This paper presents a comprehensive study of residual displacements of the bilinear single degree of freedom (SDOF) systems under the near-fault ground motions (NFGMs). Five sets of NFGMs were constructed in this study, in which the natural ones as well as the synthesized ones were both considered. By way of the nonlinear time history analyses, three different residual displacement spectrums were obtained and analyzed in detail. Utilizing the calculated data, a back propagation (BP) neural network was established to predict the residual displacements of the bilinear SDOF systems under the NFGMs. The results show that the structural parameters, including the strength reduction factor and the post-yield strength ratio, have significant and relatively consistent impacts on the residual displacement spectrum. However, the ground motion characteristics, including the fault type, the closest distance from the site to the fault rupture, the earthquake magnitude, and the site soil condition, exhibit more complex effects on the residual displacement spectrum. In addition, the proposed BP neural network can fully incorporate the parameters affecting the residual displacements of the bilinear SDOF systems under the NFGMs, while having a fairly good accuracy in predicting the residual displacements.

Vertical greenery buildings generally have high ecological, aesthetics, and economic benefits. This paper focuses on damage monitoring of a reinforced concrete (RC) planting balcony in a high-rise vertical greenery building using the electro-mechanical impedance (EMI) method. Damage evaluation of the concrete using the EMI method was first carried out through theoretical analysis and experimental investigations. They both indicate that the conductance resonant frequency (CRF) of the piezoelectric transducer has a good linear correlation with damage of the concrete. According to the experimental results, damage evaluation criteria of concrete are proposed based on the CRF. On the basis of the above work, damage monitoring of a RC planting balcony employing a total of 13 piezoelectric transducers was conducted for about 7 months. The results show the planting balcony was slightly damaged which mainly happened before planting during the whole monitoring process.

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Data stream is a type of data that continue to grow over time. For example, network security data stream will constantly be generated in the field of data security, and encrypted data stream will be generated in the privacy protection scenario. Clustering is a basic task in the analysis of data stream. In addition to the large amount of data and limited computer memory, there are the following challenges in time-decaying data stream clustering: (1) How to quickly process time-varying data stream and how to quickly save vaild data. (2) How to maintain and update clusters and track their evolution in real time. Based on the fact that the existing data stream algorithms do not provide a good strategy to the above problems, this paper proposes a dynamic clustering algorithm named SKDStream. The algorithm divides the entire data space into distinct minimal bound hypercubes, which are maintained and indexed by a newly defined structure, SKDTree, that aggregates and updates clusters in real time without requiring large primary storage. Clusters are composed of dense hypercubes. Experiments on synthetic datasets and real datasets show that the response time of the algorithm is similar to that of existing dataflow algorithms, but the quality of the generated clusters is relatively stable over time. Furthermore, the SKDStream algorithm is able to track the evolution of the number of clusters, centers, and density in real time, and compared to D-stream, SKDStream is efficient and effective in clustering.