WO2023060672A1 - 一种基于健康监测数据的桥梁冲刷动力识别方法 - Google Patents

一种基于健康监测数据的桥梁冲刷动力识别方法 Download PDF

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WO2023060672A1
WO2023060672A1 PCT/CN2021/128853 CN2021128853W WO2023060672A1 WO 2023060672 A1 WO2023060672 A1 WO 2023060672A1 CN 2021128853 W CN2021128853 W CN 2021128853W WO 2023060672 A1 WO2023060672 A1 WO 2023060672A1
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frequency
time
bridge
abnormal
scour
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French (fr)
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熊文
张嵘钊
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东南大学
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
    • G01H1/00—Measuring characteristics of vibrations in solids by using direct conduction to the detector
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
    • G01H17/00—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves, not provided for in the preceding groups
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/243—Classification techniques relating to the number of classes
    • G06F18/2433—Single-class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/12—Classification; Matching

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  • the invention relates to the technical field of bridge health monitoring, in particular to a method for dynamically identifying foundation scour depth by analyzing structural system dynamic characteristics based on bridge monitoring vibration acceleration time-history data.
  • the scour disease of bridge foundation is one of the most important reasons for the failure of bridge structure and the loss of its safety performance. Taking the United States as an example, from 1966 to 2005, 58% of the collapsed bridges (1,502) in the United States were related to the erosion of the bridge foundation structure. The US Department of Transportation has regarded the bridge foundation erosion as the structural function and safety performance of highway bridges. One of the most common causes of failure. In my country, especially in the eastern region, the pile length of expressway over-water bridges is generally within the design range of 10-50m. According to the regular inspection data of old and old bridges on-site, it is found that more than 5m is the common depth of bridge foundation scour, and the scour depth can even be measured under specific hydrological conditions. More than 10m.
  • the balanced scour of the foundation structure can reach more than 20m.
  • the bridge foundation is usually damaged without any sign, which seriously endangers the safety performance of the bridge structure and the smooth operation of the traffic road network.
  • the technical problem to be solved in the present invention is to address the deficiencies of the above-mentioned prior art, and propose a method for analyzing the dynamic characteristics of the structural system based on the bridge monitoring vibration acceleration time history data, so as to dynamically identify the scour depth of the foundation, and has wide applicability.
  • the cost is relatively low, and the technical characteristics of long-term dynamic underwater foundation scour monitoring and early warning can be realized.
  • the technical solution adopted by the present invention is: a bridge scour dynamic identification method based on health monitoring data, comprising the following steps:
  • Step 1 Collect the acceleration-time curve of the bridge foundation structure when it vibrates: use the health monitoring system to collect the acceleration-time curve of each bridge foundation structure in the scouring state when each bridge foundation structure vibrates, and analyze the acceleration-time curve Anti-interference factor preprocessing;
  • Step 2 obtain bridge scour reference mode frequency-time curve: the acceleration-time curve in step 1 is passed through Fourier transform, obtain scour reference mode frequency-time curve;
  • Step 3 determine the value of the significance level value ⁇
  • Step 3.1 using the kernel density estimation method to establish a probability distribution model for the time-frequency of the bridge scour assessment reference mode, and convert the scour reference mode frequency into a random variable that obeys the standard normal distribution;
  • Step 3.2 According to the random variable subject to the standard normal distribution, combined with the Shewhart mean control chart, initially set the value of the significance level value ⁇ , and obtain the probability distribution function corresponding to the significance level value ⁇ , and establish a normal distribution probability model;
  • Step 3.3 perform identification sensitivity calibration according to the value range of the initially set significance level value ⁇ ;
  • Step 4 Bring ⁇ into the normal distribution probability model, and calculate the upper control threshold UCL and the lower control threshold LCL for the warning of abnormal time-frequency changes of the first scoured bridge evaluation reference mode;
  • Step 5 identify the abnormal segment in the frequency segment of the scoured bridge to be identified:
  • Step 5.1 determine the frequency segment of the scour bridge to be identified that exceeds the upper control threshold UCL or the lower control threshold LCL as a time-frequency abnormal segment;
  • Step 6 Identify the abnormal time-frequency sequence in the time-frequency abnormal segment:
  • the time-frequency anomaly segment includes multiple time-frequency sequences, identifying the time-frequency anomaly sequence in the multiple time-frequency sequences:
  • the identification parameters of the time-frequency anomaly sequence include the time length ratio parameter PL /U ' of the abnormal reference frequency sequence, the time interval parameter Ts' between two adjacent abnormal frequencies, and the mean value of the scour reference frequency Variation difference parameter M s ';
  • Step 6.2 Calculate the time length ratio parameter PL/U of the abnormal frequency sequence of the abnormal segment:
  • Tab is the time length of the frequency sequence exceeding the upper control threshold UCL or the lower control threshold LCL, and Tt 0 is the total time length of the abnormal segment;
  • Ts is the time interval between two adjacent Tabs
  • Step 6.3 Calculate the time-series mean value change M s of the scour reference frequency in the time-frequency anomaly sequence:
  • M 1 is the frequency mean value of the time-frequency abnormal sequence
  • M 2 is the frequency mean value of the previous same time interval of the abnormal segment in a healthy state
  • Step 7 After the scour warning is completed for the abnormal sequence, repeat steps 5-6 to update the upper control threshold and lower control threshold of the random fluctuation of the time-frequency characteristics of the bridge scour reference mode, which will be used for the next mutation identification and scour warning prepare for.
