WO2024016415A1 - 一种桥梁多源多尺度智能分级预警方法及系统 - Google Patents

一种桥梁多源多尺度智能分级预警方法及系统 Download PDF

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WO2024016415A1
WO2024016415A1 PCT/CN2022/112799 CN2022112799W WO2024016415A1 WO 2024016415 A1 WO2024016415 A1 WO 2024016415A1 CN 2022112799 W CN2022112799 W CN 2022112799W WO 2024016415 A1 WO2024016415 A1 WO 2024016415A1
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bridge
deflection
mid
span
early warning
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PCT/CN2022/112799
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English (en)
French (fr)
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杨则英
孙英琳
曲植霖
段蓉蓉
侯和涛
田利
段抗
张宇
王成赫
程正权
单煜辉
赵凤金
王洪云
高新学
赵峰
张林林
曲伟松
杨乾一
曲建波
刘杰
于先伟
周广通
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山东大学
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/23—Design optimisation, verification or simulation using finite element methods [FEM] or finite difference methods [FDM]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/10—Geometric CAD
    • G06F30/13—Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
    • 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
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0635—Risk analysis of enterprise or organisation activities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/14—Force analysis or force optimisation, e.g. static or dynamic forces
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation

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  • the invention relates to the technical field of bridge management and maintenance, and in particular to a multi-source multi-scale intelligent hierarchical early warning method and system for a bridge.
  • Continuous rigid frame bridge refers to a continuous beam bridge with piers and beams consolidated. It usually adopts a prestressed concrete structure, has more than two main piers, and adopts a pier-beam consolidation system. Prestressed concrete continuous rigid frame bridges are widely used in bridge engineering due to their advantages of high structural stiffness, good driving smoothness and low cost.
  • the present invention provides a multi-source and multi-scale intelligent hierarchical early warning method and system for bridges, which can realize hierarchical early warning of underdeflection and cracks of continuous rigid frame bridges.
  • the underdeflection and cracks of continuous rigid frame bridges Select corresponding countermeasures according to the grade of cracks to ensure the safety and durability of the bridge structure during its service life.
  • the present disclosure provides a multi-source multi-scale intelligent hierarchical early warning method for bridges:
  • a multi-source and multi-scale intelligent hierarchical early warning method for bridges including:
  • the mid-span deflection threshold of the bridge is divided to obtain the risk assessment level
  • a further technical solution is to establish an initial finite element model of the steel bridge based on the MIDAS large-scale finite element software based on the structural geometric dimensions, component sections and positions, and material properties in the design data.
  • the influencing factors include loading age, environmental relative humidity, prestressed loss rate, crack stiffness reduction rate, and overweight rate.
  • a further technical solution is that the loading age and relative humidity of the environment are inversely proportional to the bridge deflection, and the prestressed loss rate, crack stiffness reduction rate, and overweight rate are directly proportional to the bridge deflection.
  • the determination of the bridge mid-span deflection threshold means to obtain the bridge mid-span deflection threshold in combination with the deflection span ratio stipulated in the specification requirements.
  • a further technical solution is to input the internal forces on the bridge under different degrees of deflection into the mid-span model with the largest deflection of the bridge, and obtain the crack conditions of the bridge under different degrees of deflection based on the mid-span model with the largest deflection of the bridge.
  • the risk assessment level is: when the deflection threshold reaches 60% and below, it is set as a yellow warning, and no measures are taken; when it reaches 60% to 80% of the deflection threshold, it is set as an orange warning, and appropriate measures are taken. Reinforcement measures; if more than 80% of the deflection threshold is set as a red warning, reinforcement measures must be taken as soon as possible.
  • the present disclosure provides a multi-source multi-scale intelligent hierarchical early warning system for bridges, including:
  • the model building module is used to establish the finite element model of the bridge and the mid-span model with the largest deflection of the bridge based on the actual design data of the bridge;
  • the risk assessment level division module is used to analyze the impact of various factors on bridge deflection based on the bridge finite element model, obtain the internal force on the bridge under different degrees of deflection, and determine the mid-span deflection threshold of the bridge; based on the maximum deflection of the bridge
  • the mid-span model combines different degrees of deflection and the internal forces on the bridge under different degrees of deflection to analyze the cracks in the bridge under different degrees of deflection. Based on the bridge cracks, the mid-span deflection thresholds of the bridge are divided to obtain the risk assessment level;
  • the data processing module is used to construct the corresponding relationship between various influencing factors and changes in bridge deflection and cracks;
  • the risk assessment module is used to determine the actual deflection and cracks of the bridge based on the actual values of a variety of different influencing factors, and then determine the corresponding risk assessment level.
  • the present disclosure also provides an electronic device, including a memory, a processor, and computer instructions stored in the memory and executed on the processor.
  • the computer instructions are executed by the processor, the computer instructions in the first aspect are completed. Method steps.
