Approximating Dynamic Elastic Modulus of Concrete for an Old Aqueduct Using Dynamic Tests and BP Neural Network

Abstract

Monitoring the degradation of the dynamic elastic modulus (Ed) of concrete is of great importance to track the durability deterioration for hydraulic concrete structures. For the aqueduct under investigation in this study, the dynamic elastic modulus of bent frames and moment frame supports (Ed-frame), the dynamic elastic modulus of arch trusses (Ed-arch) and the shear stiffnesses of the asphaltic bearings of U-shaped flumes (Kflume) are the main parameters to define the dynamic behavior of the structure, which have direct correlation with its vibrational characteristics and thus practicably can be estimated by a BP (back-propagation) neural network using modal frequencies as inputs. Since it is impossible to obtain sufficient experimental field data to train the network, a full-scale 3D FE model of the entire aqueduct is created, and modal analyses under different combinations of Kflume, Ed-arch and Ed-frame are conducted to generate the analytical dataset for the network. After the network’s architecture is refined by the cross-validation process and its modeling accuracy verified by the test procedure, the primary modal frequencies of the aqueduct obtained from in situ dynamic tests are put into the network to obtain the final approximations for Kflume, Ed-arch and Ed-frame, which sets an evaluation baseline of the general concrete Ed status for the aqueduct and indicates that the makeshift asphaltic bearings of U-shaped flumes basically can be treated as a three-directional hinge in the FE model. It is also found that more inputs of modal frequencies can improve the prediction accuracy of the BP neural network.