A global–local strategy with the generalized finite element framework for continuum damage models

Evangelista, Francisco; Alves, Gelson de Sousa; Moreira, José Fabiano Araújo; Paiva, Guilherme

doi:10.1016/j.cma.2020.112888

Cited by 13 publications

(3 citation statements)

References 36 publications

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“…Coupled with the concrete degradation model, it becomes advantageous to use submodeling techniques, starting from the discretization of regions of interest in which the degradation of the element occurs more intensely. Thus, crack propagation regions more consistent with experimental observations can be predicted [6].…”

Section: Introductionsupporting

confidence: 74%

Railway Infrastructure Asset Analysis Using A Global-Local Approach

Gamino,

Santos,

Bitencourt Jr

et al. 2023

Proceedings of the 11th International Conference on Fracture Mechanics of Concrete and Concrete Structures

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The global-local modeling methodology is used to evaluate specific regions of a structural system. A global model represents the entire system under study, while critical domains are assessed in detail by a submodeling routine employing more refined models. The advantages of this hierarchical modeling are related to reducing the need for complex transition regions in solid elements and the versatility in testing different geometries in the submodel region. Furthermore, it allows for a reduction in the computational power required to solve the problem. However, ensuring a good transfer of the boundary conditions between the different models is essential. For large and complex infrastructures, such as bridges, numerical analyses sometimes become time-consuming when more complex evaluations are required. Therefore, using simplified models to reproduce global behavior and more complex modeling strategies in critical locations can be an alternative to comply with this requirement. The present paper aims to employ a global-local approach to analyze a reinforced concrete railway bridge. Thus, the global and local models employed numerical analyses using solid tetrahedral finite elements. The local region presented a greater mesh discretization for the submodeling. Additionally, the local model allows inserting steel reinforcement details and specific constitutive laws for the materials utilized in the described region of the railway infrastructure. The obtained results enable the evaluation of the formation and propagation of cracks and the identification of damages located in the structural elements with greater precision. The methodology can improve condition assessment and support the inspection and maintenance of critical infrastructure assets.

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Section: Introductionsupporting

confidence: 74%

Railway Infrastructure Asset Analysis Using A Global-Local Approach

Gamino,

Santos,

Bitencourt Jr

et al. 2023

Proceedings of the 11th International Conference on Fracture Mechanics of Concrete and Concrete Structures

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“…In this sense, using an ANN is a specific and justified choice, according to authors [10][11][12][13][14][15][16][17][18][19][20], and depends on the dataset used. In Yeh's study [10], using four distinct models with varying inputs, RMSE values between 2 MPa and 4.5 MPa were obtained with the augment-neuron networks for both testing and training. This parameter assesses the accuracy of the network, and thus, in Cases 3 and 4, there is not much accuracy.…”

Section: Step 3-statistical Analysis Of the Technique According To Th...mentioning

confidence: 99%

“…Numerous studies have been striving to develop models capable of predicting the compressive strength of materials such as cement, mortar, and concrete. These models use different methodologies and data sources, such as statistical techniques, analytical analyses, mathematical calculations, numerical simulations, and computational algorithms [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Prediction of the Compressive Strength of Brazilian Concretes: A Dual-Dataset Study

et al. 2023

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Lately, several machine learning (ML) techniques are emerging as alternative and efficient ways to predict how component properties influence the properties of the final mixture. In the area of civil engineering, recent research already uses ML techniques with conventional concrete dosages. The importance of discussing its use in the Brazilian context is inserted in an international context in which this methodology is already being applied, and it is necessary to verify the applicability of these techniques with national databases or what is created from national input data. In this research, one of these techniques, an artificial neural network (ANN), is used to determine the compressive strength of conventional Brazilian concrete at 7 and 28 days by using a database built through publications in congresses and academic works and comparing it with the reference database of Yeh. The data were organized into nine variables in which the data samples for training and test sets vary in five different cases. The eight possible input variables were: consumption of cement, blast furnace slag, pozzolana, water, additive, fine aggregate, coarse aggregate, and age. The response variable was the compressive strength of the concrete. Using international data as a training set and Brazilian data as a test set, or vice versa, did not show satisfactory results in isolation. The results showed a variation in the five scenarios; however, when using the Brazilian and the reference data sets together as test and training sets, higher R2 values were obtained, showing that in the union of the two databases, a good predictive model is obtained.

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