  • the present disclosure also provides a computer-readable storage medium for storing computer instructions.
  • the computer instructions When the computer instructions are executed by a processor, the steps of the method described in the first aspect are completed.
  • This disclosure proposes a multi-source multi-scale intelligent hierarchical early warning method and system for bridges.
  • the relationship between multiple different influencing factors of the bridge and the bridge deflection and crack conditions is obtained. Relationship, and divide the bridge deflection threshold according to the crack situation, and obtain the risk assessment level. Based on the actual influencing factor value of the bridge, the risk assessment of the bridge can be realized, which facilitates the staff to make corresponding countermeasures.
  • This disclosure proposes a multi-source and multi-scale intelligent hierarchical early warning method and system for bridges. Through the analysis of cracks, a reasonable threshold for the deflection of the actual continuous rigid frame bridge is obtained, and a hierarchical early warning of the deflection and cracks of the continuous rigid frame bridge is achieved. , select corresponding countermeasures according to the level of deflection and cracks under the continuous rigid frame bridge to ensure the safety and durability of the bridge structure during its service life.
  • Figure 1 is an overall framework diagram of the multi-source multi-scale intelligent hierarchical early warning method for bridges according to the embodiment of the present invention.
  • a multi-source and multi-scale intelligent hierarchical early warning method for bridges includes:
  • Step 1 Establish the finite element model of the bridge and the mid-span model with the largest deflection of the bridge based on the actual design data of the bridge;
  • Step 2 Based on the finite element model of the bridge, analyze the impact of various factors on the bridge deflection, obtain the internal force on the bridge under different degrees of deflection, and determine the mid-span deflection threshold of the bridge;
  • Step 3 Based on the mid-span model with the largest deflection of the bridge, combined with different degrees of deflection and the internal forces on the bridge under different degrees of deflection, analyze the cracks in the bridge under different degrees of deflection;
  • Step 4 Based on the bridge cracks, divide the bridge mid-span deflection thresholds to obtain the risk assessment level;
  • Step 5 Construct the corresponding relationship between various influencing factors and the changes in bridge deflection and cracks. Based on the actual values of various influencing factors, determine the actual deflection and cracks of the bridge, and then determine the corresponding risk assessment level.
  • step 1 the entire bridge is first analyzed, and a finite element model of the bridge and a mid-span model with the largest deflection of the bridge are established based on the actual design data of the bridge.
  • Finite element analysis is an effective numerical analysis method in structural mechanics analysis and is often used in technical fields such as hydraulic engineering, civil engineering, bridges, machinery, electrical machinery, mechanics, and physics.
  • software compiled based on finite element analysis algorithms that is, finite element analysis software, includes many kinds.
  • Common general finite element software includes Midas, Abaqus, LMS-Samtech, Algor, Femap/NX Nastran, Hypermesh, COMSOL Multiphysics, FEPG et al.
  • MIDAS finite element software is used to construct a finite element model of the bridge based on the design data and construction data of the actual bridge project.
  • the design data and construction data mainly include the shape, material, size, and construction stage of the bridge.
  • the bridge finite element model is established according to the structural geometric dimensions, component sections and positions, and material properties in the design data.
  • the specific process is: first, establish all the finite element models of the finite element model based on the node coordinates of the steel bridge structure. nodes; then, all elements of the finite element model are established according to the design section, material parameters and location; finally, coupling and constraints are applied to the nodes according to the constraint conditions to obtain the bridge finite element model.
  • ABAQUS finite element software is used to construct a mid-span model with the largest deflection of the bridge.
  • the finite element model of the bridge is used to analyze the overall stress, displacement and deformation of the bridge, while the mid-span model with the largest deflection of the bridge is used to analyze the local cracks in the mid-span of the bridge.
  • Bridge cracks will seriously endanger the durability and load-bearing capacity of road bridges, and different degrees of cracks have different degrees of harm.
  • a mild degree can hinder the driver's driving comfort, and a severe degree can directly endanger vehicle and personal safety.
  • cracks have a greater impact on the overall stiffness and usability safety of the bridge.
  • the overall analysis of the bridge combined with the analysis of cracks at the maximum deflection in the mid-span can better grasp the performance of the bridge.
  • step 2 based on the constructed finite element model of the bridge, the influence of various different influencing factors on the bridge deflection is analyzed, the internal forces on the bridge under different degrees of deflection are obtained, and the mid-span deflection threshold of the bridge is determined.
  • the impact of various common influencing factors on bridge deflection is analyzed.
  • various different influencing factors of the bridge are determined.
  • the influencing factors of the deflection are screened, the factors with a smaller influence are eliminated, the factors with a larger influence are retained, and the factors with a larger influence are finally determined.
  • the selection of the above-mentioned various influencing factors that affect bridge deflection is determined based on the specific actual conditions of the project.
