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Documents published in Scipedia

• IberWQ: new simulation tool for 2D water quality modelling in rivers and shallow estuaries

  L. Cea, M. Bermudez, J. Puertas, E. Bladé, G. Corestein, E. Escolano, A. Conde, B. Bockelmann-Evans, R. Ahmadian

  Journal of hydroinformatics (2016). Vol. 18 (5), pp. 816-830

  
  

  Abstract

  The model computes the spatial and temporal evolution of several species and variables which are relevant for the evaluation of the environmental status of rivers and estuaries, including: Escherichia coli, dissolved oxygen, carbonaceous biochemical oxygen demand, organic nitrogen, ammoniacal nitrogen, nitrate–nitrite nitrogen, water temperature and salinity. A depth-averaged transport equation is solved for each variable with a mass conservative unstructured finite volume solver. IberWQ is fully coupled to the hydrodynamic module of the software Iber, a freeware simulation tool for solving the 2D shallow water equations. Both models are integrated in the same windows graphical environment, including several tools which allow the user to generate unstructured meshes adapted to the site topography, define spatially variable input parameters and visualize model outputs. We present four application examples to illustrate the possibilities of the software for the dynamic simulation of water quality in rivers and estuaries.

  Abstract
  The model computes the spatial and temporal evolution of several species and variables which are relevant for the evaluation of the environmental status of rivers and estuaries, [...]

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• Interpretation of dam monitoring data combining visualisation tools and machine learning. Eberlaste Dam Case Study

  S. F., . Kohler, . Landstorfer, A. Conde

  
  

  Abstract

  '''The installation of automatic data acquisition systems, together with the use of machine learning, allow obtaining useful information on the behaviour of dams. In this contribution, an example of application for a machine learning based predictive model is presented. Specifically, the level in a piezometer and its association with the reservoir level is studied for an embankment dam. The results show the model's ability to identify changes in dam response by taking full advantage of the available monitoring data. The flexibility of the algorithm allows different types of variables to be analysed without the need to determine a priori which are the most influential loads or how they affect the target value. The model has been implemented in a software tool that includes additional functionalities, specific for the treatment and exploration of dam monitoring data. It can be applied to different dam types and response variables.''' '''

  Abstract
  '''The installation of automatic data acquisition systems, together with the use of machine learning, allow obtaining useful information on the behaviour of dams. [...]

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• Identification of dam behavior by means of machine learning classification models

  F. Salazar, A. Conde, D. Vicente

  (2021). Numerical Analysis of Dams, DOI: 10.1007/978-3-030-51085-5_48

  
  

  Abstract

  '''The improvements in monitoring devices result in databases of increasing size showing dam behaviour. Advanced tools are required to extract useful information from such large amounts of data. Machine learning is increasingly used for that purpose worldwide: data-based models are built to estimate the dam response in front of a given combination of loads. The results of the comparison between model predictions and actual measurements can be used for decision support in dam safety evaluations. However, most of the works to date consider each device separately. A different approach is used in this contribution: a set of displacement records are jointly considered to identify patterns using a classification model. First, potential anomaly scenarios are defined and the response of the dam for each of them is obtained with numerical models under a realistic load combination. Then, the resulting displacements are used to generate a machine learning classifier. This model is later used to predict the most probable class of dam behavior corresponding to a new set of records. The methodology is applied to a double-curvature arch dam, showing great potential for anomaly detection.

  Abstract
  '''The improvements in monitoring devices result in databases of increasing size showing dam behaviour. Advanced tools are required to extract useful information [...]

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• Combination of advanced numerical methods and machine learning for dam safety assessment

  F. Salazar, B. Crookston, M. Hariri-Ardebili, D. Vicente, A. Conde

  Andre Conde's personal collection (2021). 4

  
  

  Abstract

  The availability of machine learning techniques opens up possibilities in different fields of civil engineering. Their application in conjunction with numerical simulations overcomes the limitations in traditional approaches and pave the road for some new horizons. This communication presents several applications of such a hybrid tool in design and safety assessment of dams and hydraulic structures. They include the generation of behavior prediction models from monitoring data, the identification of behavior patterns with classification models, the analysis of the seismic response of gravity dams with heterogeneous concrete in a probabilistic framework, the investigation of the performance of arch dams, and the estimation of the discharge capacity of arched labyrinth spillways.