  • the step 1 specifically includes the following steps:
  • Step 1.1 Use the health monitoring system to collect the acceleration-time curve of each bridge foundation in the scoured state, and use the filter and signal detrending function to remove the higher order in the acceleration-time curve frequency signal;
  • Step 1.2 calculate and process to obtain the missing signal length in the frequency-time curve:
  • the missing signal length is filled by the continuation filling method
  • the missing signal length is greater than the length tolerance threshold, the missing signal length is discarded;
  • Step 1.3 identifying and removing outliers in the frequency-time curve, and supplementing the removed outliers by numerical interpolation;
  • Step 1.4 remove the temperature effect in the acceleration-time curve obtained in step 1.3, and obtain the bridge scour reference mode frequency-time curve: the bridge foundation structure frequency of the bridge foundation structure at a specific temperature is measured in advance through the temperature sensor, and the EMD The algorithm decomposes the acceleration-time curve to obtain the multi-order sub-mode acceleration-time curve, and the multi-order sub-mode acceleration-time curve is obtained by Fourier transform to obtain the main frequency, and the frequency close to the frequency of the bridge infrastructure at a specific temperature is calculated. The acceleration-time curve is eliminated, and then the acceleration-time curve of the scour reference mode is obtained after reconstruction.
  • step 3.1 the specific steps of step 3.1 are:
  • K(x) is the selected kernel density function
  • l is the data length of a set time series
  • h is the set time interval value
  • i is the order
  • f i,j is the modal frequency vector The jth data of f i ;
  • ⁇ -1 ( ⁇ ) is the inverse function of the standard normal distribution function.
  • the non-normal data is transformed into a random variable subject to the standard normal distribution.
  • step 3.2 the specific steps of step 3.2 are: according to the random variable subject to the standard normal distribution, combined with the Shewhart mean control chart, initially set the value of the significance level value ⁇ , and obtain the significant The probability distribution function corresponding to the sex level value ⁇ :
  • ⁇ 0 is the overall mean
  • ⁇ is the standard deviation of the sample population
  • ⁇ is the significance level value
  • Z ⁇ /2 is the upper ⁇ /2 quantile point of the standard normal distribution
  • f is the parameter in the probability density function
  • n is the total amount of samples to be tested
  • the normal distribution probability model is established through the probability distribution function corresponding to the significance level value ⁇ .
  • the calculation method of the upper control threshold UCL is:
  • the calculation method of the lower control threshold LCL is:
  • step 3.2 the value range of the initially set significance level value ⁇ is 0.05-0.15.
  • the present invention discloses a bridge scouring power identification method based on health monitoring data.
  • the frequency of the bridge foundation structure in the scouring state is obtained.
  • the identification and early warning of the change of the bridge infrastructure can be achieved, and finally the rapid detection and diagnosis of the deformation of the bridge infrastructure can be achieved.
  • Early warning provides important support. Compared with conventional scour detection techniques, this method does not require underwater operations and direct observation of scour conditions. It only uses on-site measurement and parameter tracking of the dynamic response of the bridge superstructure.
  • Fig. 1 is a flow chart of the implementation of scour early warning of a bridge scour power identification method based on health monitoring data in the present invention
  • Fig. 2 is a schematic diagram of setting the control limit of the Shewhart control chart adopted in the present invention in calculating the abnormal warning control threshold of the time-frequency characteristic change of the scour evaluation reference mode;
  • Fig. 3a is a diagram of the scour warning results based on the measured data of a large cable-stayed bridge when the significance level ⁇ is set to 0.05 in the present invention
  • Fig. 3b is a diagram of the scour warning results based on the measured data of a large cable-stayed bridge when the significance level ⁇ is set to 0.10 in the present invention
  • Fig. 3c is a diagram of the scour warning results based on the measured data of a large cable-stayed bridge when the significance level ⁇ is set to 0.15 in the present invention.
  • the basic condition to be met by the present invention is: the bridge structure is only subjected to environmental noise or natural excitation, wherein natural excitation refers to the environment effects such as vehicle load, wave erosion, wind load, etc. without artificial excitation load.
  • natural excitation refers to the environment effects such as vehicle load, wave erosion, wind load, etc. without artificial excitation load.
  • the external signal input to the structural system can be regarded as white noise.
  • the output of various excitation responses to the bridge structure system can be obtained by relevant algorithms and its dynamic characteristics.
  • the algorithm of the present invention mainly serves the field of bridge scour health monitoring, and the applied object is the field measured data of bridge structures. Therefore, the relevant algorithm steps can be completed based on professional software toolkit writing codes. In the present embodiment, the relevant algorithm steps are completed based on the Matlab program. programming calculations.
  • a bridge scour dynamic identification method based on health monitoring data the specific steps are as follows:
  • Step 1 Collect the acceleration-time curve of the bridge foundation structure when it vibrates: use the health monitoring system to collect the acceleration-time curve of each bridge foundation structure in the scouring state when each bridge foundation structure vibrates, and analyze the acceleration-time curve Anti-interference factor preprocessing.