  • technicians can also make their own judgments based on the actual conditions of the specific project to eliminate certain influencing factors or not. , this embodiment is not limited here.
  • the influencing factors with greater influence include loading age, environmental relative humidity, prestressed loss rate, crack stiffness reduction rate, and overweight rate.
  • influencing factors After determining the influencing factors, analyze the influence of various different influencing factors on the bridge deflection. Based on the finite element model of the bridge, adjust the values of the above influencing factors, such as adjusting the loading age in days, environmental relative humidity percentage, prestress loss percentage, and stiffness. The reduction percentage and overweight rate were adjusted to various working conditions by changing the values of the influencing factors, and the changing rules of the bridge's downward deflection under different working conditions were observed. The internal forces on the bridge under different degrees of downward deflection were obtained through the MIDAS finite element software.
  • the higher the relative humidity of the environment the smaller the deflection; the longer the loading age, the smaller the deflection; the higher the prestressed loss rate, the greater the deflection; the higher the crack stiffness reduction rate, the greater the deflection; overweight The higher the rate, the greater the deflection. That is, the loading age and relative humidity of the environment are inversely proportional to the bridge deflection, and the prestressed loss rate, crack stiffness reduction rate, and overweight rate are directly proportional to the bridge deflection.
  • determining the bridge mid-span deflection threshold means to obtain the mid-span deflection threshold of the continuous rigid frame bridge under study in combination with the deflection span ratio stipulated in the code requirements.
  • the specified deflection span ratio refers to the "Highway Reinforced Concrete and Prestressed
  • the deflection-span ratio specified in "Code for Design of Concrete Bridges and Culverts" (JTG3362-2018) should not exceed 1/600.
  • step 3 based on the mid-span model with the largest deflection of the bridge, combined with different degrees of deflection and the internal forces on the bridge under different degrees of deflection, the cracks in the bridge under different degrees of deflection are analyzed.
  • the internal forces on the bridge under different degrees of deflection obtained in the above step 2 are input into the mid-span model of the bridge with the largest deflection constructed by ABAQ US finite element analysis software.
  • the mid-span model of the bridge with the largest deflection simulates the bridge cracks. Adjust the loading age days, environmental relative humidity percentage, prestress loss percentage, stiffness reduction percentage, and overweight rate to change within a certain range of values to determine the bridge deflection and cracks corresponding to different working conditions. In fact, the greater the deflection value of a continuous rigid frame bridge, the more and wider the cracks will be, and the cracks will be more serious.
  • step 4 based on the bridge cracks, the mid-span deflection thresholds of the bridge are divided to obtain the risk assessment level.
  • steps 2 and 3 the corresponding bridge mid-span deflection thresholds and crack conditions under different working conditions are obtained respectively. According to the crack conditions under different working conditions, the bridge mid-span deflection thresholds are divided to obtain the risk assessment level.
  • the analysis of crack conditions can be determined based on specific actual working conditions. For example, crack conditions can be divided into harmless, minor, and serious based on the width of the crack.
  • the mid-span deflection threshold of the bridge is divided according to the crack situation to realize the division of risk assessment levels. In this embodiment, 60% and below of the deflection threshold is set as a yellow warning. At this time, the deflection and cracks can be ignored and no Take any measures; if it reaches 60% to 80% of the deflection threshold, it is set as an orange warning, and appropriate reinforcement measures should be taken; if it reaches more than 80% of the deflection threshold, it is set as a red warning, and reinforcement measures must be taken as soon as possible.
  • step 5 the corresponding relationship between various different influencing factors and the changes in bridge deflection and cracks is constructed. Based on the actual values of various different influencing factors, the actual deflection and cracks of the bridge are determined, and then the corresponding risk assessment level is determined.
  • the values of various influencing factors and the bridge deflection and cracks under these values were obtained to construct a database corresponding to the internal force-deflection-cracks.
  • Python was used for data processing to construct a variety of Correspondence between different influencing factors and changes in bridge deflection and cracks.
  • the current actual deflection and crack conditions of the bridge can be determined.
  • the risk assessment level of the bridge can be determined. According to the risk assessment Level selects whether to take reinforcement measures.
  • This embodiment provides a multi-source multi-scale intelligent hierarchical early warning system for bridges, including:
  • the model building module is used to establish the finite element model of the bridge and the mid-span model with the largest deflection of the bridge based on the actual design data of the bridge;
  • the risk assessment level division module is used to analyze the impact of various factors on bridge deflection based on the bridge finite element model, obtain the internal force on the bridge under different degrees of deflection, and determine the mid-span deflection threshold of the bridge; based on the maximum deflection of the bridge
  • the mid-span model combines different degrees of deflection and the internal forces on the bridge under different degrees of deflection to analyze the cracks in the bridge under different degrees of deflection. Based on the bridge cracks, the mid-span deflection thresholds of the bridge are divided to obtain the risk assessment level;
  • the data processing module is used to construct the corresponding relationship between various influencing factors and changes in bridge deflection and cracks;
  • the risk assessment module is used to determine the actual deflection and cracks of the bridge based on the actual values of a variety of different influencing factors, and then determine the corresponding risk assessment level.
