Framework for measurement-system design

Numa Bertola from the Cyber Civil Infrastructure project at the Future Cities Laboratory (FCL) and Dr Marco Cinelli from the Assessing and Measuring Energy Systems Resilience module at the Future Resilient Systems (FRS) program) with the collaboration of Prof Ian Smith the principal investigator of the Cyber Civil Infrastructure project, Simon Casset from EPFL, and Salvatore Corrente from Catania University published an article about a new framework to design measurement systems using a Multi-Criteria Decision Analysis (MCDA) model in the journal Advanced Engineering Informatics.

Due to conservative design models and safe construction practices, civil infrastructure usually has unknown amounts of reserve capacity that exceed code requirements. Quantification of this reserve capacity has the potential to lead to better asset-management decisions by avoiding unnecessary replacement. Field measurements, collected during load tests, may lead to more accurate assessment of the reserve-capacity. Although the success of the monitoring depends on the sensor configuration, engineers usually place sensors based on experience.

The paper external page A multi-criteria decision framework to support measurement-system design for bridge load testing presents a framework to evaluate and rank possible measurement systems based on a tiered multi-criteria strategy. The framework takes into account five performance criteria, including the costs and robustness of the measurements, and provides an extensive evaluation of the alternatives, including the best solution defined probabilistically and for specific conditions when other near-optimal solutions might be preferred.

This study shows a good example of collaboration between the two entities of the Singapore ETH Centre. Numa Bertola from FCL provided a real-world case study of a multi-criteria problem and Marco Cinelli from FRS defined the appropriate MCDA methodology to solve the problem.

The Cyber Civil Infrastructure project at FCL combines sensor measurement data with site-inspection results and engineering knowledge to improve behaviour models. Inspired by fundamental research in model-based diagnosis, the project is developing sensor-data-interpretation methodologies for full-scale applications where complete knowledge of uncertainties is not available.

The Assessing and Measuring Energy Systems Resilience module at FRS compares accident risks across a broad range of current and future energy supply chains to establish a comprehensive set of resilience indicators with emphasis on supply security and to develop tools to support decision making and to improve conflicting energy planning processes.