Monitoring of Civil Engineering Structures Using Temporal Convolutional Networks and Meteorological Data
Nicolas Manzini, B. Hilloulin, Najwa Aidat
Structural health monitoring (SHM) of small structures is necessary to ensure their long-term sustainability. However, the large number and scattered distribution of such structures raise a significant techno-economic challenge, which calls for optimization of the monitoring solutions to be deployed. Monitoring these structures requires complementary data to better understand their behavior. Such information is typically obtained through the deployment of additional sensors measuring temperature, solar radiation, humidity, and wind speed. Nevertheless, this approach is difficult to balance for small or isolated structures, whose maintenance programs already face tight constraints to ensure durability. Optimizing the number of sensors to be installed is therefore crucial. In this context, artificial neural networks can model complex relationships between time series through learning processes and thus represent an alternative to the deployment of numerous physical sensors. Instead of multiplying measurement points on a structure, neural networks can model various phenomena affecting it (gradients, internal temperature, surface heating, drying, wind effects) from a smaller set of input data. In this context, this study focuses on the use of publicly available datasets in France This paper presents investigates the use of Temporal Convolutional Networks (TCN) for sensor data regression based on external and public data, the challenges faced with such approach and discusses their potential as a “ready-to-use” analysis tool. A case study using multiple type of sensors on concrete structures is presented.