This article investigates the seismic behavior of masonry infilled RC frames with/without openings. Four full-scale, single-story, and single-bay specimens were tested under constant vertical loads and quasi-static cyclic lateral loads. The experimental results showed that the infill wall was more influential in stiffness than in load-resisting capacity. The opening increased the ductility ratio of the structure due to the uniform distribution and slow propagation of cracks. Finally, simplified micro finite element models are established to simulate the tested specimens, which effectively predict the load-displacement response of the structures and the crack damage of masonry infill wall with acceptable accuracy.

Transportation network is of vital importance for city maintaining and reconstruction especially after extreme events. Decisions on the restoration plan of transportation network have to be made in a short period after event, but taking into account various decision-making criteria and resource constraints. This paper explains the essential problems in network restoration scheduling, and presents a novel resilience-based optimisation model for post-disaster urban transportation network restoration. The proposed method could obtain a set of general optimal restoration schedules in an efficient manner, so that decision-makers could make a final choice by weighting other preferences and experience. In the methodology, a new travel speed-based metric is introduced for performance assessment of transportation network, and three normalised independent indicators are developed to characterise network resilience from the perspective of functionality curve. Furthermore, a bi-objective optimisation model based on the recovery trajectory is recommended to search for non-dominated optimal restoration schedules. To illustrate the proposed method, heuristic algorithms are used to solve the restoration schedule optimisation problem of a hypothetical transportation network.

One of the key elements within seismic hazard analysis is the establishment of appropriate ground‐motion models (GMMs), which are used to predict the levels of ground‐motion intensities by considering various parameters (e.g., source, path, and site). Many empirical GMMs were derived on the basis of a predefined linear or nonlinear equation that is heavily dependent on the a priori knowledge of a functional form that varies between the modelers’ choices. To overcome this issue, this study develops a deep neural network (DNN) trained by the recordings from the Pacific Earthquake Engineering Research Center (PEER) Next Generation Attenuation‐West2 Project (NGA‐West2) database. To this end, we collected 20,900 ground motion recordings from the database and randomly split them into the training, validation, and testing datasets. The refined second‐order neuron is proposed to solve the problem, and the Adam optimizer is used to optimize the performance of the model. The prediction errors are evaluated by three performance indicators (i.e., $R2$⁠, root mean square error, mean absolute error), and the predictive results are compared with previous GMMs developed based on the PEER NGA‐West2 database. The between‐event and within‐event standard deviations (SDs) as well as total SDs are calculated and compared. Based on the comparisons, our model maintains consistent performance (e.g., the dependence of predicted intensity measures on seismological and site‐specific parameters) with the compared GMM. Its relatively small total SDs, especially for longer periods, confirm that the proposed model is associated with better predictive power.