CN114547868A - Cable-stayed bridge performance online evaluation prediction method based on BIM - Google Patents

Cable-stayed bridge performance online evaluation prediction method based on BIM Download PDF

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CN114547868A
CN114547868A CN202210093200.0A CN202210093200A CN114547868A CN 114547868 A CN114547868 A CN 114547868A CN 202210093200 A CN202210093200 A CN 202210093200A CN 114547868 A CN114547868 A CN 114547868A
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cable
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bim
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魏佳北
李连华
丁海峰
胡海波
张国林
范啸
肖宽怀
王卫国
刘晓剑
许俊德
宁卫强
黄正荣
张心纯
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China Railway Construction Investment Group Co Ltd
CRCC Suzhou Design and Research Institute Co Ltd
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CRCC Suzhou Design and Research Institute Co Ltd
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Abstract

The invention provides a BIM-based online cable-stayed bridge performance evaluation and prediction method, which comprises the steps of building a BIM model and building an online evaluation and prediction system based on the BIM model.

Description

Cable-stayed bridge performance online evaluation prediction method based on BIM
Technical Field
The invention relates to the field of cable-stayed bridge monitoring, in particular to a BIM-based cable-stayed bridge performance online evaluation and prediction method.
Background
At present, maintenance management of a large-span bridge is generally realized by the following two methods:
(1) the bridge health monitoring system is characterized in that a sensor is arranged at a response position of a bridge, and data acquisition can be carried out by means of technologies such as a camera and an unmanned aerial vehicle, but the bridge health monitoring system is still not perfect at present and is limited by the limitations of multiple aspects such as the scale of the health monitoring system and the monitoring technology, and the bridge health monitoring system cannot provide complete bridge health information;
(2) the manual regular inspection is carried out, the information obtained by the conventional manual visual inspection or portable one-time measurement is long in manual inspection period, many blind areas are formed, subjectivity is high, and the practical use is limited. In addition, the safety and durability of the large-span bridge are evaluated by the skilled engineering technicians, however, the investigation means and judgment criteria adopted by the traditional experience method are directly related to the personal knowledge level and experience of an evaluator, and the expert evaluation results under different background conditions have strong subjectivity and misjudgment possibility.
Disclosure of Invention
In order to solve the problems, the invention provides a BIM-based online performance evaluation and prediction method for a cable-stayed bridge.
The main content of the invention comprises:
a BIM-based cable-stayed bridge performance online evaluation and prediction method comprises the following steps:
s1, establishing a BIM corresponding to an object to be monitored according to the object to be monitored, and laying the same sensor model at a position corresponding to the BIM according to a monitoring scheme of the monitored object;
s2, establishing an online evaluation prediction system based on a BIM model, wherein the online evaluation prediction system comprises a data acquisition module, a data processing module and an early warning display module; the data acquisition module is connected with the existing real-time monitoring system and transmits data corresponding to each sensor in the existing real-time monitoring system to a corresponding sensor model in the BIM model; the data acquisition module can also receive manual inspection data; the data processing module receives the data transmitted by the data acquisition module, and creates a bridge performance evaluation index after data preprocessing and information fusion; the early warning display module receives the bridge performance evaluation index obtained by the data processing module, evaluates the service state of the bridge based on a variable weight analysis fusion method according to the established evaluation standard, establishes a cable-stayed bridge state trend change mathematical model based on a big data and time sequence method, and displays the estimation and prediction results.
Preferably, the data acquisition module comprises a real-time monitoring unit and a manual inspection unit, and the real-time monitoring unit is used for receiving data of the sensor transmitted by the existing real-time monitoring system; and the manual inspection unit receives the input of the timing inspection data.
Preferably, the data processing module receives the data of the real-time monitoring unit, performs mutual calibration and fusion with the data transmitted by the manual inspection unit after self-calibration and fusion, and generates a bridge performance evaluation index according to the created evaluation model.
Preferably, the method for establishing the cable-stayed bridge state trend change mathematical model based on the big data and time sequence method and displaying the prediction and prediction results comprises the following steps:
s21, updating a BIM (building information modeling) model according to the evaluation result of the service state of the bridge;
s22, according to the prediction scheme, selecting an acceleration sensor model at a corresponding position on the BIM model, and acquiring monitoring data of a corresponding acceleration sensor on the existing real-time monitoring system to obtain the cable force of a corresponding cable;
s23, a cable force prediction model is created by using a Holt-Winters cubic exponential smoothing method, cable force data obtained in the S22 are input into the cable force prediction model, and seasonal statistical test is performed through Krusaklas-Wallis test to evaluate the future performance evolution trend of the bridge.
Preferably, S23 includes the following sub-steps:
s231, identifying seasonal influence of the cable by using Krusakal-Wallis test, wherein the test statistic is as follows:
Figure BDA0003490008880000021
wherein R isiIndicating y in the ith seasoniA rank of (c); n isiIndicating y in the ith seasoniThe number of (2); n represents the number of all of y,
Figure BDA0003490008880000022
