A PERFORMANCE ALARMING METHOD FOR BRIDGE EXPANSION JOINTS BASED ON TEMPERATURE DISPLACEMENT RELATIONSHIP MODEL

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.

Claims

1. A performance alarming method for bridge expansion joints based on temperature displacement relationship model, wherein the specific steps of which are as follows: step 1: calculate canonically correlated temperatures of the bridge structure acquire T and D of the monitored bridge structure, T=[T.sub.1, T.sub.2, . . . , T.sub.m].sup.T represents a measurement sample of m temperature measurement point in the structural health monitoring system, D=[D.sub.1, D.sub.2, . . . , D.sub.n].sup.T represents a measurement sample of n expansion joint displacements, calculate the covariance matrix and the cross-covariance matrix of temperature and displacement monitoring data as follows: $\begin{array}{c}\begin{array}{c}\begin{array}{c}{R}_{\mathrm{TT}}=\frac{1}{l-1}.Math.\underset{t=1}{\overset{l}{.Math.}}.Math.\left\{T\left(t\right)-\stackrel{\_}{T}\right\}.Math.{\left\{T\left(t\right)-\stackrel{\_}{T}\right\}}^T\\ R_{\mathrm{DD}}=\frac{1}{l-1}.Math.\underset{t=1}{\overset{l}{.Math.}}.Math.\left\{D\left(t\right)-\stackrel{\_}{D}\right\}.Math.{\left\{D\left(t\right)-\stackrel{\_}{D}\right\}}^T\end{array}\\ R_{\mathrm{TD}}=\frac{1}{l-1}.Math.\underset{t=1}{\overset{l}{.Math.}}.Math.\left\{T\left(t\right)-\stackrel{\_}{T}\right\}.Math.{\left\{D\left(t\right)-\stackrel{\_}{D}\right\}}^T\end{array}\\ R_{\mathrm{DT}}=\frac{1}{l-1}.Math.\underset{t=1}{\overset{l}{.Math.}}.Math.\left\{D\left(t\right)-\stackrel{\_}{D}\right\}.Math.{\left\{T\left(t\right)-\stackrel{\_}{T}\right\}}^T\end{array}$ where T(t) represents the tth temperature measurement sample; T represents the mean-vector of temperature data; D(t) represents the tth displacement: measurement sample; D represents the mean-vector of displacement data; l represents the number of samples; R.sub.TT represents the covariance matrix of temperature data; R.sub.DD represents the covariance matrix of displacement data; R.sub.TD represents the cross-covariance matrix of temperature and displacement data; R.sub.DT represents the cross-covariance matrix of displacement and temperature data; determine a pair of base vectors, i.e., u.sub.1 and v.sub.1, to maximize the correlation between the linear combination of temperatures u.sub.1.sup.TT and the linear combination of displacements v.sub.1.sup.TD, their correlation coefficient is as follows: $\left(u_1,v_1\right)=\frac{u_1^T.Math.R_{\mathrm{TD}}.Math.v_1}{\sqrt{u_1^T.Math.R_{\mathrm{TT}}.Math.u_1.Math.v_1^T.Math.R_{\mathrm{DD}}.Math.v_1}}$ solving the combination coefficients of the base vectors, which maximize the correlation coefficient, can be described by the following optimization problem: $\{\begin{array}{cc}{\max }_{u_1,v_1}& u_1^T.Math.R_{\mathrm{TD}}.Math.v_1\\ s.t.& u_1^T.Math.R_{\mathrm{TT}}.Math.u_1=1\\ & v_1^T.Math.R_{\mathrm{DD}}.Math.v_1=1\end{array}$ solving the combination coefficients of the subsequent base vectors can also be described by similar optimization problems, and the combination coefficients of all base vectors can be solved by the following eigenvalue decomposition:
R.sub.TT.sup.1R.sub.TDR.sub.DD.sup.1R.sub.DT=UU.sup.T
R.sub.DD.sup.1R.sub.DTR.sub.TT.sup.1R.sub.TD=VV.sup.T where =diag(.sub.1, .sub.2, . . . , .sub.k) is a diagonal eigenvalue matrix; .sub.i=.sup.2(u.sub.i, v.sub.i) is the ith eigenvalue; U=[u.sub.1, u.sub.2, . . . , u.sub.k] and V=[v.sub.1, v.sub.2, . . . , v.sub.k] are eigenvector matrices; k=min (m,n) is the number of non-zero solutions; step 2: establish the relationship model between canonically correlated temperatures and displacements define the ith canonically correlated temperature, i.e., T.sub.c,i, i=1,2, . . . , k, as follows:
