Zhen Chen

As critical infrastructures, long, large-diameter shield tunnels require a reliable long-term seismic safety assessment. This study investigates the coupled effects of spatially non-uniform ground motions and bolt corrosion on the seismic response of long, large-diameter shield tunnels. Three types of non-uniform seismic waves (coherent, traveling, and coherent traveling) consistent with the design spectrum were generated, and a 3D discontinuous finite element model was used to analyze tunnel response under various corrosion locations and rates. Results show that non-uniform excitation significantly amplifies the seismic response in the order coherent traveling waves > traveling waves > coherent waves > uniform waves, with coherent traveling waves amplifying key seismic responses by up to 10 times compared with uniform excitation. Corrosion at circumferential joints causes more substantial responses than at longitudinal joints, and combined longitudinal corrosion shows a greater effect than single-joint corrosion. Under coherent traveling waves with high corrosion rates, the seismic response exhibits a distinct “top stronger than bottom” distribution. This study concludes that accurate seismic performance evaluation requires consideration of both variability in spatial ground motion and bolt corrosion, thereby supporting the seismic design and long-term maintenance of such structures.

Voids behind the lining that develop during long-term operation can seriously compromise the seismic safety performance of metro shield tunnels. To investigate the influence of such void defects on large-diameter shield tunnels, this study systematically analyzed the causes and distribution patterns of voids. A three-dimensional discontinuous finite element model was developed to simulate the interaction among lining segments, connecting bolts, and surrounding rock. The seismic responses, including circumferential stress, interface slip, interface opening, and bolt tensile stress, were analyzed considering coupled factors such as the void circumferential angle, radial depth, distribution location, and geological conditions. Single-factor and multi-factor sensitivity analyses were conducted to evaluate the significance of the above coupled factors on the overall seismic response. The results show that lining circumferential stress, displacement, interface opening, and bolt stress increase with void enlargement, a shift in void location from the crown to the haunch, and deterioration of geological conditions. A void located at the right haunch leads to a peak circumferential stress of 3.27 MPa, causing local segment damage. Sensitivity analysis reveals that void location is the most influential factor affecting the seismic response, while geological conditions exhibit lower sensitivity. A predictive model for the peak circumferential stress around the void was established using multiple linear regression, incorporating void position, circumferential angle, and radial depth. Within the parameter range considered in this study, this model provides a theoretical basis and practical reference for rapid seismic risk assessment and safety management of shield tunnels with void defects.

Pile foundations are extensively employed in large-diameter silos owing to their superior stability. However, conventional equal-stiffness designs often require thicker pile caps and higher reinforcement ratios, leading to increased construction costs. It promotes the study of this paper, which optimized the design of pile foundations for large-diameter silos to reduce construction costs. A three-dimensional (3D) finite element (FE) numerical model, incorporating superstructure–foundation–soil interaction, was developed using ANSYS to analyze the effects of pile cap thickness (PCT), pile length (PL), and pile diameter (PD) on maximum differential settlement (MDS). Sensitivity analysis quantified the influence of each parameter. Additionally, improvements to the automatic grouping genetic algorithm (AGGA) were proposed and implemented in Matlab to enhance design optimization. Results show that PCT has the greatest impact on MDS, followed by PL and PD. Optimal ranges are 1.7–2.3 m for PCT, 11.8–19.3 m for PL, and 0.6–1.2 m for PD. Using long-slender piles instead of short-thick piles reduces concrete consumption by 39.16% and decreases MDS by 73.5%, effectively mitigating secondary internal forces in the superstructure and pile cap. This approach enhances structural safety and overall system stability, offering a cost-effective solution for large-diameter silo foundations.

Structural damage identification (SDI) serves as an indirect approach that has the potential to meet real‐time monitoring of structures. However, the identification accuracy and efficiency of some methods need to be improved, especially when there are some uncertain interfering factors or noise. This paper presents a new optimization algorithm and an improved objective function for inverse problems of SDI, offering an effective solution for bridge damage identification under uncertain noise interference and incomplete modal data. In this study, by hybridizing the whale optimization algorithm and the sand cat swarm optimization, a novel whale‐sand cat swarm optimization (W‐SCSO) method is proposed for SDI. The cubic chaotic mapping is introduced for initialization of the W‐SCSO method, and then the lens opposition‐based learning and the stochastic differential mutation are employed to enhance the search capability and convergence accuracy of the proposed algorithm. Besides, the mode shape curvature, the frequency change ratio, and the L _1/2 sparse regularization are used to improve the objective function. Four other existing state‐of‐the‐art methods are used to verify the performance of the proposed W‐SCSO method by the CEC2017 benchmark functions and a simply supported beam finite model. The comparative analysis highlights the feasibility and effectiveness of the proposed method in the considered cases. Moreover, an aluminum alloy simply supported beam was conducted for the SDI experiment to further prove the effectiveness of the improved method in practice. Simulation and experimental results show that the proposed method effectively locates and quantifies stiffness reduction in bridge structures, which maintains high accuracy in damage identification despite potential modal incompleteness and uncertain measurement noise interference.

