A method for monitoring the slippage of a large-span, two-way fish-belly type steel truss

Abstract

This invention belongs to the field of steel truss slip monitoring technology, specifically disclosing a slip monitoring method for large-span bidirectional fish-belly steel trusses. By deploying multi-source sensors at key stress nodes of the large-span bidirectional fish-belly steel truss, it collects real-time time-series data on structural stress distribution and displacement response, constructing a stress-displacement dynamic coupling analysis model. Based on sliding window correlation analysis, the time-varying characteristics of the coupling coefficient at each monitoring point are extracted. Combined with spatial clustering and stiffness distribution characteristics, dynamic identification and division of risk areas are achieved. Furthermore, a graded early warning mechanism based on the evolution trend of the coupling coefficient is established for high, medium, and low-risk areas. This method improves the ability to identify early risks such as latent stress concentration and asynchronous slip, promoting the evolution of slip construction monitoring from passive response to proactive early warning and closed-loop control, significantly improving the safety and intelligence level of the construction process.

Description

Technical Field

[0001] This invention belongs to the field of steel truss slip monitoring technology, and specifically discloses a method for monitoring the slip of a large-span, bidirectional fish-belly type steel truss. Background Technology

[0002] Large-span, two-way fish-belly steel trusses, through an orthogonally arranged curved lattice beam system, fully utilize the mechanical properties of materials to achieve a unity of large space, lightweight design, and structural aesthetics, and are widely used in large public buildings such as stadiums and convention centers. Due to their massive size and significant weight, conventional hoisting is difficult to implement. They are typically assembled in sections on the ground and then horizontally slid into place using sliding tracks on the top of the supporting columns and a hydraulic jacking system. However, because the height and stiffness distribution of different sections in the two-way fish-belly structure is uneven, non-uniform deformation can easily occur under the same thrust, leading to asynchronous sliding and thus structural safety risks. Therefore, high-precision real-time monitoring must be implemented throughout the entire sliding construction process.

[0003] Existing monitoring systems generally use displacement as the primary indicator, while stress, displacement, and jacking parameters belong to different monitoring subsystems. This results in fragmented data, asynchronous timing, and difficulty in achieving dynamic correlation analysis between mechanical response and construction actions. In fact, the asynchronous application of jacking force directly induces internal force redistribution through the structural stiffness matrix. Such single-dimensional, isolated monitoring modes cannot identify the implicit internal force redistribution caused by stiffness unevenness, thus masking the true safety state of the structure and posing significant safety hazards.

[0004] Furthermore, due to the lack of real-time perception of the coupling relationship between asynchronous jacking and stress evolution, the system cannot predict trends and provide early warnings in the initial stage of internal force imbalance. Warning actions often lag behind the actual damage process, relying solely on passive alarms triggered by threshold exceeding limits. This prevents proactive adjustment and closed-loop optimization in construction control, severely restricting the ability to precisely control the sliding process and easily leading to systemic risks such as attitude deviation and local buckling, affecting structural positioning accuracy and construction safety. Summary of the Invention

[0005] Therefore, one objective of this application is to provide a method for monitoring the slippage of a large-span bidirectional fish-belly steel truss. By focusing on the spatiotemporal coupling of stress and displacement during the slippage construction of a large-span bidirectional fish-belly steel truss to identify risks and introducing graded early warning criteria, the problem mentioned in the background art is solved.

[0006] The objective of this invention can be achieved through the following technical solution: A method for monitoring the slippage of a large-span bidirectional fish-belly steel truss, comprising the following steps: S1: Deploying a multi-source sensor network along the key nodes of the primary and secondary trusses of the bidirectional fish-belly steel truss to collect structural stress distribution and displacement data in real time, and generating a multi-source initial monitoring dataset for the slippage process.

[0007] S2: Perform anomaly identification and data reconstruction on the multi-source initial monitoring dataset to generate a spatiotemporally aligned stress-displacement monitoring dataset.

[0008] S3: Based on the stress-displacement monitoring dataset, perform stress-displacement spatiotemporal coupling analysis to construct a structural stiffness field distribution model.

[0009] S4: Identify weak and strong stiffness regions based on the structural stiffness field distribution model, and divide high-risk, medium-risk, and low-risk regions by combining the projection of the sliding track axis.

[0010] S5: Perform stress-displacement coupling enhancement analysis on a single node based on the stress-displacement time series data of each node in each risk area, and spatially aggregate all nodes in the region that are in a coupled enhancement state to evaluate the stress-displacement coupling trend of the risk area.

[0011] S6: When a stress-displacement coupling trend is observed to increase in a risk area, a graded early warning and control mechanism based on the risk level is activated.

[0012] Combining all the above technical solutions, the positive effects of this invention are as follows: 1. By deploying multi-source sensors at key stress nodes of a large-span bidirectional fish-belly steel truss, this invention collects real-time time-series data on structural stress distribution and displacement response for coupled analysis to delineate risk areas. This allows for the establishment of a graded early warning mechanism for the coupled evolution trend in high, medium, and low-risk areas, thereby enhancing the ability to identify early risks such as latent stress concentration and asynchronous slippage. This promotes the evolution of slippage construction monitoring from passive response to active early warning and closed-loop control, significantly improving the safety and intelligence level of the construction process.

