Abstract:Double-curvature dams are unique 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 ther… Show more
“…Since the temperature field in the dam body influences the deformations of the dam and depends on the initial temperature considered, we performed a preliminary analysis to obtain a realistic thermal field to be used as the reference temperature in the body of the dam. This is a relevant issue, since thermal displacements are computed on the basis of the difference between these values and the thermal field at each time step of the simulation [16]. For this purpose, we performed a 12-year transitory analysis with a fixed value of the initial temperature (8°C) and a time step of 12 h. The resulting thermal field at the end of this preliminary calculation was taken as the initial temperature for all the scenarios considered.…”
Section: Fem Modelmentioning
confidence: 99%
“…A one-way coupling between the thermal and the mechanical problem was applied: the thermal field at the end of the preliminary transient analysis was taken as reference temperature, i.e., deviations from such a value results in thermal deformations; the hydrostatic load is applied and the stress and deformation are computed assuming elastic behaviour; the deformation field is computed as the sum of the thermal and the mechanical deformations. The numerical implementation was developed by the authors and described in detail in [16].…”
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: multi-class, 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.
“…Since the temperature field in the dam body influences the deformations of the dam and depends on the initial temperature considered, we performed a preliminary analysis to obtain a realistic thermal field to be used as the reference temperature in the body of the dam. This is a relevant issue, since thermal displacements are computed on the basis of the difference between these values and the thermal field at each time step of the simulation [16]. For this purpose, we performed a 12-year transitory analysis with a fixed value of the initial temperature (8°C) and a time step of 12 h. The resulting thermal field at the end of this preliminary calculation was taken as the initial temperature for all the scenarios considered.…”
Section: Fem Modelmentioning
confidence: 99%
“…A one-way coupling between the thermal and the mechanical problem was applied: the thermal field at the end of the preliminary transient analysis was taken as reference temperature, i.e., deviations from such a value results in thermal deformations; the hydrostatic load is applied and the stress and deformation are computed assuming elastic behaviour; the deformation field is computed as the sum of the thermal and the mechanical deformations. The numerical implementation was developed by the authors and described in detail in [16].…”
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: multi-class, 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.
“…A basic overview of the main approaches for modelling the behaviour of concrete at early ages and beyond is also provided in RILEM state-of-the-art report [ 27 ]. The investigation on the importance of some factors and numerical aspects in FE models has been recently presented in [ 28 ] for arch dams. Nevertheless, the FE modelling strategy is usually not thoroughly discussed and relies rather on the judgment of researchers performing these calculations, while some aspects involved in the FE analysis can be crucial.…”
In this paper, the focus is placed on essential aspects of finite element modelling of thermo-mechanical behaviour of massive foundation slabs at early ages. Basic decision-making issues are discussed in this work: the potential need to explicitly consider the casting process in the modelling, the necessary size of the underlying soil to be modelled and the size of the FE mesh, and the need of considering daily changes of the environmental temperature and the temperature distribution over the depth of the soil. Next, the contribution of shrinkage to early age stresses, the role of the reinforcement, and the type of mechanical model are investigated. Comparative analyses aiming to investigate the most important aspects of the FE model and some possible simplifications with negligible effect on the results are made on the example of a massive foundation slab. Finally, the results are summarized with recommendations for creating the FE models of massive slabs at early ages.
“…It is acknowledged that thermal load is one of the important reasons for concrete arch dam cracking, both during construction and operation [1][2][3][4]. During the construction, due to the exothermic reactions, a lot of hydration heat cannot be released in time, resulting in the increase of internal temperature gradient and thermal stress, which leads to cracks.…”
Section: Introductionmentioning
confidence: 99%
“…Temperature cracks problem was an important research topic in the field of high arch dam structure. To date, lots of achievements [3] related to thermal stress and temperature cracks of concrete dams mainly are focused on the influence of the diurnal temperature variation during the construction [8,9], and the annual temperature [10], seasonal temperature variations [4,11,12], solar radiation [13,14], and water temperature variations [1] during the operation. e results show that the cold wave or the diurnal temperature variation will affect the thermal field within a certain range below the concrete surface and lead to the surface cracks [15].…”
In order to make clear the cracking reasons in arch dam of Xiaowan Hydropower Station during operation period, the approach to combine ANSYS with finite element program COCE-3D is adopted. Firstly, the influence by element type and mesh size for the temperature field simulation result is analyzed. Subsequently, the three typical dam segments cut from Xiaowan arch dam are selected and the relevant finite element model is established; the effect of the measured diurnal air temperature on temperature field and temperature stress of arch dam is analyzed thoroughly. The results indicate that the temperature gradient in mass concrete becomes lower, whereas the affecting depth becomes deeper when the mesh size is too large. Therefore, it is advisable to use smaller size mesh to study the influence of the measured diurnal air temperature on the surface temperature distribution in mass concrete. The temperature of downstream zone in arch dam is significantly affected by air temperature; the changing laws of temperature field and temperature stress with the air temperature are basically consistent, which is sensitive to lower temperature. When the temperature sharply decreased, the temperature stress in the downstream zone is mainly in tensile stress state. The calculated results are basically consistent with the measured results, and the temperature stress induced by the day-night temperature difference is the important reason for the horizontal cracks on the downstream surface. The submodel analysis method is an important alternative approach to study the changing laws of temperature field of arch dam. The research results not only provide an evidence for temperature control and crack prevention of Xiaowan arch dam but also provide a reference for temperature field simulation of similar projects.
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