Data Science and Machine Learning Applied to Engineering

Nesta página são apresentadas as principais produções bibliográficas derivadas das pesquisas do grupo DASMAE.

Os bancos de dados utilizados nos trabalhos podem ser solicitados aos autores correspondetes dos trabalhos.

Integrating machine learning and Monte Carlo Simulation for probabilistic assessment of durability in RC structures affected by carbonation-induced corrosion

Felix, E.F.; Lavinicki, B.M.; Bueno, T.L.G.T.; Castro, T.C.C.; Cândido, R.A.

Journal of Building Pathology and Rehabilitation

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Influence of the ANN Hyperparameters on the Forecast Accuracy of RAC’s Compressive Strength

Almeida, T. A.C.; Felix, E.F.; de Sousa, C.M.A.; Pedroso, G.O.M.; Motta, F.M.B.; Prado, L.P.

Materials Journal

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Carbonation model for fly ash concrete based on artificial neural network: Development and parametric analysis

Felix, E.F.; Carrazedo, R.; Possan, E.

Journal of Construction and Building Materials

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A Monte Carlo-Based Approach to Assess the Reinforcement Depassivation Probability of RC Structures: Simulation and Analysis

Felix, E.F.; Falcão, I.S.; dos Santos, L.G.; Carrazedo, E.; Possan, E.

Buildings Journal

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Analysis of training parameters in the ANN learning process to map the concrete carbonation depth

Felix, E.F.; Possan, E.; Carrazedo, R.

Journal of Building Pathology and Rehabilitation

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A New Formulation to Estimate the Elastic Modulus of Recycled Concrete Based on Regression and ANN

Felix, E.F.; Possan, E.; Carrazedo, R.

Journal of Sustainability

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