COLLECTIONS

Documents published in Scipedia

• WP1–T1.4. Definition of key water quality parameters for feeding ML models

  P. of, D. Vicente

  DIGIT4WATER Project (2023). 1

  
  

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• Anomaly Detection in Dam Behaviour with Machine Learning Classification Models

  F. Salazar, A. Conde, J. Irazábal González, D. Vicente

  Water (2021). 13(17), 2387

  
  

  Abstract

  Dam safety assessment is typically made by comparison between the outcome of some predictive model and measured monitoring data. This is done separately for each response variable, and the results are later interpreted before decision making. In this work, three approaches based on machine learning classifiers are evaluated for the joint analysis of a set of monitoring variables: multiclass, two-class and one-class classification. Support vector machines are applied to all prediction tasks, and random forest is also used for multi-class and two-class. The results show high accuracy for multi-class classification, although the approach has limitations for practical use. The performance in two-class classification is strongly dependent on the features of the anomalies to detect and their similarity to those used for model fitting. The one-class classification model based on support vector machines showed high prediction accuracy, while avoiding the need for correctly selecting and modelling the potential anomalies. A criterion for anomaly detection based on model predictions is defined, which results in a decrease in the misclassification rate. The possibilities and limitations of all three approaches for practical use are discussed.

  Abstract
  Dam safety assessment is typically made by comparison between the outcome of some predictive model and measured monitoring data. This is done separately for each response [...]

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• A review on thermo-mechanical modelling of arch dams during construction and operation. Effect of the reference temperature on the stress field

  F. Salazar, D. Vicente, J. Irazábal González, I. de-Pouplana, J. San Mauro

  Archives of Computational Methods in Engineering (2020). 27 (5), 1681-1707

  
  

  Abstract

  Double-curvaturedamsareunique structures for several reasons. Their behaviour changes significantly after joint grouting, when they turn from a set of independent cantilevers into a monolithic structure with arch effect. The construction process has a relevant influence on the stress state, due to the way in which self-weight loads are transmitted, and to the effect on the dissipation of the hydration heat. Temperature variations in the dam body with respect to those existing at joint grouting generate thermal stresses that may be important in the stress state of the structure. It is thus essential to have a realistic estimate of this thermal field, also called reference or

  Abstract
  Double-curvaturedamsareunique structures for several reasons. Their behaviour changes significantly after joint grouting, when they turn from a set of independent cantilevers [...]

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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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• An Interactive Tool for Automatic Predimensioning and Numerical Modeling of Arch Dams

  D. Vicente, J. San Mauro, F. Salazar, C. Baena

  Mathematical Problems in Engineering (2017). Vol. 2017

  
  

  Abstract

  The construction of double-curvature arch dams is an attractive solution from an economic viewpoint due to the reduced volume of concrete necessary for their construction as compared to conventional gravity dams. Due to their complex geometry, many criteria have arisen for their design. However, the most widespread methods are based on recommendations of traditional technical documents without taking into account the possibilities of computer-aided design. In this paper, an innovative software tool to design FEM models of double-curvature arch dams is presented. Several capabilities are allowed: simplified geometry creation (interesting for academic purposes), preliminary geometrical design, high-detailed model construction, and stochastic calculation performance (introducing uncertainty associated with material properties and other parameters). This paper specially focuses on geometrical issues describing the functionalities of the tool and the fundamentals of the design procedure with regard to the following aspects: topography, reference cylinder, excavation depth, crown cantilever thickness and curvature, horizontal arch curvature, excavation and concrete mass volume, and additional elements such as joints or spillways. Examples of application on two Spanish dams are presented and the results obtained analyzed.

  Abstract
  The construction of double-curvature arch dams is an attractive solution from an economic viewpoint due to the reduced volume of concrete necessary for their construction [...]

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• Analysis of temporal and spatial variability of water balance components in a Mediterranean river basin: the Spanish part of the Duero basin.

  D. Vicente, L. Rodríguez-Sinobas, F. Salazar, L. Garrote

  Geophysical Research Abstracts (2019). Vol. 21, p.1

  
  

