Post-Earthquake Serviceability Assessment Using the Convolutional Neural Networks and Deep Neural Networks

Post-Earthquake Serviceability Assessment Using the Convolutional Neural Networks and Deep Neural Networks

Author:  Saiful Hasan, M.Tech (Earthquake Engineering), Jamia Millia Islamia , New Delhi, India. Hasansaiful35@gmail.com

Co-Ordinator:  Dr. SM Abbas, Senior Professor, Department of Civil Engineering, Jamia Millia Islamia, New Delhi, India. abbas_sm@hotmail.com

ABSTRACT

There have been studies using image processing techniques to assess damage of structure especially columns without sending individual to onsite. Convolutional neural networks (CNNs) and the entire Deep neural network (DNN) have shown state-of-art results in object detection and image classification tasks. This study proposed cascaded deep learning network for post-earthquake structural serviceability assessment. Major target deficiency components (crack, spalled area, transverse bar, and longitudinal bar) were used to determine the proposed damage states to assess serviceability of structure. Concrete surface cracks are major defect in civil structures. Structural Inspection which is done for the evaluation of rigidity and tensile strength of the building. Crack detection plays a major role in the building inspection, finding the cracks and determining the Structural health in the case of post-earthquake. The research paper is all about  finding the serviceability results of the structure.