Jia Guo

Damping has attracted increasing attention as an indicator for structural health monitoring (SHM), owing to its sensitivity to subtle damage that may not be reflected in natural frequencies. However, the practical application of damping-based SHM remains limited by the accuracy and robustness of damping identification methods. Enhanced Frequency Domain Decomposition (EFDD), a widely used operational modal analysis technique, offers efficiency and user-friendliness, but suffers from intrinsic deficiencies in damping identification due to bias introduced at several signal-processing stages. This study proposes to improve EFDD by integrating neural networks, replacing heuristic parameter choices with data-driven modules. Two strategies are explored: a step-wise embedding of neural modules into the EFDD workflow, and an end-to-end grid-weight framework that aggregates candidate damping estimates using a lightweight multilayer perceptron. Both approaches are validated through numerical simulations on synthetic response datasets. Their applicability was further validated through shaking-table experiments on an eight-storey steel frame and a five-storey steel–concrete hybrid structure. The proposed grid-weight EFDD demonstrated superior robustness and sensitivity in capturing early-stage damping variations, confirming its potential for practical SHM applications. The findings also revealed that the effectiveness of damping-based indicators is strongly influenced by the structural material system. This study highlights the feasibility of integrating neural network training into EFDD to replace human heuristics, thereby improving the reliability and interpretability of damping-based damage detection.

This study examines nonlinear signal-based control (NSBC) in shake table experiments with sliding structures, which have an isolation effect during an earthquake. NSBC uses a nonlinear signal obtained from the outputs of a controlled system and its linear model under the same input. Owing to the presence of the linear model, NSBC controllers are described by transfer functions, even for controlling nonlinear systems. NSBC achieved excellent control of the shake table in experiments with a specimen having nonlinear characteristics such as yielding of structural components. A sliding structure placed on a shake table significantly jeopardises its control because the nonlinear severity of sliding is greater than yielding, and its compensation has not yet been fully developed. Therefore, this study introduces NSBC into shake table experiments with sliding structures along with its linear model design to enhance their robustness, utilising the analysis stability to evaluate the design. Numerical simulations with a shake table with a sliding structure with a friction coefficient of 0.22 demonstrate the excellent performance of NSBC in table acceleration control. However, inversion-based control (IBC), a basic compensation approach, shows its ineffectiveness. In actual shake table experiments with a sliding structure with a friction coefficient of 0.2, NSBC with a reasonable linear model achieved excellent table acceleration control with almost 100% accuracy, whereas IBC was ineffective. This study clarifies that NSBC can solve the problem of control degradation caused by a sliding structure placed on the table.