@phdthesis{Tan,
  author    = {Tan, Fengjie},
  title     = {Shape Optimization Design of Arch Type Dams under Uncertainties},
  doi       = {10.25643/bauhaus-universitaet.3960},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20190819-39608},
  school      = {Bauhaus-Universit{\"a}t Weimar},
  abstract  = {Due to an increased need for hydro-electricity, water storage, and flood protection, it is assumed that a series of new dams will be built throughout the world. Comparing existing design methodologies for arch-type dams, model-based shape optimization can effectively reduce construction costs and leverage the properties of construction materials. To apply the means of shape optimization, suitable variables need to be chosen to formulate the objective function, which is the volume of the arch dam here. In order to increase the consistency with practical conditions, a great number of geometrical and behavioral constraints are included in the mathematical model. An optimization method, namely Genetic Algorithm is adopted which allows a global search. Traditional optimization techniques are realized based on a deterministic approach, which means that the material properties and loading conditions are assumed to be fixed values. As a result, the real-world structures that are optimized by these approaches suffer from uncertainties that one needs to be aware of. Hence, in any optimization process for arch dams, it is nec- essary to find a methodology that is capable of considering the influences of uncertainties and generating a solution which is robust enough against the uncertainties. The focus of this thesis is the formulation and the numerical method for the optimization of the arch dam under the uncertainties. The two main models, the probabilistic model, and non-probabilistic models are intro- duced and discussed. Classic procedures of probabilistic approaches un- der uncertainties, such as RDO (robust design optimization) and RBDO (reliability-based design optimization), are in general computationally ex- pensive and rely on estimates of the system's response variance and fail- ure probabilities. Instead, the robust optimization (RO) method which is based on the non-probabilistic model, will not follow a full probabilistic approach but works with pre-defined confidence levels. This leads to a bi-level optimization program where the volume of the dam is optimized under the worst combination of the uncertain parameters. By this, robust and reliable designs are obtained and the result is independent of any as- sumptions on stochastic properties of the random variables in the model. The optimization of an arch-type dam is realized here by a robust optimiza- tion method under load uncertainty, where hydraulic and thermal loads are considered. The load uncertainty is modeled as an ellipsoidal expression. Comparing with any traditional deterministic optimization (DO) method, which only concerns the minimum objective value and offers a solution candidate close to limit-states, the RO method provides a robust solution against uncertainties. All the above mentioned methods are applied to the optimization of the arch dam to compare with the optimal design with DO methods. The re- sults are compared and analyzed to discuss the advantages and drawbacks of each method. In order to reduce the computational cost, a ranking strategy and an ap- proximation model are further involved to do a preliminary screening. By means of these, the robust design can generate an improved arch dam structure which ensures both safety and serviceability during its lifetime.},
  subject      = {Wasserbau},
  language  = {en}
}
@inproceedings{TahaSherifHegger2004,
  author    = {Taha, M. M. Reda and Sherif, Alaa and Hegger, Josef},
  title     = {A nouvelle approach for predicting the shear cracking angle in RC and PC beams using artificial neural networks},
  doi       = {10.25643/bauhaus-universitaet.107},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20111215-1071},
  year      = {2004},
  abstract  = {The truss model for predicting shear resistance of reinforced concrete beams has usually been criticized because of its underestimation of the concrete shear strength especially for beams with low shear reinforcement. Two challengers are commonly encountered in any truss model and are responsible for its inaccurate shear strength prediction. First: the cracking angle is usually assumed empirically and second the shear contribution of the arching action is usually neglected. This research introduces a nouvelle approach, by using Artificial Neural Network (ANN) for accurately evaluating the shear cracking angle of reinforced and prestressed concrete beams. The model inputs include the beam geometry, concrete strength, the shear reinforcement ratio and the prestressing stress if any. ...},
  subject      = {Neuronales Netz},
  language  = {en}
}
@inproceedings{SwaddiwudhipongThoLiu2004,
  author    = {Swaddiwudhipong, Somsak and Tho, Kee Kiat and Liu, Zishun},
  title     = {Material characterization using artificial neural network},
  doi       = {10.25643/bauhaus-universitaet.155},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20111215-1550},
  year      = {2004},
  abstract  = {Indentation experiments have been carried out over the past century to determine hardness of materials. Modern indentation machines have the capability to continuously monitor load and displacement to high precision and accuracy. In recent years, research interests have focussed on methods to extract material properties from indentation load-displacement curves. Analytical methods to interpret the indentation load-displacement curves are difficult to formulate due to material and geometric nonlinearities as well as complex contact interactions. In the present study, an artificial neural network model was constructed for interpretation of indentation load-displacement curves. Large strain-large deformation finite element analyses were first carried out to simulate indentation experiments. The data from finite element analyses were then used to train the artificial neural network model. The artificial neural network model was able to accurately determine the material properties when presented with load-displacement curves which were not used in the training process. The proposed artificial neural network model is robust and directly relates the characteristics of the indentation loaddisplacement curve to the elasto-plastic material properties.},
  subject      = {Neuronales Netz},
  language  = {en}
}