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• A methodology for dam safety evaluation and anomaly detection based on boosted regression trees

  F. Salazar, J. González, M. Toledo

  (2016). Proceedings on 8th European Workshop On Structural Health Monitoring (EWSHM 2016)

  
  

  Abstract

  Many countries are implementing new dam safety regulations that often include more restrictive standards. This, together with the increasing average age of dams, results in a greater need for dam control and maintenance works. The advances in information and communications technologies improved the performance of dam monitoring systems, so a large amount of information on the dam behaviour can be collected. This has led to the use of more powerful tools for its analysis, many of which were first developed in the field of machine learning (e. g. neural networks). They offer some advantages over the conventional statistical methods. However, their capacity for early detection of anomalies has seldom been studied. As a result, they are far from being fully accepted by practitioners, whose analyses are often restricted to the interpretation of simple plots of time series data, together with basic statistical models. The present work describes a methodology for anomaly detection in dam behaviour, with the following features: a) The prediction model is based on boosted regression trees (BRTs). b) Causal and auto-regressive models are combined to detect different types of anomalies. c) It is checked whether the values of the external variables fall within the range of the training data. The performance of the proposed methodology was assessed through its application to a test case corresponding to an actual 100-m height arch dam, in operation since 1980. Artificial data were generated by means of a finite element model. Different anomalies were later added in order to test the anomaly detection capability. The method can be applied to other response variables and dam typologies, due to the great flexibility of BRTs, which automatically select the most relevant inputs.

  Abstract
  Many countries are implementing new dam safety regulations that often include more restrictive standards. This, together with the increasing average age of dams, results in [...]

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• Modelación física y numérica de aliviaderos en laberinto con fondo poliédrico

  J. Mauro, F. Salazar, M. Toledo, F. Caballero, C. Ponce-Farfán, T. Ramos

  Ingeniería del agua (2016). Vol. 20 (3), pp. 127-138

  
  

  Abstract

  Un número importante de presas en España deben ampliar sus aliviaderos para cumplir criterios de seguridad distintos a los de su construcción. En este contexto, cobran interés soluciones de aliviaderos no convencionales como los aliviaderos en laberinto, que permiten aumentar la capacidad de desagüe de estructuras existentes con una inversión moderada. Para la optimización del diseño de este tipo de aliviaderos se desarrolla el proyecto POLILAB, en cuyo marco se llevaron a cabo las campañas de modelación física y numérica que se exponen en este artículo. Se describen los resultados más relevantes en lo relativo a la inclusión de fondos poliédricos en aliviaderos en laberinto como refuerzo estructural, contemplando además las modificaciones que implican con respecto a soluciones en laberinto con fondo plano, en lo relativo a capacidad de desagüe, patrón de flujo y acciones hidrodinámicas sobre los contornos del laberinto.

  Abstract
  Un número importante de presas en España deben ampliar sus aliviaderos para cumplir criterios de seguridad distintos a los de su construcción. En este [...]

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• Hydraulic and stability analysis of the supporting layer of wedge-shaped blocks

  F. Salazar, J. Mauro, A. Laresa, M. Toledo

  (2016). Protections 2016: 2nd International Seminar on Dam Protection Against Overtopping". Fort Collins, CO: Colorado State University, p. 13-15

  
  

  Abstract

  Wedge shaped blocks (WSB) are attracting increasing attention as protection against overtopping for earth and rock-fill dams. However, there are limited examples of application and some aspects of the technology merit additional research and improvement. One key issue is the design of drainage and supporting layer for WSB protections. During overtopping, part of the overflow leaks through the joints between blocks, hence circulating through the granular material. The permeability and thickness of the supporting layer must be sufficient to prevent the flow from generating pressure on the bottom side of the blocks, which contributes to its destabilization. However, it must also be structurally stable to avoid undesirable deformations on the downstream face. Both the material permeability and the layer thickness determine the hydraulic behavior of this element. These, together with the weight of the blocks and the slope of the downstream face, directly influence mass and block stability. These aspects should be taken into account for the numerical modeling of seepage through the supporting layer. To this end, an application of the open source software Kratos Multi-physics was employed. A parametric study was conducted to quantify the influence of each design variable in the safety factor against mass sliding of the supporting and drainage layer.

