Standard filtering techniques for structural parameter estimation assume that the input force is either known or can be replicated using a known white Gaussian model. Unfortunately for structures subjected to seismic excitation, the input time history is unknown and also no previously known representative model is available. This invalidates the aforementioned idealization. To identify seismic induced damage in such structures using filtering techniques, force must therefore also be estimated. In this paper, the input force is considered to be an additional state that is estimated in parallel to the structural parameters. Two concurrent filters are employed for parameters and force respectively. For the parameters, an interacting Particle-Kalman filter is used to target systems with correlated noise. Alongside this, a second filter is used to estimate the seismic force acting on the structure. In the proposed algorithm, the parameters and the inputs are estimated as being conditional on each other, thus ensuring stability in the estimation. The proposed algorithm is numerically validated on a sixteen degrees-of-freedom mass-spring-damper system and a five-story building structure.The stability of the proposed filter is also tested by subjecting it to a sufficiently long measurement time history. The estimation results confirm the applicability of the proposed algorithm.

Standard filtering techniques for structural parameter estimation assume that the input force either is known exactly or can be replicated using a known white Gaussian model. Unfortunately for structures subjected to seismic excitation, the input time history is unknown and also no previously known representative model is available. This invalidates the aforementioned idealization. To identify seismic induced damage in such structures using filtering techniques, a novel algorithm is proposed to estimate the force as additional state in parallel to the system parameters. Two concurrent filters are employed for parameters and force respectively. For the parameters, interacting Particle-Kalman filter [1] is employed targeting systems with correlated noise. Alongside a second filter is employed to estimate the seismic force acting on the structure. The proposal is numerically validated on a sixteen degrees-of-freedom mass-spring-damper system. The estimation results confirm the applicability of the proposed algorithm.

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The objective of the study summarised, hereafter, is to compare square pulsed and pulsed thermography for defect detection and characterisation of carbon fibre-reinforced polymer (CFRP) plates used as structural reinforcement in Civil Engineering applications. For this purpose, two specimens built with cement concrete support were manufactured in the laboratory. They were reinforced with CFRP plates bonded to their surface and different artificial defects were inserted during gluing. Two types of thermal excitations (pulse and square pulse optical heating) have been studied and applied to these specimens. In parallel, numerical simulations were carried out as well. Defect detection was rapidly performed using singular value decomposition both on experimental and simulated thermal image sequences. A 1D multi-layer thermal quadrupole model coupled to an estimation procedure was studied. Validity domain of the 1D hypothesis is studied. Then this model is used to characterise the defects and the glue layer thickness. In addition, a sensitivity study of the proposed model is also presented. Furthermore, 3D numerical simulations were conducted to study the reliability of the estimation procedure vs. the two types of thermal excitations. The estimation procedure on numerical data provides results closer to experimental observations when applied to the square pulse method and it is less disturbed by white noise, even though a preliminary estimation residue analysis indicated that the heat pulse method should be, in theory, more precise. Finally, the estimation procedure studied herein was applied to experimental data acquired on laboratory specimens. Results obtained are analysed and perspectives for the presented method are discussed.

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