Effects of material properties on structural behaviour and safety evaluation of an old arch dam

Safety evaluation is an important task to verify performance of a dam during its service. For an old dam, the material properties vary from the designed value significantly affecting structural performance. This study investigates the effects of material properties of dam concrete and foundation rock on the structural behaviour of an old concrete arch dam during operation. The dam safety is evaluated by using a three-dimensional finite element model (FEM). All main loads, such as water pressure, dam self-weight, and thermal load, are considered in the analysis. An existing 54-year-old concrete arch dam, located in a tropical climate region in Thailand, is employed as a case study. The analysis results show that deformation modulus of the foundation, modulus elasticity of concrete, and the variation of reservoir water level during operation are key factors affecting the dam response. With a deformability modulus of foundation and elasticity modulus of dam concrete, which are about 20 GPa and 44.2 GPa, respectively, a good agreement in dam deflections between the analysis and the current monitored data from plumb line equipment can be obtained. It should be noted that the material properties are different from designed value significantly. Finally, safety evaluation can be properly conducted based on current material properties.

Temperature Field Online Reconstruction for In-Service Concrete Arch Dam Based on Limited Temperature Observation Data Using AdaBoost-ANN Algorithm

Temperature is one of the factors affecting the safety operation of concrete arch dams. To accurately reconstruct the temperature field of the concrete arch dam online based on the temperature data of several typical dam sections, this paper proposes the AdaBoost-ANN algorithm. The algorithm uses artificial neural network (ANN) to establish a training set of the measured temperature data and the temperature field of the concrete arch dam obtained by the three-dimensional finite element model; these trained artificial neural networks are used as weak classifiers of the AdaBoost algorithm. Then, the AdaBoost-ANN algorithm is used to establish the mapping relationship between the measured temperature data and the temperature field, and the online reconstruction of the temperature field of the concrete arch dam is realized. The case study shows that the temperature field of the concrete arch dam can be accurately established by AdaBoost-ANN algorithm based on limited temperature observation data. The algorithm is more time-saving and labor-saving than the finite element method and is convenient for online reconstruction of the temperature field and assessment of the safety status of the concrete arch dam.