[0013] 2. After collecting the original time-series data of stress and displacement at key stress nodes of a long-span bidirectional fish-belly steel truss, this invention first performs outlier identification and data reconstruction, which effectively improves the accuracy and consistency of the monitoring data. This provides a high-fidelity, spatiotemporally synchronized input data foundation for subsequent stress-displacement coupled dynamic analysis and structural state evolution trend identification, ensuring the reliability and engineering applicability of the risk identification and graded early warning model. Attached Figure Description

[0014] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.

[0015] Figure 1 This is a diagram illustrating the implementation steps of the method of the present invention.

[0016] Figure 2This is a flowchart illustrating the operation of assessing the stress-displacement coupling trend in the risk area in this invention.

[0017] Figure 3 This is a schematic diagram of the risk-level-based hierarchical early warning and control mechanism in this invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] See Figure 1 As shown, the present invention proposes a method for monitoring the slippage of a large-span bidirectional fish-belly steel truss, including the following steps: S1: Deploy a multi-source sensor network along the key nodes of the main and secondary trusses of the bidirectional fish-belly steel truss to collect structural stress distribution and displacement data in real time, and generate a multi-source initial monitoring dataset for the slippage process.

[0020] The specific implementation process of the above steps is as follows: fiber optic stress sensor arrays and laser displacement sensor arrays are arranged at the intersection nodes of the main truss and the secondary truss, the mid-span nodes and the support nodes.

[0021] During the slip process, stress and displacement data of each node are collected synchronously at a fixed sampling frequency, and timestamps and spatial location identifiers are added.

[0022] As an application illustration of the above operations, the sensor array is deployed at the intersection of the main truss and the secondary truss, the mid-span node, and the support node because these three types of nodes are the most unfavorable positions for structural stress and are the areas most prone to instability, buckling, or connection failure during the sliding process. Specifically, the intersection of the main truss and the secondary truss is a key connection point for the transmission of structural forces in space. It bears the convergence of multi-directional internal forces and complex stress concentration, and is a weak area prone to local buckling or connection damage during the sliding process.

[0023] Mid-span node: Located in the peak bending moment region of a large-span structure, it is a typical control section with the largest deflection and the most unfavorable stress, directly reflecting the structural stiffness performance and load-bearing safety margin.

[0024] Support node: As the connection interface between the sliding track and the structure, it bears all vertical loads and transmits horizontal thrust.

[0025] In summary, the three types of nodes mentioned above represent the key force transmission path nodes, the most unfavorable stress section, and the boundary constraint location of the structure, respectively. Deploying a high-precision sensor array provides representative key data support for subsequent coupling analysis and risk identification.

[0026] It should be noted that during the data acquisition process at the nodes, stress monitoring characterizes the internal force distribution of the structure under sliding loads, reflecting the bearing capacity and stress concentration of key sections; while displacement monitoring reflects the sliding kinematic state, including attitude displacement, and is an important indicator for evaluating the structural deformation performance, construction accuracy, and stability. The coordinated monitoring of both can achieve a comprehensive understanding of the coupled behavior of structural internal forces and deformations.

[0027] S2: Perform anomaly identification and data reconstruction on the multi-source initial monitoring dataset to generate a spatiotemporally aligned stress-displacement monitoring dataset.

[0028] The above steps are implemented as follows: each node is divided into several neighborhood node groups based on the spatial proximity and force correlation of the nodes.

[0029] Applying to the above implementation, the process of dividing the neighborhood node group is as follows: taking the structural design reference point of the steel truss as the origin, defining the direction of the main truss axis as the X-axis, defining the vertical gravity direction as the Z-axis, determining the Y-axis according to the right-hand rule, establishing a three-dimensional rectangular coordinate system, and determining the three-dimensional coordinates of each node under the established coordinate system.

[0030] Based on the three-dimensional coordinates of each node, and taking each node as the reference center, calculate the Euclidean distance between it and all other nodes.

[0031] Node pairs that meet any of the following conditions are considered to have geometric proximity and constitute the initial set of spatially adjacent nodes: a) The Euclidean distance between the two nodes is less than or equal to the preset spatial proximity threshold.

[0032] It should be noted that the aforementioned spatial proximity threshold reflects the maximum allowable distance between two nodes considered to be geometrically close in the spatial distribution of the structure. It is usually determined based on the typical span length of the structure or the average span of the members. For example, it can be taken as 1.2 to 1.5 times the standard span length of the main truss to ensure coverage of a complete load-bearing unit.

[0033] b) Construct topological connections based on the spatial distribution of all nodes, if two nodes belong to the same triangular unit and share an edge.