  • Step 1.1 Use the health monitoring system to collect the acceleration-time curve of each bridge foundation in the scoured state, and use the filter and signal detrending function to remove the higher order in the acceleration-time curve frequency signal;
  • Step 1.2 calculate and process to obtain the missing signal length in the frequency-time curve:
  • the missing signal length is filled by the continuation filling method
  • the missing signal length is greater than the length tolerance threshold, the missing signal length is discarded;
  • Step 1.3 identifying and removing outliers in the frequency-time curve, and supplementing the removed outliers by numerical interpolation;
  • Step 1.4 remove the temperature effect in the acceleration-time curve obtained in step 1.3, and obtain the bridge scour reference mode frequency-time curve: the bridge foundation structure frequency of the bridge foundation structure at a specific temperature is measured in advance through the temperature sensor, and the EMD The algorithm decomposes the acceleration-time curve to obtain the multi-order sub-mode acceleration-time curve, and the multi-order sub-mode acceleration-time curve is obtained by Fourier transform to obtain the main frequency, and the frequency close to the frequency of the bridge infrastructure at a specific temperature is calculated. The acceleration-time curve is eliminated, and then the acceleration-time curve of the scour reference mode is obtained after reconstruction.
  • Step 2 Obtain the frequency-time curve of the reference mode of scour of the bridge: the acceleration-time curve in step 1 is transformed by Fourier to obtain the frequency-time curve of the reference mode of scour.
  • Step 3 Determine the value of the significance level value ⁇
  • Step 3.1 using the kernel density estimation method to establish a probability distribution model for the time-frequency of the bridge scour assessment reference mode, and convert the scour reference mode frequency into a random variable that obeys the standard normal distribution;
  • K(x) is the selected kernel density function
  • l is the data length of a set time series
  • h is the set time interval value
  • i is the order
  • f i,j is the modal frequency vector The jth data of f i ;
  • ⁇ -1 ( ⁇ ) is the inverse function of the standard normal distribution function.
  • the non-normal data is transformed into a random variable subject to the standard normal distribution.
  • Step 3.2 according to the random variable subject to the standard normal distribution, combined with the Shewhart mean control chart, initially set the value of the significance level value ⁇ , in this embodiment, the initially set significance level value ⁇
  • the value range of is 0.05-0.15, and the probability distribution function corresponding to the significance level value ⁇ is obtained, and the normal distribution probability model is established;
  • the value of the significance level value ⁇ is preliminarily set, and the probability distribution function corresponding to the significance level value ⁇ is obtained:
  • ⁇ 0 is the overall mean
  • ⁇ is the standard deviation of the sample population
  • ⁇ is the significance level value
  • Z ⁇ /2 is the upper ⁇ /2 quantile point of the standard normal distribution
  • f is the parameter in the probability density function
  • n is the total amount of samples to be tested
  • the normal distribution probability model is established through the probability distribution function corresponding to the significance level value ⁇ .
  • Step 3.3 Carry out recognition sensitivity calibration according to the value range of the initially set significance level value ⁇ .
  • Step 4 Bring ⁇ ' into the normal distribution probability model, and calculate the upper control threshold UCL and lower control threshold LCL of the abnormal time-frequency change warning of the first scoured bridge evaluation reference mode:
  • Step 5 identify the abnormal segment in the frequency segment of the scoured bridge to be identified:
  • Step 5.1 Determine the frequency segment of the scoured bridge to be identified that exceeds the upper control threshold UCL or the lower control threshold LCL as a time-frequency abnormal segment.
  • Step 6 Identify the abnormal time-frequency sequence in the time-frequency abnormal segment:
  • the time-frequency anomaly segment includes multiple time-frequency sequences, identifying the time-frequency anomaly sequence in the multiple time-frequency sequences: setting the identification parameters of the time-frequency anomaly sequence, the identification parameters of the time-frequency anomaly sequence include the abnormal reference frequency sequence
  • Step 6.2 Calculate the time length ratio parameter PL/U of the abnormal frequency sequence of the abnormal segment:
  • Tab is the time length of the frequency sequence exceeding the upper control threshold UCL or the lower control threshold LCL, and Tt 0 is the total time length of the abnormal segment;
  • Ts is the time interval between two adjacent Tabs
  • Step 6.3 Calculate the time-series mean value change M s of the scour reference frequency in the time-frequency anomaly sequence:
  • M 1 is the frequency mean value of the time-frequency abnormal sequence
  • M 2 is the frequency mean value of the previous same time interval of the abnormal segment in a healthy state
  • Step 7 After the scour warning is completed for the abnormal sequence, repeat steps 5-6 to update the upper control threshold and lower control threshold of the random fluctuation of the time-frequency characteristics of the bridge scour reference mode, which will be used for the next mutation identification and scour warning prepare for.
  • the above method steps and basic formula principles can also be loaded on a computer or other programmable data processing equipment, so that a series of operation steps are executed on the computer or other programmable equipment to realize computer processing, so that the computer or other programmable equipment can execute
  • the instructions are provided to implement the functions or steps specified in one process or multiple processes of the flowchart.