represents an average rank of the ith season;
Figure BDA0003490008880000023
-overall average rank;
s232, if H is calculated in S231>χα 2(L-1), if the cable is considered to have seasonal influence, a cable force prediction model is created according to a Holt-Winters cubic exponential smoothing method, and the time for predicting the structural degradation to a threshold value is calculated as follows:
Figure BDA0003490008880000024
wherein the content of the first and second substances,
Figure BDA0003490008880000025
suppose that the current moment of a certain measuring point is t1The predicted value of the cable force at the time t is
Figure BDA0003490008880000026
At (t)1+tm) The cable force measuring value reaches the limit value;
s233, if the calculated H in S233 is not more than χα 2(L-1), if the cable has no seasonal influence, the cable force variation trend is obtained by means of the sexual fitting, namely
Figure BDA0003490008880000027
The corresponding time to predict structural degradation to the threshold is:
Figure BDA0003490008880000031
s234, predicting the time t when the structure is degraded to a threshold value according to the single cablemAnd calculating the cable force evaluation value of the monitoring point.
Preferably, the method for calculating the cable force evaluation value of the monitoring point in S234 is as follows:
when t ism>T0,xjWhen t is 100m＝0，xjWhen the cable force evaluation value of the measuring point is equal to 0, the cable force evaluation value of the measuring point is
Figure BDA0003490008880000032
Preferably, the creating of the cable force prediction model by using the Holt-Winters cubic exponential smoothing method comprises the following steps:
establishing an addition mode model:
Figure BDA0003490008880000033
in the formula, atIs the horizontal part of the sequence, btThe trend part of the sequence, stFor the seasonal part of the sequence, L is the period length of one season; alpha, beta and gamma are smoothing coefficients and are between 0 and 1, in the equation set, the first equation is a horizontal equation, the second equation is a trend equation, and the third equation is a seasonal equation which indicates that the seasonal variation value of t period is a weighted average of the difference between the observed value and the horizontal value of t period and the seasonal variation value of t-L period;
the addition mode model is used for sequence prediction, and the predicted value of l step length is predicted forward in t period as follows:
Figure BDA0003490008880000034
wherein, at、btRespectively, the level value and the trend value, s, of the t periodt+l-LIs the seasonal variation value of t + L-L.
Preferably, the initial value in the formula (1) is:
wherein the initial value of the first equation
Figure BDA0003490008880000035
Initial value of the second equation
Figure BDA0003490008880000036
Initial value of the third equation: s1＝y1-a0,s2＝y2-a0,…sL＝yL-a0。
Preferably, the method for creating the evaluation criterion includes: generating a cable-stayed bridge judgment matrix based on an expert questionnaire method, carrying out fuzzy C-means clustering through an FCM algorithm, classifying the weight given by each expert, and constructing and distinguishing indexes of the logical property of the expert judgment matrix through the maximum characteristic root of the judgment matrix.
Preferably, the method for evaluating the service state of the bridge based on the variable weight analysis fusion method comprises the following steps: and performing state evaluation by adopting an analytic hierarchy process, wherein the evaluation index of the cable force of the stay cable is a dead load characteristic value extracted by eliminating a temperature effect and a live load effect, and the cable force of the stay cable at the symmetrical position of the structure is selected for mutual calibration evaluation.
Compared with the prior art, the online performance evaluation and prediction method for the cable-stayed bridge based on the BIM has the following beneficial effects: the method is characterized in that real-time monitoring data and regular manual inspection data acquired by the existing bridge health management system are fused, comprehensive and accurate monitoring and evaluation of the large-span cable-stayed bridge are realized by adopting an analytic hierarchy process, and meanwhile, based on a big data and time sequence method, a cable force prediction model is created to extract corresponding trend changes, so that not only can a degradation trend of a real structure be obtained, but also sequence indexes can be evaluated.
Detailed Description
The technical solution protected by the present invention will be specifically explained below.
A BIM-based cable-stayed bridge performance online evaluation and prediction method comprises the following steps:
s1, establishing a corresponding BIM (building information modeling) model according to an object to be monitored, wherein the BIM model comprises bridge towers, guys, main beams, foundations, railings, expansion joints and other members, and arranging the same sensor model at a position corresponding to the BIM model according to a monitoring scheme of the monitored object so as to simulate a real bridge;
s2, establishing an online evaluation prediction system based on a BIM model, wherein the online evaluation prediction system comprises a data acquisition module, a data processing module and an early warning display module; the data acquisition module is connected with the existing real-time monitoring system and transmits data corresponding to each sensor in the existing real-time monitoring system to a corresponding sensor model in the BIM model; the data acquisition module can also receive manual inspection data; specifically, the data acquisition module comprises a real-time monitoring unit and a manual inspection unit, wherein the real-time monitoring unit is used for receiving data of a sensor transmitted by the existing real-time monitoring system; and the manual inspection unit receives the input of the timing inspection data.
