CN112577461B - Large-span beam bridge state prediction method and system based on deflection separation - Google Patents
Large-span beam bridge state prediction method and system based on deflection separationDownload PDFInfo
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Abstract
Description
Translated fromChinese技术领域technical field
本发明涉及梁桥工程技术领域，尤其涉及一种基于挠度分离的大跨径梁桥状态的预测方法及系统。The invention relates to the technical field of girder bridge engineering, in particular to a method and system for predicting the state of a large-span girder bridge based on deflection separation.
背景技术Background technique
大跨度预应力混凝土梁桥是50～300m跨度范围内极具竞争的结构，国内外均得到了广泛的应用。然而，这类梁桥在运营的过程中易出现腹板开裂和长期下挠的病害问题。一方面，由于目前涉及到的预应力与徐变计算理论不完善、施工质量难保证和运营环境复杂等问题，使得大跨径梁桥的下挠问题难以解决。另一方面，全球气候变化使得梁桥所处的环境侵蚀越发严重，加上我国超载重载也对大跨径梁桥造成严重的影响。因此，为了能够有效管理大跨径梁桥的运营安全，迫切需要能够对梁桥状态进行预测的方法。The long-span prestressed concrete girder bridge is a very competitive structure in the span range of 50-300m, and has been widely used at home and abroad. However, such girder bridges are prone to the problems of web cracking and long-term deflection during the operation. On the one hand, due to the imperfect prestress and creep calculation theory involved, the difficulty in guaranteeing construction quality and the complex operating environment, it is difficult to solve the problem of deflection of long-span girder bridges. On the other hand, global climate change has made the environment where girder bridges are located more and more severely eroded. In addition, overloading and heavy loads in our country also have a serious impact on long-span girder bridges. Therefore, in order to effectively manage the operational safety of long-span girder bridges, methods that can predict the state of girder bridges are urgently needed.
现有的基于挠度的梁桥状态的预测方法，通常将测量的挠度数据直接与各类经验(或计算)限值进行对比，从而判断出当前梁桥的结构状态，然而这种方法指标体系评价过程简单，导致梁桥状态的预测精度不高，进而导致预警的过程中出现报错或乱报的现象。不仅如此，现有的基于挠度的梁桥状态的预测方法，对梁桥挠度下所有成分都采用单一的评估方法，并未考虑到不同挠度成分对梁桥的结构状态有着不同的影响，进而导致梁桥状态的预测精度低。The existing deflection-based beam bridge state prediction method usually directly compares the measured deflection data with various empirical (or calculated) limits to determine the current structural state of the beam bridge. However, this method evaluates the index system. The process is simple, which leads to the low accuracy of the prediction of the state of the girder and bridge, which in turn leads to the phenomenon of error or random reporting in the process of early warning. Not only that, the existing deflection-based beam bridge state prediction method adopts a single evaluation method for all components under the deflection of the beam bridge, and does not consider that different deflection components have different effects on the structural state of the beam bridge, which leads to The prediction accuracy of the beam bridge state is low.
发明内容SUMMARY OF THE INVENTION
本发明实施例提供了一种基于挠度分离的大跨径梁桥状态的预测方法，能够根据梁桥挠度成分采取对应的评估方法，提高梁桥状态预测结果的准确度。The embodiment of the present invention provides a method for predicting the state of a large-span girder bridge based on deflection separation, which can adopt a corresponding evaluation method according to the deflection component of the girder bridge, thereby improving the accuracy of the state prediction result of the girder bridge.
为了解决上述技术问题，本发明实施例提供了一种基于挠度分离的大跨径梁桥状态的预测方法，包括：In order to solve the above technical problems, the embodiment of the present invention provides a method for predicting the state of a large-span girder bridge based on deflection separation, including:
获取梁桥的挠度监测数据；Obtain deflection monitoring data of girder bridges;
根据信号分离方法对所述挠度监测数据进行分离，得到车辆挠度数据、温度挠度数据和长期下挠数据；The deflection monitoring data is separated according to the signal separation method to obtain vehicle deflection data, temperature deflection data and long-term deflection data;
分别对所述车辆挠度数据、所述温度挠度数据和所述长期下挠数据进行评估，获得所述车辆挠度数据对应的第一评估结果、所述温度挠度数据对应的第二评估结果和所述长期下挠数据对应的第三评估结果；Evaluate the vehicle deflection data, the temperature deflection data, and the long-term downward deflection data respectively, and obtain a first evaluation result corresponding to the vehicle deflection data, a second evaluation result corresponding to the temperature deflection data, and the The third evaluation result corresponding to the long-term bending data;
基于信息融合算法对所述第一评估结果、所述第二评估结果和所述第三评估结果进行计算，得到用于预警操作的梁桥状态。Based on the information fusion algorithm, the first evaluation result, the second evaluation result and the third evaluation result are calculated to obtain the beam bridge state for early warning operation.