[0034] It should be noted that the steel truss is discretized into a spatial grid composed of members and nodes. Its geometry can be divided into multiple triangular elements, which are the basic units in finite element analysis or structural modeling. If two nodes are two vertices of a certain triangular element and they are directly connected by an actual member, that is, they share an edge, then they are considered to be directly connected in the structural topology.

[0035] An example of applying the above operation is to construct a set of fully connected node pairs between the reference center and other nodes, using each node in the above three-dimensional coordinate system as the reference center. In this set, other nodes whose Euclidean distance between the reference center and other nodes is less than a preset spatial proximity threshold, or whose reference center and other nodes belong to the same triangular unit and share a side, are selected to form a set of spatially adjacent nodes for each node.

[0036] It is important to understand that in continuous or semi-continuous structural systems, physical fields such as stress and displacement fields exhibit spatial continuity, and the responses of adjacent regions typically show a smooth transition. Therefore, the mechanical responses of spatially adjacent nodes are highly correlated. Based on this, identifying geometrically adjacent nodes through Euclidean distance or topological relationships aligns with the spatial distribution patterns of field variables.

[0037] Based on the structural force transmission mechanism, consecutive nodes located on the same force transmission path are selected from the spatial adjacent node group and divided into a group to form a neighborhood node group.

[0038] It is important to understand that the load transfer of a steel truss structure follows a clear mechanical path, such as the force chain from the secondary truss at mid-span → main truss → support node. Nodes on the same force transmission path exhibit coordinated deformation and synchronous response during the stress process. Grouping nodes located on the same force transmission chain together can reflect the actual coupling relationship of structural behavior and avoid the incorrect aggregation of nodes with no direct mechanical connection.

[0039] The above operations divide spatially adjacent, continuous nodes located on the same force transmission path into neighborhood node groups. This is based on the fact that large-span steel trusses, as typical continuous or semi-continuous spatial structural systems, exhibit a smooth and gradually changing distribution of stress and displacement fields under external loads. Since adjacent nodes share members or connection points and are in the same mechanical force transmission chain, their responses show significant spatial correlation and co-evolution characteristics. Therefore, at any given time, the monitored values ​​of each node within this neighborhood should fluctuate slightly around the local mean; significant deviations can be considered abnormal.

[0040] This neighborhood partitioning mechanism provides a mechanically meaningful local reference range for anomaly identification in subsequent monitoring data. Compared to traditional methods that rely on a globally fixed threshold, this strategy can adapt to differences in response amplitude and gradient changes in different regions, effectively improving the sensitivity and accuracy of anomaly detection and avoiding false alarms in high-stress areas or missed alarms in low-sensitivity areas due to a one-size-fits-all threshold.

[0041] In neighborhood partitioning, relying solely on geometric proximity while ignoring structural force transmission paths can lead to the incorrect grouping of nodes with no direct mechanical connection, thereby weakening the inherent consistency of neighborhood responses, reducing the reliability of anomaly identification, and even causing misjudgments. Therefore, integrating both spatial topology and mechanical path criteria is crucial to ensuring the physical rationality and engineering applicability of monitoring data analysis.

[0042] At each fixed sampling time, the monitoring data within each neighborhood node group are statistically analyzed. The sample mean and standard deviation of the monitoring data corresponding to all nodes in the group are calculated, and confidence intervals are constructed accordingly. Specifically, the confidence intervals include stress confidence intervals and displacement confidence intervals.

[0043] Understandably, when the sample size is moderate, the sampling distribution of the sample mean approximately follows a normal distribution. Furthermore, in engineering monitoring, the responses of similar nodes under the same operating conditions exhibit strong spatial correlation. Their dispersion is mainly caused by measurement errors, local structural differences, and environmental disturbances, and can be considered as small random fluctuations around a local mean, satisfying the approximate normality assumption. Therefore, constructing confidence intervals based on the mean and standard deviation is statistically reasonable.

[0044] For example, when applied to the above operations, the stress confidence interval can be expressed as: ,in This represents the average stress of the neighboring nodes. This represents the standard deviation of stress at neighboring nodes. This represents the confidence coefficient, which can be set to 3, representing a 99.7% confidence level.

[0045] The displacement confidence interval can be expressed as ,in This represents the average displacement of the neighboring nodes. This represents the standard deviation of displacement of neighboring nodes.

[0046] The collected values ​​of each node in the group at that moment are compared with the confidence interval of the group: if the collected value of a node exceeds the confidence interval, it is determined that there is an abnormal response at that moment and it is marked as an abnormal node.

[0047] The data collected from the abnormal node is reconstructed by spatial interpolation using the remaining nodes in the neighboring node group to which the abnormal node belongs as reference nodes.

[0048] Furthermore, it can be understood that, under the assumption of structural continuity, measurements of normal nodes surrounding anomaly nodes can reflect the expected response at that location. Reconstructing anomalous data through spatial interpolation can maintain the smoothness and physical plausibility of the data in the spatial domain, which is superior to simple time series interpolation.

[0049] A spatiotemporally aligned stress-displacement monitoring dataset is generated by integrating the multi-source initial monitoring datasets after anomaly identification and reconstruction.

