An assessment framework for sensor-based detection of critical structural conditions with consideration of load uncertainty

The assessment of the condition of structures subjected to ageing and extreme loading conditions becomes increasingly important. Highly accurate sensor technologies and advanced structural identification methods can help to detect critical structural conditions and thus prevent collapse. The paper presents a general methodology for the evaluation of the measurement quality of sensor setups based on the identification of target structural properties like section forces, deflections or stresses. The target quantities that are relevant for the structural assessment will be identified from readily measurable deformation quantities such as inclinations. The proposed framework includes considerations of uncertainty in load pattern and uses mathematical optimisation techniques to relate the measured quantities to the range of target properties and thus to the ability of identifying critical structural conditions. The methods are applied to sample structures and the effect of uncertainty in the loading and uncertainty in the measurement process are studied for an inclinometer-based monitoring system. The quality of competing sensor setups will be evaluated for a single target quantity and expanded to multiple target properties. The methodology developed can be used for optimising sensor setups in the context of detection systems.