作为优选方案，所述根据信号分离方法对所述挠度监测数据进行分离，得到车辆挠度数据、温度挠度数据和长期下挠数据，具体为：As a preferred solution, the deflection monitoring data is separated according to the signal separation method to obtain vehicle deflection data, temperature deflection data and long-term deflection data, specifically:
通过低通滤波器对所述挠度监测数据进行滤波，获得所述车辆挠度数据和滤波数据；Filter the deflection monitoring data through a low-pass filter to obtain the vehicle deflection data and filtered data;
基于联合字典的稀疏分离方法对所述滤波数据进行分离，获得所述温度挠度数据和所述长期下挠数据。The filtered data is separated by a sparse separation method based on a joint dictionary to obtain the temperature deflection data and the long-term down deflection data.
作为优选方案，所述分别对所述车辆挠度数据、所述温度挠度数据和所述长期下挠数据进行评估，获得所述车辆挠度数据对应的第一评估结果、所述温度挠度数据对应的第二评估结果和所述长期下挠数据对应的第三评估结果，具体为：As a preferred solution, the vehicle deflection data, the temperature deflection data, and the long-term downward deflection data are evaluated respectively, and a first evaluation result corresponding to the vehicle deflection data and a first evaluation result corresponding to the temperature deflection data are obtained. The second evaluation result and the third evaluation result corresponding to the long-term deflection data are as follows:
从预设的车辆挠度数据表中，提取与所述车辆挠度数据对应的第一分值区间，并根据内插法和所述第一分值区间对所述车辆挠度数据进行插值，获得所述车辆挠度数据对应的第一评估结果；From a preset vehicle deflection data table, extract a first score interval corresponding to the vehicle deflection data, and perform interpolation on the vehicle deflection data according to an interpolation method and the first score interval to obtain the The first evaluation result corresponding to the vehicle deflection data;
从预设的温度挠度数据表中，提取与所述温度挠度数据对应的第二分值区间，并根据内插法和所述第二分值区间对所述温度挠度数据进行插值，获得所述温度挠度数据对应的第二评估结果；From a preset temperature deflection data table, extract a second score interval corresponding to the temperature deflection data, and perform interpolation on the temperature deflection data according to the interpolation method and the second score interval to obtain the The second evaluation result corresponding to the temperature deflection data;
从预设的长期下挠数据表中，提取与所述长期下挠数据对应的第三分值区间，并根据内插法和所述第三分值区间对所述长期下挠数据进行插值，获得所述长期下挠数据对应的第三评估结果。From the preset long-term deflection data table, extract a third score interval corresponding to the long-term deflection data, and perform interpolation on the long-term deflection data according to the interpolation method and the third score interval, A third evaluation result corresponding to the long-term deflection data is obtained.
作为优选方案，所述车辆挠度数据表是根据红色预警值、橙色预警值和黄色预警值获得，具体为：As a preferred solution, the vehicle deflection data table is obtained according to the red warning value, the orange warning value and the yellow warning value, specifically:
将规范定义的活载作用下挠度经验限值作为所述红色预警值；The empirical limit of deflection under the action of live load defined in the specification is taken as the red warning value;
将规范定义的汽车荷载模型加载下的桥梁挠度计算值作为所述橙色预警值；Take the calculated value of bridge deflection under the loading of the vehicle load model defined by the code as the orange warning value;
将运营随机车流与桥梁作用下的极值挠度作为所述黄色预警值；Take the extreme deflection under the action of random traffic flow and bridge as the yellow warning value;
根据所述红色预警值、所述橙色预警值和所述黄色预警值划分为四个区间，并根据所述四个区间构建所述车辆挠度数据表。According to the red early warning value, the orange early warning value and the yellow early warning value, it is divided into four sections, and the vehicle deflection data table is constructed according to the four sections.
作为优选方案，所述温度挠度数据表是根据红色预警值、橙色预警值和黄色预警值获得，具体为：As a preferred solution, the temperature deflection data table is obtained according to the red warning value, the orange warning value and the yellow warning value, specifically:
将第一温度下的挠度效应值作为所述红色预警值；Taking the deflection effect value at the first temperature as the red warning value;
将第二温度下的挠度效应值作为所述橙色预警值；Taking the deflection effect value at the second temperature as the orange warning value;
将第三温度下的挠度效应值作为所述黄色预警值；Taking the deflection effect value at the third temperature as the yellow warning value;
根据所述红色预警值、所述橙色预警值和所述黄色预警值划分为四个区间，并根据所述四个区间构建所述温度挠度数据表。According to the red warning value, the orange warning value and the yellow warning value, it is divided into four sections, and the temperature deflection data table is constructed according to the four sections.