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Abstract

一种基于健康监测数据的桥梁冲刷动力识别方法，包括：采集桥梁基础结构振动时的加速度时间曲线：利用健康监测系统采集每个桥梁基础结构在冲刷状态下，每个桥梁基础结构产生振动时的加速度-时间曲线；计算得到首次冲刷桥梁评估基准模态的时频变化异常警戒的警戒控制阀值；识别待识别冲刷桥梁频率片段中的异常片段；识别时频异常片段中的异常时频序列：异常序列完成冲刷预警后，更新桥梁冲刷基准模态时频特征自身随机波动的警戒控制阀值，为下一次的异变识别与冲刷预警做准备。本方法通过进行结构系统动力特性分析，从而动态识别基础冲刷深度，并可实现长期动态水下基础冲刷监测及预警。

Description

一种基于健康监测数据的桥梁冲刷动力识别方法 技术领域
本发明涉及桥梁健康监测技术领域，具体涉及到一种基于桥梁监测振动加速度时程数据进行结构系统动力特性分析，从而动态识别基础冲刷深度的方法。
背景技术
桥梁基础冲刷病害是当今桥梁结构功能失效、丧失其安全性能的最主要原因之一。以美国为例，从1966年至2005年，全美倒塌桥梁中(1502座)58％的破坏桥梁与桥梁基础结构冲刷病害相关，美国交通部已将桥梁基础冲刷看作公路桥梁结构功能及安全性能失效的最常见原因之一。在我国特别是东部区域高速公路过水桥梁桩长一般为10-50m设计范围，根据老、旧桥现场病害定期检测数据发现，5m以上为桥梁基础冲刷常见深度，特定水文条件下冲刷深度甚至可超过10m。而对于跨越江河湖海的大型桥梁，基础结构平衡冲刷可达20m以上。另外，由于冲刷发生于水面以下，桥梁基础被冲刷破坏通常没有任何征兆，严重危及桥梁结构的安全性能以及交通公路网络的顺利运营。
为提前预测局部冲刷形态发展，合理进行运营阶段的桥梁基础结构安全评估与加固决策，防止桥梁基础冲刷而导致的结构灾难性倒塌，显然需要长期且定期地针对桥梁基础冲刷状态进行检测与诊断。长期以来，桥梁基础冲刷状态主要基于日常检查的主观经验判断，准确性不高。尽管针对个别特大型桥梁可进行冲刷模型实验，但模型相似比难以确定，且实验人力物力花费高额，也无法广泛应用于一般桥梁设计；近年来，新出现的水下检测设备，诸如声呐、TDR技术、多波束探测系统、水下机器人检测基础冲刷深度还受到地形因素限制，单次检测费用高昂，无法实现长期动态的水下监测。
发明内容
本发明要解决的技术问题是针对上述现有技术的不足，而提出一种基于桥梁监测振动加速度时程数据进行结构系统动力特性分析，从而动态识别基础冲刷深度的方法，且具有广泛适用性，费用相对低廉，并可实现长期动态水下基础冲刷监测及预警的技术特点。
为解决上述技术问题，本发明采用的技术方案是：一种基于健康监测数据的桥梁冲刷动力识别方法，包括以下步骤：
步骤1、采集桥梁基础结构振动时的加速度时间曲线：利用健康监测系统采集每个桥梁基础结构在冲刷状态下，每个桥梁基础结构产生振动时的加速度-时间曲线，并对加速度-时间曲线进行抗干扰因素预处理；
步骤2、获得桥梁冲刷基准模态频率-时间曲线：将步骤1中的加速度-时间曲线通过傅里 叶变换，得到冲刷基准模态频率-时间曲线；
步骤3、确定显著性水平值α的取值；
步骤3.1、利用核密度估计方法建立桥梁冲刷评估基准模态时频的概率分布模型，将冲刷基准模态频率转换为服从标准正态分布的随机变量；
步骤3.2、根据服从标准正态分布的随机变量，结合休哈特均值控制图，初步设定显著性水平值α的取值，并获得显著性水平值α对应的概率分布函数，建立正态分布概率模型；
步骤3.3、根据初步设定的显著性水平值α的取值范围，进行识别灵敏度校准；
步骤4、将α带入正态分布概率模型，计算得到首次冲刷桥梁评估基准模态的时频变化异常警戒的上控制阀值UCL和下控制阀值LCL；
步骤5、识别待识别冲刷桥梁频率片段中的异常片段：
步骤5.1、将超过上控制阀值UCL或下控制阀值LCL的待识别冲刷桥梁的频率片段判定为时频异常片段；
步骤6、识别时频异常片段中的异常时频序列：
步骤6.1、所述时频异常片段包含多个时频序列，识别多个时频序列中的时频异常序列：
设置时频异常序列的识别参数，时频异常序列的识别参数包括异常基准频率序列时间长度比参数P L/U'、相邻两个异常频率之间的时间间隔参数Ts'、冲刷基准频率均值变化差值参数M s'；
步骤6.2、计算异常片段的异常频率序列的时间长度比参数P L/U：
P L/U＝T ab/T t0
其中，Tab为超过上控制阀值UCL或下控制阀值LCL的频率序列时间长度，Tt 0为异常片段总时间长度；
计算异常片段的Ts，Ts为相邻的两个Tab之间的时间间隔；
当P L/U>P L/U'，且Ts<Ts'时，则判定该时频序列为异常序列，并进入步骤6.3，反之，则判定该时频序列为无异常；
步骤6.3、计算时频异常序列中的冲刷基准频率时序均值变化差M s：
M s＝|M 1-M 2|