  Abstract

  Water balances have been identified as an effective tool for assessing the amount of water and its availability in a region. In particular, the System of Environmental-Economic Accounting for Water (SEEA-W) has emerged as one of the most complete methodologies to undertake this action. Despite the great effort made to offer a common methodology for elaborating the SEEA-W tables, there are multiple aspects that prevent its standardization in a generalized way. This leads both to multiple approaches in the realization of water balances and to the introduction of high uncertainty in the accounts obtained.The data series variability, both temporal and spatial, is one of the factors that can introduce large uncertainty into the water balances if they are not correctly evaluated. This aspect has not yet been specifically studied, and most of the case studies in the literature only provide the water balance values for a short period (usually a natural or hydrological year) and for a specific basin. In the case of having data corresponding to several years, average values are typically used to elaborate the calculations. In the present study, the SEEA-W methodology was applied to the Spanish part of Duero river basin. Each component of the water asset accounts was calculated using simulated data from three models: SIMPA (rainfall-runoff model), ASTER (snow-related processes model) and SIMGES (water allocation and management model). These models were developed and calibrated by different Spanish entities. Then, the uncertainty associated with the temporal and spatial irregularity of the data series was also estimated.The Duero basin is located in a semi-arid Mediterranean region, where hydrological processes are affected by high intra- and inter-annual variability. To analyse this issue both monthly and annual resolutions were used. The first allowed identifying a significant intra-annual variability. The second was analysed using a period of 26 years, from 1980 to 2006. The results show a high variability for certain hydrological components which can lead to a great degree of uncertainty. In addition, each hydrological year was classified as dry, average or wet and water balances were calculated under the same classification. This approach would improve the estimation of water resources including the prediction of future climate changes scenarios.Regarding the spatial variability, the water resources of the basin, like many of the large Mediterranean rivers, are not evenly distributed and showed large differences in hydrological processes. These differences are caused by climatic and geomorphological differences (the area comprises an extensive central plateau with a peripheral mountain arch). In addition, the analysis considers the different human activities coexisting within the basin and the heterogeneity of their spatial distribution. The water balances split into a more disaggregated territorial units could help to detect the areas with both positive and negative balances and improve the water resources management in the whole basin.

  Abstract
  Water balances have been identified as an effective tool for assessing the amount of water and its availability in a region. In particular, the System of Environmental-Economic [...]

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• Algoritmo adaptativo para la estimación de pérdidas de agua en redes basado en análisis avanzado de Caudales Mínimos Nocturnos (CMN)

  D. Vicente, M. AGUILAR, F. Salazar, J. Helmbrecht

  F. Salazar's Conference proceedings (2019). 2

  
  

  Abstract

  El método del Caudal Mínimo Nocturno (CMN) es un concepto ampliamente utilizado para la estimación de fugas y otros objetivos estratégicos en empresas operadoras de agua. Si bien es un procedimiento de fácil aplicabilidad, existe un gran abanico de submétodos de aplicación, pudiendo introducirse una gran incertidumbre en los cálculos si no se seleccionan correctamente los criterios a utilizar. En este estudio se presenta un algoritmo capaz de asistir en la elección del mejor enfoque para el cálculo de CMN en función de los datos disponibles, las características de cada caso y el objetivo perseguido. Para elaborar dicho algoritmo se han ejecutado tres acciones: (i) revisión exhaustiva de literatura especializada y recopilación de submétodos utilizados en casos reales a nivel global, (ii) elaboración de un algoritmo multi-criterio capaz de seleccionar la mejor opción en cada fase del cálculo y (iii) presentación de una plataforma informática, llamada WatEner, en la que se integrará progresivamente el algoritmo propuesto.

  Abstract
  El método del Caudal Mínimo Nocturno (CMN) es un concepto ampliamente utilizado para la estimación de fugas y otros objetivos estratégicos en empresas [...]

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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)

• Water scarcity in the Spanish part of the Douro basin: current status and future scenarios

  DJ Vicente, L Rodríguez-Sinobas

  EGU General Assembly Conference Abstracts, 2016

• Application of the System of Environmental Economic Accounting for Water SEEAW to the Spanish part of the Duero basin: lessons learned

  DJ Vicente, L Rodríguez-Sinobas, L Garrote, R Sánchez

  Science of the Total Environment - STOTEN, 2016

• Lessons learned from the elaboration of SEEAW tables in the Duero basin

  DJ Vicente, L Garrote, J Rodríguez, R Sánchez

  EGU General Assembly Conference Abstracts 17, 10479, 2015

• Pressure management in water distribution systems: Current status, proposals and future trends

  DJ Vicente, L Garrote, R Sánchez, D Santillán

  Journal of Water Resources Planning and Management, 2015

• Modelación y caracterización de estaciones de regulación de presión en redes de distribución de agua urbana

  R Sánchez, DJ Vicente, L Rodríguez-Sinobas

  IV Jornadas de Ingeniería del Agua, 2015

• Comparación de diferentes métodos de determinación de la presión media en sistemas de distribución de agua, para la estimación de caudal fugado

  DJ Vicente

  VIII Seminario Líneas Prioritarias RLHE (Red de Laboratorios de Hidráulica ..., 2014

• Treatment of solar radiation by spatial and temporal discretization for modeling the thermal response of arch dams

  D Santillán, E Salete, DJ Vicente, MÁ Toledo

  Journal of Engineering Mechanics 140 (11), 05014001, 2014

• Funcionamiento de reguladores de presión en paralelo y en serie

  R Sánchez, L Juana Sirgado, L Rodríguez Sinobas, DJ Vicente, ...

  III Jornadas de Ingeniería del Agua, 2013

• Hacia el diseño óptimo de un plan de Gestión de Presiones en redes de distribución de agua urbana

  II Jornadas de Ingeniería del Agua, 2011