[0050] This invention first collects the original time-series data of stress and displacement at key stress nodes of a long-span bidirectional fish-belly steel truss, and then performs outlier identification and data reconstruction processing. This effectively improves the accuracy and consistency of the monitoring data, providing a high-fidelity, spatiotemporally synchronized input data foundation for subsequent stress-displacement coupled dynamic analysis and structural state evolution trend identification, and ensuring the reliability and engineering applicability of the risk identification and graded early warning model.

[0051] S3: Based on the stress-displacement monitoring dataset, perform stress-displacement spatiotemporal coupling analysis to construct a structural stiffness field distribution model.

[0052] Preferably, the specific implementation process of the above steps is as follows: calculate the stress change rate and displacement change rate of each node in adjacent time periods based on the stress-displacement monitoring dataset, and define the ratio of the two as the equivalent tangential stiffness index at that node.

[0053] It should be noted that, within the elastic range, the stiffness of a material or structure is defined as the ratio of stress increment to strain increment. This ratio reflects the force required per unit deformation and is a core parameter for measuring a structure's ability to resist deformation. Constructing an "equivalent stiffness index" by using the measured ratio of stress to displacement change rate is essentially a data-driven estimation of the stiffness of a local structure.

[0054] The equivalent tangent stiffness index of each node is associated with its spatial coordinates. A continuous stiffness field distribution model is constructed using spatial interpolation, and a spatial distribution cloud map of the equivalent stiffness of the structure is generated based on this model.

[0055] In the specific implementation of the above operations, the generation method of the structural equivalent stiffness spatial distribution cloud map is as follows: using a BIM or finite element visualization platform, the stiffness field distribution model obtained by interpolation is mapped to the structural surface or spatial grid, and the stiffness level of different regions is rendered by color gradient such as blue-yellow-red to generate the structural equivalent stiffness spatial distribution cloud map. This cloud map intuitively presents the local stiffness evolution state and spatial non-uniformity of the structure during the slip process.

[0056] S4: Identify weak and strong stiffness regions based on the structural stiffness field distribution model, and divide high-risk, medium-risk, and low-risk regions by combining the projection of the sliding track axis.

[0057] More preferably, the specific implementation process of the above steps is as follows: calculate the global mean of the equivalent stiffness index of all nodes in the equivalent stiffness spatial distribution cloud map of the structure.

[0058] Regions with stiffness indices below the global average are identified as weak stiffness regions, typically corresponding to areas of stress concentration, loose connections, or high risk of local buckling. Regions with stiffness indices above the global average are identified as stiffness-enhancing regions, which may originate from abrupt changes in local stiffness, increased constraints, or load redistribution effects.

[0059] The overlapping area between the weak stiffness region and the projection area of ​​the sliding track axis is classified as a high-risk region, the stiffness-enhanced region that is far from the track is classified as a low-risk region, and the remaining regions are classified as medium-risk regions.

[0060] The aforementioned "away from the track" refers to the spatial location of the stiffness-enhancing zone being outside the vertical projection influence zone of the sliding track and not participating in the main horizontal thrust transmission or support constraint. The mechanical response of this region is primarily dominated by its own weight and local loads, with minimal influence from the sliding force. In a specific embodiment, the centerline of the sliding track is projected upwards along the vertical Z-axis onto the space where the steel truss is located, forming the track projection zone. A lateral tolerance range, such as ±1.5 times the track width, is set; any area outside this zone is considered "away from the track."

[0061] As an explanation of the above operations, the area where the weak stiffness region and the projection area of ​​the central axis of the sliding track on the structural plane intersect in the plane or three-dimensional space is defined as a high-risk region because it has the dual disadvantages of local stiffness degradation and direct bearing of sliding load transfer. It is prone to risks such as local buckling, node instability or support eccentric loading. If the stiffness strengthening region is far from the projection area of ​​the sliding track, that is, not on the main force transmission path, and is less affected by the sliding force effect, it indicates that although there is a stiffness change or stress concentration trend in this region, it does not directly participate in the core sliding force process and has little impact on the overall stability of the structure. Therefore, it is classified as a low-risk region.

[0062] The above-mentioned risk zone division of a large-span bidirectional fish-belly steel truss during the sliding process constructs a spatial risk zoning model based on the coupling relationship between the structural stiffness distribution characteristics and the sliding force domain. Compared with the traditional method of using a uniform threshold or homogenized criteria for the overall structure, this significantly improves the spatial orientation of risk identification and provides a physically based zoning decision support framework for the subsequent differentiated monitoring deployment, hierarchical early warning mechanism and active control strategy formulation.

[0063] S5: Perform stress-displacement coupling enhancement analysis on a single node based on the stress-displacement time series data of each node in each risk area, and spatially aggregate all nodes in the region that are in a coupled enhancement state to evaluate the stress-displacement coupling trend of the risk area.