  • This embodiment provides an electronic device, including a memory, a processor, and computer instructions stored in the memory and run on the processor.
  • the computer instructions are run by the processor, the multi-source multi-scale bridge is completed as described above. Steps in the intelligent hierarchical early warning method.
  • This embodiment also provides a computer-readable storage medium for storing computer instructions.
  • the steps in the multi-source multi-scale intelligent hierarchical early warning method for bridges are completed as described above.
  • Embodiment 1 Each step involved in the above Embodiments 2 to 4 corresponds to the method Embodiment 1.
  • the term "computer-readable storage medium” shall be understood to include a single medium or multiple media that includes one or more sets of instructions; and shall also be understood to include any medium capable of storing, encoding, or carrying instructions for use by a processor.
  • the executed instruction set causes the processor to perform any method in the present invention.
  • each module or each step of the present invention described above can be implemented by a general-purpose computer device. Alternatively, they can be implemented by program codes executable by the computing device, so that they can be stored in a storage device. The device is executed by a computing device, or they are respectively made into individual integrated circuit modules, or multiple modules or steps among them are made into a single integrated circuit module.
  • the invention is not limited to any specific combination of hardware and software.

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Abstract

一种桥梁多源多尺度智能分级预警方法及系统，该方法包括：建立桥梁有限元模型和桥梁挠度最大的中跨模型；基于构建的模型，分析多种不同影响因素对桥梁下挠的影响，得到不同下挠程度下桥梁受到的内力，进而分析相应的桥梁裂缝情况，同时确定桥梁中跨挠度阈值；基于桥梁裂缝情况，对桥梁中跨挠度阈值进行划分，得到风险评估等级；构建多种不同影响因素与挠度和裂缝变化的对应关系，根据多种不同影响因素的实际值，确定桥梁的实际挠度和裂缝情况，进而确定对应的风险评估等级。桥梁多源多尺度智能分级预警方法实现了对连续刚构桥下挠及裂缝的分级预警，根据连续刚构桥下挠及裂缝的等级选择相应的应对措施，保证服役期桥梁结构的安全性和耐久性。