作为优选方案，所述长期下挠数据表是根据红色预警值、橙色预警值和黄色预警值获得，具体为：As a preferred solution, the long-term bending data table is obtained according to the red early warning value, the orange early warning value and the yellow early warning value, specifically:
基于最小二乘法的数学逼近方法对已有的下挠数据建立时变挠跨比模型，以所述时变挠跨比模型的高分位值作为所述红色预警值；A time-varying torsion-span ratio model is established based on the mathematical approximation method of the least squares method for the existing downward deflection data, and the high quantile value of the time-varying torsion-span ratio model is used as the red warning value;
将所述时变挠跨比模型的中分值为作为所述橙色预警值；Taking the median score value of the time-varying torsion-span ratio model as the orange warning value;
将所述时变挠跨比模型的低分值为作为所述黄色预警值；Taking the low score value of the time-varying torsion-span ratio model as the yellow warning value;
根据所述红色预警值、所述橙色预警值和所述黄色预警值划分为四个区间，并根据所述四个区间构建所述长期下挠数据表。According to the red warning value, the orange warning value and the yellow warning value, it is divided into four intervals, and the long-term deflection data table is constructed according to the four intervals.
作为优选方案，在获取梁桥的挠度监测数据之前，还包括：As a preferred solution, before acquiring the deflection monitoring data of the girder bridge, it also includes:
根据梁桥监测点确定出所述挠度监测数据为中跨跨中挠度数据或次中跨跨中挠度数据或边跨跨中挠度数据。According to the monitoring points of the girder bridge, it is determined that the deflection monitoring data is mid-span mid-span deflection data or secondary mid-span mid-span deflection data or side span mid-span deflection data.
相应地，本发明还提供一种基于挠度分离的大跨径梁桥状态的预测系统，包括：Correspondingly, the present invention also provides a system for predicting the state of a large-span girder bridge based on deflection separation, including:
数据获取模块，用于获取梁桥监测点下的挠度监测数据；The data acquisition module is used to acquire the deflection monitoring data under the monitoring point of the girder bridge;
信号分离模块，用于根据信号分离方法对所述挠度监测数据进行分离，得到车辆挠度数据、温度挠度数据和长期下挠数据；a signal separation module for separating the deflection monitoring data according to the signal separation method to obtain vehicle deflection data, temperature deflection data and long-term deflection data;
评估模块，用于分别对所述车辆挠度数据、所述温度挠度数据和所述长期下挠数据进行评估，获得所述车辆挠度数据对应的第一评估结果、所述温度挠度数据对应的第二评估结果和所述长期下挠数据对应的第三评估结果；An evaluation module, configured to evaluate the vehicle deflection data, the temperature deflection data and the long-term downward deflection data respectively, and obtain a first evaluation result corresponding to the vehicle deflection data and a second evaluation result corresponding to the temperature deflection data the evaluation result and the third evaluation result corresponding to the long-term deflection data;
预警模块，用于基于信息融合算法对所述第一评估结果、所述第二评估结果和所述第三评估结果进行计算，得到用于预警操作的梁桥状态。The early warning module is configured to calculate the first evaluation result, the second evaluation result and the third evaluation result based on the information fusion algorithm to obtain the beam bridge state for the early warning operation.
实施本发明实施例，具有如下有益效果：Implementing the embodiment of the present invention has the following beneficial effects:
本发明实施例提供的基于挠度分离的大跨径梁桥状态的预测方法，该方法先获取梁桥监测点下的挠度监测数据；根据信号分离方法对挠度监测数据进行分离，得到车辆挠度数据、温度挠度数据和长期下挠数据；分别对车辆挠度数据、温度挠度数据和长期下挠数据进行评估，获得车辆挠度数据对应的第一评估结果、温度挠度数据对应的第二评估结果和长期下挠数据对应的第三评估结果；基于信息融合算法对第一评估结果、第二评估结果和第三评估结果进行计算，得到用于预警操作的梁桥状态。相比于现有的梁桥状态预测方法，本发明技术方案不会对所有挠度成分均采用相同的评估方法，而是会考虑不同挠度成分对梁桥结构状态的影响，并根据挠度成分采取对应的评估方法，再通过信息融合方法对各个挠度成分的评估结果进行融合，从而提高梁桥状态预测结果的准确度。In the method for predicting the state of a large-span girder bridge based on deflection separation provided by the embodiment of the present invention, the method first obtains the deflection monitoring data under the monitoring point of the girder bridge; separates the deflection monitoring data according to the signal separation method to obtain vehicle deflection data, Temperature deflection data and long-term deflection data; evaluate the vehicle deflection data, temperature deflection data and long-term deflection data respectively, and obtain the first evaluation result corresponding to the vehicle deflection data, the second evaluation result corresponding to the temperature deflection data and the long-term deflection The third evaluation result corresponding to the data; the first evaluation result, the second evaluation result and the third evaluation result are calculated based on the information fusion algorithm to obtain the beam bridge state used for the early warning operation. Compared with the existing beam bridge state prediction method, the technical solution of the present invention does not use the same evaluation method for all deflection components, but considers the influence of different deflection components on the structural state of the beam bridge, and takes corresponding measures according to the deflection components. Then, the evaluation results of each deflection component are fused by the information fusion method, so as to improve the accuracy of the state prediction results of the girder bridge.
