The inverse finite element method (iFEM) is an advanced shape sensing framework that serves as a powerful tool for structural health monitoring (SHM). It enables real-time reconstruction of structural deformations through in-situ strain measurements. However, the precision of the iFEM reconstruction heavily relies on the number and placement of strain sensors. In this study, the proposed approach is able to break through the limitations of regularity in sensor placement while further reduce the requirement for the number of sensors. One major contribution of this paper is to leverage an extended data-driven approach based on singular value decomposition (SVD) to realize strain field pre-regression. The combination of the SVD-based extended data-driven approach with the iFEM framework containing physical information improves the credibility and interpretability, rendering it more appealing for practical applications. The use of SVD method makes iFEM better resistant to noise disturbances. Furthermore, it exhibits strong scalability, and is not restricted by specific data-driven algorithms. To validate the feasibility of proposed improved iFEM framework, the precision of the pre-regressed smooth strain measurements and iFEM predictions based on different types of inverse elements with varying orders is evaluated. The research results show that precise reconstruction of structural deformations can be realized, even with sparser strain sensor placement. It provides a new perspective for the development of iFEM-based SHM technologies.

The earthquake simulation shaking table is an indispensable equipment for earthquake research. A new seismic wave generation and control method based on the twin rotary valve is proposed to meet the requirements of the flexible regulation of simulated seismic waves for large-scale testing. Meanwhile, the static and dynamic regulation characteristics are analyzed based on the mathematical model of the novel system aiming to further improve the frequency bandwidth and amplitude of the system. The research results show that the novel structure and control method are feasible in terms of the flexible regulation of the waveform frequency and amplitude, which provides new ideas to further enhance the level of the seismic wave control technology for large-scale testing.

Strain sensor placement strongly influences the accuracy and cost of deformation reconstruction with the inverse finite element method (iFEM). This study develops a common sensor layout for several loading regimes by coupling the four-node inverse shell element with a Lichtenberg-inspired local search embedded in the Non-dominated Sorting Genetic Algorithm III (LA-NSGA-III). At a prescribed sensor count, the layout is optimized by simultaneously minimizing reconstruction residuals for multiple training loads. The method is assessed using DTLZ benchmarks and a plate example under equal objective-evaluation budgets, with separate controls for the multi-load formulation and the local search operator. Across 20 paired plate runs with 23 sensor sites, the proposed search reduces the mean joint training residual by 18.65% relative to NSGA-III, with mean displacement errors of 0.73% and 0.77% for the two training loads. Additional pressure, torsional and off-center force cases, a withheld combined load, strain noise, and sensor loss are used to assess a fixed layout under changing conditions. A cantilever plate test demonstrates strain-based displacement reconstruction, with laser peak displacement measurements used to calibrate the FE reference model. The method supports the offline design of a single sensor layout for iFEM reconstruction under multiple anticipated loading conditions, reducing the need to reposition sensors when the load changes.