其中，M 1为时频异常序列的频率均值，M 2为异常片段的前一个相同时间间隔的健康状态下的频率均值；
当M s≤M s'，则判定该异常序列为正常信号振荡；当M s＞M s'，则对异常序列进行冲刷预 警；
步骤7、异常序列完成冲刷预警后，重复步骤5-步骤6，更新桥梁冲刷基准模态时频特征自身随机波动的上控制阀值和下控制阀值，为下一次的异变识别与冲刷预警做准备。
作为本发明的进一步的优选方案，所述步骤1中，具体包括以下步骤：
步骤1.1、利用健康监测系统采集每个桥梁基础在冲刷状态下，获得每个桥梁基础振动时的加速度-时间曲线后，并利用滤波器和信号去趋势函数，去除加速度-时间曲线中的高阶频率信号；
步骤1.2、计算并处理得到频率-时间曲线中缺失的信号长度：
首先，定义索引结构体missing：
Figure PCTCN2021128853-appb-000001
其中，s m、e m分别为第m段缺失数据的起、止索引；k为缺失数据的总段数；第m段缺失的信号长度即为missing.longm＝e m-s m；
当缺失的信号长度小于长度容忍临界值时，采用延拓填补法填充缺失的信号长度；
当缺失的信号长度大于长度容忍临界值时，则舍弃缺失的信号长度；
步骤1.3、识别并剔除频率-时间曲线中的离群值，并采用数值插值法补充剔除的离群值；
步骤1.4、去除步骤1.3处理得到的加速度-时间曲线中的温度效应，得到桥梁冲刷基准模态频率-时间曲线：预先通过温度传感器测得桥梁基础结构在特定温度下的桥梁基础结构频率，通过EMD算法分解加速度-时间曲线得到多阶子模态加速度-时间曲线，将多阶子模态加速度-时间曲线通过傅里叶变换获得主要频率，并将其中与特定温度下的桥梁基础结构频率相近的加速度-时间曲线剔除，再重构后得到冲刷基准模态加速度-时间曲线。
作为本发明的进一步的优选方案，所述步骤3.1具体步骤为：
将待识别的频率信号作为一维连续的样本向量f i，并利用选定的核密度函数得到样本向量f i的核密度估计向量PDF(f i)，
Figure PCTCN2021128853-appb-000002
Figure PCTCN2021128853-appb-000003
Figure PCTCN2021128853-appb-000004
其中，K(x)是选定的核密度函数，l是设定的1个时间序列的数据长度，h是设定的时间间隔值，i是阶次，f i,j是模态频率向量f i的第j个数据；
通过核密度估计向量PDF(f i)，计算得到样本向量f i的累计概率估计向量CDF(f i)：
Figure PCTCN2021128853-appb-000005
最后，对累积概率估计向量CDF(f i)进行标准正态分布函数的逆变换，将其转换为Q统计量：
Q i＝Φ -1(CDF(f i))
其中，Φ -1(·)为标准正态分布函数的反函数，此时完成将非正态数据转换为服从标准正态分布的随机变量。
作为本发明的进一步的优选方案，所述步骤3.2具体步骤为：根据服从标准正态分布的随机变量，结合休哈特均值控制图，初步设定显著性水平值α的取值，并获得显著性水平值α对应的概率分布函数：
Figure PCTCN2021128853-appb-000006
其中，μ 0为总体均值，σ为样本总体标准差，α为显著性水平值，Z α/2为标准正态分布的上α/2分位点，f为概率密度函数的中的参数，n为待测样本的总量；
通过显著性水平值α对应的概率分布函数得到建立正态分布概率模型。
作为本发明的进一步的优选方案，所述步骤4中，所述上控制阀值UCL的计算方式为：
Figure PCTCN2021128853-appb-000007
所述下控制阀值LCL的计算方式为：
Figure PCTCN2021128853-appb-000008
作为本发明的进一步的优选方案，其特征在于：在步骤3.2中，所述初步设定的显著性水平值α的取值范围为0.05-0.15。
作为本发明的进一步的优选方案，所述步骤6.1中，令P L/U'＝1％，Ts'＝0.1s、M s'＝0.01Hz。
本发明具有如下有益效果：
1、本发明公开了一种基于健康监测数据的桥梁冲刷动力识别方法，通过采集桥梁基础结构在冲刷状态下的加速度随时间的变化情况，得到桥梁基础结构在冲刷状态下的变化 振动变化频率，从而达到桥梁基础结构变化情况的识别预警，最终达到桥梁基础结构变形情况的快速检测与诊断，为有选择的进一步水下检测及区域性的桥梁冲刷快速筛查提供理论依据，为桥梁结构安全的提前预警提供重要支撑，与常规冲刷检测技术相比，该方法不需要水下作业，不需要直接观测冲刷状态，仅通过对桥梁上部结构动力学响应的现场量测与参数跟踪。
2、通过对采集到的桥梁基础结构在冲刷状态下的变化振动变化频率提出温度效应的影响，提高对桥梁基础结构的变形程度的识别准确性。
附图说明
图1是本发明一种基于健康监测数据的桥梁冲刷动力识别方法的冲刷预警实施流程图；
图2是本发明在计算冲刷评估基准模态的时频特征变化异常警戒控制阈值，采用的休哈特控制图控制限设定示意图；