  Abstract
  The availability of machine learning techniques opens up possibilities in different fields of civil engineering. Their application in conjunction with numerical simulations [...]

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• AN INTERACTIVE TOOL FOR DATA ANALYSIS AND MACHINE-LEARNING MODEL FITTING WITH APPLICATION TO HYDRO-ENVIRONMENT SYSTEMS

  F. Salazar, A. Conde, D. Vicente

  
  

  Abstract

  The advances in sensors and communication technologies open great possibilities in the management and maintenance of engineering systems. In general, the performance of monitoring devices has undergone relevant improvements in terms of both accuracy and reliability, which have resulted in more information available on the behaviour of the structure under consideration. However, the investments made in the modernization of the monitoring systems are not recovered unless complemented by applications capable of handling such large and diverse information. In this contribution, we present a software tool for importing, exploring, cleaning and analysing monitoring data. Also, it allows for fitting machine-learning behaviour models, as well as interpreting the response of the system to the actions or loads in operation. It was initially developed for dam safety assessment, but can be used -with minor changes- for other engineering systems. The methodology and the overall structure can be categorized in two main sections: (i) the monitoring data can be uploaded, cleaned, completed and analysed and (ii) the machine-learning model can be fitted to predict the variables of interest of the system. The same model can then be used for online detection of anomalies by comparing predictions with recorded behaviour. For example, this allows the identification of abnormal displacements in dams for a given load combination. The software can be equally run locally or in the cloud, with appropriate safe access. It has been written in the R language using the Shiny package for interactivity with the following functionalities: zooming and showing information for data exploration, selecting time periods to interpolate and choosing the training parameters to fit behaviour models.

  Abstract
  The advances in sensors and communication technologies open great possibilities in the management and maintenance of engineering systems. In general, the performance of monitoring [...]

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• Tratamiento y análisis inteligente de datos del comportamiento de estructuras hidráulicas

  A. Conde, F. Salazar, D. Vicente

  VI Jornadas de Ingeniería del Agua. 23-24 de Octubre. Toledo

  
  

  Abstract

  El rendimiento de los dispositivos de monitorización ha experimentado mejoras permitiendo disponer de más información sobre el comportamiento de las estructuras. Sin embargo, las inversiones realizadas en modernización de sistemas de medida no se recuperan a menos que se complementen con aplicaciones capaces de manejar una información tan amplia y diversa. En esta contribución, se presenta una herramienta de software para importar, explorar, limpiar y analizar datos de monitorización. Además, permite la generación de modelos de predicción basados en machine learning, así como la interpretación de la respuesta del sistema a las acciones o cargas en funcionamiento. La metodología y la estructura general se dividen en dos fases: i) carga, depuración, completado y análisis datos, y ii) generación e interpretación de modelos predictivos basados en machine learning. El mismo modelo puede utilizarse para la detección de anomalías comparando las predicciones con el comportamiento registrado.

  Abstract
  El rendimiento de los dispositivos de monitorización ha experimentado mejoras permitiendo disponer de más información sobre el comportamiento de las estructuras. [...]

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Articles (Documents not available in Scipedia)

• Hydraulic model study of the intake-outlet of a pumped-storage hydropower plant

  M Bermúdez, L Cea, J Puertas, A Conde, A Martín, J Baztán

  Engineering Applications of Computational Fluid Mechanics 11 (1), 483-495, 2017

• IberWQ: new simulation tool for 2D water quality modelling in rivers and shallow estuaries

  L Cea, M Bermudez, J Puertas, E Blade, G Corestein, E Escolano, ...

  Journal of Hydroinformatics 18 (5), 816-830, 2016