Structural damage identification based on metaheuristic algorithms is an important part of structural health monitoring with great potential. However, the metaheuristic intelligent algorithms probably have flaws of slow convergence speed and low calculation accuracy, which need to be improved to address engineering optimization problems. In this paper, the black widow optimization (BWO) algorithm is used for structural damage identification. In addition, a multistrategy fusion–improved BWO (IBWO) algorithm is proposed by introducing the tent chaotic mapping, the golden sine equation, the gazelle wandering equation, and the boundary treatments. First, in the population initialization stage, tent chaotic mapping is introduced to improve the quality of the initial solution. Second, the golden sine strategy is used to acquire the optimal solution quickly in local search. Then, the motion equation of the gazelle algorithm is employed to enhance the global search ability and avoid the algorithm falling into the local optimal solution. Finally, the boundary processing strategy is presented to reduce the calculation of solutions and improve the optimization efficiency. A novel damage identification objective function is redefined by combining the modal assurance criterion and the modal flexibility. Then, a two‐story rigid frame structure is utilized for numerical simulations. Moreover, experimental studies with a simply supported beam were carried out to verify the performance of the proposed damage identification method. Simulation results and experimental studies demonstrate that, even with the interference of strong noise, the IBWO algorithm has a higher accuracy and efficiency in damage identification compared to the BWO algorithm.

Swarm intelligence algorithms and finite element model update technology are important issues in the field of structural damage detection. However, the complexity of engineering structural models normally leads to low computational efficiency and large detection errors in structural damage detection. To solve these problems, a simulated annealing-artificial hummingbird algorithm (SA-AHA) is proposed based on the artificial hummingbird algorithm (AHA). The Sobol sequence is used to improve the identification efficiency by optimizing the initial population distribution of the AHA. Then, the simulated annealing strategy is introduced to improve the detection accuracy by enhancing the global search ability of the AHA. In addition, a novel objective function is presented by combining modal flexibility residual, natural frequency residual, and trace sparse constraint of the structural model. Numerical simulations of a simply supported beam and a two-story rigid frame are carried out to verify the superiority of the proposed SA-AHA and the objective function. Simulation results demonstrate that the SA-AHA is better than the AHA in terms of damage computational efficiency and damage identification accuracy. Moreover, the new objective function can be more excellently applied to the SA-AHA than the previous one, which can be effectively used to locate and estimate the damage of the proposed SA-AHA in structure. Finally, experimental studies are carried out to verify the proposed method.

Damage detection of structures based on swarm intelligence optimization algorithms is an effective method for structural damage detection and key parts of the field of structural health monitoring. Based on the chimp optimization algorithm (ChOA) and the whale optimization algorithm, this paper proposes a novel hybrid whale-chimp optimization algorithm (W-ChOA) for structural damage detection. To improve the identification accuracy of the ChOA, the Sobol sequence is adopted in the population initialization stage to make the population evenly fill the entire solution space. In addition, to improve the local search ability of the traditional ChOA, the bubble-net hunting mechanism and the random search mechanism of the whale optimization algorithm are introduced into the position update process of the ChOA. In this paper, the validity and applicability of the proposed method are illustrated by a two-story rigid frame model and a simply supported beam model. Simulations show that the presented method has much better performance than the ChOA, especially in dealing with multiple damage detection cases. The W-ChOA has good performance in both overcoming misjudgment and improving computational efficiency, which should be a preferred choice in adoption for structural damage detection.

In this study, a modified truncated singular value decomposition (MTSVD) method is proposed for the identification of dynamic moving forces on simply-supported beams. By regularizing the truncated singular value decomposition (TSVD) method, the MTSVD method focuses on overcoming the ill-posed problems that intrinsically exist in moving force identification. Two regularization parameters, namely, regularization matrix and truncating point are the most important regularization parameters affecting the performance of the MTSVD method. The accuracy and efficiency of the MTSVD method is shown by comparing the results with the conventional counterpart SVD and TSVD methods. In addition, the proposed method is also compared with a similar method recently proposed by the author, that is, the piecewise polynomial truncated singular value decomposition (PP-TSVD) method. Numerical simulations demonstrate that the performance of the MTSVD method is significantly improved compared with the PP-TSVD method in high noise level cases.

In this study, a particle swarm optimization with a sigmoid increasing inertia weight (SIPSO) algorithm is proposed for structural damage identification based on the optimization of structural vibration response constraints. In view of the existing problems for particle swarm optimization algorithms used for structural damage identification, such as low accuracy of damage identification and easy misjudgment of damage location, the sigmoid increasing inertia weight is introduced to improve the global and local search ability of the algorithm. Simulation results show that the parameters of the sigmoid increasing inertia weight have a significant effect on the performance of the SIPSO algorithm for structural damage identification. Compared with similar improved particle swarm optimization algorithms, the SIPSO algorithm has some advantages of fast convergence speed, high identification accuracy, and strong robustness ability in structural damage identification.