogenous inputs", "Structural health monitoring; Varying environmental and operational conditions; Damage detection and localization; Gaussian process regression; Autoregressive with exogenous inputs; Distributed sensor network; Mode shape curvatures", "Distributed sensor network", "Mode shape curvatures", "damage detection and localization", "Varying environmental and operational conditions", "Gaussian process regression"]}, "links": [{"href": "http://www.mdpi.com/2224-2708/9/3/41/pdf"}, {"href": "https://doi.org/10.3929/ethz-b-000445427"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Sensor%20and%20Actuator%20Networks", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3929/ethz-b-000445427", "name": "item", "description": "10.3929/ethz-b-000445427", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3929/ethz-b-000445427"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-09-08T00:00:00Z"}}, {"id": "10.3929/ethz-b-000482924", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:21:19Z", "type": "Journal Article", "created": "2021-05-03", "title": "Vibration\u2010based monitoring of a small\u2010scale wind turbine blade under varying climate and operational conditions. Part II: A numerical benchmark", "description": "Open AccessThis paper constitutes the numerical companion of the experimental work on vibration-based monitoring of a small-scale wind turbine (WT) blade. In this second part, a numerical benchmark is established for condition assessmentof a Windspot 3.5-kW WT blade. The aim is to supplement the companion experimental work with a physical model exposed to diverse operational conditions, loading scenarios, and damage patterns that are not easily explorable and controllable in the laboratory. To this end, a finite element (FE) model of the considered blade is developed and subjected to a number of artificial damage scenarios, which are dynamically tested under both environmental and operational variability. The paper offers a detailed description of the numerical benchmark and the underlying assumptions, as well as the spectrum of operational conditions, the measured quantities, and the wind load model. Finally, we provide an overview and demonstration of the stand-alone application for time history analysis and generation of synthetic vibration data, which is made available via an open-access code in Sonkyo-Benchmark repository", "keywords": ["condition assessment", "structural health monitoring", "operational and environmental variability", "13. Climate action", "0202 electrical engineering", " electronic engineering", " information engineering", "wind turbine blade", "02 engineering and technology", "numerical benchmark", "condition assessment; damage detection; numerical benchmark; operational and environmental variability; structural health monitoring; system identification; wind turbine blade", "7. Clean energy", "damage detection", "system identification"]}, "links": [{"href": "https://doi.org/10.3929/ethz-b-000482924"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Structural%20Control%20and%20Health%20Monitoring", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3929/ethz-b-000482924", "name": "item", "description": "10.3929/ethz-b-000482924", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3929/ethz-b-000482924"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-05-03T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Structural+health+monitoring&f=json", "hreflang": "en-US"}, {"rel": "alternate", "type": "text/html", "title": "This document as HTML", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Structural+health+monitoring&f=html", "hreflang": "en-US"}, {"rel": "collection", "type": "application/json", "title": "Collection URL", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main", "hreflang": "en-US"}, {"type": "application/geo+json", "rel": "first", "title": "items (first)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Structural+health+monitoring&", "hreflang": "en-US"}, {"rel": "last", "type": "application/geo+json", "title": "items (last)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Structural+health+monitoring&offset=11", "hreflang": "en-US"}], "numberMatched": 11, "numberReturned": 11, "distributedFeatures": [], "timeStamp": "2026-07-26T11:13:20.421449Z"}