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With the rapid pace of economic and social development, the complexity and diversity of building structures continue to evolve. Over their operational lifespan, structures are subjected to various forms of degradation, including material aging and environmental erosion, which can significantly diminish their durability. Consequently, the development of robust structural health monitoring systems becomes imperative. These systems not only track the evolving performance trends of structures but also predict potential failures, extending their operational lifespan and ensuring the safety of occupants and assets. This study addresses the challenges inherent in extracting detailed structural feature information and enhancing the accuracy of predictive models. Focused on the cantilever beam test model as a research framework, the research explores an innovative approach to structural state prediction. It integrates Variational Mode Decomposition (VMD) and Convolutional Neural Network (CNN) methodologies to effectively analyze and forecast structural conditions. The study highlights a significant improvement in prediction accuracy and overall model performance by comparing CNN’s predictions using original structural signals with those processed through VMD. The results show that when the number of modes $K=5$, compared with the original signal (when $K=0$), the growth rate of the $R$-value at each measuring point can reach a maximum of 288.13%, and the average growth rate is 101.07%. This indicates that the VMD-CNN can significantly improve the prediction performance of the model.

The existing prediction methods of penetration depth do not take the influence of attack angle or angular velocity of projectile (i.e. effect of attack angle) into account. The main purpose of this work is to develop an approximate approach to predict the penetration depth for a rigid projectile into concrete target covered with yaw-inducing overlays. A quasi-static, elastic–plastic contact model of projectile impacting on irregular barrier is built based on Hertz’s contact theory, and the expression of effect of attack angle are also presented. The stress on projectile–target interface is derived according to spherical cavity expansion model and mechanical property of target, where reduction coefficient is introduced to consider the effect of free surface, and the penetration resistances are further obtained. Differential equations of motion for projectile arbitrarily oblique into target covered with yaw-inducing overlays are given by employing the initial conditions of projectile striking on irregular barrier. Then a simplified formula of depth-of-penetration (DOP) with effect of attack angle is presented by introducing an influence coefficient of oblique impact and an influence coefficient of yaw-inducing overlays into the depth of normal penetration. Predictions from the simplified formula are validated by comparison with ballistic experimental data for a 57mm diameter, 4.44kg, and ogive-nose, semi-armor-piercing with striking velocities between 320 and 705m/s.

The assembled tube-gusset K-joint by bolts is a commonly used connection form in steel tubular transmission towers. At present, main existing research or design codes for steel tubular transmission towers regard this K-joint as either rigid or pinned connections, which do not consider the semi-rigid behavior of K-joint. In this paper, the semi-rigid behavior of K-joint in steel tubular transmission towers is investigated and a direct prediction (DP) method is proposed to evaluate the semi-rigid behavior of K-joints based on the support vector regression (SVR) model, especially to predict the moment–rotation curve of semi-rigid K-joints. First, the establishment and validation of the finite element (FE) model of semi-rigid K-joints are conducted. Second, a dataset of 144 samples generated by the FE model is used to train and test the SVR model. Finally, the accuracy assessment of the proposed DP method and comparison with other existing methods, including the Kishi–Chen model, EC3 model and ANN-based two-step prediction method, are presented. The accuracy assessment shows that predicted values of the proposed DP method based on the SVR model exhibit good agreement with the numerical analysis values, which indicates the quite high accuracy of this method. Additionally, the comparison reveals that the proposed DP method based on the SVR model for predicting moment–rotation curves is rather more accurate than other aforementioned methods. Therefore, the proposed DP method based on the SVR model is of high reliability in predicting the semi-rigid behavior of K-joints in steel tubular transmission towers, which affords an alternative way for further engineering analysis and initial design purposes.

Results from 23 cyclic tests, including 18 cantilever-typed steel bridge piers and five beam-to-column connections, are presented to investigate their ductile fracture behavior as related to the seismic design of steel bridge structures, and based on shell and fiber models, two evaluation methods of ductile crack initiation are proposed. The effect of various parameters, including plate width-thickness and column slenderness ratios, cross-section shape, loading history, repeated earthquakes and initial weld defect is investigated experimentally. Among these parameters, width-thickness ratio, loading history and initial weld defect are shown to have significant influence on ductile fracture behavior. The test data suggest that for unstiffened box specimens, current seismic design provision limits on ultimate strain may not provide sufficient ductility for seismic design. On the other hand, based on the experimental results, two damage index-based evaluation methods respectively using shell model and fiber model are successfully employed to predict ductile fracture of steel bridge structures. Comparisons between experimental and analytical results show that they can predict ductile fracture behavior with good accuracy across the specimen geometries, steel types, loading histories and initial weld defects.

Based on the theoretical analysis of basic material mechanics, a prediction model for bending energy absorption has been established. In order to investigate the energy absorption characteristics of 22MnB5 hot stamped steel under plane bending strain state, on the basis of German VDA 238-100 standard, the limit tip cold bending performance test of 22MnB5 hot stamped steel samples with different grain sizes was tested. The results show that the prediction value of the sample’s bending energy absorption increased with the decrease of grain size which is consistent with the general regularity. The difference between test results and prediction results of bending energy absorption value is also small which shows that the model is practical and accurate.