Smart condition assessment, surveillance and management of critical bridges
| Reference number | |
| Coordinator | KUNGLIGA TEKNISKA HÖGSKOLAN - KTH Avdelningen för bro- och stålbyggnad |
| Funding from Vinnova | SEK 1 497 000 |
| Project duration | October 2016 - December 2018 |
| Status | Completed |
| Venture | The strategic innovation program InfraSweden |
Purpose and goal
The overall aim of the project was to develop an integrated system for monitoring, communication, condition assessment and decision support for critical bridges. Equipment for wireless measurements of the real response of bridges has been developed and tested on the Old Lidingö Bridge in Stockholm. Methods have been developed to make use of the measured response for condition assessment and planning of maintenance actions. By cloud based services for information distribution, the results from the measurements can be visualized in an App.
Expected results and effects
The results of the project show that reliable wireless sensor networks are available for long-term measurements on bridges. We have also seen that it is possible to harvest energy from vibrations of train passages, even if the contribution is small, and how the scheduling of activities can extend the service life of batteries. By the theoretical models developed, measured data can be used to extend the service life of existing bridges with known damages. The results of the project are expected to contribute to a more extensive use of monitoring to keep critical bridges in service.
Planned approach and implementation
An evaluation of experimental and commercial equipment for wireless measurements has been conducted with the Old Lidingö Bridge as case study. Data from the measurements have been used to develop and test routines for communication and energy harvesting, and as input for case studies within the theoretical parts of the project. Models for condition assessment and damage detection has involved theoretical modelling of degeneration processes and service life prediction, furthermore, machine learning by artificial neural networks (ANN).