附图说明Description of drawings
图1是本发明提供的一种基于挠度分离的大跨径梁桥状态的预测方法的第一实施例的流程示意图；1 is a schematic flowchart of a first embodiment of a method for predicting the state of a large-span girder bridge based on deflection separation provided by the present invention;
图2是本发明提供的一种基于挠度分离的大跨径梁桥状态的预测方法的车辆挠度数据表；2 is a vehicle deflection data table of a method for predicting the state of a large-span girder bridge based on deflection separation provided by the present invention;
图3是本发明提供的一种基于挠度分离的大跨径梁桥状态的预测方法的温度挠度数据表；3 is a temperature deflection data table of a method for predicting the state of a large-span girder bridge based on deflection separation provided by the present invention;
图4是本发明提供的一种基于挠度分离的大跨径梁桥状态的预测方法的长期下挠数据表；4 is a long-term deflection data table of a method for predicting the state of a large-span girder bridge based on deflection separation provided by the present invention;
图5本发明提供的一种基于挠度分离的大跨径梁桥状态的预测方法的分值表；Figure 5 is a score table of a method for predicting the state of a large-span girder bridge based on deflection separation provided by the present invention;
图6本发明提供的一种基于挠度分离的大跨径梁桥状态的预测方法的挠度监测数据的分离效果图；6 is a diagram of the separation effect of deflection monitoring data of a method for predicting the state of a large-span girder bridge based on deflection separation provided by the present invention;
图7本发明提供的一种基于挠度分离的大跨径梁桥状态的预测方法的梁桥状态评级与评分表；7 is a girder bridge state rating and scoring table based on a method for predicting the state of a large-span girder bridge based on deflection separation provided by the present invention;
图8本发明提供的一种基于挠度分离的大跨径梁桥状态的预测方法的信息权重表；FIG. 8 is an information weight table of a method for predicting the state of a large-span girder bridge based on deflection separation provided by the present invention;
图9是本发明提供的一种基于挠度分离的大跨径梁桥状态的预测系统的第二实施例的结构示意图。9 is a schematic structural diagram of a second embodiment of a system for predicting the state of a large-span girder bridge based on deflection separation provided by the present invention.
具体实施方式Detailed ways
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.
第一实施例：First embodiment:
参见图1，是本发明提供的一种基于挠度分离的大跨径梁桥状态的预测方法的一种实施例的流程示意图。如图1，该方法包括步骤101至步骤104，各步骤具体如下：Referring to FIG. 1 , it is a schematic flowchart of an embodiment of a method for predicting the state of a large-span girder bridge based on deflection separation provided by the present invention. As shown in Figure 1, the method includes
步骤101：获取梁桥的挠度监测数据。Step 101: Obtain deflection monitoring data of the girder bridge.
在本实施例中，在步骤101之前，根据梁桥监测点确定出挠度检测数据为中跨跨中挠度数据或次中跨跨中挠度数据或边跨跨中挠度数据。需说明的是，梁桥的各个挠度对梁桥状态的影响是存在差异的，因此，根据梁桥监测点确定出挠度检测数据的类别，有利于提高梁桥状态预测结果的准确度。In this embodiment, before
步骤102：根据信号分离方法对挠度监测数据进行分离，得到车辆挠度数据、温度挠度数据和长期下挠数据。Step 102: Separating the deflection monitoring data according to the signal separation method to obtain vehicle deflection data, temperature deflection data and long-term down deflection data.
在本实施例中，步骤102具体为：通过低通滤波器对挠度监测数据进行滤波，获得车辆挠度数据和滤波数据；基于联合字典的稀疏分离方法对滤波数据进行分离，获得温度挠度数据和长期下挠数据。In this embodiment,
步骤103：分别对车辆挠度数据、温度挠度数据和长期下挠数据进行评估，获得车辆挠度数据对应的第一评估结果、温度挠度数据对应的第二评估结果和长期下挠数据对应的第三评估结果。Step 103: Evaluate the vehicle deflection data, the temperature deflection data and the long-term deflection data respectively, and obtain a first evaluation result corresponding to the vehicle deflection data, a second evaluation result corresponding to the temperature deflection data, and a third evaluation corresponding to the long-term deflection data result.
