Hasan Katkhuda

Stiffness identification under uncertain blast loading

We develop a finite element-based time-domain system identification algorithm which will identify structural parameters at the local element level without using input excitation information and with limited output response information. The proposed method has no restriction on types of input forces to be used to excite a structure. Blast loading and the uncertainty associated with modeling it are specifically addressed. The efficiency and accuracy of the algorithm are verified using several examples. For verification purposes, both noise-free and noise-included output response measurements are considered. The results indicate that the proposed method identifies the structural parameters at the element level very well

Damage detection in steel structures with semi-rigid connections using unscented Kalman filter

Purpose The purpose of this paper is to detect damages in steel structures with actual connections, i.e. semi-rigid connections. The method will detect the damages by tracking the changes in the stiffness of structural members using only a limited number of dynamic responses and without knowing the type or time history of the dynamic force applied on the structure. Design/methodology/approach The paper proposes a technique that combines the iterative least-square and unscented Kalman filter (UKF) methods to identify the stiffness of beams and columns in typical two-dimensional steel-framed structures with semi-rigid connections. The detection of damages is by using nonlinear time-domain structural health monitoring method. Findings The technique is verified by using numerical examples using noise-free and noise-included dynamic responses from two different types of dynamic forces: harmonic and blast loads. The results showed that the UKF method with iterative least-square is a powerful approach to identify and detect damages in structures that have nonlinear behavior and the method was able to detect the damages in beams with a very high accuracy for noise-free and noise-included dynamic responses. In addition, the optimum number and locations of dynamic responses (accelerometer sensors) required for damage detection were determined. Originality/value This paper fulfills an identified need to detect damages in steel structures using only a limited number of accelerometer sensors.

Two-stage system identification approach for three-dimensional structural systems

A two-stage finite element system identification (SI) technique is proposed in this paper to identify stiffness of elements and detect damages in three-dimensional framed structures. The technique combines in stage 1 the iterative least-square and in stage 2 the unscented Kalman filter (UKF) to identify the stiffness of elements using only limited measured response time histories from only four to six accelerometers instead of dozens of accelerometers of the whole structure and assuming the time history of dynamic load applied on structure is unknown. The method will identify the stiffness and detect the damages in the elements by tracking the changes in the recordable dynamic output responses between damaged and undamaged states. The optimum number and locations of accelerometers were studied in this paper. The algorithm is verified using numerical examples. The results showed clearly that the technique can identify damaged and undamaged three-dimensional steel framed structures and the minimum number of sensors required for such frames.

Structural health assessment after an impact

An innovative technique to assess structural health just after subjected to impulsive loadings (blasts, explosions, etc.) underdevelopment at the University of Arizona was experimentally verified and is presented in this paper. The authors called it the G eneralized I terative L east S quare E xtended K alman F ilter with U nknown I nput (GILS-EKF-UI) method. The system is represented by finite elements and a Kalman filter-based system identification (SI) technique is used to identify the system. Some of the major characteristics of the method are that it does not require information on input excitation and can identify a system with limited noise-contaminated response information measured at few node points. To implement the Kalman-filter based algorithm, the information on the input excitation and the initial state vector must be available. The authors proposed a two-stage approach. In the first stage, based on the limited measured response information available at the locations of the sensors, a substructure is identified. After the completion of the first stage, the input excitation information that caused the responses and the stiffness of all the elements in the substructure can be evaluated. Then, in stage 2, the Kalman-filter based algorithm is used to identify the whole structure. The experimental verification of the method is emphasized in this paper.Copyright