MS9.8 - Dynamics of Railway infrastructures

Examining the correctness of the structural health state and projecting its future strength are the two main goals of Structural Health Monitoring (SHM). Recently, researchers have been closely studying the inclinometer-based structural damage detection procedures. For instance, the measurement of vertical displacements is one of the most crucial challenges in monitoring railway bridges which can be granted using high-resolution tiltmeters. However, due to the high price of commercial inclinometers, this method can only be applied to unique structures with a sizeable Structural Health Monitoring (SHM) budget. Therefore, increasing the accuracy of low-cost angular sensors through numerous strategies has garnered a lot of interest in the field of structural evaluation. Nevertheless, it is possible to extract meaningful information from these inexpensive electronics that can offer helpful insights for SHM systems with adequate code and the correct mode to leverage their characteristics and potential. In this work, a revolutionary, low-cost tiltmeter is introduced. It detects inclination using fusion tech-nology, which combines an accelerometer, a gyroscope, and a magnetometer. This device uses open-source Internet of Things (IoT) microcontroller technology (Arduino due + Raspberry Pi), enabling wireless data streaming and free commercial software for data collection. The issues with the collection of angular data are discussed in this paper, along with remedies. Not only are the coding and location concerns of these sensors thoroughly addressed, but also in-depth solutions to the challenges raised above are offered, along with instructions on assembling and preparing the sensors.

This study summarizes the experimental and computational studies carried out for the robust-ness assessment of steel riveted bridges based on truss-type structures. This paper describes a unique case of a 21 m full-scale bridge span tested under laboratory conditions with an exten-sive monitoring system to evaluate structural behavior and robustness as damage progressed in its elements after the removal of up to two diagonals. A computational analysis was also per-formed to examine other possible damages not included in the experiment, such as the sudden loss of the bottom or top chords, columns, or beams. This paper also presents the preliminary findings and approaches that are being developed as part of an ongoing project, which aims to anticipate failure propagation in this type of bridge by using robustness analysis methods, SHM of real structures and Artificial Intelligence. The recommendations already extracted from the study are currently being applied in three similar bridge case studies and will also be employed in the future to predict the collapse of bridges prior to the catastrophic event.