在本实施例中，步骤103中对车辆挠度数据进行评估，获得第一评估结果，具体为：从预设的车辆挠度数据表中，提取与车辆挠度数据对应的第一分值区间，并根据内插法和第一分值区间对车辆挠度数据进行插值，获得车辆挠度数据对应的第一评估结果。In this embodiment, the vehicle deflection data is evaluated in
在本实施例中，车辆挠度数据表的构建过程，具体为：首先，将规范定义的活载作用下挠度经验限值L/600作为红色预警值，其中L为梁桥跨径，本实施例的中跨跨中的跨径为160m、次中跨跨中的跨径为100m、边跨跨中的跨径为50m。其次，将规范定义的汽车荷载模型加载下的桥梁挠度计算值作为橙色预警值，具体为：将规范车道荷载模型作用于挠度测点的位移影响线最不利位置，即挠度效应计算结果最大的布载位置，通过有限元模型计算得到包含冲击效应的挠度汽车荷载效应值。紧接着，将运营随机车流与桥梁作用下的极值挠度作为黄色预警值，譬如，选取该桥址位置4个月的动态称重数据，对车辆荷载数据展开调研分析，统计货车交通量和荷载分布特性，采用随机车流模拟方法，将模拟的随机车流通过车桥耦合方式加载到桥梁结构上，获取其荷载效应时程，并通过基于区组最大值的广义极值外推方法计算极值挠度，即选择每天的挠度最大值作为基础样本，采用广义极值分布模型对数据进行极大似然拟合，获得广义极值分布的各个参数，通过确定设计基准期荷载效应分位值方法，确定黄色预警值；最后，根据红色预警值、橙色预警值和黄色预警值划分为四个区间，从而根据四个区间构建车辆挠度数据表，具体详情可参见图2；本实施例中小于黄色预警值的第一区间的分值区间为[75,100]，对应的等级为1；橙色预警值与黄色预警值之间的第二区间的分值区间为[50,75]，对应的等级为2；红色预警值与橙色预警值之间的第三区间的分值区间的分值区间为[25,50]，对应的等级为3；大于红色预警值的第四区间的分值区间为[0,25]，对应的等级为4；具体详情可参见图5。In this embodiment, the construction process of the vehicle deflection data table is as follows: first, the empirical limit value L/600 of deflection under the action of live load defined in the specification is taken as the red warning value, where L is the span of the beam bridge, this embodiment The span of the middle span is 160m, the span of the second middle span is 100m, and the span of the side span is 50m. Secondly, the calculated value of the bridge deflection under the loading of the vehicle load model defined by the code is taken as the orange warning value. Specifically, the code lane load model is applied to the most unfavorable position of the displacement influence line of the deflection measuring point, that is, the layout with the largest deflection effect calculation result. The load position is calculated by the finite element model, and the deflection vehicle load effect value including the impact effect is obtained. Next, take the extreme deflection of the operation random traffic flow and the bridge as the yellow warning value. For example, select the dynamic weighing data of the bridge site for 4 months, conduct research and analysis on the vehicle load data, and count the traffic volume and load of trucks. distribution characteristics, the random traffic flow simulation method is used to load the simulated random traffic flow onto the bridge structure through the vehicle-bridge coupling method to obtain the load effect time history, and the extreme value deflection is calculated by the generalized extreme value extrapolation method based on the block maximum value. , that is, select the maximum deflection value of each day as the basic sample, use the generalized extreme value distribution model to perform maximum likelihood fitting on the data, and obtain each parameter of the generalized extreme value distribution. Yellow early warning value; finally, according to the red early warning value, the orange early warning value and the yellow early warning value, it is divided into four intervals, so as to construct the vehicle deflection data table according to the four intervals, the specific details can be seen in Figure 2; in this embodiment, it is less than the yellow early warning value The score interval of the first interval is [75, 100], and the corresponding level is 1; the score interval of the second interval between the orange early warning value and the yellow early warning value is [50, 75], and the corresponding level is 2; red The score interval of the third interval between the warning value and the orange warning value is [25,50], and the corresponding level is 3; the score interval of the fourth interval greater than the red warning value is [0,25] ], the corresponding level is 4; see Figure 5 for details.
在本实施例中，步骤103中对温度挠度数据进行评估，获得第二评估结果，具体为：从预设的温度挠度数据表中，提取与温度挠度数据对应的第二分值区间，并根据内插法和第二分值区间对温度挠度数据进行插值，获得温度挠度数据对应的第二评估结果。In this embodiment, the temperature deflection data is evaluated in
在本实施例中，温度挠度数据表的构建过程，具体为：首先，将升温25℃计算的挠度效应值作为红色预警值；其次，将升温20℃计算的挠度效应值作为橙色预警值；紧接着，将升温15℃计算的挠度效应值作为黄色预警值；最后，根据红色预警值、橙色预警值和黄色预警值划分为四个区间，从而根据四个区间构建车辆挠度数据表，具体详情可参见图3；本实施例中小于黄色预警值的第一区间的分值区间为[75,100]，对应的等级为1；橙色预警值与黄色预警值之间的第二区间的分值区间为[50,75]，对应的等级为2；红色预警值与橙色预警值之间的第三区间的分值区间的分值区间为[25,50]，对应的等级为3；大于红色预警值的第四区间的分值区间为[0,25]，对应的等级为4；具体详情可参见图5。In this embodiment, the construction process of the temperature deflection data table is specifically as follows: first, the deflection effect value calculated at a temperature of 25°C is taken as the red warning value; secondly, the deflection effect value calculated at a temperature of 20°C is taken as the orange warning value; Then, the deflection effect value calculated at a temperature rise of 15°C is taken as the yellow warning value; finally, it is divided into four intervals according to the red warning value, the orange warning value and the yellow warning value, so that the vehicle deflection data table is constructed according to the four intervals. Referring to Figure 3; in this embodiment, the score interval of the first interval smaller than the yellow warning value is [75,100], and the corresponding level is 1; the score interval of the second interval between the orange warning value and the yellow warning value is [75,100] 50,75], the corresponding level is 2; the score interval of the third interval between the red early warning value and the orange early warning value is [25,50], and the corresponding level is 3; The score interval of the fourth interval is [0, 25], and the corresponding level is 4; see Figure 5 for details.
