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1.发明申请公开(公告)号：US20190228117A1
公开(公告)日：2019-07-25
申请号：US16336807
申请日：2018-03-14
Applicant: DALIAN UNIVERSITY OF TECHNOLOGY
Inventor： Tinghua YI , Haibin HUANG , Hongnan LI
Abstract: The present invention belongs to the technical field of health monitoring for civil structures, and a performance alarming method for bridge expansion joints based on temperature displacement relationship model is proposed. First, the canonically correlated temperature is proposed to maximize the correlation between bridge temperature field and expansion joint displacement; second, a temperature displacement relationship model for bridge expansion joints is established based on canonically correlated temperatures; then, a mean-value control chart is constructed to the error of temperature displacement relationship model; finally, reasonable control limits are determined for the mean-value control chart. A more accurate temperature displacement relationship model can be established based on canonically correlated temperatures, which is of important value to improve the performance alarming ability for expansion joint.
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2.发明申请公开(公告)号：US20190391037A1
公开(公告)日：2019-12-26
申请号：US16342922
申请日：2018-03-23
Applicant: Dalian University of Technology
Inventor： Tinghua YI , Haibin HUANG , Hongnan LI
Abstract: Health monitoring for civil structures, and a performance alarming method for long-span bridge girder considering time-varying effects. First, establish accurate relationship model between temperature and strain fields to eliminate the temperature effect in the girder strain; second, build principal component analysis model for the girder strain after eliminating temperature effect to further eliminate the effects of wind and vehicle loads. Then, construct the performance alarming index and determine its reasonable threshold for the strain after eliminating the effects of temperature, wind and vehicle loads. Finally, construct the performance degradation locating index based on the contribution analysis.
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3.发明申请公开(公告)号：US20190121838A1
公开(公告)日：2019-04-25
申请号：US16090911
申请日：2018-02-12
Applicant: Dalian University of Technology
Inventor： Tinghua YI , Haibin HUANG , Hongnan LI
IPC: G06F17/18
Abstract: The present invention belongs to the technical field of health monitoring for civil structures, and a dynamically non-Gaussian anomaly identification method is proposed for structural monitoring data. First, define past and current observation vectors for the monitoring data and pre-whiten them; second, establish a statistical correlation model for the whitened past and current observation vectors to obtain dynamically whitened data; then, divide the dynamically whitened data into two parts, i.e., the system-related and system-unrelated parts, which are further modelled by the independent component analysis; finally, define two statistics and determine their corresponding control limits, respectively, it can be decided that there is anomaly in the monitoring data when each of the statistics exceeds its corresponding control limit. The non-Gaussian and dynamic characteristics of structural monitoring data are simultaneously taken into account, based on that the defined statistics can effectively identify anomalies in the data.
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4.发明申请公开(公告)号：US20190122131A1
公开(公告)日：2019-04-25
申请号：US16090744
申请日：2018-02-12
Applicant: Dalian University of Technology
Inventor： Tinghua YI , Haibin HUANG , Hongnan LI , Shuwei MA
Abstract: The present invention belongs to the technical field of health monitoring for civil structures, and an anomaly identification method considering spatial-temporal correlation is proposed for structural monitoring data. First, define current and past observation vectors for the monitoring data and pre-whiten them; second, establish a statistical correlation model for the pre-whitened current and past observation vectors to simultaneously consider the spatial-temporal correlation in the monitoring data; then, divide the model into two parts, i.e., the system-related and system-unrelated parts, and define two corresponding statistics; finally, determine the corresponding control limits of the statistics, and it can be decided that there is anomaly in the monitoring data when each of the statistics exceeds its corresponding control limit.
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