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To obtain an effective data mining method for cable-stayed bridge damage diagnosis, the algorithm of the cable-stayed bridge damage diagnosis model based on data mining was studied, and a data mining method is proposed. This method is oriented to the damage diagnosis of cable-stayed bridges. After algorithm comparison, the support vector machine (SVM) and limit gradient-boosting (XGBoost) algorithms, with advantages in damage location and quantification, are combined and optimized to obtain the damage diagnosis model for cable-stayed bridges. First, a refined benchmark finite element model is established by Abaqus, and postprocessing data such as vibration frequency and modal curvature are used as a data mining dataset. Second, feature se-lection is conducted, and the damage-sensitive modal curvature change rate index is selected as the feature of data mining. Next, the SVM and XGBoost algorithms are optimized by grid and random search, and the optimized SVM and XGBoost algorithms are used to locate and quantify the damage. Finally, the damage diagnosis model for cable-stayed bridges is obtained. Taking a cable-stayed bridge as an example, the proposed method is applied and analyzed, and the results show the effectiveness of the proposed method.

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This study aimed to investigate the axial compression performance of concrete-filled circular-end aluminum tube (RECFAT) columns, utilizing four specimens with varying parameters such as cross-sectional aspect ratio and cross-sectional aluminum content. Axial compression tests and ABAQUS finite element extended parameter analyses were conducted, with key mechanical performance indicators such as specimen failure morphology, ultimate bearing capacity, load–displacement curve, and load–strain curve being obtained. The influence of various variation parameters on the axial compression performance of the specimen was analyzed. The results indicated that the majority of specimens underwent oblique shear failure due to local bulging of the aluminum tube plane, while specimens with an aspect ratio of 4.0 experienced overall instability failure. As the aspect ratio increased, the bearing capacity improvement coefficient and ductility coefficient of the specimen decreased and the initial stiffness of the specimen gradually decreased. As the aluminum content increased, the initial stiffness decreased, with the critical aspect ratio for overall instability being between 2.0 and 2.5. The optimal aluminum content was recommended to be between 8.5% and 13.5%. When the aspect ratio was around 2.0, the lateral strain of the round-ended aluminum tube developed faster and the constraint effect was the best. The finite element model accurately reproduced the oblique shear bulging of the round-ended aluminum tube and the internal concrete V-shaped collapse, with the axial load–displacement curve being in good agreement. Improving the strength of aluminum alloy was more conducive to improving the axial compression bearing capacity of RECFAT than increasing the strength of concrete. A simplified model and calculation method for RECFAT was proposed, with an error of less than 1%.

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