And the data processing module receives the data transmitted by the data acquisition module, and creates a bridge performance evaluation index based on an analytic hierarchy process after data preprocessing and information fusion. Specifically, the data processing module receives the data of the real-time monitoring unit, performs mutual calibration fusion with the data transmitted by the manual inspection unit after self-calibration fusion, and generates a bridge performance evaluation index according to the created evaluation model; the above-mentioned analytic hierarchy process is to divide the evaluation index into qualitative index, quantitative index and sequence index, such as coating degradation area, corrosion area, bolt loss rate, etc. which belong to the indexes that can be evaluated by quantification, and cable force, displacement and stress, etc. are called sequence index.
The early warning display module receives the bridge performance evaluation index obtained by the data processing module, evaluates the service state of the bridge based on a variable weight analysis fusion method according to the established evaluation standard, establishes a cable-stayed bridge state trend change mathematical model based on a big data and time sequence method, and displays the estimation and prediction results. The method for evaluating the service state of the bridge based on the variable weight analysis fusion method comprises the following steps: and performing state evaluation by adopting an analytic hierarchy process, wherein the evaluation index of the cable force of the stay cable is a dead load characteristic value extracted by eliminating a temperature effect and a live load effect, and the cable force of the stay cable at the symmetrical position of the structure is selected for mutual calibration evaluation.
Preferably, the method for establishing the cable-stayed bridge state trend change mathematical model based on the big data and time sequence method and displaying the prediction and prediction results comprises the following steps:
s21, updating a BIM (building information modeling) model according to the evaluation result of the service state of the bridge;
s22, according to the prediction scheme, selecting an acceleration sensor model at a corresponding position on the BIM model, and acquiring monitoring data of a corresponding acceleration sensor on the existing real-time monitoring system to obtain the cable force of a corresponding cable;
s23, a cable force prediction model is created by using a Holt-Winters cubic exponential smoothing method, cable force data obtained in the S22 are input into the cable force prediction model, and seasonal statistical test is performed through Krusaklas-Wallis test to evaluate the future performance evolution trend of the bridge.
In order to establish the change of the cable force with time through the Holt-winter model, judge whether the cable force sequence has seasonality from the quantitative point of view, and then evaluate the cable force according to the influence of the seasonality, in the embodiment, the krusaklai-Wallis test is used for performing a statistical test of the seasonality, and specifically, the S23 comprises the following sub-steps:
s231, identifying seasonal influence of the cable by using Krusadal-Wallis test, wherein test statistic is as follows:
Figure BDA0003490008880000051
wherein R isiIndicating y in the ith seasoniA rank of (c); n isiIndicating y in the ith seasoniThe number of (2); n represents the number of all of y,
Figure BDA0003490008880000052
represents an average rank of the ith season;
Figure BDA0003490008880000053
-overall average rank;
s232, if H is calculated in S231>χα 2(L-1), if the cable is considered to have seasonal influence, a cable force prediction model is created according to a Holt-Winters cubic exponential smoothing method, and the time for predicting the structural degradation to a threshold value is calculated as follows:
Figure BDA0003490008880000054
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003490008880000055
suppose that the current moment of a certain measuring point is t1The predicted value of the cable force at the time t is
Figure BDA0003490008880000056
At (t)1+tm) The cable force measuring value reaches the limit value;
s233, if the calculated H in S233 is not more than chiα 2(L-1), if the cable has no seasonal influence, the cable force variation trend is obtained by means of the sexual fitting, namely
Figure BDA0003490008880000061
The corresponding time to predict structural degradation to the threshold is:
Figure BDA0003490008880000062
s234, predicting the time t when the structure is degraded to a threshold value according to the single cablemAnd calculating the cable force evaluation value of the monitoring point.
Preferably, the method for calculating the cable force evaluation value of the monitoring point in S234 is as follows:
when t ism>T0,xjWhen t is 100m＝0，xjWhen the cable force evaluation value of the measuring point is equal to 0, the cable force evaluation value of the measuring point is
Figure BDA0003490008880000063
Preferably, the creating of the cable force prediction model by using the Holt-Winters cubic exponential smoothing method comprises the following steps:
establishing an addition mode model:
Figure BDA0003490008880000064
in the formula, atIs the horizontal part of the sequence, btThe trend part of the sequence, stFor the seasonal part of the sequence, L is the period length of one season;alpha, beta and gamma are smoothing coefficients and are between 0 and 1, in the equation set, the first equation is a horizontal equation, the second equation is a trend equation, and the third equation is a seasonal equation which indicates that the seasonal variation value of t period is a weighted average of the difference between the observed value and the horizontal value of t period and the seasonal variation value of t-L period;
the addition mode model is used for sequence prediction, and the predicted value of l step length is predicted forward in t period as follows:
Figure BDA0003490008880000065
wherein, at、btRespectively, the level value and the trend value, s, of the t periodt+l-LIs the seasonal variation value of t + L-L.
Preferably, the initial value in the formula (1) is:
wherein the initial value of the first equation
Figure BDA0003490008880000066
Initial value of the second equation
Figure BDA0003490008880000071
Initial value of the third equation: s1＝y1-a0,s2＝y2-a0,…sL＝yL-a0。
The method for creating the evaluation criterion comprises the following steps: generating a cable-stayed bridge judgment matrix based on an expert questionnaire method, carrying out fuzzy C-means clustering through an FCM algorithm, classifying the weight given by each expert, and constructing and distinguishing indexes of the logical property of the expert judgment matrix through the maximum characteristic root of the judgment matrix.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.

