A large scale SHM system: A case study on pre-stressed bridge and cloud architecture

In recent decades, external prestressing is increasingly being used especially in motorway and railway bridge structures due to the substantial savings in terms of construction time and costs. In such systems, internal and external steel tendons work together with concrete elements to withstand external actions. This means that the deterioration or failure of these elements reduces structural safety in a meaningful way. Real time monitoring of prestressing tendons can provide useful information on the health of the bridge under service loads, detecting possible fatigue, corrosion and damage/deterioration processes. However, most of the currently used structural monitoring systems are rather expensive and time consuming to install. Although many papers address high density sensing as the proper solution thanks to the “internet of things” tool, both for hardware and software, there are not so many applications in which this approach is really put into service. This paper describes the application of MEMS accelerometers in a high performance and cost-effective SHM system for bridge structures. In particular, data from a real time monitoring system installed in a box section composite highway bridge are presented. The external tendons of this bridge have been instrumented with a total number of 88 triaxial accelerometers. Changes in the dynamic characteristics of the monitored elements have been analyzed by detecting the shift in tendons’ dynamic behavior. The main challenge was collecting a huge amount of data and find a way to properly process them, not requiring the operator’s direct action, unless the observed situation is out of the “normal” scenario. For this purpose, simple but easy-to-implement specific data processing algorithms have been tested in order to check the real feasibility of such a SHM system first, and then to analyze the collected sensor data and provide an efficient real time damage detection.