The rapid assessment of post-earthquake building damage for rescue and reconstruction is a crucial strategy to reduce the enormous number of human casualties and economic losses caused by earthquakes. Conventional machine learning (ML) approaches for this problem usually employ one-hot encoding to cope with categorical features, and their overall procedure is neither sufficient nor comprehensive. Therefore, this study proposed a three-stage approach, which can directly handle categorical features and enhance the entire methodology of ML applications. In stage I, an integrated data preprocessing framework involving subjective–objective feature selection was proposed and performed on a dataset of buildings after the 2015 Gorkha earthquake. In stage II, four machine learning models, KNN, XGBoost, CatBoost, and LightGBM, were trained and tested on the dataset. The best model was judged by comprehensive metrics, including the proposed risk coefficient. In stage III, the feature importance, the relationships between the features and the model’s output, and the feature interaction effects were investigated by Shapley additive explanations. The results indicate that the LightGBM model has the best overall performance with the highest accuracy of 0.897, the lowest risk coefficient of 0.042, and the shortest training time of 12.68 s due to its relevant algorithms for directly tackling categorical features. As for its interpretability, the most important features are determined, and information on these features’ impacts and interactions is obtained to improve the reliability of and promote practical engineering applications for the ML models. The proposed three-stage approach can provide a reference for the overall ML implementation process on raw datasets for similar problems.

To improve seismic performance of a traditional viscous damper, an innovative viscous damper is proposed, in which the conventional damping medium is replaced by a mixture of oil and silt. This new medium is expected to increase damping force and solve the problem that arises out of the fact that the energy dissipation capacity of a conventional fluid viscous damper is low for small displacements. Firstly, the design concept and device configurations are introduced, then a cyclic loading test is applied to investigate the damper’s energy dissipation mechanism in different conditions. Experimental results show that the damper exhibits displacement-dependent characteristics for small displacements, indicating that the silt has changed its damping mechanism. Furthermore, the effect of multiple test parameters on the damping force is analyzed, showcasing that an increase in silt content can visibly increase the damping force. According to experimental data, fitting models of the damping force are obtained and verified, thus promoting further engineering applications.

The reinforced concrete (RC) frames infilled with masonry walls are widely used in buildings. It has been well recognized that the arrangement of tie-columns can improve the wall integrity and seismic performance. However, the existing cast-in-situ tie-column (CSTC) has some problems, and a new type of fabricated tie-column (FTC) which can be recycled for secondary use is proposed in this study. Two specimens, the wall constrained by the cast-in-situ tie-columns (W-CSTC) and the wall constrained by the fabricated tie-columns (W-FTC), were designed and constructed. Low cyclic loading tests were carried out and some parameters, such as the failure modes, hysteretic curves and so forth, were used to evaluate the applicability of the FTC. The results show the W-FTC has a certain initial stiffness and strength, favorable deformation capacity, and the FTC can not only enhance the wall integrity to meet the functional requirements of tie-columns, but also solve the connection problems and reduce the adverse effects on the frame structure.

This paper presents an unconditionally stable integration method, which introduces a linearly implicit algorithm featuring an explicit displacement expression. The technique that is being considered integrates one Newton iteration into the mean acceleration method. The stability of the proposed algorithm in solving equations of motion containing nonlinear restoring force and nonlinear damping force is analyzed using the root locus method. The objective of this investigation was to assess the accuracy and consistency of the proposed approach in contrast to the Chang method and the CR method. This is achieved by analyzing the dynamic response of three distinct structures: a three-layer shear structure model outfitted with viscous dampers, a three-layer shear structure model featuring metal dampers, and an eight-story planar frame structure. Empirical evidence indicates that the algorithm in question exhibits a notable degree of precision and robustness when applied to nonlinear dynamic problem-solving.