T.sub.c,i=u.sub.i.sup.TT establish a temperature displacement relationship model for bridge expansion joints, using canonically correlated temperatures, as follows: $\left[\begin{array}{c}{\hat{D}}_1\\ {\hat{D}}_2\\ \mathrm{.Math.}\\ {\hat{D}}_n\end{array}\right]=\left[\begin{array}{cccc}{}_{1,1}& {}_{1,2}& \mathrm{.Math.}& {}_{1,k}\\ {}_{2,1}& {}_{2,2}& \mathrm{.Math.}& {}_{2,k}\\ \mathrm{.Math.}& \mathrm{.Math.}& & \mathrm{.Math.}\\ {}_{n,1}& {}_{n,2}& \mathrm{.Math.}& {}_{n,k}\end{array}\right]\left[\begin{array}{c}{T}_{c,1}\\ T_{c,2}\\ \mathrm{.Math.}\\ T_{c,k}\end{array}\right]+\left[\begin{array}{c}{}_{1,0}\\ {}_{2,0}\\ \mathrm{.Math.}\\ {}_{n,0}\end{array}\right]$ where {circumflex over (D)}.sub.i represents the estimated displacement of the ith expansion joint, i=1,2, . . . , n; represents the regression coefficient; step 3: construct control chart based alarming method define the error of temperature displacement relationship model as follows:
E.sub.i={circumflex over (D)}.sub.iD.sub.i where E.sub.i represents the model error of the ith expansion joint displacement, i=1,2, . . . , n; Let E(t) represent the error sequence of an expansion joint, t=1,2, . . . , l, the mean-value and standard variation of which are as follows: $\begin{array}{c}\stackrel{\_}{E}=\frac{1}{l}.Math.\underset{t=1}{\overset{l}{.Math.}}.Math.E\left(t\right)\\ {}_E=\sqrt{\frac{1}{l-1}.Math.\underset{t=1}{\overset{l}{.Math.}}.Math.{\left\{E\left(t\right)-\stackrel{\_}{E}\right\}}^2}\end{array}$ where represents the mean-value of the error sequence; .sub.E represents the standard variation of the error sequence; construct a mean-value control chart to the error sequence to realize performance alarming of expansion joints, and the three parameters of the mean-value control chart are as follows:
UCL=+.sub.E
CL=
LCL=.sub.E where UCL represents the upper control limit; CL represents the center line; LCL represents the lower control limit; represents a scaling factor which can be determined according to a given significance level; step 4: determine reasonable control limit calculate the absolute value of the error sequence, and estimate its probability density function, then obtain the cumulative density function and the inverse cumulative density function; as a result, the control limit of the absolute error sequence, i.e., L, is calculated as:
L=F.sup.1(1) where F.sup.1(.Math.) represents the inverse cumulative density function of the absolute error sequence; represents the significance level; the calculation formula for the scaling factor is as: $=\frac{L}{{}_E}$ the upper and lower control limits can then be determined through the scaling factor; feed the newly acquired temperature and displacement monitoring data into the temperature displacement relationship model of bridge expansion joints, and the model prediction error of an expansion joint E can be obtained; the criteria for judging performance degradation of expansion joints is as:
E>UCL
E<LCL it can be decided that the performance of expansion joints degrades if the above formula is satisfied.

Description

DETAILED DESCRIPTION

[0026] The following details is used to further describe the specific implementation process of the present invention.

[0027] The monitoring data of temperatures and expansion joint displacements, acquired during 14 months, from a long-span bridge is used to verify the validity of the present invention. The monitoring data acquired during the first 12 months is used as training dataset, which represents the intact state of expansion joints; whereas the monitoring data acquired during the last 2 months is used as testing dataset, which represents the unknown state of expansion joints.

[0028] The detailed implementation process is as follows:

[0029] (1) Obtain canonically correlated temperatures from the training dataset (the solution process can be seen in the sole figure), and then establish temperature displacement relationship model for bridge expansion joints using canonically correlated temperatures.

[0030] (2) Construct mean-value control chart to the modelling error of the temperature displacement relationship model, and calculate the corresponding upper and lower control limits of the control chart.

[0031] (3) Simulate performance degradation of expansion joints in the testing dataset; feed the testing data into the temperature displacement relationship model to obtain the prediction error of the expansion joint displacement; Compare the prediction error with the upper and lower control limits, and trigger a performance alarm when the error falls beyond the control limits; results show that the alarming rate achieves more than 99%, when the performance degradation of expansion joints achieves a severity of 8 mm.