![]() ![]() (2006), "Structural health monitoring of civil infrastructure", Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 365(1851), 589-622. (2002), "Modal identification and damage detection on a concrete highway bridge by frequency domain decomposition", The Structural Engineering World Conference: SEWC. (1997), "Time-delay neural networks in damage detection of railway bridges", Adv. ![]() (2019), "Artificial neural network methods for the solution of second order boundary value problems", Comput. Anitescu, C., Atroshchenko, E., Alajlan, N.The proposed method not only can distinguish the damage type, but also it can accurately identify damage level. ![]() The measurements of the Nam O bridge, a truss railway bridge in Vietnam, is used to illustrate the method. ![]() Two machine learning algorithms are used one for classifying the type and location of damage, whereas the other for finding the severity of damage. The outputs of the ANNs are the damage type, location and severity. The transmissibility damage indicators are calculated and stored as ANNs inputs. In each scenario, the vibration responses at the considered nodes are recorded and then used to calculate the transmissibility functions. Both single damage and multiple damage cases are simulated in the bridge model with different scenarios. A finite element (FE) model is constructed for the bridge and updated using FE software and experimental data. Sensors are installed in the truss joints in order to measure the bridge vibration responses under train and ambient excitations. The network not only can predict the existence of damage, but also can classify the damage types and identity the location of the damage. A new approach method, which makes use of the input parameters calculated from the transmissibility function, is proposed. This paper proposes the use of transmissibility functions combined with a machine learning algorithm, Artificial Neural Networks (ANNs), to assess damage in a truss bridge. ![]()
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