Academic literature on the topic 'Arch dams'

Conducting a case study, this thesis investigates the dynamic behavior of Karun IV arch dam (Iran) under the effect of maximum feasible thermal input. A three-dimensional finite element model is created using ANSYS software. The thesis determines the impact of thermal loadings under two scenarios of normal and minimum water levels. The dam-foundation, dam-water and water-foundation interactions are considered in the modeling to accurately capture its dynamic response. Furthermore, in addition to the water compressibility, appropriate wave absorbing boundaries are used for the reservoir far end and bottom and for the foundation. Stability, static, modal, and thermal analyses are conducted as initial conditions. Using three orthogonal earthquake components, linear dynamic time history analyses with and without thermal loading are performed. The comparison of the results verified that the application of thermal effects on the dynamic analysis increases the maximum tension, changes the maximum pressure, and decreases the displacements in the dam body. This study concludes that thermal loading must be considered in the dynamic analysis of an arch dam as it can worsen the tensile cracks.<br>Applied Science, Faculty of<br>Engineering, School of (Okanagan)<br>Graduate

Includes abstract.<br>Includes bibliographical references.<br>This document underscores various temperature related components and procedures for undertaking a successful performance analysis of concrete arch dams. Arch dams experience high temperature variations, which are generally assessed using finite element models. Deterioration of arch dams is caused principally by thermal effects, with 19% cases attributed to freezing and thawing, and 9% to temperature variations (Daoudu et al., 1997). The temperature loading conditions cause a high expanse of stresses at the various interface locations due to change in environmental conditions. Past research has shown that stresses caused by temperature change can be larger than those from reservoir loading (Bureau of Reclamation, 1977). The proposed finite element model for this study focuses on the performance assessment of arch dams in operation due to thermal loading.

Includes bibliographical references.<br>To date, dam failures have resulted in significant losses in the commercial economy and in human life. Raising awareness in the field of structural health monitoring is neccesary to develop contemporary structural analysis and monitoring methods to ensure the integrity of these structures. Hence, the research aims at developing an analytical formulation that can be used in the dynamic modelling of arch dams, for structural health monitoring purposes. A hypothetical arch dam model was created to investigate the influence of a reservoir’s orientation and geometry on the dam’s dynamic properties, and the discrepancies between the Westergaard and Fluid Structure Interaction (FSI) analysis methods. The two analysis methods were then utilized to develop an updatable finite element model, in a case study pertaining to the 72m high Roode Elsberg concrete-arch dam. Thereafter, ambient vibration tests were conducted on the Roode Elsberg dam to measure its dynamic properties and validate the finite element models. The excitation on the dam was provided by the wind and the reservoir flowing over the spillway. Vibrations of the dam were measured and recorded by accelerometers placed on the cantilevered arch blocks. Finally, the Frequency Domain Decomposition (FDD) algorithm was used to analyse the acquired data and identify the natural frequencies of the dam.

The temperature field of concrete dams in operational stages is dominantly influenced by variations in environmental seasonal temperature and climatic conditions. Cyclic seasonal temperature and associated thermally induced stresses have been found to contribute significantly to long term degradation of strength and stiffness of concrete dams raising concerns about their durability (Leger et al. 1993). In this study, a critical review of the current state-of-the-art of temperature models for determining temperature distribution including the main environmental parameters influencing the temperature distribution for concrete dams in operation has been undertaken. It is found that, the heat flow in the concrete dams currently is approximated as conduction only and the classical Fourier heat conduction models are adopted as the governing equations to define heat flow mechanisms in the dam. The solution to Fourier heat models is accurately achieved through the finite element analysis using finite element models of the dam to the determine the temperature field.

This study explores the buckling behaviour of thin walled and curved concrete arch dams investigated as a function of various parameters of interest, the focus being on the influence of dam thickness on the elastic buckling behaviour.

Societies around the world are heavily dependent on civil engineering infrastructures such as concrete dams that provide necessities such as water supply for irrigation, hydroelectric power generation and prevention of floods. As a result, it is important to ensure that concrete dams are protected such that their failure is avoided. To ensure the structural safety of these structures, concrete dams are continuously monitored by sensors installed on the dam to detect any unusual behaviour. Data collected by the sensors include environmental variables (temperature and water levels) and dam responses (deformations, stresses, strains, natural frequencies) which indicate the structural behaviour of dams. This implies that research in the analysis of the collected data is very important. Methods used in the analysis of dam monitoring data include data-driven models, physical-based models and hybrid models. Data-driven models utilise environmental variables as independent factors and dam responses as dependent factors. The trends in the dam responses can be learnt for purposes of monitoring and prediction by understanding the interactions between environmental variables and dam responses. Dam specialists have mainly focused on predicting the static deformations of dam walls using environmental variables through statistical modelling. Dynamic properties such as natural frequencies also provide valuable information on the structural behaviour of dams as they are influenced by the changes in environmental variables. To the best of my knowledge, there is no scientific literature that has studied the influence of environmental variables on natural frequencies through statistical modelling. The increase in the amount of data collected from monitoring devices installed on dams has led to the use of advanced statistical models to extract important information about the behaviour of dams. Machine learning algorithms have been developed to solve problems of large data sets and nonlinearity between variables. In particular, there are no studies that exist in the prediction of natural frequencies using measured environmental variables (water level and temperature). Therefore, the purpose of this study was to understand the effect of water level and temperature on natural frequencies and deformations. The case study used in this thesis is Roode Elsberg dam, a concrete arch dam located in South Africa. Natural frequency data used was collected between December 2014 and June 2017 while deformation data used was between January 2012 and June iii 2016. Observations indicated that water level was the dominant factor driving the variation of natural frequencies with temperature affecting the natural frequency variations in periods of constant water levels. On the other hand, temperature was the driving factor in deformation variations with water level also affecting deformation variation. Due to the nonlinear relationship between environmental variables and dam responses, a machine learning-based algorithm known as Gaussian process regression models were developed to predict natural frequencies. In Gaussian process regression, the choice of a covariance function is very important in producing good results. The ability of the different covariance functions in Gaussian process regression models, to predict natural frequencies and dam deformations, was studied. The performance of Gaussian process regression models was compared with other machine learning algorithms (BRT, SVR and ANN), multivariate adaptive regression splines and the commonly used MLR models in the prediction of natural frequencies and deformations. Results suggested that the GPR model is the most suitable and more accurate in the prediction of dam responses. Finally, robust statistics are introduced in the identification of anomalies in the collected data. Furthermore, univariate methods are used to identify any abnormalities in dam behaviour. Results indicated, there were no abnormalities in the dam behaviour.