Description

一种桥梁多源多尺度智能分级预警方法及系统
本发明要求于2022年7月22日提交中国专利局、申请号为202210868232.3、发明名称为“一种桥梁多源多尺度智能分级预警方法及系统”的中国专利申请的优先权，其全部内容通过引用结合在本发明中。
技术领域
本发明涉及桥梁管养技术领域，尤其涉及一种桥梁多源多尺度智能分级预警方法及系统。
背景技术
本部分的陈述仅仅是提供了与本公开相关的背景技术信息，不必然构成现有技术。
连续刚构桥是指墩梁固结的连续梁桥，通常采用预应力混凝土结构，有两个以上主墩，采用墩梁固结体系。预应力混凝土连续刚构桥因其结构刚度大、行车平顺性好、造价低的优势在桥梁工程中广泛应用。
然而，连续刚构桥在长期服役过程中，随着使用年限的增加，连续刚构的跨中不断下挠，这会使桥梁运营期内出现不良线形而引起行车乘客的不舒适感，甚至危及行车安全。即，桥通车后各种荷载造成梁体受弯而造成跨中向下的位移即下挠度，而连续刚构桥中将出现的跨中严重下挠问题，进而影响桥梁结构的安全性和耐久性，限制连续刚构桥在工程中的进一步应用。因此，对连续刚构桥进行下挠预测风险评估及预警研究，对保证服役期桥梁结构的安全性和耐久性具有重要意义。
发明内容
为解决上述现有技术的不足，本发明提供了一种桥梁多源多尺度智能分级预警方法及系统，能够实现对连续刚构桥下挠及裂缝的分级预警，根据连续刚构桥下挠及裂缝的等级选择相应的应对措施，保证服役期桥梁结 构的安全性和耐久性。
第一方面，本公开提供了一种桥梁多源多尺度智能分级预警方法：
一种桥梁多源多尺度智能分级预警方法，包括：
根据桥梁的实际设计数据建立桥梁有限元模型和桥梁挠度最大的中跨模型；
基于桥梁有限元模型，分析多种不同影响因素对桥梁下挠的影响，得到不同下挠程度下桥梁受到的内力，同时确定桥梁中跨挠度阈值；
基于桥梁挠度最大的中跨模型，结合不同下挠程度和不同下挠程度下桥梁受到的内力，分析不同下挠程度下桥梁裂缝情况；
基于桥梁裂缝情况，对桥梁中跨挠度阈值进行划分，得到风险评估等级；
构建多种不同影响因素与桥梁挠度和裂缝变化的对应关系，根据多种不同影响因素的实际值，确定桥梁的实际挠度和裂缝情况，进而确定对应的风险评估等级。
进一步的技术方案，基于MIDAS大型有限元软件，按照设计数据中的结构几何尺寸、构件截面与位置、材料性质建立钢桥初始有限元模型。
进一步的技术方案，所述影响因素包括加载龄期、环境相对湿度、预应力损失率、裂缝刚度折减率、超重率。
进一步的技术方案，加载龄期、环境相对湿度与桥梁挠度成反比，预应力损失率、裂缝刚度折减率、超重率与桥梁挠度呈正比。
进一步的技术方案，所述确定桥梁中跨挠度阈值是指，结合规范要求所规定的挠跨比获取桥梁中跨挠度阈值。
进一步的技术方案，将不同下挠程度下桥梁受到的内力输入到桥梁挠度最大的中跨模型中，依据桥梁挠度最大的中跨模型得到不同下挠程度下的桥梁裂缝情况。
进一步的技术方案，所述风险评估等级为：以达到挠度阈值的60％及 以下设定为黄色预警，不采取任何措施；以达到挠度阈值的60％～80％设定为橙色预警，适当采取加固措施；以达到挠度阈值的80％以上设定为红色预警，必须尽快采取加固措施。
第二方面，本公开提供了一种桥梁多源多尺度智能分级预警系统，包括：
模型搭建模块，用于根据桥梁的实际设计数据建立桥梁有限元模型和桥梁挠度最大的中跨模型；
风险评估等级划分模块，用于基于桥梁有限元模型，分析多种不同影响因素对桥梁下挠的影响，得到不同下挠程度下桥梁受到的内力，同时确定桥梁中跨挠度阈值；基于桥梁挠度最大的中跨模型，结合不同下挠程度和不同下挠程度下桥梁受到的内力，分析不同下挠程度下桥梁裂缝情况；基于桥梁裂缝情况，对桥梁中跨挠度阈值进行划分，得到风险评估等级；
数据处理模块，用于构建多种不同影响因素与桥梁挠度和裂缝变化的对应关系；
风险评估模块，用于根据多种不同影响因素的实际值，确定桥梁的实际挠度和裂缝情况，进而确定对应的风险评估等级。
第三方面，本公开还提供了一种电子设备，包括存储器和处理器以及存储在存储器上并在处理器上运行的计算机指令，所述计算机指令被处理器运行时，完成第一方面所述方法的步骤。
第四方面，本公开还提供了一种计算机可读存储介质，用于存储计算机指令，所述计算机指令被处理器执行时，完成第一方面所述方法的步骤。
以上一个或多个技术方案存在以下有益效果：
1、本公开提出了一种桥梁多源多尺度智能分级预警方法及系统，通过对桥梁整体、桥梁挠度和桥梁局部裂缝的分析，得到桥梁多个不同影响因素与桥梁挠度和裂缝情况之间的关系，并依据裂缝情况对桥梁挠度阈值进行划分，划分得到风险评估等级，根据桥梁的实际影响因素值即可实现桥 梁的风险评估，便于工作人员做出相对应的应对措施。
