Advances in Engineering Software最新文献

Periodic structures have attracted considerable attention in lightweight design due to their high specific strength and stiffness. Despite this, existing topology optimization research on these structures typically focuses on deterministic, single-load cases. To address the limitations arising from real-world, variable load conditions, this study presents a robust method for the topology optimization of periodic structures under both multiple and uncertain load cases. The proposed model integrates the uncertainty of the load magnitude, direction, and excitation frequency, employing the weighted sum of the mean and standard deviation of the dynamic structural compliance modulus as the objective function, constrained by the volume fraction of the structure. A method for uncertainty quantification is introduced, utilizing the bivariate dimension reduction technique and Gauss-type quadrature. Leveraging the displacement superposition principle in linear elastomers, we provide a method to calculate the mean and standard deviation of the dynamic structural compliance modulus under these complex load cases. Additionally, the sensitivity of the objective function concerning design variables is derived. The effectiveness of the proposed method is verified through numerical examples, revealing the effect of load uncertainty on the topology optimization of periodic structures.

The assessment of seismic resilience in bridge networks holds significant importance for urban disaster prevention and mitigation efforts. Unlike individual bridges, there has been limited efficiency in assessing bridge networks. A seismic resilience assessment methodology for bridge networks using ensemble learning methods is proposed in this paper. Initially, a comprehensive resilience index is proposed, integrating both structural and functional aspects of bridge networks. Using 3 ensemble learning methods, 9 parameters related to network structure and traffic characteristics are chosen as input variables for predicting the seismic resilience index. Finite element models of 18 bridges are constructed and combined to generate 3500 sets of virtual bridge networks for model training. The predictive accuracy of models trained using the 3 ensemble methods exceeds 89 %, and the expected values of peak ground acceleration (PGA) and functional loss rate are the most influential features. The methodology offers insights into the application of ensemble learning for bridge network seismic resilience assessment.

Composite materials have been widely applied in aerospace, automotive, and construction industries, making the non-destructive testing of these materials crucial. Planar electrical capacitance tomography (ECT), as a permittivity visualization technology, holds significant potential for development in the field of non-destructive testing. However, the underdetermination of its inverse problem often poses a key challenge to the imaging quality. To alleviate the underdetermination of the inverse problem and improve the image reconstruction quality of planar ECT, an image reconstruction method based on nonconvex overlapping group sparsity (NOGS) regularization is proposed. Firstly, the l_2,1 overlapping group sparse regularization model for normalized permittivity is established. Secondly, nonconvex functions are utilized as the external functions of the l_2,1 norm to form a NOGS regularization model. Finally, a Fast Non-Convex Overlapping Group Sparse Algorithm (FaNogSa) based on the LBP solution is proposed to solve the model for image reconstruction. To validate the effectiveness of this method, simulations, and experiments are conducted, and comparisons are made with the Tikhonov algorithm, Landweber algorithm, l₁ norm method, Laplace Prior-Based Efficient Sparse Bayesian Learning (L-ESBL), student's T Prior-Based Efficient Sparse Bayesian Learning (S-ESBL), and method by combining the density-based spatial clustering of applications with noise clustering algorithm and self-adaptive alternating direction method of multipliers (DBSCAN-SADMM) algorithm. Results demonstrate that NOGS outperforms other algorithms in terms of reconstruction accuracy, convergence time, and robustness. Among NOGS, NOGS (atan) performs the best, NOGS (abs) performs the worst, and NOGS (log) falls in between.

This study presents an innovative approach to mitigating seismic responses in multi-storey buildings equipped with a base-isolation (BI) system and passive friction-tuned mass dampers (PFTMDs). The key innovation lies in the combined use of a BI system and a PFTMD system, as well as the activation of this mechanical system by controllers. Additionally, the research design optimizes the parameters of these devices specifically for each earthquake scenario and compares the results to the average of the optimal parameters, which has not been investigated in previous studies. In this study, a 10-storey structure is modeled, featuring a BI system beneath the first floor and a PFTMD system on the roof. The parameters for the BI, PFTMD, BI-PFTMD, and BI-active FTMD (BI-AFTMD) systems are independently optimized using a multi-objective particle swarm optimization (MOPSO) algorithm. To enhance the passive BI-PFTMD system, a proportional-integral-derivative (PID) controller is incorporated into the friction-tuned mass damper system, resulting in the BI-AFTMD hybrid control system that adjusts the final control force transmitted to the structure. The seismic performance of these systems is assessed for the 10-storey building under both far-field and near-field earthquakes. The findings reveal that these control systems significantly decrease average peak displacement, acceleration, and inter-storey drift as compared to an uncontrolled structure, especially when system parameters are optimized for the same earthquake scenario. Using average optimal parameters, the BI-AFTMD system achieves the most substantial reduction in average peak displacement, while the BI system offers the greatest reduction in average peak acceleration and inter-storey drift.