The frequent occurrence of major earthquakes highlights the structural challenges posed by long-period ground motions (LPGMs). This study investigates the seismic performance and resilience of five reinforced concrete (RC) columns with different high-strength steel bars under LPGM-induced cyclic loading, both experimentally and numerically. The results show that low-bond and debonded high-strength steel bars significantly enhance self-centering capabilities and reduce residual drift, with lateral force reductions of 7.6% for normal cyclic loading and 19.2% for multiple reversed cyclic loading. The concrete damage in the hinge zone of the columns was increased; however, the significant inside damage of the concrete near the steel bars made it easier to restore the columns for the damage accumulation caused by multiple loading. Based on the experiment, a numerical model was developed for the columns, and a simplified model was proposed to predict energy dissipation capacity, providing practical design methods for resilient RC structures that may be attacked by LPGMs.

Multi-principal element alloys (MPEAs) are characterized by a high-dimensional materials design space, and data-driven models can be considered as the best tools to describe the structure–property relationship in this class of materials. Predicting the prevalence of an intermetallic (IM) phase in a high-entropy alloy (HEA) regime of MPEAs has become a very important research direction recently. In this work, Automatic Featurization capability has been deployed computationally to extract composition and property features from the datasets of MPEAs. Data visualization has been performed, and through principal component analysis, the relative impacts of the input features on the two principal components have been specified. Artificial neural network is then trained upon the set of compostion, property and phase information features. A GUI interface is subsequently developed on top of the prediction model to enable the user-friendly computer environment for detection of the IM phase in a compositionally complex alloy.

The application of machine learning (ML) in structural engineering is receiving increasing attention recently. This paper experimentally studies three self-restoring reinforced concrete (SRRC) columns reinforced with low-bond ultra-high strength rebars, to first discuss the reliability and evaluation of the SRRC columns under multiple reversed cyclic (MRC) loads induced by strong earthquakes, and to also first introduce the Transformer method into the analysis and discussion of structural tests. The tests confirmed the superior seismic behavior and high self-centering performance of the columns and presented how MRC loads affect the seismic performance of SRRC columns in terms of the lateral load-carrying capacity and energy dissipation capacity. Superior to conventional methods, a high-accuracy Transformer-based model is proposed to evaluate the plastic hinge height (PHL) of the tested SRRC columns compared with the other three algorithms (MLP, KNN, and XGBoost). Furthermore, the Shapley Additive exPlanations (SHAP) approach is adopted to explain the insight relationship between the structural parameters and PHL of the columns.

Traditional fiber-reinforced polymer (FRP) retrofit methods can restore the strength of reinforced concrete columns well, but stiffness is also partly restored. To increase the initial stiffness of retrofitted columns, this study investigated the seismic behavior of retrofitted resilient reinforced concrete (RRC) columns that were retrofitted by different methods, including high-strength mortar retrofit, carbon fiber-reinforced polymer (CFRP) retrofit, and CFRP and steel plate retrofit. In addition, the effect of the axial load was also considered. Quasi-static tests were conducted twice on five specimens, i.e., before and after repairing. The first test was used to create earthquake damage, and the second test was used to compare the seismic behavior of the retrofitted columns. The experimental results indicated that the CFRP retrofit method, whether with a steel plate or not, can restore the lateral resistance capacity well; furthermore, the drift-hardening behavior and self-centering performance were well maintained. The residual drift ratio of the CFRP-retrofitted column was less than 0.5%, even at a drift ratio of 3.5%, and less than 1% at the 6% drift ratio. However, the initial stiffness was only partly restored using the CFRP sheet. The introduction of steel plates was beneficial in restoring the initial stiffness, and the stiffness recovery rate remained above 90% when CFRP sheets and steel plates were used simultaneously. The strain distribution of the CFRP sheet showed that the steel plate did work at the initial loading stage, but the effect was limited. By using the steel plate, the CFRP hoop strain on the south side was reduced by 68% at the 6% drift ratio in the push direction and 38% in the pull direction. The axial strain of CFRP cannot be ignored due to the larger value than the hoop strain, which means that the biaxial stress condition should be considered when using an FRP sheet to retrofit RC columns.