Claims (10)

1. A BIM-based cable-stayed bridge performance online evaluation and prediction method is characterized by comprising the following steps:
s1, establishing a BIM corresponding to an object to be monitored according to the object to be monitored, and laying the same sensor model at a position corresponding to the BIM according to a monitoring scheme of the monitored object;
s2, establishing an online evaluation prediction system based on a BIM model, wherein the online evaluation prediction system comprises a data acquisition module, a data processing module and an early warning display module; the data acquisition module is connected with the existing real-time monitoring system and transmits data corresponding to each sensor in the existing real-time monitoring system to a corresponding sensor model in the BIM model; the data acquisition module can also receive manual inspection data; the data processing module receives the data transmitted by the data acquisition module, and after data preprocessing and information fusion, a bridge performance evaluation index is created based on an analytic hierarchy process; the early warning display module receives the bridge performance evaluation index obtained by the data processing module, evaluates the service state of the bridge based on a variable weight analysis fusion method according to the established evaluation standard, establishes a cable-stayed bridge state trend change mathematical model based on a big data and time sequence method, and displays the estimation and prediction results.
2. The BIM-based cable-stayed bridge performance online evaluation and prediction method according to claim 1, characterized in that the data acquisition module comprises a real-time monitoring unit and a manual inspection unit, wherein the real-time monitoring unit is used for receiving data of a sensor transmitted by the existing real-time monitoring system; and the manual inspection unit receives the input of the timing inspection data.
3. The BIM-based cable-stayed bridge performance online evaluation and prediction method according to claim 2, characterized in that the data processing module receives the data of the real-time monitoring unit, performs mutual calibration and fusion with the data transmitted from the manual inspection unit after self-calibration and fusion, and generates a bridge performance evaluation index according to the created evaluation model.
4. The BIM-based cable-stayed bridge performance online evaluation and prediction method according to claim 1, characterized in that the method for establishing a cable-stayed bridge state trend change mathematical model based on big data and a time series method and displaying the prediction and prediction results comprises the following steps:
s21, updating a BIM (building information modeling) model according to the evaluation result of the service state of the bridge;
s22, according to the prediction scheme, selecting an acceleration sensor model at a corresponding position on the BIM model, and acquiring monitoring data of a corresponding acceleration sensor on the existing real-time monitoring system to obtain the cable force of a corresponding cable;
s23, a cable force prediction model is created by using a Holt-Winters cubic exponential smoothing method, cable force data obtained in the S22 are input into the cable force prediction model, and seasonal statistical test is performed through Krusaklas-Wallis test to evaluate the future performance evolution trend of the bridge.
5. The BIM-based cable-stayed bridge performance online evaluation and prediction method according to claim 4, wherein S23 comprises the following sub-steps:
s231, identifying seasonal influence of the cable by using Krusadal-Wallis test, wherein test statistic is as follows:
Figure FDA0003490008870000021
wherein R isiIndicating y in the ith seasoniA rank of (c); n isiIndicating y in the ith seasoniThe number of (2); n represents the number of all of y,
Figure FDA0003490008870000022
Figure FDA0003490008870000023
represents an average rank of the ith season;
Figure FDA0003490008870000024
-overall average rank;
s232, if H is calculated in S231>χα 2(L-1), if the cable is considered to have seasonal influence, a cable force prediction model is created according to a Holt-Winters cubic exponential smoothing method, and the time for predicting the structural degradation to a threshold value is calculated as follows:
Figure FDA0003490008870000025
wherein the content of the first and second substances,
