Using Low-cost IoT-based inclinometers for damage detection of a Bridge model

Abstract

Nowadays, researchers are paying close attention to using inclinometers for Structural Health Monitoring (SHM) applications. Moreover, the applications based on using inclinometers can detect the magnitude and location of bridge pathologies. However, as these applications are based on expensive commercial inclinometers, their use is typically exclusive to the SHM of structures with a high monitoring budget. There is a gap in the literature with the development and validation of low-cost accurate angular-meters for decreasing the monitoring cost of inclinometer-based damage detection applications. This work aims to develop low-cost IoT-based inclinometers for detecting damage in bridge structures. The Low-cost Adaptable Reliable Angle-meter (LARA) is a novel inclinometer that accurately measures an induced inclination by combining the measurements of five gyroscopes and five accelerometers. The accuracy, resolution, Allan variance, and standard deviation of LARA are examined through laboratory experiments and are compared with those obtained by numerical slope calculations and a commercial inclinometer (HI-INC). For further experimental validation, a robotic vehicle model is designed and developed to simulate a moving load over a bridge model. The vehicle model integrates IoT technology and can be utilized in different damage detection experiments. The outcomes of a load test experiment using a simple beam model demonstrate the high accuracy (0.003 degrees) of LARA measurements. LARA may be used for structural damage identification and location in bridges utilizing inclinometers because of its low cost and high accuracy