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Seismic performance of concrete columns confined by high‑strength stirrups

Hou Chongchi, Zheng Wenzhong, Liu Pengfei, Wang Qinghe, Qi ShaoBo

Archives of Civil and Mechanical Engineering

2023

Vol. 23, no. 2

art. no. e69, 2023

The concrete columns confined by high-strength stirrups exhibited higher bearing capacity and better deformation ability. Based on the test results of concrete columns confined by high-strength stirrups under lateral cyclic loading, it is found that stirrup yield strength could not be used directly in calculating bearing capacity, because the high-strength stirrup could not yield at the peak point. Moreover, according to the seismic performance of a total of 49 sets of confined concrete columns from this paper and other 5 research papers, an easy-to-use model of skeleton curve is proposed by using a set of empirical equations to calculate the characteristic points of skeleton curve. Furthermore, based on the proposed model of skeleton curve, hysteretic rules are developed for the unloading and reloading stages by providing calculating formula of unloading stiffness and ignoring the effect of strength degradation. Finally, the proposed model of skeleton curve and hysteretic rules are verified and evaluated by comparing the calculated curves and experimental curves.

2D inversion of magnetotelluric data using deep learning technology

Liao Xiaolong, Shi Zeyu, Zhang Zhihou, Yan Qixiang, Liu Pengfei

Acta Geophysica

2022

Vol. 70, no 3

1047--1060

The inverse problem of magnetotelluric data is extremely difficult due to its nonlinear and ill-posed nature. The existing gradient-descent approaches for this task surface from the problems of falling into local minima and relying on reliable initial models, while statistical-based methods are computationally expensive. Inspired by the excellent nonlinear mapping ability of deep learning, in this study, we present a novel magnetotelluric inversion method based on fully convolutional networks. This approach directly builds an end-to-end mapping from apparent resistivity and phase data to resistivity anomaly model. The implementation of the proposed method contains two stages: training and testing. During the training stage, the weight sharing mechanism of fully convolutional network is considered, and only the single anomalous body model samples are used for training, which greatly shortens the modeling time and reduces the difficulty of network training. After that, the unknown combinatorial anomaly model can be reconstructed from the magnetotelluric data using the trained network. The proposed method is tested in both synthetic and field data. The results show that the deep learning-based inversion method proposed in this paper is computationally efficient and has high imaging accuracy.