Surrogate models are widely used in engineering design and optimization to substitute computationally expensive simulations for efficient approximation of system behaviours. However, since actual system behaviours are usually not known a priori, it is very challenging to select the most appropriate surrogate model for a specific application. To tackle this, ensemble models that combine different surrogate models have been developed based on global measures and local measures respectively. This article proposes a novel ensemble of surrogates to take advantage of both global and local measures, and a unified strategy is conceived over the entire design space with proper trade-off between these two measures. The effectiveness of the proposed model is tested with 38 mathematical problems and an engineering optimization example. It is concluded that the proposed model has superior accuracy while keeping comparable robustness and efficiency with other ensemble models. The proposed model is also extended to non-uniform experimental design.z

The flexibility matrix finds extensive application in detecting damage in civil and mechanical engineering structures, a focus explored in-depth within this paper. This study meticulously examines distinct components of the flexibility matrix, namely the proposed Diagonal of the Flexibility Matrix Vector (DFV) and a singular row/column of the flexibility matrix (RFV). Both DFV and RFV can be represented uniquely as a specialized form-a weighted combination of the Hadamard product of two mode shapes. Sensitivity analysis reveals that DFV undergoes a sharp change in proximity to damage, establishing it as a dependable indicator for pinpointing damage locations. Conversely, this characteristic is not consistently observed with RFV. The results of numerical simulations conducted on a 16-story frame structure corroborate the outcomes of the sensitivity analysis, underscoring the efficacy of DFV not only in localizing damage but also in gauging its severity. To illustrate the potential and limitations of DFV/RFV in practical scenarios, damage localization for the I-40 Bridge is provided.

Vibration-based damage identification methods have been received widespread attention these days for its property of global damage identification. This paper presents a damage identification method using the Auto correlation function at Maximum point value Vector (AMV) of vision-based displacement response of the structure. Sensitivity analysis for the normalized AMV to the local stiffness shows there is a sharp change of the damage index around the damage location, which means it is a good indicator for the damage. The influence of the excitation frequency on the detect ability of this proposed method is verified by the numerical simulation and non-contact damage identification experiment of a shear frame.

Displacement signals hold a paramount position in the field of bridge structural health monitoring, yet getting displacement signals presents certain challenges. This paper introduces an acceleration signal integration method based on Variational Mode Decomposition (VMD) with the aim of improving signal quality, enhancing the accuracy of the integration process, and obtaining velocity and displacement signals. The method initially employs VMD to decompose and reconstruct the raw acceleration signal to eliminate noise components. Subsequently, an integration operation is performed in the frequency domain to obtain displacement signals. Furthermore, by applying VMD's decomposition-reconstruction method once again, low-frequency noise caused by frequency domain integration is effectively removed. Ultimately, accurate velocity and displacement signals are successfully obtained. This research validates the effectiveness of the proposed method through simulation and experimentation. Additionally, we compare this method with a wavelet packet-based acceleration signal integration method and a method solely using frequency domain integration. The results demonstrate that the method proposed in this paper exhibits significant advantages in terms of signal-to-noise ratio, kurtosis error, and root mean square error compared to the other two methods. This study indicates that the proposed approach can effectively reduce noise interference, achieve precise conversion of acceleration signals, and provide a feasible solution for bridge structural health monitoring.

Rapid hot-air drying is indispensable, but kernel fissuring remains a major barrier to its broader adoption. This review critically collates and compares studies on empirical tests and numerical simulation, covering hygrothermal-induced stress, the role of glass transition temperature in grain strength, mechanical impact loading, multi-field simulation methods, and in situ imaging-based validation. Achieving effective rapid drying requires a clear understanding of the grain’s coupled hygrothermal and mechanical behavior. This remains a persistent challenge due to the grain’s heterogeneous structure and oversimplified modeling assumptions. Broadly speaking, research follows two distinct paths: one rooted in empirical experiments, the other in multi-field numerical simulation. The two approaches differ substantially in their explanatory frameworks, predictive capabilities, and ability to adapt to real processing conditions. A key point emphasized in this review is that in situ micro-CT imaging is the bridge between the two approaches: it delivers real-time structural data for model calibration and validation, enabling closed-loop integration of experimental observations and numerical predictions. The integrated technical framework proposed here provides a theoretical basis for reducing grain breakage loss and optimizing drying and conveying equipment.