Abstract

Approximately 70,000 Spanish off-stream reservoirs, many of them irrigation ponds, need to be evaluated in terms of their potential hazard to comply with the new national Regulation of the Hydraulic Public Domain. This requires a great engineering effort to evaluate different scenarios with two-dimensional hydraulic models, for which many owners lack the necessary resources. This work presents a simplified methodology based on machine learning to identify risk zones at any point in the vicinity of an off-stream reservoir without the need to elaborate and run full two-dimensional hydraulic models. A predictive model based on random forest was created from datasets including the results of synthetic cases computed with an automatic tool based on the two-dimensional numerical software Iber. Once fitted, the model provided an estimate on the potential hazard considering the physical characteristics of the structure, the surrounding terrain and the vulnerable locations. Two approaches were compared for balancing the dataset: the synthetic minority oversampling and the random undersampling. Results from the random forest model adjusted with the random undersampling technique showed to be useful for the estimation of risk zones. On a real application test the simplified method achieved 91% accuracy.

Abstract

A predictive study framework for dams is proposed using Machine Learning (ML) algorithms programmed in R. This study is capable of estimating the relationship between dam response and internal properties or external forces. For a better efficiency of the analysis with predictive models, a previous step has been developed consisting of database cleaning and expansion processes. An interactive interface for each of the two phases has been developed using the Shiny package with the aim of making these processes accessible also to professionals who do not possess knowledge of laborious programming scripts.

Abstract

La presa de Sant Ponç entró en funcionamiento en 1954. Es de gravedad de planta recta, con 59,5 m de altura sobre cimientos. Su aliviadero consta de un vertedero recto de tres vanos de 17 m cada uno regulados por compuertas. La estructura de disipación es singular e incluye un tramo de solera no hormigonado que ha sufrido erosiones. Aunque no comprometen la seguridad de la presa, se ha diseñado una solución para la mejora de su funcionamiento, que se ha basado fundamentalmente en los resultados de modelos numéricos. Los avances en las prestaciones de estas herramientas permiten analizar con detalle el comportamiento hidráulico de las alternativas planteadas en diversas situaciones de vertido, así como el de los desagües intermedios y de fondo.

Abstract

La Baells arch dam has a height above the foundations of 102 m. It entered service in 1976 and since then it had correct performance, which has been controlled with a very complete monitoring system and conventional interpretation techniques. This communication presents the results of a pilot study in which machine learning techniques have been applied to analyze their monitoring data. These techniques offer greater flexibility than conventional procedures for generating dam response prediction models, as well as for identifying relationships between devices of a different nature. This allows a better understanding of the behavior of the structure and greater safety control.

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

The main aim of structural safety control is the multiple assessments of the expected dam behaviour based on models and the measurements and parameters that characterise the dam’s response and condition. In recent years, there is an increase in the use of data-based models for the analysis and interpretation of the structural behaviour of dams. Multiple Linear Regression is the conventional, widely used approach in dam engineering, although interesting results have been published based on machine learning algorithms such as artificial neural networks, support vector machines, random forest, and boosted regression trees. However, these models need to be carefully developed and properly assessed before their application in practice. This is even more relevant when an increase in users of machine learning models is expected. For this reason, this paper presents extensive work regarding the verification and validation of data-based models for the analysis and interpretation of observed dam’s behaviour. This is presented by means of the development of several machine learning models to interpret horizontal displacements in an arch dam in operation. Several validation techniques are applied, including historical data validation, sensitivity analysis, and predictive validation. The results are discussed and conclusions are drawn regarding the practical application of data-based models.

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

Conventional and high-speed train lines are being constructed all over the world with the objective of improving the mobility of both people and goods. Most of these infrastructures are built with railway ballast, a granular material whose main functions are resisting train loads and facing climate actions. The growth in popularity of railway infrastructures has led to an increasing interest in the development of numerical models to evaluate their performance and improve their maintenance. To this respect, the Discrete Element Method (DEM) is an approach that considers the discontinuous nature of granular materials, such as railway ballast. Moreover, it can also be used to compute the behaviour of continuum materials, such as rails and bearing plates, applying the so-called bonded DEM. The code used is developed within DEMPack, a specific software tool for modelling physical problems using the DEM. After evaluating the different geometrical alternatives for representing railway ballast and considering the high amount of material involved in the full-scale tests (more than 130,000 particles) the calculations were carried out using the most efficient option, spheres with rolling friction. This geometrical simplification is not suitable for small-scale tests, however, previous analysis showed that it is accurate enough for reproducing the macroscopic behaviour of the ballast layer. The numerical results correctly capture the effect of changing several parameters such as ballast compaction, inter-particle friction or grain size. It can be concluded that the DEM increases the possibilities for analysing innovative solutions aiming to improve ballasted tracks design, maintenance and performance, since real case-scenarios can be studied with enough accuracy and feasible time.

Abstract

En el presente trabajo se estudia la capacidad de desagüe de aliviaderos con compuertas radiales mediante la aplicación de un código de cálculo numérico al caso del aliviadero de la presa de Oliana.