A Practical Engineering Approach to Interpreting Measurement Data in Uncertain Contexts

Measurement data interpretation has the potential to improve asset management through quantification of reserve capacity and execution of well-engineered interventions. Many recent proposals for data interpretation that include uncertainty are not appropriate for the context of large civil-engineering structures. Furthermore, statistical formulations are usually incomprehensible to practicing engineers. This paper describes error domain model falsification (EDMF), a methodology that has been specially developed for large infrastructure asset-management activities. Following a description of EDMF, it is compared with other approaches that have recently been proposed to accommodate uncertainties. The compatibility of these methods with engineering practice is then evaluated. Finally, a recent full-scale case study in Singapore is briefly described. This methodology is a useful addition to the data-interpretation toolkit that is available to asset managers.