  Abstract
  Wedge shaped blocks (WSB) are attracting increasing attention as protection against overtopping for earth and rock-fill dams. However, there are limited examples of application [...]

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• Applications of numerical methods in design and evaluation of overtopping protection systems

  F. Salazar, J. Mauro, A. Larese, M. Toledo

  (2016). Protections 2016: 2nd International Seminar on Dam Protection Against Overtopping". Fort Collins, CO: Colorado State University, p. 1-10

  
  

  Abstract

  The development of overtopping protection systems often requires detailed analyses of complex physical phenomena. This hinders the comprehensive knowledge of their behavior, and therefore the development of suitable design criteria. In recent years, the authors have developed and validated different methods, combining continuous, particle and discrete numerical techniques, to obtain accurate and reliable solutions of different numerical problems involving fluid-soil-structure interaction. In this contribution, some applications of these methods to the study of dam protection against overtopping are presented. The main advantages of this approach include the ability to extract results of the governing variables (pressure, velocity) at any location of the domain, and the possibility to consider scenarios without the restrictions of the experimental facilities (flow rates, size, scale effects). In particular, the contribution gathers examples of application of numerical methods in a) analysis of rockfill dam stability against overtopping, including seepage evolution and deformation of the downstream shoulder, and b) stability analysis of wedge-shaped-blocks subjected to vandalism.

  Abstract
  The development of overtopping protection systems often requires detailed analyses of complex physical phenomena. This hinders the comprehensive knowledge of their behavior, [...]

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• Advances in the understanding of the hydraulic behavior of wedge-shape block spillways

  R. Morán, M. Toledo, F. Salazar, F. Caballero, J. Mauro

  (2016). International Seminar on Dam Protection Against Overtopping. Protections 2016

  
  

  Abstract

  The technology of wedge shaped blocks (WSB) for spillways has had a rising development through the last forty years, and has proved its reliability for practical applications with spillways executed during the last decade. Ongoing additional research aims to complete the understanding of the hydraulic behavior, improve current design criteria and make this technology available to practitioners, in order to consider this type of spillway together with the conventional alternatives when designing an embankment dam spillway. A summary is here shown of recent research performed by the consortium formed by the research organizations: Research Group in Dam Safety (SERPA) of the Technical University of Madrid (UPM) and the International Center for Numerical Methods in Engineering (CIMNE) and the company PREHORQUISA. Even though the experimental campaign has not been completed, some preliminary conclusions can be addressed about the developed pressures along the block tread and the leakage flow from the joints among blocks and the drainage holes.

  Abstract
  The technology of wedge shaped blocks (WSB) for spillways has had a rising development through the last forty years, and has proved its reliability for practical applications [...]

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• Aula Invertida en la Ingeniería del Terreno Asistida por Ordenador

  A. Arcos, J. Arcos, M. Fernández, J. González, C. Monsó, C. Manget, F. Salazar

  (2017). Comunicaciones y ponencias. IV Congreso Internacional sobre Aprendizaje, Innovación y Competitividad-CINAIC.

  
  

  Abstract

  Para superar las limitaciones de la metodología tradicional en la enseñanza de AutoCAD, la Unidad Docente de Expresión Gráfica de la Universidad Politécnica de Madrid está llevando a cabo un Proyecto de Innovación Educativa para implementar una metodología de Aula Invertida (AI) con el apoyo de vídeos educativos. En este trabajo se presentan los resultados de una primera aplicación práctica de la metodología de AI llevada a cabo durante el curso 2016/2017 sobre el tema “La impresión en AutoCAD”. La aplicación de la metodología ha incluido el empleo de un grupo de control y la realización de una prueba objetiva sobre el uso de AutoCAD, la cual ha supuesto un reto organizativo para la Unidad Docente. Los resultados del estudio muestran un mejor rendimiento de los alumnos que han empleado AI. Sin embargo, esta mejora no parece deberse directamente al AI como técnica de enseñanza sino a su aspecto motivador que incide de manera general en el rendimiento de los alumnos.

