公开(公告)号：US11169136B2

公开(公告)日：2021-11-09

申请号：US17049194

申请日：2019-11-25

Applicant: QINGDAO UNIVERSITY OF TECHNOLOGY

Inventor： Fanxiu Chen ,  Bin Zhang ,  Pengfei Guo ,  Zuquan Jin

IPC: G01B11/16 ,  G01N33/38 ,  G01L5/1627 ,  G01N17/00 ,  G06T7/00 ,  H04N5/225 ,  H04N5/247

Abstract: A method for measuring corrosion-expansion force during cracking of concrete due to corrosion and expansion of reinforcing steel; wherein, deformation on a surface of reinforced concrete is photographed based on a digital image correlation (DIC) method, a full-field displacement and a full-field strain on a surface of the concrete are analyzed and calculated, a relationship between corrosion-expansion force and the strain on the surface of the concrete is found through an established theoretical model, and corrosion-expansion force of reinforcing steel and a change rule of the corrosion-expansion force are calculated. Therefore, the method is simple and includes with safe and reliable operations, scientific principles, and low costs, so that a change in corrosion-expansion force during corrosion and expansion of reinforced concrete can be monitored in real time.

公开(公告)号：US20230410012A1

公开(公告)日：2023-12-21

申请号：US18091663

申请日：2022-12-30

Applicant: Qingdao University of Technology

Inventor： Liming Zhang ,  Zhongyuan Liu ,  Yu Cong ,  Xiaoshan Wang ,  Zaiquan Wang ,  Fanxiu Chen ,  Jinfeng Cao

IPC: G06Q10/0635 ,  G06Q50/26 ,  G06F18/25 ,  G06N3/0442 ,  G06N3/08

CPC classification number: G06Q10/0635 ,  G06Q50/265 ,  G06F18/251 ,  G06N3/0442 ,  G06N3/08

Abstract: A project disaster warning method and system based on collaborative fusion of multi-physical field monitoring data is provided. The method includes: acquiring and preprocessing multi-sensor real-time monitoring data of potentially dangerous parts of a project structure; normalizing multi-physical field monitoring time sequence data to construct a normalized sample matrix; analyzing sensitivities of various physical field monitoring indicators to a safety state of a project by using a multivariate statistical method; guiding initialization training of a LSTM network according to the sensitivities, and obtaining output results of the LSTM network; obtaining basic probability assignments of various warning levels after fusion according to an improved D-S evidence theory based on Chebyshev distance, with the output results of the LSTM network as evidence inputs; determining disaster danger levels of the potentially dangerous parts of the project structure using a basic probability assignment-based decision method.