WO2021046846A1 - 一种高铁桥梁损耗监控系统 - Google Patents

一种高铁桥梁损耗监控系统 Download PDF

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WO2021046846A1
WO2021046846A1 PCT/CN2019/105838 CN2019105838W WO2021046846A1 WO 2021046846 A1 WO2021046846 A1 WO 2021046846A1 CN 2019105838 W CN2019105838 W CN 2019105838W WO 2021046846 A1 WO2021046846 A1 WO 2021046846A1
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support
bridge
displacement
longitudinal displacement
cloud computing
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PCT/CN2019/105838
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English (en)
French (fr)
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丁幼亮
丁李
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南京东南建筑机电抗震研究院有限公司
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Priority to PCT/CN2019/105838 priority Critical patent/WO2021046846A1/zh
Publication of WO2021046846A1 publication Critical patent/WO2021046846A1/zh

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    • E—FIXED CONSTRUCTIONS
    • E01—CONSTRUCTION OF ROADS, RAILWAYS, OR BRIDGES
    • E01D—CONSTRUCTION OF BRIDGES, ELEVATED ROADWAYS OR VIADUCTS; ASSEMBLY OF BRIDGES
    • E01D22/00—Methods or apparatus for repairing or strengthening existing bridges ; Methods or apparatus for dismantling bridges
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/08—Construction