2、本公开提出了一种桥梁多源多尺度智能分级预警方法及系统，通过对裂缝的分析，获得实际连续刚构桥挠度的合理阈值，实现对连续刚构桥下挠及裂缝的分级预警，根据连续刚构桥下挠及裂缝的等级选择相应的应对措施，保证服役期桥梁结构的安全性和耐久性。
附图说明
构成本发明的一部分的说明书附图用来提供对本发明的进一步理解，本发明的示意性实施例及其说明用于解释本发明，并不构成对本发明的不当限定。
图1为本发明实施例所述桥梁多源多尺度智能分级预警方法的整体框架图。
具体实施方式
应该指出，以下详细说明都是示例性的，旨在对本发明提供进一步的说明。除非另有指明，本文使用的所有技术和科学术语具有与本发明所属技术领域的普通技术人员通常理解的相同含义。
需要注意的是，这里所使用的术语仅是为了描述具体实施方式，而非意图限制根据本发明的示例性实施方式。如在这里所使用的，除非上下文另外明确指出，否则单数形式也意图包括复数形式，此外，还应当理解的是，当在本说明书中使用术语“包含”和/或“包括”时，其指明存在特征、步骤、操作、器件、组件和/或它们的组合。
实施例一
本实施例提供了一种桥梁多源多尺度智能分级预警方法：
如图1所示，一种桥梁多源多尺度智能分级预警方法，包括：
步骤1、根据桥梁的实际设计数据建立桥梁有限元模型和桥梁挠度最大的中跨模型；
步骤2、基于桥梁有限元模型，分析多种不同影响因素对桥梁下挠的影 响，得到不同下挠程度下桥梁受到的内力，同时确定桥梁中跨挠度阈值；
步骤3、基于桥梁挠度最大的中跨模型，结合不同下挠程度和不同下挠程度下桥梁受到的内力，分析不同下挠程度下桥梁裂缝情况；
步骤4、基于桥梁裂缝情况，对桥梁中跨挠度阈值进行划分，得到风险评估等级；
步骤5、构建多种不同影响因素与桥梁挠度和裂缝变化的对应关系，根据多种不同影响因素的实际值，确定桥梁的实际挠度和裂缝情况，进而确定对应的风险评估等级。
在本实施例中，步骤1中，首先对桥梁整体进行分析，根据桥梁的实际设计数据建立桥梁有限元模型和桥梁挠度最大的中跨模型。
有限元分析是结构力学分析中的一种有效的数值分析方法，常应用于水工、土建、桥梁、机械、电机、力学、物理学等技术领域。现有技术中，基于有限元分析算法编制的软件，即有限元分析软件，包含多种，常见通用有限元软件包括Midas、Abaqus、LMS-Samtech、Algor、Femap/NX Nastran、Hypermesh、COMSOL Multiphysics、FEPG等。在本实施例中，根据桥梁的实际工程的设计资料、施工资料等，应用MIDAS有限元软件构建桥梁的有限元模型，该设计资料、施工资料主要是包括桥梁的形状、材料、尺寸、施工阶段、施工步骤、环境因素等。具体的，基于MIDAS大型有限元软件，按照设计数据中的结构几何尺寸、构件截面与位置、材料性质建立桥梁有限元模型，具体流程为：首先，以钢桥结构节点坐标建立有限元模型的所有节点；然后，按照设计截面、材料参数以及所在位置建立有限元模型的所有单元；最后，依据约束条件对节点施加耦合和约束，得到桥梁有限元模型。
同样，在本实施例中，根据桥梁的实际工程的设计资料、施工资料等，应用ABAQUS有限元软件构建桥梁挠度最大的中跨模型。
桥梁有限元模型是用于对桥梁整体受力及位移变形情况进行分析，而 桥梁挠度最大的中跨模型是用于对桥梁跨中局部裂缝情况进行分析。
桥梁裂缝问题会严重危害道路桥梁的耐久性与承载力，且不同程度的裂缝，其危害程度也不尽相同，程度轻可妨碍司机驾车的舒适度，程度重可直接危害车辆及人身安全。也就是说，裂缝对桥梁的整体刚度及使用性安全性影响较大，对桥梁进行整体分析结合跨中最大挠度处的裂缝分析，能够更好把握桥梁的性能。
步骤2中，基于构建的桥梁有限元模型，分析多种不同影响因素对桥梁下挠的影响，得到不同下挠程度下桥梁受到的内力，同时确定桥梁中跨挠度阈值。
具体的，基于建立的MIDAS桥梁有限元模型，分析多种常见影响因素对桥梁下挠的影响，首先，根据连续刚构桥研究现状，结合连续刚构桥工程实际情况，确定多种不同影响桥梁下挠的影响因素，对该影响因素进行筛选，剔除影响较小的因素，保留影响较大的因素，最终确定影响较大的影响因素。上述多种不同影响桥梁下挠的影响因素的选择根据工程的具体实际情况确定，同样，针对该影响因数的筛选，技术人员也可根据具体工程实际而自行判定，剔除某些影响因素或不剔除，本实施例在此并不做限定。在本实施例中，最终确定影响较大的影响因素包括加载龄期、环境相对湿度、预应力损失率、裂缝刚度折减率、超重率。
