Active vibration control of tensegrity structures for performance enhancement: A comparative study

This study performs a novel control efficiency assessment approach that compares performance of optimal control algorithms regarding vibration of tensegrity structures. Due to complex loading conditions and the inherent characteristics of tensegrities, e.g. geometrical nonlinearity, the quantization of control efficiency in active control of tensegrity constitutes a challenging task especially for different control algorithms. As a first step, an actuator energy input, comprising the strain energy of tensegrity elements and their internal forces work, is set to constant levels for the linearquadratic regulator(LQR). Afterwards, the actuator energy of the linear-quadratic Gaussian(LQG) is iterated with identical actuator energy input in LQR. A double layer tensegrity grid is employed to compare the control efficiencies between LQR and LQG with five different control scenarios. The results demonstrate the efficiency and robustness in reducing the dynamic response of tensegrity structures, and a theoretical guideline is provided to search optimal control options in controlling actual tensegrities.     

Validation of (not‐historical) large‐event near‐fault ground‐motion simulations for use in civil engineering applications

Ground‐motion simulations generated from physics‐based wave propagation models are gaining increasing interest in the engineering community for their potential to inform the performance‐based design and assessment of infrastructure residing in active seismic areas. A key prerequisite before the ground‐motion simulations can be used with confidence for application in engineering domains is their comprehensive and rigorous investigation and validation. This article provides a four‐step methodology and acceptance criteria to assess the reliability of simulated ground motions of not historical events, which includes (1) the selection of a population of real records consistent with the simulated scenarios, (2) the comparison of the distribution of Intensity Measures (IMs) from the simulated records, real records, and Ground‐Motion Prediction Equations (GMPEs), (3) the comparison of the distribution of simple proxies for building response, and (4) the comparison of the distribution of Engineering Demand Parameters (EDPs) for a realistic model of a structure. Specific focus is laid on near‐field ground motions (<10km) from large earthquakes (M_w7), for which the database of real records for potential use in engineering applications is severely limited. The methodology is demonstrated through comparison of (2490) near‐field synthetic records with 5 Hz resolution generated from the Pitarka et al (2019) kinematic rupture model with a population of (38) pulse‐like near‐field real records from multiple events and, when applicable, with NGA‐W2 GMPEs. The proposed procedure provides an effective method for informing and advancing the science needed to generate realistic ground‐motion simulations, and for building confidence in their use in engineering domains.     

Conditional bias-penalized Kalman filter for improved estimation and prediction of extremes

Kalman filter (KF) and its variants are widely used for real-time state updating and prediction in environmental science and engineering. Whereas in many applications the most important performance criterion may be the fraction of the times when the filter performs satisfactorily under different conditions, in many other applications estimation and prediction specifically of extremes, such as floods, droughts, algal blooms, etc., may be of primary importance. Because KF is essentially a least squares solution, it is subject to conditional biases (CB) which arise from the error-in-variable, or attenuation, effects when the model dynamics are highly uncertain, the observations have large errors and/or the system being modeled is not very predictable. In this work, we describe conditional bias-penalized KF, or CBPKF, based on CB-penalized linear estimation which minimizes a weighted sum of error variance and expectation of Type-II CB squared and comparatively evaluate with KF through a set of synthetic experiments for one-dimensional state estimation under the idealized conditions of normality and linearity. The results show that CBPKF reduces root mean square error (RMSE) over KF by 10–20% or more over the tails of the distribution of the true state. In the unconditional sense CBPKF performs comparably to KF for nonstationary cases in that CBPKF increases RMSE over all ranges of the true state only up to 3%. With the ability to reduce CB explicitly, CBPKF provides a significant new addition to the existing suite of filtering techniques for improved analysis and prediction of extreme states of uncertain environmental systems.