图3a是本发明在设定显著性水平α为0.05时，依据某大型斜拉桥实测数据冲刷预警结果图；
图3b是本发明在设定显著性水平α为0.10时，依据某大型斜拉桥实测数据冲刷预警结果图；
图3c是本发明在设定显著性水平α为0.15时，依据某大型斜拉桥实测数据冲刷预警结果图。
具体实施方式
下面结合附图和具体较佳实施方式对本发明作进一步详细的说明。
本发明的描述中，需要理解的是，术语“左侧”、“右侧”、“上部”、“下部”等指示的方位或位置关系为基于附图所示的方位或位置关系，仅是为了便于描述本发明和简化描述，而不是指示或暗示所指的装置或元件必须具有特定的方位、以特定的方位构造和操作，“第一”、“第二”等并不表示零部件的重要程度，因此不能理解为对本发明的限制。本实施例中采用的具体尺寸只是为了举例说明技术方案，并不限制本发明的保护范围。
本发明需满足的基本条件为：桥梁结构仅受环境噪声或自然激励的情况，其中自然激励是指在不施加人工激励荷载的情况下，仅存在车辆荷载、波浪冲刷、风荷载等环境作用效应，结构系统所受外部信号输入均可视为白噪声。此后，桥梁结构系统所受的各项激励响应输出，均可由相关算法获得及动力特性。
本发明算法主要服务桥梁冲刷健康监测领域，所应用对象为桥梁结构野外实测数据，因而，相关算法步骤可基于专业软件工具包编写代码完成，在本实施例中，基于Matlab程序完成相关算法步骤的编程计算。
一种基于健康监测数据的桥梁冲刷动力识别方法，具体步骤如下：
步骤1、采集桥梁基础结构振动时的加速度时间曲线：利用健康监测系统采集每个桥梁 基础结构在冲刷状态下，每个桥梁基础结构产生振动时的加速度-时间曲线，并对加速度-时间曲线进行抗干扰因素预处理。
步骤1.1、利用健康监测系统采集每个桥梁基础在冲刷状态下，获得每个桥梁基础振动时的加速度-时间曲线后，并利用滤波器和信号去趋势函数，去除加速度-时间曲线中的高阶频率信号；
步骤1.2、计算并处理得到频率-时间曲线中缺失的信号长度：
首先，定义索引结构体missing：
Figure PCTCN2021128853-appb-000009
其中，s m、e m分别为第m段缺失数据的起、止索引；k为缺失数据的总段数；第m段缺失的信号长度即为missing.longm＝e m-s m；
当缺失的信号长度小于长度容忍临界值时，采用延拓填补法填充缺失的信号长度；
当缺失的信号长度大于长度容忍临界值时，则舍弃缺失的信号长度；
步骤1.3、识别并剔除频率-时间曲线中的离群值，并采用数值插值法补充剔除的离群值；
步骤1.4、去除步骤1.3处理得到的加速度-时间曲线中的温度效应，得到桥梁冲刷基准模态频率-时间曲线：预先通过温度传感器测得桥梁基础结构在特定温度下的桥梁基础结构频率，通过EMD算法分解加速度-时间曲线得到多阶子模态加速度-时间曲线，将多阶子模态加速度-时间曲线通过傅里叶变换获得主要频率，并将其中与特定温度下的桥梁基础结构频率相近的加速度-时间曲线剔除，再重构后得到冲刷基准模态加速度-时间曲线。
步骤2、获得桥梁冲刷基准模态频率-时间曲线：将步骤1中的加速度-时间曲线通过傅里叶变换，得到冲刷基准模态频率-时间曲线。
步骤3、确定显著性水平值α的取值
步骤3.1、利用核密度估计方法建立桥梁冲刷评估基准模态时频的概率分布模型，将冲刷基准模态频率转换为服从标准正态分布的随机变量；
将待识别的频率信号作为一维连续的样本向量f i，并利用选定的核密度函数得到样本向量f i的核密度估计向量PDF(f i)，
Figure PCTCN2021128853-appb-000010
Figure PCTCN2021128853-appb-000011
Figure PCTCN2021128853-appb-000012
其中，K(x)是选定的核密度函数，l是设定的1个时间序列的数据长度，h是设定的时间间隔值，i是阶次，f i,j是模态频率向量f i的第j个数据；
通过核密度估计向量PDF(f i)，计算得到样本向量f i的累计概率估计向量CDF(f i)：
Figure PCTCN2021128853-appb-000013
最后，对累积概率估计向量CDF(f i)进行标准正态分布函数的逆变换，将其转换为Q统计量：
Q i＝Φ -1(CDF(f i))
其中，Φ -1(·)为标准正态分布函数的反函数，此时完成将非正态数据转换为服从标准正态分布的随机变量。
步骤3.2、根据服从标准正态分布的随机变量，结合休哈特均值控制图，初步设定显著性水平值α的取值，在本实施例中，所述初步设定的显著性水平值α的取值范围为0.05-0.15，并获得显著性水平值α对应的概率分布函数，建立正态分布概率模型；
根据服从标准正态分布的随机变量，结合休哈特均值控制图，初步设定显著性水平值α的取值，并获得显著性水平值α对应的概率分布函数：
Figure PCTCN2021128853-appb-000014
