KEYWORDS: Education and training, Vibration, Structural health monitoring, Matrices, Accelerometers, Mathematical modeling, Systems modeling, Deep learning, Data acquisition, Damage detection
In the field of structural health monitoring, the adoption of intelligent systems able to automatically detect changes in a structure are evidently attractive. A change in the baseline configuration can be an early predictor of a structural defect that has to be monitored before it reaches critical conditions. When there is no prior knowledge on the system, deep learning models such as autoencoders could effectively detect a change and enhance the capability to determine the damage location. In this paper a deep learning approach is applied to a test rig consisting of a small building model composed by four floors connected by bending springs. Modifications of the system are simulated by changing stiffness of the spring. This algorithm is compared with traditional approach based on modal parameters by carrying out experimental tests to validate the hypothesis.
KEYWORDS: Education and training, Bridges, Windows, Sensors, Deep learning, Neural networks, Distance measurement, Structural health monitoring, Signal processing, Sensor networks
The estimation of trains weight could be useful under certain circumstances. For instance, in the field of structural health monitoring, some considerations can be derived from the evaluation of the load spectrum that an infrastructure has to withstand in its lifetime. One approach to estimate the train weight is based on the use of strain gauges mounted on the rail. The procedure allows to associate the local deformations with the load on an axle. However, strain gauges present several limitations: they are regarded as delicate sensors, and their replacement is burdensome and time-consuming. Moreover, their life is usually short when subjected to weathering and numerous load cycles. For these reasons, this paper proposes a novel methodology that relies on the use of more robust sensors mounted on a bridge structure for the estimation of the train load, alongside other information, such as the number of axles, the train speed, and the train class. The idea consists in the estimation of the train load starting from a network of sensors mounted on a bridge. A deep learning model is particularly suitable to achieve this task. The sensors network must consist of robust and easy-to-replace transducers (such as velocimeters mounted on the bridge structure). In this way, when the strain gauges are removed, the system is still able to estimate the loads passing on the bridge.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.