Definitions

  • the invention relates to the field of construction, in particular to a high-speed rail bridge loss monitoring system.
  • the present invention provides a high-speed rail bridge loss monitoring system.
  • the invention provides a high-speed rail bridge loss monitoring system, which includes a set of sensors, a cloud computing system, an expert system and an early warning system;
  • the sensor sends the monitoring signal to the cloud computing system
  • the cloud computing system establishes a coordinate system for the high-speed rail bridge according to the three-dimensional space position, and divides it into multiple monitoring areas; according to the real-time monitoring signal of the sensor, the bridge status in each monitoring area is obtained, and its loss trend is judged through calculation, and the loss trend is determined. Classify the status of each monitoring area, and calculate and analyze the impact of the early warning area on other areas; the cloud computing system calculates the longitudinal displacement of the support that eliminates the influence of the temperature of the main beam, the cumulative value of the longitudinal displacement of the support caused by the action of the train, and the high-speed rail bridge Clearance between supports and beam ends to determine the vulnerability of high-speed railway bridge supports;
  • the expert system According to the state of each monitoring area of the bridge calculated by the cloud computing system, the expert system provides management and maintenance recommendations for each monitoring area of the bridge;
  • the early warning system receives the early warning signal of the cloud computing system and issues an early warning.
  • connection mode of the sensor, cloud computing system, expert system and early warning system is wireless connection.
  • the sensors include: an axial force sensor, a horizontal displacement sensor, a relative displacement sensor, an acceleration sensor, and a temperature and humidity sensor.
  • the calculation method of the bearing longitudinal displacement to eliminate the influence of the main beam temperature is: a) Using 10 minutes as the calculation interval, calculate the 10-min average value D of the bearing longitudinal displacement D and the 10-min average value of the main beam temperature T respectively Value T; b) Select the monitoring data of n days (n is a natural number) after the completion of the high-speed railway bridge construction and use the linear regression method to establish the correlation model between the longitudinal displacement D of the support and the temperature T of the main beam.
  • the regression model parameters are from the least two Calculated by multiplication; c) Eliminate the influence of the main beam temperature on the longitudinal displacement of the support, select the reference temperature as T, and normalize the original test value D of the longitudinal displacement of the support according to the correlation model between the support longitudinal displacement D and the main beam temperature T To the reference temperature T, the longitudinal displacement D of the support that eliminates the influence of the temperature of the main beam is obtained.
  • the calculation method of the high-speed rail bridge support and beam end gap is: a) Construct a three-dimensional rectangular coordinate system, and the displacements of the rotation angle displacement sensors in the three groups of support rotation angle displacement detection mechanisms are h1, h2, h3, and h3 sensor
  • the fulcrum is taken as the origin
  • the directions of h3 and h1 are taken as the x-axis
  • the directions of h3 and h2 are taken as the y-axis
  • their faces are vertically upward as the z-axis to construct a three-dimensional coordinate system
  • b) the calculation of the rotation angle, h1, h2, and h3 are composed of three sets of supports
  • the rotation angle displacement sensor in the rotation angle displacement detection mechanism is measured.
  • L is determined when the sensor is installed. It is the side length of an isosceles right-angled triangle.
  • the rotation angle is derived from these 4 quantities.
  • the expert system uses big data and neural network methods to calculate the optimal maintenance plan after inputting a large amount of bridge information, aging process, and maintenance methods.
  • the high-speed rail bridge loss monitoring system provided by the present invention is proposed for the real-time and accuracy requirements of the high-speed railway bridge support maintenance management.
  • the method is simple and easy to implement, convenient for practical engineering applications, and can accurately evaluate the cumulative displacement caused by the train action on the support.
  • the impact of seat damage effectively improves the accuracy of the vulnerability assessment of the bearing.
  • the high-speed rail bridge loss monitoring system includes a set of sensors, a cloud computing system, an expert system, and an early warning system; the connection mode of the sensors, the cloud computing system, the expert system, and the early warning system is wireless connection.
  • the sensor sends the monitoring signal to the cloud computing system;
  • the sensor includes: an axial force sensor, a horizontal displacement sensor, a relative displacement sensor, an acceleration sensor, and a temperature and humidity sensor.
  • the cloud computing system establishes a coordinate system for the high-speed rail bridge according to the three-dimensional space position, and divides it into multiple monitoring areas; according to the real-time monitoring signal of the sensor, the bridge status in each monitoring area is obtained, and its loss trend is judged through calculation, and the loss trend is determined. Classify the status of each monitoring area, and calculate and analyze the impact of the early warning area on other areas; the cloud computing system calculates the longitudinal displacement of the support that eliminates the influence of the temperature of the main beam, the cumulative value of the longitudinal displacement of the support caused by the action of the train, and the high-speed rail bridge Clearance between supports and beam ends to determine the vulnerability of high-speed railway bridge supports;
  • the calculation method of the bearing longitudinal displacement to eliminate the influence of the main beam temperature is: a) Using 10 minutes as the calculation interval, calculate the 10-min average value D of the bearing longitudinal displacement D and the 10-min average value of the main beam temperature T respectively Value T; b) Select the monitoring data of n days (n is a natural number) after the completion of the high-speed railway bridge construction and use the linear regression method to establish the correlation model between the longitudinal displacement D of the support and the temperature T of the main beam.
  • the regression model parameters are from the least two Calculated by multiplication; c) Eliminate the influence of the main beam temperature on the longitudinal displacement of the support, select the reference temperature as T, and normalize the original test value D of the longitudinal displacement of the support according to the correlation model between the support longitudinal displacement D and the main beam temperature T To the reference temperature T, the longitudinal displacement D of the support that eliminates the influence of the temperature of the main beam is obtained.
  • the calculation method of the high-speed rail bridge support and beam end gap is: a) Construct a three-dimensional rectangular coordinate system, and the displacements of the rotation angle displacement sensors in the three groups of support rotation angle displacement detection mechanisms are h1, h2, h3, and h3 sensor
  • the fulcrum is taken as the origin
  • the directions of h3 and h1 are taken as the x-axis
  • the directions of h3 and h2 are taken as the y-axis
  • their faces are vertically upward as the z-axis to construct a three-dimensional coordinate system
  • b) the calculation of the rotation angle, h1, h2, and h3 are composed of three sets of supports
  • the rotation angle displacement sensor in the rotation angle displacement detection mechanism is measured.
  • L is determined when the sensor is installed. It is the side length of an isosceles right-angled triangle.
  • the rotation angle is derived from these 4 quantities.
  • the expert system gives advice on the management and maintenance of each monitoring area of the bridge; the expert system uses big data and neural networks after inputting a large amount of bridge information, aging process, and maintenance methods The method calculates to obtain the optimal maintenance plan.
  • the early warning system receives the early warning signal of the cloud computing system and issues an early warning.