Figure FDA0003490008870000026
suppose that the current moment of a certain measuring point is t1The predicted value of the cable force at the time t is
Figure FDA0003490008870000027
At (t)1+tm) The cable force measuring value reaches the limit value;
s233, if the calculated H in S233 is not more than χα 2(L-1), if the cable has no seasonal influence, the cable force variation trend is obtained by means of the sexual fitting, namely
Figure FDA0003490008870000028
The corresponding time to predict structural degradation to the threshold is:
Figure FDA0003490008870000029
s234, predicting the time t when the structure is degraded to a threshold value according to the single cablemAnd calculating the cable force evaluation value of the monitoring point and calculating the corresponding scoring weight of the cable force.
6. The BIM-based cable-stayed bridge performance online evaluation and prediction method according to claim 5, characterized in that the method for calculating the cable force evaluation value of the monitoring point in S234 is as follows:
when t ism>T0,xjWhen t is 100m＝0，xjWhen the cable force evaluation value of the measuring point is equal to 0, the cable force evaluation value of the measuring point is
Figure FDA00034900088700000210
7. The BIM-based cable-stayed bridge performance online evaluation and prediction method of claim 5, wherein the step of creating a cable force prediction model by using a Holt-Winters cubic exponential smoothing method comprises the following steps:
establishing an addition mode model:
Figure FDA0003490008870000031
in the formula, atIs the horizontal part of the sequence, btThe trend part of the sequence, stFor the seasonal part of the sequence, L is the period length of one season; alpha, beta and gamma are smoothing coefficients and are between 0 and 1, in the equation set, the first equation is a horizontal equation, the second equation is a trend equation, and the third equation is a seasonal equation which indicates that the seasonal variation value of t period is a weighted average of the difference between the observed value and the horizontal value of t period and the seasonal variation value of t-L period;
the addition mode model is used for sequence prediction, and the predicted value of l step length is predicted forward in t period as follows:
Figure FDA0003490008870000032
wherein, at、btRespectively, the level value and the trend value, s, of the t periodt+l-LIs the seasonal variation value of t + L-L.
8. The BIM-based cable-stayed bridge performance online evaluation and prediction method according to claim 7, characterized in that the initial values in the formula (1) are as follows:
wherein the initial value of the first equation
Figure FDA0003490008870000033
Initial value of the second equation
Figure FDA0003490008870000034
Initial value of the third equation: s1＝y1-a0,s2＝y2-a0,…sL＝yL-a0。
9. The BIM-based cable-stayed bridge performance online evaluation and prediction method according to claim 1, characterized in that the evaluation criterion creating method comprises the following steps: generating a cable-stayed bridge judgment matrix based on an expert questionnaire method, carrying out fuzzy C-means clustering through an FCM algorithm, classifying the weight given by each expert, and constructing and distinguishing indexes of the logical property of the expert judgment matrix through the maximum characteristic root of the judgment matrix.
10. The BIM-based cable-stayed bridge performance online evaluation and prediction method according to claim 1, wherein the method for evaluating the service state of the bridge based on the variable weight analysis fusion method comprises the following steps: and performing state evaluation by adopting an analytic hierarchy process, wherein the evaluation index of the cable force of the stay cable is a dead load characteristic value extracted by eliminating a temperature effect and a live load effect, and the cable force of the stay cable at the symmetrical position of the structure is selected for mutual calibration evaluation.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115048998A (en) * 2022-06-13 2022-09-13 大连理工大学 Cable-stayed bridge group cable force abnormity identification and positioning method based on monitoring data

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115048998A (en) * 2022-06-13 2022-09-13 大连理工大学 Cable-stayed bridge group cable force abnormity identification and positioning method based on monitoring data

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