Methodology Maps for Model-Based Sensor-Data Interpretation to Support Civil-Infrastructure Management

With increasing urbanization and depleting reserves of raw materials for construction, sustainable management existing infrastructure will be an important challenge in this century. Structural sensing has the potential to increase knowledge behavior improve engineering decision making asset management. Model-based methodologies such as residual minimization (RM), Bayesian model updating (BMU) error-domain falsification (EDMF) have been proposed interpret monitoring data support Application these requires approximations assumptions related class, complexity uncertainty estimations, which ultimately affect accuracy interpretation subsequent making. This paper introduces methodology maps order provide guidance appropriate use methodologies. The development is supported by in-house evaluations nineteen full-scale cases since 2016 a two-decade assessment applications model-based Nineteen studies include structural identification, fatigue-life assessment, post-seismic risk geotechnical-excavation quantification. In some cases, much, previously unknown, reserve capacity quantified. RM BMU may useful when computational constraints are satisfied. EDMF special implementation BMU. It more compatible with usual characteristics, nature typically available evaluation concepts than other most applicable contexts high magnitudes uncertainties, including significant levels bias sources systematic uncertainty. also provides additional practical advantages due its ease flexibility information changes. paper, observations leveraged develop maps. These guide users selecting through reference conditions constraints. improves asset-management thus expected lead lower maintenance costs compared current practice.