Interpretation of concrete dam behaviour with artificial neural network and multiple linear regression models

• Published in: Engineering structures Vol. 33; no. 3; pp. 903 - 910
• Main Author: Mata, J.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.03.2011; Elsevier
• Subjects: Applied sciences; Artificial neural network; Artificial neural networks; Assessments; Building structure; Buildings. Public works; Ceteris paribus; Computation methods. Tables. Charts; Concrete dam; Concrete structure; Concretes; Construction (buildings and works); Dam behaviour; Dams; Dams and subsidiary installations; Exact sciences and technology; Hydraulic constructions; Mathematical models; Multiple linear regression; Neural networks; Regression; Regression analysis; Stresses. Safety; Structural analysis. Stresses; Artificial neural network; Multiple linear regression; Ceteris paribus; Dam behaviour; Concrete dam; Linear regression; Neural network; Modeling; Multiple regression; Dam; Regression model; Statistical model; Concrete construction; Behavior
• ISSN: 0141-0296; 1873-7323
• DOI: 10.1016/j.engstruct.2010.12.011

The safety control of large dams is based on the measurement of some important quantities that characterize their behaviour (like absolute and relative displacements, strains and stresses in the concrete, discharges through the foundation, etc.) and on visual inspections of the structures. In the more important dams, the analysis of the measured data and their comparison with results of mathematical or physical models is determinant in the structural safety assessment. In its lifetime, a dam can be exposed to significant water level variations and seasonal environmental temperature changes. The use of statistical models, such as multiple linear regression (MLR) models, in the analysis of a structural dam’s behaviour has been well known in dam engineering since the 1950s. Nowadays, artificial neural network (NN) models can also contribute in characterizing the normal structural behaviour for the actions to which the structure is subject using the past history of the structural behaviour. In this work, one important aspect of NN models is discussed: the parallel processing of the information. This study shows a comparison between MLR and NN models for the characterization of dam behaviour under environment loads. As an example, the horizontal displacement recorded by a pendulum is studied in a large Portuguese arch dam. The results of this study show that NN models can be a powerful tool to be included in assessments of existing concrete dam behaviour.