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  • Human Resources & Organizations (AREA)
  • Strategic Management (AREA)
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  • General Health & Medical Sciences (AREA)
  • Game Theory and Decision Science (AREA)
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  • Health & Medical Sciences (AREA)
  • Architecture (AREA)
  • Civil Engineering (AREA)
  • Structural Engineering (AREA)
  • General Engineering & Computer Science (AREA)
  • Machines For Laying And Maintaining Railways (AREA)
  • Bridges Or Land Bridges (AREA)

Abstract

本发明提供的一种高铁桥梁损耗监控系统，包括一组传感器、云计算系统、专家系统以及预警系统；所述传感器将监测信号发送至云计算系统；所述云计算系统将高铁桥梁按三维空间位置建立坐标系，并分为多个监控区域；所述专家系统根据云计算系统计算获得的桥梁各监控区域的状态，给出桥梁各监控区域管养建议；所述预警系统接收云计算系统的预警信号，并发出预警。本发明提供的高铁桥梁损耗监控系统是针对高速铁路桥梁支座养护管理的实时性和准确性要求提出的，方法简单易行，方便实际工程的应用，可准确评估列车作用引起的累积位移对支座损伤的影响，有效提高了支座易损性评估的精度。

Description

一种高铁桥梁损耗监控系统 技术领域
本发明涉及建筑领域，具体涉及一种高铁桥梁损耗监控系统。
背景技术
随着我国建筑行业的发展以及用地的紧缩，为了缩短项目工期，多建筑密集高效施工已成为土木行业的常态。塔吊作为施工领域的重要角色，其作业环境日益复杂，然而传统吊装作业采用操作员与吊装员相互配合的方式指挥塔吊交叉重叠作业。虽然这些专业人员需要考取相关驾驶证和具备一定文化水平方能上岗，但这种方法总体上智能化较低，信息对接不准确或及时，难以应对某些复杂情况。近年来塔吊机碰撞事故高发也逐渐印证了这一点。传统的依靠人工值守的监测技术逐渐无法满足现代化施工工地上塔吊机群的防碰撞预警需求，急需一类能够实时监控塔吊机吊装运行且能发出预警信号的建筑施工群塔防碰撞预警监控方法。
发明概述
技术问题
为了解决现有技术的缺陷，本发明提供了一种高铁桥梁损耗监控系统。
问题的解决方案
技术解决方案
本发明提供的一种高铁桥梁损耗监控系统，包括一组传感器、云计算系统、专家系统以及预警系统；
所述传感器将监测信号发送至云计算系统；
所述云计算系统将高铁桥梁按三维空间位置建立坐标系，并分为多个监控区域；根据传感器实时监测信号，获得各监控区域内的桥梁状态，并通过计算判断其损耗趋势，将损耗趋势将各监控区域的状态进行分类，同时计算分析预警区域对其他区域的影响情况；云计算系统计算消除主梁温度影响的支座纵向位移、列车作用引起的支座纵向位移的累积值、高铁桥梁支座及梁端间隙，从而判 断高速铁路桥梁支座易损性；
所述专家系统根据云计算系统计算获得的桥梁各监控区域的状态，给出桥梁各监控区域管养建议；
所述预警系统接收云计算系统的预警信号，并发出预警。
所述传感器、云计算系统、专家系统以及预警系统的连接方式为无线连接。
所述传感器包括：轴力传感器、水平位移传感器、相对位移传感器、加速度传感器、温湿度传感器。
所述消除主梁温度影响的支座纵向位移的计算方法为：a)以10分钟为计算区间，分别计算支座纵向位移D的10-min平均值D和主梁温度T的10-min平均值T；b)选取高速铁路桥梁施工建成后n天(n为自然数)的监测数据并采用线性回归的方法建立支座纵向位移D和主梁温度T的相关性模型，回归模型参数由最小二乘法计算得到；c)消除主梁温度对支座纵向位移的影响，选取参考温度为T，根据支座纵向位移D和主梁温度T的相关性模型将支座纵向位移原始测试值D归一化至参考温度T，得到消除主梁温度影响的支座纵向位移D。