[0064] As one possible approach to the above scheme, the stress-displacement coupling enhancement analysis of a single node is implemented as follows: extract the stress time series data and displacement time series data of each node in each risk region during the slip process.

[0065] Sliding window correlation analysis is performed on the stress and displacement data of each node to calculate the stress-displacement coupling coefficient of each node in each time window, thereby forming a stress-displacement coupling coefficient sequence that reflects the dynamic evolution process of its mechanical response coordination.

[0066] In the specific operation of the above scheme, the Pearson linear correlation coefficient of the stress and displacement sequence within each window interval is calculated as the local stress-displacement coupling coefficient of the node in that period. It reflects the degree of linear coordination and mechanical correlation between stress response and displacement deformation at the node. The higher the absolute value, the more coordinated the internal force redistribution and geometric deformation of the structure in that region and period, which is in line with the expected response of the material constitutive relation.

[0067] It is important to understand that the stress and displacement of a steel truss structure under external loads are two physical manifestations of the same mechanical process, interconnected through material constitutive relations and the structural stiffness matrix. During slippage, the structural stress state continuously changes, and the coordinated evolution of stress and displacement reflects the overall or local stiffness characteristics and force transmission behavior of the structure. Therefore, analyzing the dynamic correlation between these two factors can effectively reveal the intrinsic mechanical state of the structure.

[0068] Slip-line construction is a typical non-stationary dynamic process, with structural response characteristics evolving over time. Traditional global correlation analysis cannot capture this time-varying feature. The sliding time window method can decompose long sequences into multiple local steady-state segments, enabling dynamic tracking of time-varying coupling relationships, which aligns with the physical nature of actual working conditions.

[0069] The stress-displacement coupling coefficient sequence of each node is linearly fitted within a set observation period, and its regression slope is extracted as a trend criterion. When the regression slope is greater than zero, the node is determined to be in a state of enhanced coupling, which represents an intensified redistribution of internal forces in the structure.

[0070] Applying to the above operational instructions, the observation period can cover multiple consecutive sliding time windows, and it is generally recommended to include three or more sliding time windows to ensure that the trend analysis has sufficient time series coverage and statistical stability.

[0071] Furthermore, the linear trend fitting can be performed using the least squares method to fit the straight line. When the regression slope is greater than zero, the coupling coefficient is enhanced, indicating that the stress and displacement response are enhanced synchronously, the local stiffness distribution of the structure changes, and the load redistribution is active. This indicates that the redistribution of internal forces in the structure is intensified, and there may be phenomena such as uneven support stress, local stress concentration, or evolution of connection state.

[0072] See Figure 2 As shown, as another possible way to implement the above scheme, the stress-displacement coupling trend in the risk area is judged by the following process: count the number of nodes in each risk area that are in the enhanced coupling state, and record the corresponding nodes as enhanced coupling nodes.

[0073] Based on the three-dimensional spatial coordinates of all coupling enhancement nodes, the coupling enhancement nodes are topologically connected and geometrically reconstructed to form a spatially continuous coupling enhancement region, which is used to characterize the local response concentration area where the internal force coordination of the structure is significantly improved.

[0074] It is important to understand that when a steel truss structure undergoes internal force redistribution under external loads, the changes in its mechanical response do not occur in isolation, but rather exhibit spatial aggregation in localized areas. The concentrated distribution of coupled reinforcement nodes reflects the physical processes of structural stiffness adjustment, stress concentration, or changes in force transmission paths. Therefore, aggregating discrete nodes into continuous regions aligns with the actual spatial morphology of structural damage or state evolution.

[0075] The ratio of the volume of the coupling enhancement region to the total volume of the risk region is calculated as the enhancement coverage, which is used to assess the spatial penetration range of anomalous mechanical behavior within the target region.

[0076] Extract the geometric centroid coordinates of the coupling enhancement region and compare them with the geometric center of the risk region. Calculate the Euclidean distance between the two as the enhancement centroid offset distance to assess whether there is an eccentric development tendency in the spatial distribution of the anomalous response.

[0077] It should be understood that the critical stress area of ​​a steel truss structure is usually located at the design center. If the centroid of the coupled reinforcement area is close to the center of the risk area, it indicates that the anomaly occurs in the high stress area and the potential harm is great. Conversely, if the offset is large, it may only be the response of the edge members and the risk is low.

[0078] The enhanced coverage of each risk area and the enhanced centroid offset distance are used to construct a coupling enhancement significance index through weighted linear fusion.

[0079] For example, the specific expression for the coupling enhancement significance index is as follows: In the formula Indicates the significance index of enhanced coupling. This indicates enhanced coverage. Indicates the distance of the increased centroid offset. Characteristic scales representing risk regions, such as the length of the maximum diagonal. , These represent the weighting coefficients for enhancing coverage and enhancing centroid offset distance, respectively. This reflects the relative importance of coverage breadth and location concentration.

