http://repositorio.lnec.pt:8080/jspui/handle/123456789/40 DBB 2026-04-04T20:59:29Z http://repositorio.lnec.pt:8080/jspui/handle/123456789/1018952 Reforço de barragens de betão com recurso a aumentos da secção transversal Conde Silva, J.; Serra, C. A adição de elevados volumes de betão por forma aumentar a secção transversal de barragens de betão é um procedimento de reforço relativamente comum. Estas modificações de secções de barragens incluem a adição de betão ao paramento de jusante, ao pé de jusante ou sobre o coroamento, tendo este último o objetivo de aumentar a altura da barragem. Estas medidas de reforço podem ser implementadas através da colocação convencional de betão em barragens ou através de betão compactado com cilindros. Este artigo visa compilar algumas das informações mais relevantes sobre este tópico, incluindo referências a casos de sucesso. 2025-10-01T00:00:00Z http://repositorio.lnec.pt:8080/jspui/handle/123456789/1018951 Reabilitação de barragens de betão através de sobreposição de camadas Conde Silva, J.; Serra, C. A reabilitação de barragens de betão com recurso a argamassas e betões é uma prática amplamente adotada, impulsionada tanto por vantagens técnicas como económicas. Este artigo tem o objetivo de consolidar as informações mais pertinentes sobre a utilização de argamassas e betões no âmbito de intervenções de reabilitação de barragens, através adição de uma camada à estrutura existente. Após algumas considerações relativas à compatibilidade entre os materiais, abordar-se-ão os remendos (patches) com argamassa ou betão, que visam solucionar problemas com um volume relativamente pequeno de material de reparação, e as sobreposições (overlays) de betão, que geralmente envolvem uma maior quantidade de material, podendo contribuir para o reforço estrutural. 2025-10-01T00:00:00Z http://repositorio.lnec.pt:8080/jspui/handle/123456789/1018925 Developing Statistical and Multilayer Perceptron Neural Network Models for a Concrete Dam Dynamic Behaviour Interpretation Sejas, A.; Pereira, S.; Mata, J.; Cunha, A. Caldeira, L.; Pina, C.; Viseu, T. This work focuses on the dynamic monitoring behaviour of concrete dams, with a specific emphasis on the Baixo Sabor dam as a case study. The main objective of the dynamic monitoring is to continuously observe the dam’s behaviour, ensuring it remains within expected patterns and issuing alerts if deviations occur. The monitoring process relies on on-site instruments and behaviour models that use pattern recognition, thereby avoiding explicit dependence on mechanical principles. The undertaken work aimed to develop, calibrate, and compare statistical and machine learning models to aid in interpreting the observed dynamic behaviour of a concrete dam. The methodology included several key steps: operational modal analysis of acceleration time series, characterisation of the temporal evolution of observed magnitudes and influential environmental and operational variables, construction and calibration of predictive models using both statistical and machine learning methods, and the comparison of their effectiveness. Both Multiple Linear Regression (MLR) and Multilayer Perceptron Neural Network (MLP-NN) models were developed and tested. This work emphasised the development of several MLP-NN architectures. MLP-NN models with one and two hidden layers, and with one or more outputs in the output layer, were performed. The aim of this work is to assess the performance of MLP-NN models with different numbers of units in the output layer, in order to understand the advantages and disadvantages of having multiple models that characterise the observed behaviour of a single quantity or a single MLP-NN model that simultaneously learns and characterises the observed behaviour for multiple quantities. The results showed that while both MLR and MLP-NN models effectively captured and predicted the dam’s behaviour, the neural network slightly outperformed the regression model in prediction accuracy. However, the linear regression model is easier to interpret. In conclusion, both methods of linear regression and neural network models are suitable for the analysis and interpretation of monitored dynamic behaviour, but there are advantages in adopting a single model that considers all quantities simultaneously. For large-scale projects like the Baixo Sabor dam, Multilayer Perceptron Neural Networks offer significant advantages in handling intricate data relationships, thus providing better insights into the dam’s dynamic behaviour. 2025-11-09T00:00:00Z http://repositorio.lnec.pt:8080/jspui/handle/123456789/1018923 Influence of rock mass hydraulic properties and jointing pattern on hydromechanical behaviour of gravity dams Braga Farinha, M. L.; Azevedo, N.; Oliveira, S. In concrete dam foundations, rock mass discontinuities and spatial variations in permeability give rise to complex seepage patterns, making monitoring and safety evaluation particularly challenging. An accurate safety assessment of concrete gravity dams requires the use of coupled models that: • capture the interaction between mechanical and hydraulic behaviour; and • effectively simulate the geometry and properties of rock foundation discontinuities. In this study, a coupled hydromechanical analysis is performed on four gravity dams of two different heights using Parmac2D-Fflow, a fully coupled 2D numerical model capable of simulating discontinuities in the foundation, as well as the effects of grout curtains and drainage systems. Numerical simulations were carried out by gradually increasing the water head at the reservoir bottom and applying the corresponding hydrostatic pressure to the upstream face of the dam, thereby simulating the reservoir filling process. This loading phase was followed in all cases by an unloading phase, representing the subsequent emptying of the reservoir. The analyses were conducted under various behavioural scenarios, accounting for a non-linear response at the dam/foundation interface and both linear elastic and non-linear behaviours within the foundation. Several key parameters were investigated, including joint pattern, joint stiffness, strength of the dam/foundation interface and hydraulic properties. 2025-10-01T00:00:00Z