Optimal Sensors Placement in Dynamic Damage Detection of Beams Using a Statistical Approach

Abstract

Structural monitoring plays a central role in civil engineering; in particular, optimal sensor positioning is essential for correct monitoring both in terms of usable data and for optimizing the cost of the setup sensors. In this context, we focus our attention on the identification of the dynamic response of beam-like structures with uncertain damages. In particular, the non-localized damage is described using a Gaussian distributed random damage parameter. Furthermore, a procedure for selecting an optimal number of sensor placements has been presented based on the comparison among the probability of damage occurrence and the probability to detect the damage, where the former can be evaluated from the known distribution of the random parameter, whereas the latter is evaluated exploiting the closed-form asymptotic solution provided by a perturbation approach. The presented case study shows the capability and reliability of the proposed procedure for detecting the minimum number of sensors such that the monitoring accuracy (estimated by an error function measuring the differences among the two probabilities) is not greater than a control small value.

Detecting Damage Evolution of Masonry Structures through Computer-Vision-Based Monitoring Methods

“…The problem of vibration-based damage detection has been dealt with for long time in the scientific community [1][2][3][4]. Nevertheless, during the last decades, many innovative technologies as well as optimized sensors-placing criteria have emerged, allowing more and more sustainable and extensive monitoring campaigns [5,6]. Dynamic monitoring can be performed under either seismic or ambient excitation (usually due to wind, traffic, etc.)…”

Interface modeling in load transfer mechanisms of multi-leaf masonry panels

“…Numerical model calibrated on experimental data enables to verify the behaviour of structural system with respect to different boundary conditions and integrity both in linear and non-linear field. Dynamic identification procedures depend by recorded signal quality which can be affected by temperature effects [8][9][10], by signal-to-noise ratio [11], by the difficulty to distinguish the significant modal shape [12][13][14] and on optimal configuration in number and position of accelerometers [15][16][17].…”