在本实施例中，步骤103中的对长期下挠数据进行评估，获得第三评估结果，具体为：从预设的长期下挠数据表中，提取与长期下挠数据对应的第三分值区间，并根据内插法和第三分值区间对长期下挠数据进行插值，获得长期下挠数据对应的第三评估结果。In this embodiment, evaluating the long-term deflection data in
在本实施例中，长期下挠数据表的构建过程，具体为：基于最小二乘法的数学逼近方法对已有的下挠数据中的挠跨比(＝下挠量/主跨跨径)建立时变挠跨比模型；其中，最小二乘法的拟合方程为：δ/L＝-0.00324Y2+0.17231Y，δ为长下挠成分预警值，Y为梁桥服役年限，L为梁桥跨径，对于本实施例而言，中跨跨中的跨径L2为160m、次中跨跨中的跨径L1为100m、边跨跨中的跨径L0为50m。首先，将最小二乘法的拟合方程δ/L＝-0.00324Y2+0.17231Y作为时变挠跨比模型的中分值，即为橙色预警值，其物理含义为目前统计梁桥的长期下挠数据的一般水平。其次，考虑到回归样本的差异性，取时变挠跨比模型的中分值的5％概率值(相当于橙色预警值的0.7463倍)作为时变挠跨比模型的低分值，即为黄色预警值，此时黄色预警值的拟合方程为：δ/L＝-0.00242Y2+0.12859Y。第三，取变挠跨比模型的中分值的95％概率值(相当于橙色预警值的1.2349倍)作为时变挠跨比模型的高分值，即为红色预警值，此时红色预警值的拟合方程为：δ/L＝-0.004001Y2+0.21279Y，具体详情可参见图4。最后，根据红色预警值、橙色预警值和黄色预警值划分为四个区间，从而根据四个区间构建车辆挠度数据表；其中，小于黄色预警值的第一区间的分值区间为[75,100]，对应的等级为1；橙色预警值与黄色预警值之间的第二区间的分值区间为[50,75]，对应的等级为2；红色预警值与橙色预警值之间的第三区间的分值区间的分值区间为[25,50]，对应的等级为3；大于红色预警值的第四区间的分值区间为[0,25]，对应的等级为4；具体详情可参见图5。In this embodiment, the construction process of the long-term deflection data table is specifically: establishing the deflection-span ratio (= deflection amount/main span span) in the existing deflection data based on the mathematical approximation method of the least squares method Time-varying torsion-span ratio model; among them, the fitting equation of the least squares method is: δ/L=-0.00324Y2 +0.17231Y, δ is the early warning value of the long deflection component, Y is the service life of the girder bridge, and L is the girder bridge As for the span, for this embodiment, the span L2 in the middle span is 160 m, the span L1 in the secondary middle span is 100 m, and the span L0 in the side span is 50 m. First, the fitting equation of the least squares method δ/L=-0.00324Y2 +0.17231Y is used as the median score of the time-varying deflection-span ratio model, which is the orange early warning value, and its physical meaning is the current statistical long-term General level of torsion data. Secondly, considering the difference of regression samples, the probability value of 5% of the median score of the time-varying torsion-span ratio model (equivalent to 0.7463 times of the orange early warning value) is taken as the low score of the time-varying torsion-span ratio model, which is The yellow early warning value, the fitting equation of the yellow early warning value at this time is: δ/L=-0.00242Y2 +0.12859Y. Third, take the 95% probability value (equivalent to 1.2349 times of the orange early warning value) of the median score of the time-varying torsion-span ratio model as the high score of the time-varying torsion-span ratio model, which is the red early warning value. At this time, the red early warning The fitting equation of the value is: δ/L=-0.004001Y2 +0.21279Y, and the specific details can be found in FIG. 4 . Finally, according to the red warning value, the orange warning value and the yellow warning value, it is divided into four intervals, so as to construct the vehicle deflection data table according to the four intervals; among them, the score interval of the first interval smaller than the yellow warning value is [75, 100], The corresponding grade is 1; the score interval of the second interval between the orange warning value and the yellow warning value is [50,75], and the corresponding grade is 2; the third interval between the red warning value and the orange warning value is The score interval of the score interval is [25, 50], and the corresponding level is 3; the score interval of the fourth interval greater than the red warning value is [0, 25], and the corresponding level is 4; for details, please refer to the figure 5.
