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.

Abstract: The presented invention belongs to the technical field of data analysis for structural health monitoring, and relates to a method of the mode order determination for the modal identification of engineering structures. The presented invention first calculates the structural natural frequencies for every order by eigensystem realization algorithm. Then the modal responses for every natural frequency are extracted. After obtaining the square mean root of modal responses, the modal response contribution index (MRCI) is calculated by summation of square mean root for every degree-of-freedom. The relation map between mode order and MRCI is drawn. The mode order is determined by the obvious gap between two adjacent MRCI according to the relation map. This order is also the truncated order of singular matrix in the eigensystem realization algorithm, which is useful to identify other modal parameters accurately.

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.

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.