Masonry infilled RC frames are one of the most common structural forms, the damage of which, in earthquake events, usually cause serious losses. The determination of the seismic performance target is the key foundation of performance-based seismic evaluation and design for masonry infilled RC frames. In this paper, an extensive database of experimental tests on infilled RC frames loaded in an in-plane direction is collated. According to the crack propagation and elastic-plastic characteristics of infilled RC frames, the damage process is divided into four stages, and then the criteria of the damage states (DS) are proposed. In addition, the seismic performance targets expressed as inter-story drift ratio (IDR) for the four stages are suggested, which would support the performance-based in-plane seismic analysis of infilled RC frames. Finally, the proposed in-plane seismic performance target is utilized to analyze the fragility of two masonry infilled RC frame structures.

Pile groups are designed to sustain complex loads in various engineering. During the design of a pile group, the obvious pile group effect should be considered for closely spaced pile groups. However, the group effect considered by different scholars varies, which makes it hard for engineers to consider the pile group effect for the design of a pile group. In this study, the finite element (FE) method is proposed to advance our understanding of the variations of pile group effects developed by different researchers, based on the concept of soil–pile relative stiffness. The relationship between soil–pile relative stiffness and normalized lateral load–displacement curves and bending moment profile response of the pile group is investigated. The results show that the pile group effect increases with the increase in soil–pile relative stiffness; the pile group effect increases with the decrease in pile spacing, increases with the increase in of number of piles in the group, and is significantly affected by pile group arrangement as well.

Reinforced concrete (RC) frames are designed based on the strong column-weak beam (SCWB) philosophy to reduce structural damage and collapse during earthquakes. The SCWB design philosophy is ensured by the required minimum flexural strength ratio of columns to beams (FSRCB) in the seismic code. Quantifying the relationship between the FSRCB and the collapse capacity of the frames may facilitate the efficient assessment of the seismic performance of the existing or newly designed RC frames. This paper investigates the influence of different FSRCBs on the collapse capacity of three- and nine-story RC frames designed according to Chinese seismic codes. The results show that the collapse capacities of the RC frames can be efficiently improved by increasing the FSRCB, and the collapse capacities of frames with FSRCB = 2.0 are improved by approximately 1.6–2.0 times compared with those of the frames with FSRCB = 1.2. Compared with the middle- or high-rise (nine-story) frames, it is more efficient to improve the collapse capacity for low-rise (three-story) frames by increasing the value of CBFSR. The logarithmic standard deviation of the collapse capacity of the RC frames designed according to the Chinese seismic codes ranges from 0.5–0.9, which is larger than the proposed maximum logarithmic standard deviation (0.4) in FEMA P695.

The damage pattern of masonry-infilled reinforced concrete (RC) frame structures in earthquake events is complicated, and understanding the detailed failure behavior of these structures and modeling it accurately has been a challenging task. In this paper, the extended finite element method (XFEM) is introduced to reproduce arbitrary cracks initiating and propagating in concrete frame and masonry units, combined with interface elements to model various behaviors of masonry-infilled RC frames. Within the finite element analysis program FEAP, a user element subroutine is adopted for the incorporation of XFEM and two types of extended finite elements with and without crack tip enrichments are built to simulate the behavior of concrete material for frame members and masonry blocks for the infill panel, respectively. In addition, a macro command is created to check the crack-propagation criterion and update crack and enrichment information. Furthermore, numerical examples are performed with existing test data, which reveal the efficiency of the implementation procedure. A comparison of the analytical and experimental results show that the proposed modeling can be used to predict the crack and failure process and the load-bearing capacity curves of the structures and reflect accurately the interaction of masonry infill and RC frames.

A high-pass cut-off frequency in filtering is critical to processing strong-motion records. The various processing procedures available nowadays are based on their own needs and are not universal. Regardless of the methods, a visual inspection of the filtered acceleration integration to displacement is required to determine if the selected filter passband is appropriate. A better method is to use a traversal search combined with visual inspection to determine the cut-off frequency, which is the traditional method. However, this method is inefficient and unsuitable for processing massive strong-motion records. In this study, convolutional neural networks (CNNs) were used to replace visual inspection to achieve the automatic judgment of the reasonableness of the filtered displacement time series. This paper chose the pre-trained deep neural network (DNN) models VGG19, ResNet50, InceptionV3, and InceptionResNetV2 for transfer learning, which are only trained in the fully connected layer or in all network layers. The effect of adding probability constraints on the results when predicting categories was analyzed as well. The results obtained through the VGG19 model, in which all network layers are trained and probability constraints are added to the prediction, have the lowest errors compared to the other models. The coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are 0.82, 0.038, 0.026, and 2.99%, respectively.

In structural response array observation, the vibration response of a structure during an earthquake or from the natural environment is recorded and stored using high-sensitivity strong motion seismographs, and the dynamic characteristics of the structure are analyzed and determined using random signal data processing technology. Due to the use of field test data for analysis, this may be the most accurate and effective way to obtain the actual characteristics of the structure, which can be further used to verify the accuracy of theoretical analysis, experimental results, and numerical simulations. Therefore, this technique plays an important role in earthquake prevention and disaster reduction, with the application of strong motion observation data. In this paper, field vibration tests were performed on the highest reinforced masonry structure in China. With the test environmental vibration data, the natural vibration frequency values and mode shapes of the structure were identified using the peak picking method. A numerical modal analysis was then performed to verify the accuracy of the field test results. In addition, the structural response records obtained during an earthquakes in Songyuan were also used to identify the natural vibration frequency of the structure and the changes in the natural vibration frequency before, during, and after the earthquake. The results showed that the structure was not damaged during the earthquake and remained in an elastic state.