其中，μ 0为总体均值，σ为样本总体标准差，α为显著性水平值，Z α/2为标准正态分布的上α/2分位点，f为概率密度函数的中的参数，n为待测样本的总量；
通过显著性水平值α对应的概率分布函数得到建立正态分布概率模型。
步骤3.3、根据初步设定的显著性水平值α的取值范围，进行识别灵敏度校准。
步骤4、将α’带入正态分布概率模型，计算得到首次冲刷桥梁评估基准模态的时频变化异常警戒的上控制阀值UCL和下控制阀值LCL：
Figure PCTCN2021128853-appb-000015
Figure PCTCN2021128853-appb-000016
步骤5、识别待识别冲刷桥梁频率片段中的异常片段：
步骤5.1、将超过上控制阀值UCL或下控制阀值LCL的待识别冲刷桥梁的频率片段判定为时频异常片段。
步骤6、识别时频异常片段中的异常时频序列：
步骤6.1、所述时频异常片段包含多个时频序列，识别多个时频序列中的时频异常序列：设置时频异常序列的识别参数，时频异常序列的识别参数包括异常基准频率序列时间长度比参数P L/U'、相邻两个异常频率之间的时间间隔参数Ts'、冲刷基准频率均值变化差值参数M s'，在本实施例中令P L/U'＝1％，Ts'＝0.1s、M s'＝0.01Hz。
步骤6.2、计算异常片段的异常频率序列的时间长度比参数P L/U：
P L/U＝T ab/T t0
其中，Tab为超过上控制阀值UCL或下控制阀值LCL的频率序列时间长度，Tt 0为异常片段总时间长度；
计算异常片段的Ts，Ts为相邻的两个Tab之间的时间间隔；
当P L/U>P L/U'，且Ts<Ts'时，则判定该时频序列为异常序列，并进入步骤6.3，反之，则判定该时频序列为无异常。
步骤6.3、计算时频异常序列中的冲刷基准频率时序均值变化差M s：
M s＝|M 1-M 2|
其中，M 1为时频异常序列的频率均值，M 2为异常片段的前一个相同时间间隔的健康状态下的频率均值；
当M s≤M s'，则判定该异常序列为正常信号振荡；当M s＞M s'，则对异常序列进行冲刷预警。
步骤7、异常序列完成冲刷预警后，重复步骤5-步骤6，更新桥梁冲刷基准模态时频特征自身随机波动的上控制阀值和下控制阀值，为下一次的异变识别与冲刷预警做准备。
以上方法步骤及基本公式原理也可装载到计算机或其他可编程数据处理设备上，使得在计算机或其他可编程设备上执行一系列操作步骤以实现计算机处理，从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程中指定的功能或步骤。
以上详细描述了本发明的优选实施方式，但是，本发明并不限于上述实施方式中的具体细节，在本发明的技术构思范围内，可以对本发明的技术方案进行多种等同变换，这些等同变换均属于本发明的保护范围。

Claims (7)

  1. 一种基于健康监测数据的桥梁冲刷动力识别方法，其特征在于：包括以下步骤：
    步骤1、采集桥梁基础结构振动时的加速度时间曲线：利用健康监测系统采集每个桥梁基础结构在冲刷状态下，每个桥梁基础结构产生振动时的加速度-时间曲线，并对加速度-时间曲线进行抗干扰因素预处理；
    步骤2、获得桥梁冲刷基准模态频率-时间曲线：将步骤1中的加速度-时间曲线通过傅里叶变换，得到冲刷基准模态频率-时间曲线；
    步骤3、确定显著性水平值α的取值；
    步骤3.1、利用核密度估计方法建立桥梁冲刷评估基准模态时频的概率分布模型，将冲刷基准模态频率转换为服从标准正态分布的随机变量；
    步骤3.2、根据服从标准正态分布的随机变量，结合休哈特均值控制图，初步设定显著性水平值α的取值，并获得显著性水平值α对应的概率分布函数，建立正态分布概率模型；
    步骤3.3、根据初步设定的显著性水平值α的取值范围，进行识别灵敏度校准；
    步骤4、将α带入正态分布概率模型，计算得到首次冲刷桥梁评估基准模态的时频变化异常警戒的上控制阀值UCL和下控制阀值LCL；
    步骤5、识别待识别冲刷桥梁频率片段中的异常片段：
    步骤5.1、将超过上控制阀值UCL或下控制阀值LCL的待识别冲刷桥梁的频率片段判定为时频异常片段；
    步骤6、识别时频异常片段中的异常时频序列：
    步骤6.1、所述时频异常片段包含多个时频序列，识别多个时频序列中的时频异常序列：
    设置时频异常序列的识别参数，时频异常序列的识别参数包括异常基准频率序列时间长度比参数P L/U'、相邻两个异常频率之间的时间间隔参数Ts'、冲刷基准频率均值变化差值参数M s'；步骤6.2、计算异常片段的异常频率序列的时间长度比参数P L/U：
    P L/U＝T ab/T t0
    其中，Tab为超过上控制阀值UCL或下控制阀值LCL的频率序列时间长度，Tt 0为异常片段总时间长度；
    计算异常片段的Ts，Ts为相邻的两个Tab之间的时间间隔；
    当P L/U>P L/U'，且Ts<Ts'时，则判定该时频序列为异常序列，并进入步骤6.3，反之，则判定该时频序列为无异常；