  Abstract
  Para superar las limitaciones de la metodología tradicional en la enseñanza de AutoCAD, la Unidad Docente de Expresión Gráfica de la Universidad [...]

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• Development of a predictive model for rheumatoid arthritis mortality using random survival forest

  L. Rodríguez-Rodríguez, J. Lezcano, E. Soudah, J. Jover, I. González-Álvarez, F. Salazar, L. Abásolo

  Annals of the Rheumatic Diseases (2017). Vol. 76 (S2), pp. 520

  
  

  Abstract

   Different machine learning methods have been used to develop predictive models of high quality and precision [1]. Among them, Random Survival Forests (RSF) has been proposed as an alternative to traditional survival models [2], being able to overcome most of the limitation of traditional survival techniques, such as Cox proportional hazards models. Objectives Our objective was to develop and internally validate a predictive model for rheumatoid arthritis (RA) mortality using Random Survival Forests (RSF). Methods Retrospective longitudinal study involving 1,461 patients diagnosed with RA between January 1994 and August 2011, and followed at the outpatient clinic of the Rheumatology Department of the Hospital Clínico San Carlos (Madrid, Spain) until death or September 2013. Demographic and clinical-related variables collected during the first two years after disease diagnosis were used. RSF models were developed, based on 1,000 trees. 100 iterations of each model were performed to measure the mean and standard deviation (SD) of the predictive error and the integrated Brier score (IBS). Missing values were imputed using the function implemented by the randomForestSRC package [3]. The predictive capacity of the variables was assessed using the “variable importance” (VIMP). Two models were constructed using the log-rank (MLG) or log-rank score (MLGS) splitting rules. The model with the lowest prediction error was selected. Next, those variables with negative VIMP were excluded and a final model developed. Results 148 patients died (10.1%). MLG showed the lowest prediction error. All variables exhibited a positive VIMP. Final model showed a mean (SD) prediction error and IBS of 0.187 (0.002) and 0.150 (0.003) respectively. The most important predictor variables were age at diagnosis, median erythrocyte sedimentation rate and number of hospital admissions in the first 2 years after RA diagnosis. Conclusions We developed an accurate and precise model for RA mortality using RSF. Age and disease activity showed the highest influence in mortality.

  Abstract
   Different machine learning methods have been used to develop predictive models of high quality and precision [1]. Among them, Random Survival Forests (RSF) has been [...]

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• Development of a predictive model of radiological damage in patients with rheumatoid arthritis based on artificial intelligence

  J. Lezcano, J. Ivorra-Cortes, R. López, J. Martín, B. Fernando-Gutiérrez, M. González-Gay, A. Balsa, I. González-Álvaro, F. Salazar, L. Abásolo, L. Rodríguez-Rodríguez

  Annals of the Rheumatic Diseases (2018). Vol. 77 (S2), pp. 215

  
  