步骤104：基于信息融合算法对第一评估结果、第二评估结果和第三评估结果进行计算，得到用于预警操作的梁桥状态。Step 104: Calculate the first evaluation result, the second evaluation result and the third evaluation result based on the information fusion algorithm to obtain the beam bridge state used for the early warning operation.
在本实施例中，步骤104具体为：根据D-S证据理论和信息权重表，对第一评估结果、第二评估结果和第三评估结果进行计算，得到梁桥状态的预测结果，以便于根据梁桥状态的预测结果进行相应的预警操作。In this embodiment,
在本实施例中，信息权重表的构建方法，具体为：首先，确定不同评价等级的权重系数；基于模糊一致矩阵法，通过均匀标度方法确定各个评价等级的模糊一致性矩阵为rxy＝0.5+(y-x)/8，式中x和y分别对应评价等级；按行求和计算不同评价等级的权重系数为
为了更好的说明本实施例的流程和原理，以下面以测试5天的挠度数据为例进行说明：In order to better illustrate the process and principle of this embodiment, the following is an example of the deflection data tested for 5 days:
步骤一，通过位移传感器获取中跨跨中、次中跨跨中、边跨跨中分别对应的挠度监测数据，并对挠度监测数据进行去噪。In
步骤二，首先，通过Butterworth低通滤波器对步骤一获取的挠度监测数据进行分离获得车辆挠度数据和滤波数据，具体为：设置Butterworth低通滤波器的截断频率为采样频率的0.05倍，这吻合车载作用下挠度信号的频段范围，因此，经过Butterworth低通滤波器获取的数据就为车辆挠度数据，剩下的挠度监测数据就为滤波数据，其次，采用基于联合字典的稀疏分离方法对滤波数据进行短周期(周期为1天)和长周期(周期为1年)的温度挠度效应分离，获得温度挠度数据，剩下的滤波数据就为长期下挠数据，由于本实例的测试周期仅为5天，因此不考虑长期下挠数据。需说明的是，若测试周期远大于1年时，温度挠度数据需要分别构建符合温度日温差和温度年温差的周期性挠度信号原子，譬如，测试周期为1天时，日温差的周期性挠度信号原子为φ1(t)＝sin(πt/12)，测试周期为1年时，年温差信号周期为1年，年温差的周期性挠度信号原子为φ2(t)＝sin(πt/4380)，并基于稀疏正则化方法和快速压缩阈值迭代法进行优化求解，得到原子的参与系数，从而分离挠度日温差效应和挠度年温差效应，具体详情可参见图6。Step 2: First, the deflection monitoring data obtained in
步骤三，譬如，步骤二获取的中跨跨中的车辆挠度数据为-53.70mm、次中跨跨中的车辆挠度数据为-49.33mm，边中跨跨中的车辆挠度数据为24.10mm，由车辆挠度数据表(图2)和分值表中(图5)，得到车辆挠度数据对应的分值区间，即中跨跨中的车辆挠度数据为-53.70mm对应的分值区间为[75,100]，次中跨跨中的车辆挠度数据为-49.33mm对应的分值区间为[25,50]，边中跨跨中的车辆挠度数据为24.10mm对应的分值区间为[50,75]。并基于内插法和分值区间，获得对应的评估结果，例如，中跨跨中的车辆挠度数据为-53.70mm的评估结果为82.80，次中跨跨中的车辆挠度数据为-49.33mm的评估结果为49.80，边中跨跨中的车辆挠度数据为24.10mm的评估结果为66.20。譬如，步骤二获取的中跨跨中的温度挠度数据为7.40mm、次中跨跨中的温度挠度数据为2.23mm、边中跨跨中的温度挠度数据为2.14mm，,由温度挠度数据表(图3)和分值表中(图5)，得到温度挠度数据对应的分值，即中跨跨中的温度挠度数据为7.40mm对应的分值区间为[75,100]，次中跨跨中的温度挠度数据为2.23mm对应的分值区间为[75,100]，边中跨跨中的温度挠度数据为2.14mm对应的分值区间为[75,100]。因此，本实施例中的中跨跨中的温度挠度数据的评估结果为76.40，次中跨跨中的温度挠度数据的评估结果为85.70，次中跨跨中的温度挠度数据的评估结果为78.30；具体详情可参见图7。
步骤四，根据信息融合算法和信息权重，对步骤三获取的评估结果进行计算，即分别将图7中的评估分值乘以对应的权重系数，例如中跨跨中车辆挠度的评估分值是82.80，其权重系数对应图8中为0.2068，如此根据所有评估分值及其权重系数的乘积求和，得到梁桥状态的综合评分为76.50，评级为1。Step 4: Calculate the evaluation result obtained in
由上可见，本发明实施例提供的基于挠度分离的大跨径梁桥状态的预测方法，该方法先获取梁桥监测点下的挠度监测数据；根据信号分离方法对挠度监测数据进行分离，得到车辆挠度数据、温度挠度数据和长期下挠数据；分别对车辆挠度数据、温度挠度数据和长期下挠数据进行评估，获得车辆挠度数据对应的第一评估结果、温度挠度数据对应的第二评估结果和长期下挠数据对应的第三评估结果；基于信息融合算法对第一评估结果、第二评估结果和第三评估结果进行计算，得到用于预警操作的梁桥状态。相比于现有的梁桥状态预测方法，本发明技术方案不会对所有挠度成分均采用相同的评估方法，而是会考虑不同挠度成分对梁桥结构状态的影响，并根据挠度成分采取对应的评估方法，再通过信息融合方法对各个挠度成分的评估结果进行融合，从而提高梁桥状态预测结果的准确度。As can be seen from the above, the method for predicting the state of a large-span girder bridge based on deflection separation provided by the embodiment of the present invention first obtains the deflection monitoring data under the monitoring point of the girder bridge; separates the deflection monitoring data according to the signal separation method, and obtains: Vehicle deflection data, temperature deflection data, and long-term deflection data; evaluate the vehicle deflection data, temperature deflection data, and long-term deflection data respectively, and obtain the first evaluation result corresponding to the vehicle deflection data and the second evaluation result corresponding to the