    步骤6.3、计算时频异常序列中的冲刷基准频率时序均值变化差M s：
    M s＝|M 1-M 2|
    其中，M 1为时频异常序列的频率均值，M 2为异常片段的前一个相同时间间隔的健康状态下的频率均值；
    当M s≤M s'，则判定该异常序列为正常信号振荡；当M s＞M s'，则对异常序列进行冲刷预警；
    步骤7、异常序列完成冲刷预警后，重复步骤5-步骤6，更新桥梁冲刷基准模态时频特征自身随机波动的上控制阀值和下控制阀值，为下一次的异变识别与冲刷预警做准备。
  2. 根据权利要求1所述的基于健康监测数据的桥梁冲刷动力识别方法，其特征在于：所述步骤1中，具体包括以下步骤：
    步骤1.1、利用健康监测系统采集每个桥梁基础在冲刷状态下，获得每个桥梁基础振动时的加速度-时间曲线后，并利用滤波器和信号去趋势函数，去除加速度-时间曲线中的高阶频率信号；
    步骤1.2、计算并处理得到频率-时间曲线中缺失的信号长度：
    首先，定义索引结构体missing：
    Figure PCTCN2021128853-appb-100001
    其中，s m、e m分别为第m段缺失数据的起、止索引；k为缺失数据的总段数；第m段缺失的信号长度即为missing.longm＝e m-s m；
    当缺失的信号长度小于长度容忍临界值时，采用延拓填补法填充缺失的信号长度；
    当缺失的信号长度大于长度容忍临界值时，则舍弃缺失的信号长度；
    步骤1.3、识别并剔除频率-时间曲线中的离群值，并采用数值插值法补充剔除的离群值；
    步骤1.4、去除步骤1.3处理得到的加速度-时间曲线中的温度效应，得到桥梁冲刷基准模态频率-时间曲线：预先通过温度传感器测得桥梁基础结构在特定温度下的桥梁基础结构频率，通过EMD算法分解加速度-时间曲线得到多阶子模态加速度-时间曲线，将多阶子模态加速度-时间曲线通过傅里叶变换获得主要频率，并将其中与特定温度下的桥梁基础结构频率相近的加速度-时间曲线剔除，再重构后得到冲刷基准模态加速度-时间曲线。
  3. 根据权利要求1所述的基于健康监测数据的桥梁冲刷动力识别方法，其特征在于：所述步骤3.1具体步骤为：
    将待识别的频率信号作为一维连续的样本向量f i，并利用选定的核密度函数得到样本向量f i的核密度估计向量PDF(f i)，
    Figure PCTCN2021128853-appb-100002
    Figure PCTCN2021128853-appb-100003
    Figure PCTCN2021128853-appb-100004
    其中，K(x)是选定的核密度函数，l是设定的1个时间序列的数据长度，h是设定的时间间隔值，i是阶次，f i,j是模态频率向量f i的第j个数据；
    通过核密度估计向量PDF(f i)，计算得到样本向量f i的累计概率估计向量CDF(f i)：
    Figure PCTCN2021128853-appb-100005
    最后，对累积概率估计向量CDF(f i)进行标准正态分布函数的逆变换，将其转换为Q统计量：
    Q i＝Φ -1(CDF(f i))
    其中，Φ -1(·)为标准正态分布函数的反函数，此时完成将非正态数据转换为服从标准正态分布的随机变量。
  4. 根据权利要求3所述的基于健康监测数据的桥梁冲刷动力识别方法，其特征在于：所述步骤3.2具体步骤为：根据服从标准正态分布的随机变量，结合休哈特均值控制图，初步设定显著性水平值α的取值，并获得显著性水平值α对应的概率分布函数：
    Figure PCTCN2021128853-appb-100006
    其中，μ 0为总体均值，σ为样本总体标准差，α为显著性水平值，Z α/2为标准正态分布的上α/2分位点，f为概率密度函数的中的参数，n为待测样本的总量；
    通过显著性水平值α对应的概率分布函数得到建立正态分布概率模型。
  5. 根据权利要求4所述的基于健康监测数据的桥梁冲刷动力识别方法，其特征在于：所述步骤4中，所述上控制阀值UCL的计算方式为：
    Figure PCTCN2021128853-appb-100007
    所述下控制阀值LCL的计算方式为：
    Figure PCTCN2021128853-appb-100008
  6. 根据权利要求1所述的基于健康监测数据的桥梁冲刷动力识别方法，其特征在于：在步骤3.2中，所述初步设定的显著性水平值α的取值范围为0.05-0.15。
  7. 根据权利要求1所述的基于健康监测数据的桥梁冲刷动力识别方法，其特征在于：所述步骤6.1中，令P L/U'＝1％，Ts'＝0.1s、M s'＝0.01Hz。
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