[0080] In the above expression By introducing the characteristic scale of the risk region to normalize the reinforcement centroid offset distance, the aim is to eliminate the dimensional influence of spatial distance and transform it into a dimensionless relative distance parameter independent of the structural geometry. A smaller reinforcement centroid offset distance indicates that the spatial distribution of the coupled reinforcement region is closer to the core stress area or geometric center of the risk region, and its impact on the overall structural safety is more significant. Therefore, the expression uses [the relevant term]. This maps relationships with smaller distances and higher risks to positive contribution terms.

[0081] The weighting coefficients for enhancing coverage and centroid offset distance are set as follows: Collect historical slip construction data, including stress-displacement time-series responses of key nodes in multiple completed projects and typical abnormal events recorded during construction (such as stress over-limit, connection slip, etc.), and construct a structural risk sample library.

[0082] For each risk region, the corresponding enhanced coverage and normalized centroid offset terms are extracted as feature parameters during the critical evolution period before the occurrence of abnormal events.

[0083] The coupling enhancement significance index of each sample is calculated by traversing the preset weight combination, and the coupling enhancement significance index is used as the risk criterion to evaluate the classification performance of "abnormal" and "normal" states under different weights. The classification accuracy is used as the evaluation index.

[0084] Finally, the weight combination that maximizes the overall classification accuracy is selected as the optimal recommendation value.

[0085] The coupling enhancement significance index of each risk region is compared with the configured effective threshold. If the coupling enhancement significance index of a certain risk region reaches or exceeds the effective threshold, it is judged that the risk region has an upward trend of stress-displacement coupling.

[0086] The effective threshold mentioned above can be identified from the time series curve of the coupling enhancement significance index of the risk area corresponding to the abnormal event in the historical data. The early warning trigger point that is significantly rising and close to the critical state is the earliest identifiable mutation point before the abnormality occurs. The coupling enhancement significance index corresponding to this point is used as the effective threshold.

[0087] The above-mentioned method for judging the coupling trend of the entire risk area based on the coupling state of a single node introduces the dual criteria of spatial volume ratio and centroid spatial consistency and uses weighted fusion to construct a coupling enhancement significance index, realizing a comprehensive evaluation of both breadth and location dimensions. This reflects the quantification of the spatial morphological characteristics of the abnormal response, effectively distinguishes between local sporadic anomalies and regional mechanical state evolution, and improves the accuracy and physical credibility of early warning decisions.

[0088] S6: When a stress-displacement coupling trend is observed to increase in a risk area, a graded early warning and control mechanism based on the risk level is activated.

[0089] See Figure 3 As shown, in the optional implementation of the above steps, the graded early warning control mechanism includes the following: when a stress-displacement coupling upward trend occurs in a high-risk area, a first-level early warning is activated to reduce the sliding speed of the jacking point in the corresponding risk area.

[0090] It should be noted that when a significant coupling enhancement trend is identified within a high-risk area, indicating that the critical component is undergoing a drastic redistribution of internal forces, the high-risk area, typically located at supports, mid-span, or main force transmission paths—locations that decisively influence overall stability, may experience stiffness degradation or loss of deformation compatibility, potentially leading to local instability. This instability can then spread to surrounding areas via the force transmission chain, resulting in overall slip system imbalance. To avoid exacerbating system instability due to additional dynamic effects introduced by continuous high-speed propulsion or excessively rapid load accumulation, it is necessary to actively reduce the slip velocity, slow down the load application rate, suppress rapid redistribution of internal forces, and reduce the dynamic impact on the structural response.

[0091] When a stress-displacement coupling upward trend appears in the medium-risk area, a level-two early warning is activated, triggering a local jacking force adjustment.

[0092] It should be noted that when a significant trend of enhanced coupling is identified within a medium-risk area, it indicates that the region is undergoing a marked redistribution of internal forces or local stiffness degradation, possibly caused by asynchronous slippage, track deviation, or connection slippage. Implementing global thrust adjustments at this time would not only result in a sluggish response but also easily cause unnecessary disturbances to already coordinated areas, potentially inducing new imbalances. Therefore, spatially based local closed-loop control is adopted to implement targeted regulation in areas of abnormal evolution, thereby improving control effectiveness.

[0093] When an upward trend in stress-displacement coupling occurs in a low-risk area, a level 3 early warning is activated, triggering a self-checking procedure for all network nodes to verify the sensor's working status.

[0094] It should be noted that when a continuous upward trend in the coupling coefficient occurs in a low-risk area, this area is typically far from the main force transmission path, has high design stiffness, and is less affected by sliding forces. Theoretically, its mechanical response remains stable, and the change in coupling relationship is minimal. Therefore, such anomalies may not originate from the actual evolution of the structure, but rather reflect anomalies in the monitoring system itself or environmental interference factors. Triggering comprehensive monitoring system verification, such as sensor zero-point calibration, data link checks, and temperature compensation parameter updates, aims to quickly identify and eliminate monitoring error sources, ensuring that subsequent data analysis is based on high-fidelity and reliable measured data.