temperature deflection data The third evaluation result corresponding to the long-term bending data; the first evaluation result, the second evaluation result and the third evaluation result are calculated based on the information fusion algorithm to obtain the beam bridge state used for the early warning operation. Compared with the existing beam bridge state prediction method, the technical solution of the present invention does not use the same evaluation method for all deflection components, but considers the influence of different deflection components on the structural state of the beam bridge, and takes corresponding measures according to the deflection components. Then, the evaluation results of each deflection component are fused by the information fusion method, so as to improve the accuracy of the state prediction results of the girder bridge.
第二实施例Second Embodiment
请参见图9，是本发明提供的一种基于挠度分离的大跨径梁桥状态的预测系统的第二实施例的结构示意图，该系统包括数据获取模块201、信号分离模块202、评估模块203和预警模块204。Please refer to FIG. 9 , which is a schematic structural diagram of a second embodiment of a system for predicting the state of a large-span girder bridge based on deflection separation provided by the present invention. The system includes a
数据获取模块201，用于获取梁桥的挠度监测数据；The
信号分离模块202，用于根据信号分离方法对挠度监测数据进行分离，得到车辆挠度数据、温度挠度数据和长期下挠数据；The
评估模块203，用于分别对车辆挠度数据、温度挠度数据和长期下挠数据进行评估，获得车辆挠度数据对应的第一评估结果、温度挠度数据对应的第二评估结果和长期下挠数据对应的第三评估结果；The
预警模块204，用于基于信息融合算法对第一评估结果、第二评估结果和第三评估结果进行计算，得到用于预警操作的梁桥状态。The
由上可见，本发明技术方案会考虑不同挠度成分对梁桥结构状态的影响，并根据挠度成分采取对应的评估方法，再通过信息融合方法对各个挠度成分的评估结果进行融合，从而提高梁桥状态预测结果的准确度。It can be seen from the above that the technical solution of the present invention will consider the influence of different deflection components on the structural state of the girder bridge, and adopt corresponding evaluation methods according to the deflection components, and then fuse the evaluation results of each deflection component through the information fusion method, thereby improving the beam bridge. Accuracy of state prediction results.
本领域普通技术人员可以理解实现上述实施例方法中的全部或部分流程，是可以通过计算机程序来指令相关的硬件来完成，的程序可存储于一计算机可读取存储介质中，该程序在执行时，可包括如上述各方法的实施例的流程。其中，的存储介质可为磁碟、光盘、只读存储记忆体(Read-Only Memory，ROM)或随机存储记忆体(Random Access Memory，RAM)等。Those of ordinary skill in the art can understand that the realization of all or part of the processes in the methods of the above embodiments can be accomplished by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and the program can be executed when the program is executed. , may include the flow of the above-mentioned method embodiments. The storage medium may be a magnetic disk, an optical disk, a read-only memory (Read-Only Memory, ROM), or a random access memory (Random Access Memory, RAM) or the like.
以上是本发明的优选实施方式，应当指出，对于本技术领域的普通技术人员来说，在不脱离本发明原理的前提下，还可以做出若干改进和润饰，这些改进和润饰也视为本发明的保护范围。The above are the preferred embodiments of the present invention. It should be pointed out that for those skilled in the art, some improvements and modifications can be made without departing from the principles of the present invention, and these improvements and modifications are also regarded as the present invention. the scope of protection of the invention.
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