Hysteresis is of critical importance to structural safety under severe dynamic loading conditions. One of the widely used hysteretic models for civil structures is the Bouc-Wen model, the effectiveness of which depends on suitable model parameters. The locally non-differentiable governing equation of the conventional Bouc-Wen model poses difficulty on existing identification algorithms, especially the extended Kalman filter, which relies on linearized system equations to propagate state estimates and covariance. In addition, the standard extended Kalman filter usually does not incorporate parameter constraints, and therefore may result in unreasonable estimates. In this article, a modified and differentiable Bouc-Wen model, together with a constrained extended Kalman filter (CEKF), is proposed to identify the hysteretic model parameters in a reliable way. The partial derivatives of the differentiable Bouc-Wen model with respect to hysteretic parameters can be easily calculated for implementing the identification algorithm. Constrained extended Kalman filter restricts the Kalman gain to ensure that the estimates of parameters satisfy constraints from physical laws. Parameter identification using simulated and experimental data collected from a four-story structure demonstrates that constrained extended Kalman filter can achieve more reliable identification results than the standard extended Kalman filter.

Strain and crack are critical indicators of structural safety. As a novel sensing device, a patch antenna sensor can be utilized to wirelessly estimate structural strain and surface crack growth through resonance frequency shift. The main challenges for the sensor are other effects such as temperature fluctuation that can generate unwanted resonance frequency shift and result in large noise in the measurement. Another challenge for existing designs of patch antenna sensor is the limited interrogation distance. In this research, thermally stable patch antenna sensors are investigated for more reliable measurement. Fabricated on a substrate material with a steady dielectric constant, a new passive (battery-free) patch antenna sensor is designed to improve reliability under temperature fluctuations. In addition, another newly designed dual-mode patch antenna sensor is proposed to achieve a longer interrogation distance. Extensive experiments are conducted to characterize the patch antenna sensor performance, including thermal stability, tensile strain sensing, and emulated crack sensing. The two new patch antenna sensors are demonstrated to be effective in wireless strain and crack measurements and have potential applications in structural health monitoring (SHM).

The unscented Kalman filter (UKF) can be used to identify model parameters of structural systems from the measurement data. However, the standard UKF may provide unreliable and nonphysical estimates, since no parameter constraints are incorporated in the identification process. This paper discusses and compares several constrained UKF (CUKF) methods for parameter identification of structural systems. The effectiveness and robustness of the methods are evaluated through numerical simulation on a Bouc–Wen hysteretic system. The results demonstrate that with properly handling of the constraints, the identification accuracy can be improved. The proposed CUKF method is further validated using experimental data collected from a full-scale reinforced concrete structure. Based on the identified model parameters, the updated models can achieve more accurate simulation responses than the initial model.

Structural health monitoring (SHM) has proven effective in early damage detection and in facilitating structural condition assessment. Conventional SHM systems feature comprehensive sensing for large infrastructures, such as long-span bridges and high-rise buildings. However, the high cost of these systems limits their feasibility for small to medium-sized structures. Inspired by lightweight design principle from the automotive industry, lightweight monitoring offers a promising complementary approach to conventional SHM. This paper provides an overview of the concepts, techniques, methodologies, and applications of lightweight monitoring for SHM. For sensing devices, non-contact, wireless, and robotic sensing are explored for their potential to enable fast and convenient data acquisition. For monitoring scheme, the concept of targeted sensing is introduced as an alternative to comprehensive sensing, focusing on the monitoring of critical structural behaviors using purpose-built devices. Additionally, the development of cloud-edge-end collaborative framework enables optimized deployment of computing resources for SHM. With regard to data analysis, the paper examines methods for data preprocessing, data fusion, and structural evaluation, with particular emphasis on artificial intelligence-driven approaches that enable fast and automatic analysis. Finally, this paper offers insights into the future of lightweight monitoring in SHM. By organizing and clearly identifying SHM techniques suitable for lightweight monitoring practices, this review serves as a practical guide for advancing the adoption of lightweight SHM and provides a foundation for further innovation in this field.