Intelligent information acquisition methods and seismic damage prediction of rural masonry building groups

Masonry structures (MS) are numerous and widely distributed in rural areas of China, and these structures have features such as generally poor seismic performance, variety of structural forms, insufficient data on mechanics analysis and randomness. Building information is the precondition and basis for seismic damage predictions (SDP), while traditional methods by manual field survey are inefficient and uneconomical. In this paper, an intelligent method combining fuzzy inference and image recognition was proposed to get building information. An image recognition model for MS based on convolutional neural network was established, and image measurement technology was used to obtain geometric information. The hidden information identification method for MS was given based on fuzzy inference model, which can effectively obtain the hidden information such as wall thickness, seismic detailing and material strength. The mechanics model of building and spatially variable ground motion model were established based on building information and site information, respectively. The SDP was then conducted through structural seismic response analysis. Finally, the accuracy and efficiency of the proposed method were verified by a numerical example of MS groups in a village located in north China. The results show that the image identification and measurement techniques based on photos can effectively obtain the type of building structure and geometric information. The proposed fuzzy inference method can efficiently and accurately identify the hidden information of the masonry buildings, and seismic damage of the rural buildings can be effectively predicted through the numerical analysis.