所述列车作用引起的支座纵向位移的累积值的计算方法为：a)选取高速铁路桥梁施工建成后n天(n为自然数)的监测数据，计算支座纵向位移D的累积位移R；b)从支座纵向位移D的时间序列中提取列车凌晨不通车时段m小时内(m为自然数)的支座纵向位移，计算列车不通车时段内的累积位移R；c)计算列车作用引起的支座纵向位移的累积值R＝R-R×24/m。
所述高铁桥梁支座及梁端间隙的计算方法为：a)构建三维直角坐标系，三组支座转角位移检测机构中的转角位移传感器的位移量分别为h1、h2、h3，以h3传感器支点作为原点，h3和h1方向作为x轴，h3和h2方向作为y轴，他们的面垂直向上作为z轴，构建三维坐标系；b)转角计算，h1、h2、h3是由三组支座转角位移检测机构中的转角位移传感器测量得到的，L是安装传感器的时候决定的，它是等腰直角三角的边长，通过这4个量推导计算出转角。
所述速铁路桥梁支座易损性评估的计算方法为：a)计算支座易损性指标S＝R/n；b)对所有支座计算支座易损性指标S并排序，S最大的支座易损性最大，需要重点养护。
专家系统是通过输入大量桥梁信息、老化过程、保养方式后，利用大数据及神经网络方法计算获得最优保养方案。
发明的有益效果
有益效果
本发明提供的高铁桥梁损耗监控系统是针对高速铁路桥梁支座养护管理的实时性和准确性要求提出的，方法简单易行，方便实际工程的应用，可准确评估列车作用引起的累积位移对支座损伤的影响，有效提高了支座易损性评估的精度。
发明实施例
具体实施方式
下面结合实施例，对本发明的具体实施方式作进一步详细描述。以下实施例用于说明本发明，但不用来限制本发明的范围。
高铁桥梁损耗监控系统，包括一组传感器、云计算系统、专家系统以及预警系统；所述传感器、云计算系统、专家系统以及预警系统的连接方式为无线连接。
所述传感器将监测信号发送至云计算系统；所述传感器包括：轴力传感器、水平位移传感器、相对位移传感器、加速度传感器、温湿度传感器。
所述云计算系统将高铁桥梁按三维空间位置建立坐标系，并分为多个监控区域；根据传感器实时监测信号，获得各监控区域内的桥梁状态，并通过计算判断其损耗趋势，将损耗趋势将各监控区域的状态进行分类，同时计算分析预警区域对其他区域的影响情况；云计算系统计算消除主梁温度影响的支座纵向位移、列车作用引起的支座纵向位移的累积值、高铁桥梁支座及梁端间隙，从而判断高速铁路桥梁支座易损性；
所述消除主梁温度影响的支座纵向位移的计算方法为：a)以10分钟为计算区间，分别计算支座纵向位移D的10-min平均值D和主梁温度T的10-min平均值T；b)选取高速铁路桥梁施工建成后n天(n为自然数)的监测数据并采用线性回归的方法建立支座纵向位移D和主梁温度T的相关性模型，回归模型参数由最小二乘法计算得到；c)消除主梁温度对支座纵向位移的影响，选取参考温度为T，根据支座 纵向位移D和主梁温度T的相关性模型将支座纵向位移原始测试值D归一化至参考温度T，得到消除主梁温度影响的支座纵向位移D。
所述列车作用引起的支座纵向位移的累积值的计算方法为：a)选取高速铁路桥梁施工建成后n天(n为自然数)的监测数据，计算支座纵向位移D的累积位移R；b)从支座纵向位移D的时间序列中提取列车凌晨不通车时段m小时内(m为自然数)的支座纵向位移，计算列车不通车时段内的累积位移R；c)计算列车作用引起的支座纵向位移的累积值R＝R-R×24/m。
所述高铁桥梁支座及梁端间隙的计算方法为：a)构建三维直角坐标系，三组支座转角位移检测机构中的转角位移传感器的位移量分别为h1、h2、h3，以h3传感器支点作为原点，h3和h1方向作为x轴，h3和h2方向作为y轴，他们的面垂直向上作为z轴，构建三维坐标系；b)转角计算，h1、h2、h3是由三组支座转角位移检测机构中的转角位移传感器测量得到的，L是安装传感器的时候决定的，它是等腰直角三角的边长，通过这4个量推导计算出转角。
所述速铁路桥梁支座易损性评估的计算方法为：a)计算支座易损性指标S＝R/n；b)对所有支座计算支座易损性指标S并排序，S最大的支座易损性最大，需要重点养护。
所述专家系统根据云计算系统计算获得的桥梁各监控区域的状态，给出桥梁各监控区域管养建议；专家系统是通过输入大量桥梁信息、老化过程、保养方式后，利用大数据及神经网络方法计算获得最优保养方案。
所述预警系统接收云计算系统的预警信号，并发出预警。
以上所述实施例仅表达了本发明的若干实施方式，其描述较为具体和详细，但并不能因此而理解为对发明专利范围的限制。应当指出的是，对于本领域的普通技术人员来说，在不脱离本发明构思的前提下，还可以做出若干变形和改进，这些都属于本发明的保护范围。因此，本发明专利的保护范围应以所附权利要求为准。