  Abstract

  Background Rheumatoid arthritis (RA) is a chronic inflammatory disease associated with increased mortality and disability. Although different factors associated with prognosis have been identified, it is still difficult to predict the evolution of a specific patient. Objectives Our objective is to train and validate a predictive model of disease severity using radiological damage as a surrogate marker, based on Artificial Intelligence techniques, and using clinical and genetic data. Methods Four independent cohorts were included (892 patients with 1667 hand X-rays). Radiological damage was measured with the Sharp/van-der-Heijde score (SvdH). The variables to be predicted [total value of SvdH, erosion component (ES) and joint narrowing (NS)] were logarithmically transformed. As clinical predictors, age at onset of symptoms, sex, duration of the disease at the time of each radiograph, year of onset of symptoms and presence of rheumatoid factor were used. As genetic variables, the single nucleotide polymorphism data obtained from the Immunochip genotyping platform (Illumina) were used. In addition, an interaction between each polymorphism and the duration of the disease was introduced. Three cohorts were used for the selection of variables, generation of predictive models and internal validation. The fourth cohort was used to perform the external validation of the models. Regression trees with random effects were generated using the R package ‘REEMtree’. The goodness of fit of the models was measured using the root mean squared error (RMSE) and the intraclass correlation coefficient (ICC). Results In the cohorts where the predictive models were developed, the RMSEs for total SvdH, ES and NS were 3.16, 1.02 and 2.29 units of the Sharp/van-der-Heijde score, respectively. The ICCs were 0.96, 0.87 and 0.95, respectively. In the external validation cohort, the RMSEs were 7.13, 3.53 and 4.81 units of the Sharp/van-der-Heijde score, respectively. The ICCs were 0.90, 0.78 and 0.88. For the total SvdH, the best fit model contained the variables ‘age of onset of the symptoms of RA’ and the interaction between duration of the disease and 3 polymorphisms: rs10752907, rs4405161 and rs2501617. For the ES, it contained the variables ‘age of onset of AR symptoms’, the polymorphism rs7769752 and the interaction between disease duration and 6 polymorphisms: rs12410412, rs117029499, rs72925969, rs869186, rs11258464, rs4781952. For the NS, it contained the variables ‘age of onset of AR symptoms’, ‘gender’, and the interaction between disease duration and 9 polymorphisms: rs3814055, rs1020822, rs13157991, rs152294, rs2914190, rs10824537, rs2637229, rs114136906 and rs4958241. Conclusions It is possible to generate predictive models of radiological damage of great precision using Artificial Intelligence techniques. This could allow early stratification of patients according to prognosis. It is necessary to validate these models in other populations

  Abstract
  Background Rheumatoid arthritis (RA) is a chronic inflammatory disease associated with increased mortality and disability. Although different factors associated with prognosis [...]

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• Machine learning based seismic stability assessment of dams with heterogeneous concrete

  F. Salazar, M. Hariri-Ardebili

  (2019). 3rd Meeting of EWG Dams and Earthquakes. An International Symposium, Proceedings

  
  

  Abstract

  Nowadays, the probabilistic risk assessment (PRA) is one of the standard tools for safety evaluation of the existing dams. Although many factors are involved in risk-informed condition assessment of infra-structures, a detailed numerical simulation plays the key role. A comprehensive numerical model usually includes the interaction of the dam with foundation and reservoir, nonlinearity of the concrete, and appropriate seismic wave propagation. Often, the concrete is assumed to be a homogenous material which facilitates the transient simulations. However, in reality, the mass concrete has a heterogeneous nature especially in the case of old dams suffering from aging and deterioration. The objective of this joint contribution is to evaluate the seismic response of a typical gravity dam with heterogeneous concrete material. Since the simulations are cast in the context of probabilistic methods, a relatively large number of analyses are required to fully understand the impact of concrete random field on the structural responses (i.e. displacements, and stresses). Therefore, a Random Forest (RF) meta-model is adopted in order to reduce the computational burden of the probabilistic simulations. RF is an effective technique in the regression analyses of the systems with large number of inputs, and can provide a measure of input variables' importance. This technique is useful to identify the critical areas within the dam body which deterioration may affect the seismic stability of the dam and jeopardize its integrity.

  Abstract
  Nowadays, the probabilistic risk assessment (PRA) is one of the standard tools for safety evaluation of the existing dams. Although many factors are involved in risk-informed [...]

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• Soft Computing and Machine Learning in Dam Engineering

  F. Salazar, M. Hariri-Ardebili, F. Pourkamali-Anaraki, J. Mata

  Water (2020). Speciañ Journal Issue

  
  

  Abstract

  In this Special Issue, we solicit high-quality original research articles focused on state-of-the-art techniques and methods employed in the design and analysis of dams. We welcome both theoretical and application papers of high technical standards across various disciplines, thus facilitating an awareness of techniques and methods in one area that may apply to other areas. We seek high-quality submissions of original research articles as well as review articles on all aspects related to artificial intelligence in dam engineering and appurtenant structures that have the potential for practical application.

  Abstract
  In this Special Issue, we solicit high-quality original research articles focused on state-of-the-art techniques and methods employed in the design and analysis of dams. We [...]

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