Lei Li

Currently, the partial substitution of mineral aggregates with rubber particles in the preparation of rubber concrete (RC) is an effective method for recycling waste rubber materials. However, the mechanism of interfacial interactions in RC at high temperatures is not well understood. This study aims to explore the effect of high temperature on intermolecular interactions at the RC interface and its relationship with macroscopic mechanical properties. Molecular dynamics (MD) simulation technology was employed to construct an RC interface model. The temperature is controlled at room temperature (300 K), medium low temperature (320 K, 340 K, 360 K), and high temperature (500 K, 700 K). The interface model was analyzed from multiple dimensions such as binding energy, turning radius, and interface structure. The results show that the higher the temperature, the more easily water molecules aggregate at the interface of the two phases. As the temperature increases, the proportion of water molecules at the interface increases from 6% to 16%. Since rubber and water molecules cannot form hydrogen bonds, the formation of chemical bonds at the interface between the two phases is affected, leading to a decrease in RC binding energy. The interface bonding energy decreases by 12.6% at a temperature of 700 K. In addition, the radius of gyration of rubber is proportional to temperature. As the temperature increases, the average radius of gyration increases from 5.8 Å to 6.15 Å, and the numerical fluctuation amplitude is greater, resulting in a relatively loose and unstable rubber structure. Furthermore, the bonding strength in RC mainly comes from non-hydrogen bond interactions, and high temperatures cause an increase in bond length while reducing the strength and stability of chemical bonds. Finally, high temperatures increase the atomic movement speed in natural rubber (NR). As the temperature increases, the diffusion coefficients of H_NR and C_NR increase from 0.08 and 0.04 to 1.835 and 1.473, respectively, preventing good connections between atoms at the interface. The study provides nanoscale insights for optimizing RC.

Machine Learning (ML) has developed rapidly in recent years, achieving exciting advancements in applications such as data mining, computer vision, natural language processing, data feature extraction, and prediction. ML methods are increasingly being utilized in various aspects of seismic engineering, such as predicting the performance of various construction materials, monitoring the health of building structures or components, forecasting their seismic resistance, predicting potential earthquakes or aftershocks, and evaluating the residual performance of post-earthquake damaged buildings. This study conducts a scientometric-based review on the application of machine learning in seismic engineering. The Scopus database was selected for the data search and retrieval. During the data analysis, the sources of publications relevant to machine learning applications in seismic engineering, relevant keywords, influential authors based on publication count, and significant articles based on citation count were identified. The sources, keywords, and publications in the literature were analyzed and scientifically visualized using the VOSviewer software tool. The analysis results will help researchers understand the trending and latest research topics in the related field, facilitate collaboration among researchers, and promote the exchange of innovative ideas and methods.

Evaluation of the residual seismic capacity (RSC) of post-earthquake damaged buildings is instrumental to the formation of reasonable recovery strategies. At present, incremental dynamic analysis (IDA) that considers the mainshock and aftershock is the method most frequently used to evaluate the RSC of damaged structures. However, the mainshock-induced structural damage determined using the IDA method may be inconsistent with the damage observed in actual engineering. This inconsistency could potentially lead to an unreasonable evaluation result. To overcome this drawback, it is necessary to evaluate the RSC of damaged structures according to their observed damage instead of that obtained by the IDA. In this paper, a method of evaluating the RSC of damaged reinforced concrete (RC) columns is proposed. First, the damage degree and distribution of the damaged columns were evaluated via visual inspection after mainshocks. Then, a numerical model was developed to predict the residual behavior of damaged columns subjected to aftershocks. After that, the RSC of damaged columns was estimated based on fragility analysis. The degradation of the collapse capacity of damaged columns was quantified by the collapse fragility index (CFI), and a parameter analysis was conducted to study the effect of structural parameters on the CFI of damaged columns. Lastly, an empirical model for predicting the CFI was proposed, facilitating the application of this study in actual post-earthquake assessments. The parameter analysis indicates that the axial load ratio of the columns and the degree of damage degree accumulated during mainshocks have a significant effect on the CFI. Additionally, the proposed empirical model can effectively predict the degradation of the collapse capacity of RC columns in existing test data, with an accuracy of 0.82.