Claims (8)

  1. 一种高铁桥梁损耗监控系统，其特征在于：包括一组传感器、云计算系统、专家系统以及预警系统；
    所述传感器将监测信号发送至云计算系统；
    所述云计算系统将高铁桥梁按三维空间位置建立坐标系，并分为多个监控区域；根据传感器实时监测信号，获得各监控区域内的桥梁状态，并通过计算判断其损耗趋势，将损耗趋势将各监控区域的状态进行分类，同时计算分析预警区域对其他区域的影响情况；云计算系统计算消除主梁温度影响的支座纵向位移、列车作用引起的支座纵向位移的累积值、高铁桥梁支座及梁端间隙，从而判断高速铁路桥梁支座易损性；
    所述专家系统根据云计算系统计算获得的桥梁各监控区域的状态，给出桥梁各监控区域管养建议；
    所述预警系统接收云计算系统的预警信号，并发出预警。
  2. 根据权利要求1所述的一种高铁桥梁损耗监控系统，其特征在于：所述传感器、云计算系统、专家系统以及预警系统的连接方式为无线连接。
  3. 根据权利要求1所述的一种高铁桥梁损耗监控系统，其特征在于：所述传感器包括：轴力传感器、水平位移传感器、相对位移传感器、加速度传感器、温湿度传感器。
  4. 根据权利要求1所述的一种高铁桥梁损耗监控系统，其特征在于：所述消除主梁温度影响的支座纵向位移的计算方法为：a)以10分钟为计算区间，分别计算支座纵向位移D的10-min平均值D和主梁温度T的10-min平均值T；b)选取高速铁路桥梁施工建成后n天(n为自然数)的监测数据并采用线性回归的方法建立支座纵向位移D和主梁温度T的相关性模型，回归模型参数由最小二乘法计算得到；c)消除主梁温度对支座纵向位移的影响，选取参考温度为T，根据支座纵向位移D和主梁温度T的相关性模型将支座纵向位移原始测试 值D归一化至参考温度T，得到消除主梁温度影响的支座纵向位移D。
  5. 根据权利要求1所述的一种高铁桥梁损耗监控系统，其特征在于：所述列车作用引起的支座纵向位移的累积值的计算方法为：a)选取高速铁路桥梁施工建成后n天(n为自然数)的监测数据，计算支座纵向位移D的累积位移R；b)从支座纵向位移D的时间序列中提取列车凌晨不通车时段m小时内(m为自然数)的支座纵向位移，计算列车不通车时段内的累积位移R；c)计算列车作用引起的支座纵向位移的累积值R＝R-R×24/m。
  6. 根据权利要求1所述的一种高铁桥梁损耗监控系统，其特征在于：所述高铁桥梁支座及梁端间隙的计算方法为：a)构建三维直角坐标系，三组支座转角位移检测机构中的转角位移传感器的位移量分别为h1、h2、h3，以h3传感器支点作为原点，h3和h1方向作为x轴，h3和h2方向作为y轴，他们的面垂直向上作为z轴，构建三维坐标系；b)转角计算，h1、h2、h3是由三组支座转角位移检测机构中的转角位移传感器测量得到的，L是安装传感器的时候决定的，它是等腰直角三角的边长，通过这4个量推导计算出转角。
  7. 根据权利要求1所述的一种高铁桥梁损耗监控系统，其特征在于：所述速铁路桥梁支座易损性评估的计算方法为：a)计算支座易损性指标S＝R/n；b)对所有支座计算支座易损性指标S并排序，S最大的支座易损性最大，需要重点养护。
  8. 根据权利要求1所述的一种高铁桥梁损耗监控系统，其特征在于：专家系统是通过输入大量桥梁信息、老化过程、保养方式后，利用大数据及神经网络方法计算获得最优保养方案。
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