在确定影响因素后，分析多种不同影响因素对桥梁下挠的影响，基于该桥梁有限元模型，调节上述影响因素数值，如调节加载龄期天数、环境相对湿度百分率、预应力损失百分率、刚度折减百分率、超重率，通过变动影响因素数值，调节出多种工况，观察该桥梁在不同工况下的下挠变化规律，通过MIDAS有限元软件得到不同下挠程度下桥梁受到的内力。
其中，环境相对湿度越高，下挠越小；加载龄期越长，下挠越小；预应力损失率越高，下挠越大；裂缝刚度折减率越高，下挠越大；超重率越高，下挠越大。即加载龄期、环境相对湿度与桥梁挠度成反比，预应力损 失率、裂缝刚度折减率、超重率与桥梁挠度呈正比。
此外，确定桥梁中跨挠度阈值是指，结合规范要求所规定的挠跨比获取所研究的连续刚构桥梁中跨挠度阈值，其中，该规定挠跨比是指根据《公路钢筋混凝土及预应力混凝土桥涵设计规范》(JTG3362-2018)规定的挠跨比，挠跨比不应超过1/600。
步骤3中，基于桥梁挠度最大的中跨模型，结合不同下挠程度和不同下挠程度下桥梁受到的内力，分析不同下挠程度下桥梁裂缝情况。
将上述步骤2中得到的不同下挠程度下桥梁受到的内力输入到ABAQ US有限元分析软件构建的桥梁挠度最大的中跨模型中，该桥梁挠度最大的中跨模型模拟出桥梁裂缝情况，通过调节加载龄期天数、环境相对湿度百分率、预应力损失百分率、刚度折减百分率、超重率在一定数值范围内变化，确定不同工况对应的桥梁挠度和裂缝情况。实际上，连续刚构桥下挠值越大，裂缝越多越宽，裂缝情况更严重。
步骤4中，基于桥梁裂缝情况，对桥梁中跨挠度阈值进行划分，得到风险评估等级。通过步骤2和步骤3分别获取不同工况下相对应的桥梁中跨挠度阈值和裂缝情况，根据不同工况下的裂缝情况，对桥梁中跨挠度阈值进行划分，得到风险评估等级。
对裂缝情况的分析可根据具体实际工况确定，如根据裂缝的宽度将裂缝情况划分为无害、轻微和严重等。根据裂缝情况对对桥梁中跨挠度阈值进行划分，实现风险评估等级的划分，在本实施例中，以达到挠度阈值的60％及以下设定为黄色预警，此时挠度及裂缝可忽略，不采取任何措施；以达到挠度阈值的60％～80％设定为橙色预警，应当适当采取加固措施；以达到挠度阈值的80％以上设定为红色预警，必须尽快采取加固措施。
步骤5中，构建多种不同影响因素与桥梁挠度和裂缝变化的对应关系，根据多种不同影响因素的实际值，确定桥梁的实际挠度和裂缝情况，进而确定对应的风险评估等级。
具体的，通过上述步骤得到的多种不同影响因素的数值及该数值下的桥梁挠度和裂缝情况，以此构建内力-挠度-裂缝三者相对应的数据库，采用Python进行数据处理，构建多种不同影响因素与桥梁挠度和裂缝变化的对应关系。在此基础上，根据该桥梁的实际的多个或单个影响因素数值，即可确定该桥梁当前的实际挠度和裂缝情况，结合确定的挠度阈值，进而确定该桥梁的风险评估等级，根据风险评估等级选择是否采取加固措施。
本实施例上述方案，通过对裂缝的分析，获得实际连续刚构桥挠度的合理阈值，实现对连续刚构桥下挠及裂缝的分级预警，根据连续刚构桥下挠及裂缝的等级选择相应的应对措施，保证服役期桥梁结构的安全性和耐久性。
实施例二
本实施例提供了一种桥梁多源多尺度智能分级预警系统，包括：
模型搭建模块，用于根据桥梁的实际设计数据建立桥梁有限元模型和桥梁挠度最大的中跨模型；
风险评估等级划分模块，用于基于桥梁有限元模型，分析多种不同影响因素对桥梁下挠的影响，得到不同下挠程度下桥梁受到的内力，同时确定桥梁中跨挠度阈值；基于桥梁挠度最大的中跨模型，结合不同下挠程度和不同下挠程度下桥梁受到的内力，分析不同下挠程度下桥梁裂缝情况；基于桥梁裂缝情况，对桥梁中跨挠度阈值进行划分，得到风险评估等级；
数据处理模块，用于构建多种不同影响因素与桥梁挠度和裂缝变化的对应关系；
风险评估模块，用于根据多种不同影响因素的实际值，确定桥梁的实际挠度和裂缝情况，进而确定对应的风险评估等级。
实施例三
本实施例提供了一种电子设备，包括存储器和处理器以及存储在存储器上并在处理器上运行的计算机指令，所述计算机指令被处理器运行时， 完成如上所述的桥梁多源多尺度智能分级预警方法中的步骤。
实施例四
本实施例还提供了一种计算机可读存储介质，用于存储计算机指令，所述计算机指令被处理器执行时，完成如上所述的桥梁多源多尺度智能分级预警方法中的步骤。
以上实施例二至四中涉及的各步骤与方法实施例一相对应，具体实施方式可参见实施例一的相关说明部分。术语“计算机可读存储介质”应该理解为包括一个或多个指令集的单个介质或多个介质；还应当被理解为包括任何介质，所述任何介质能够存储、编码或承载用于由处理器执行的指令集并使处理器执行本发明中的任一方法。