[0095] As a further innovation of the above implementation, the following operation is performed to trigger the local jacking force adjustment: based on the relative position of the coupling enhancement area identified in the medium-risk area on the sliding path, the upstream control area is defined by extending a certain distance (such as 1 to 2 standard segment lengths) in the opposite direction of sliding, and the downstream adjustment area is defined by extending a certain distance in the positive direction of sliding, and the jacking equipment on the corresponding support point is located.

[0096] It should be clarified that the upstream region refers to the adjacent structural segment that is positioned forward in the coupling enhancement region along the slip direction. It is usually the driving side for load transfer, and excessive thrust will exacerbate the front-end constraint effect.

[0097] The downstream region refers to the structural segment placed in the rear of the sliding direction. It is the driven side. Appropriately increasing the thrust helps to release local stress concentration and promote overall coordinated deformation.

[0098] The appearance of a coupled enhancement region often indicates that the area is undergoing significant internal force redistribution. Its spatial location and evolution direction imply the current structural stress bottleneck. By analyzing its upstream and downstream relationships, the main path of load transfer can be identified, allowing for differential dynamic control along the force transmission path and precise intervention.

[0099] In each round of adjustment, the output of the jacking equipment in the upstream area is reduced by 5% each time, while the output of the jacking equipment in the downstream area is increased by 5% each time.

[0100] The aforementioned adjustment of the top thrust is based on the spatial orientation of the coupling enhancement region, implementing a differential thrust strategy of upstream unloading and downstream boosting. Furthermore, through small-step closed-loop iteration, it avoids new dynamic disturbances caused by sudden changes in force values ​​and guides the internal forces of the structure to evolve towards an equilibrium state.

[0101] It is important to note that each jacking point is executed according to a preset synchronous timing sequence to ensure overall attitude stability during the adjustment process.

[0102] After each adjustment, the stress-displacement coupling state of the risk area is reassessed until the adjustment stops when the stress-displacement coupling coefficient of the risk area no longer shows an upward trend.

[0103] The parameters involved in the above formula are all dimensionless and calculated numerically. The formula is a formula obtained from the most recent real situation by collecting a large amount of data and simulating it with software. The preset parameters in the formula are set by those skilled in the art according to the actual situation.

[0104] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0105] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0106] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0107] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0108] Finally, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for monitoring the slippage of a large-span, bidirectional fish-belly type steel truss, characterized in that, Includes the following steps: S1: Deploy a multi-source sensor network along the key nodes of the main and secondary trusses of the bidirectional fish-belly steel truss to collect structural stress distribution and displacement data in real time and generate a multi-source initial monitoring dataset for the slip process. S2: Perform anomaly identification and data reconstruction on the multi-source initial monitoring dataset to generate a spatiotemporally aligned stress-displacement monitoring dataset; S3: Based on the stress-displacement monitoring dataset, perform spatiotemporal coupling analysis of stress and displacement to construct a structural stiffness field distribution model; The S3 includes the following implementation: Calculate the stress change rate and displacement change rate of each node in adjacent time intervals based on the stress-displacement monitoring dataset, and define the ratio of the two as the equivalent tangent stiffness index at the node; associate the equivalent tangent stiffness index of each node with its spatial coordinates, construct a continuous stiffness field distribution model using spatial interpolation, and generate a spatial distribution cloud map of the structural equivalent stiffness based on the model. S4: Identify weak and strong stiffness regions based on the structural stiffness field distribution model, and divide high-risk, medium-risk, and low-risk regions by combining the projection of the sliding track axis. S5: Perform stress-displacement coupling enhancement analysis on a single node based on the stress-displacement time series data of each node in each risk area, and spatially aggregate all nodes in the region that are in a coupled enhancement state to evaluate the stress-displacement coupling trend of the risk area. The stress-displacement coupling enhancement analysis of the single node is as follows: extract the stress time series data and displacement time series data of each node in each risk region during the slip process; Sliding window correlation analysis is performed on the stress and displacement data of each node to calculate the stress-displacement coupling coefficient of each node in each time window, thereby forming a stress-displacement coupling coefficient sequence. The stress-displacement coupling coefficient sequence of each node is fitted with a linear trend within a set observation period, and its regression slope is extracted as a trend criterion index. When the regression slope is greater than zero, the node is determined to be in a state of enhanced coupling. S6: When a stress-displacement coupling trend is observed to increase in a risk area, a graded early warning and control mechanism based on the risk level is activated.

2. The method for monitoring the slippage of a large-span, bidirectional fish-belly type steel truss as described in claim 1, characterized in that: The specific implementation process of S1 is as follows: Fiber grating stress sensor arrays and laser displacement sensor arrays are arranged at the intersection of the main truss and the secondary truss, the mid-span node and the support node. During the slip process, stress and displacement data of each node are collected synchronously at a fixed sampling frequency, and timestamps and spatial location identifiers are added.