本领域技术人员应该明白，上述本发明的各模块或各步骤可以用通用的计算机装置来实现，可选地，它们可以用计算装置可执行的程序代码来实现，从而，可以将它们存储在存储装置中由计算装置来执行，或者将它们分别制作成各个集成电路模块，或者将它们中的多个模块或步骤制作成单个集成电路模块来实现。本发明不限制于任何特定的硬件和软件的结合。
以上所述仅为本发明的优选实施例而已，并不用于限制本发明，对于本领域的技术人员来说，本发明可以有各种更改和变化。凡在本发明的精神和原则之内，所作的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。
上述虽然结合附图对本发明的具体实施方式进行了描述，但并非对本发明保护范围的限制，所属领域技术人员应该明白，在本发明的技术方案的基础上，本领域技术人员不需要付出创造性劳动即可做出的各种修改或变形仍在本发明的保护范围以内。

Claims (10)

  1. 一种桥梁多源多尺度智能分级预警方法，其特征是，包括：
    根据桥梁的实际设计数据建立桥梁有限元模型和桥梁挠度最大的中跨模型；
    基于桥梁有限元模型，分析多种不同影响因素对桥梁下挠的影响，得到不同下挠程度下桥梁受到的内力，同时确定桥梁中跨挠度阈值；
    基于桥梁挠度最大的中跨模型，结合不同下挠程度和不同下挠程度下桥梁受到的内力，分析不同下挠程度下桥梁裂缝情况；
    基于桥梁裂缝情况，对桥梁中跨挠度阈值进行划分，得到风险评估等级；
    构建多种不同影响因素与桥梁挠度和裂缝变化的对应关系，根据多种不同影响因素的实际值，确定桥梁的实际挠度和裂缝情况，进而确定对应的风险评估等级。
  2. 如权利要求1所述的一种桥梁多源多尺度智能分级预警方法，其特征是，基于MIDAS大型有限元软件，按照设计数据中的结构几何尺寸、构件截面与位置、材料性质建立钢桥初始有限元模型。
  3. 如权利要求1所述的一种桥梁多源多尺度智能分级预警方法，其特征是，所述影响因素包括加载龄期、环境相对湿度、预应力损失率、裂缝刚度折减率、超重率。
  4. 如权利要求3所述的一种桥梁多源多尺度智能分级预警方法，其特征是，加载龄期、环境相对湿度与桥梁挠度成反比，预应力损失率、裂缝刚度折减率、超重率与桥梁挠度呈正比。
  5. 如权利要求1所述的一种桥梁多源多尺度智能分级预警方法，其特 征是，所述确定桥梁中跨挠度阈值是指，结合规范要求所规定的挠跨比，获取桥梁中跨挠度阈值。
  6. 如权利要求1所述的一种桥梁多源多尺度智能分级预警方法，其特征是，基于桥梁挠度最大的中跨模型，结合不同下挠程度和不同下挠程度下桥梁受到的内力，分析不同下挠程度下桥梁裂缝情况，是指：
    将不同下挠程度下桥梁受到的内力输入到桥梁挠度最大的中跨模型中，依据桥梁挠度最大的中跨模型得到不同下挠程度下桥梁裂缝情况。
  7. 如权利要求1所述的一种桥梁多源多尺度智能分级预警方法，其特征是，所述风险评估等级为：以达到挠度阈值的60％及以下设定为黄色预警，不采取任何措施；以达到挠度阈值的60％～80％设定为橙色预警，适当采取加固措施；以达到挠度阈值的80％以上设定为红色预警，必须尽快采取加固措施。
  8. 一种桥梁多源多尺度智能分级预警系统，其特征是，包括：
    模型搭建模块，用于根据桥梁的实际设计数据建立桥梁有限元模型和桥梁挠度最大的中跨模型；
    风险评估等级划分模块，用于基于桥梁有限元模型，分析多种不同影响因素对桥梁下挠的影响，得到不同下挠程度下桥梁受到的内力，同时确定桥梁中跨挠度阈值；基于桥梁挠度最大的中跨模型，结合不同下挠程度和不同下挠程度下桥梁受到的内力，分析不同下挠程度下桥梁裂缝情况；基于桥梁裂缝情况，对桥梁中跨挠度阈值进行划分，得到风险评估等级；
    数据处理模块，用于构建多种不同影响因素与桥梁挠度和裂缝变化的对应关系；
    风险评估模块，用于根据多种不同影响因素的实际值，确定桥梁的实际挠度和裂缝情况，进而确定对应的风险评估等级。
  9. 一种电子设备，其特征是：包括存储器和处理器以及存储在存储器上并在处理器上运行的计算机指令，所述计算机指令被处理器运行时，完成如权利要求1-7中任一项所述的一种桥梁多源多尺度智能分级预警方法的步骤。
  10. 一种计算机可读存储介质，其特征是：用于存储计算机指令，所述计算机指令被处理器执行时，完成如权利要求1-7中任一项所述的一种桥梁多源多尺度智能分级预警方法的步骤。
PCT/CN2022/112799 2022-07-22 2022-08-16 一种桥梁多源多尺度智能分级预警方法及系统 WO2024016415A1 (zh)

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