3. The method for monitoring the slippage of a large-span, bidirectional fish-belly type steel truss as described in claim 1, characterized in that: The specific implementation process of S2 is as follows: Each node is divided into several neighborhood node groups based on the spatial proximity and force correlation of the nodes. At each fixed sampling time, the monitoring data in each neighboring node group is statistically analyzed, the sample mean and standard deviation of the monitoring data corresponding to all nodes in the group are calculated, and confidence intervals are constructed accordingly. Compare the collected values ​​of each node in the group at that moment with the confidence interval of the group: if the collected value of a node exceeds the confidence interval, it is determined that there is an abnormal response at that moment and it is marked as an abnormal node. The data collected from the abnormal node is reconstructed by spatial interpolation using the remaining nodes in the neighboring node group to which the abnormal node belongs as reference nodes. A spatiotemporally aligned stress-displacement monitoring dataset is generated by integrating the multi-source initial monitoring datasets after anomaly identification and reconstruction.

4. The method for monitoring the slippage of a large-span, bidirectional fish-belly type steel truss as described in claim 3, characterized in that: The process of dividing each node into several neighborhood node groups based on the spatial proximity and force correlation of the nodes is described below: Using the structural design reference point of the steel truss as the origin, the X-axis is defined according to the direction of the main truss axis, the Z-axis is defined according to the direction of vertical gravity, and the Y-axis is determined according to the right-hand rule to establish a three-dimensional rectangular coordinate system. Based on the three-dimensional coordinates of each node, with each node as the reference center, calculate the Euclidean distance between it and all other nodes. Node pairs that satisfy any of the following conditions are determined to have geometric proximity and constitute the initial set of spatially adjacent nodes; a) The Euclidean distance between two nodes is less than or equal to a preset spatial proximity threshold; b) Construct topological connections based on the spatial distribution of all nodes, if two nodes belong to the same triangular unit and share an edge; Based on the structural force transmission mechanism, consecutive nodes located on the same force transmission path are selected from the spatial adjacent node group and divided into a group to form a neighborhood node group.

5. The method for monitoring the slippage of a large-span, bidirectional fish-belly type steel truss as described in claim 1, characterized in that: S4 includes the following implementation details: Calculate the global mean of the equivalent stiffness index of all nodes in the equivalent stiffness spatial distribution cloud map of the structure; Regions with stiffness indices below the global average are identified as regions with weak stiffness, while regions with stiffness indices above the global average are identified as regions with strong stiffness. The overlapping area between the weak stiffness region and the projection area of ​​the sliding track axis is classified as a high-risk region, the stiffness-enhanced region that is far from the track is classified as a low-risk region, and the remaining regions are classified as medium-risk regions.

6. The method for monitoring the slippage of a large-span, bidirectional fish-belly type steel truss as described in claim 1, characterized in that: The stress-displacement coupling trend in the risk area is determined by the following process: Count the number of nodes in the enhanced coupling state in each risk area and record the corresponding nodes as enhanced coupling nodes; Based on the three-dimensional spatial coordinates of all coupling enhancement nodes, the coupling enhancement nodes are topologically connected and geometrically aggregated to reconstruct a coupling enhancement region with spatial continuity; The ratio of the volume of the coupled enhancement region to the total volume of the risk region to which it belongs is used as the enhancement coverage. Extract the geometric centroid coordinates of the coupling enhancement region and compare them with the geometric center of the risk region. Calculate the Euclidean distance between the two as the enhancement centroid offset distance. The enhanced coverage of each risk area and the enhanced centroid offset distance are used to construct a coupled enhancement significance index through weighted linear fusion; The coupling enhancement significance index of each risk region is compared with the configured effective threshold. If the coupling enhancement significance index of a certain risk region reaches or exceeds the effective threshold, it is judged that the risk region has an upward trend of stress-displacement coupling.

7. The method for monitoring the slippage of a large-span, bidirectional fish-belly type steel truss as described in claim 1, characterized in that: The risk-level-based graded early warning and control mechanism includes the following: When a stress-displacement coupling upward trend appears in a high-risk area, a Level 1 early warning is activated to reduce the sliding velocity of the jacking point in the corresponding risk area. When a stress-displacement coupling upward trend appears in the medium-risk area, a level-two early warning is activated, triggering local jacking force adjustment; When an upward trend in stress-displacement coupling occurs in a low-risk area, a level 3 early warning is activated, triggering a self-checking procedure for all network nodes to verify the sensor's working status.

8. The method for monitoring the slippage of a large-span, bidirectional fish-belly type steel truss as described in claim 7, characterized in that: The implementation details for triggering local thrust adjustment are as follows: Based on the relative position of the coupling enhancement area identified in the medium-risk area on the sliding path, the upstream control area is defined by extending a certain distance in the opposite direction of sliding, and the downstream adjustment area is defined by extending a certain distance in the positive direction of sliding, and the jacking equipment on the corresponding support point is located. In each round of adjustment, the output of the jacking equipment in the upstream area is reduced, while the output of the jacking equipment in the downstream area is increased simultaneously. After each adjustment, the stress-displacement coupling state of the risk area is reassessed until the adjustment stops when the stress-displacement coupling coefficient of the risk area no longer shows an upward trend.

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