Method and system for identifying hazard sources and analyzing risks in bridge construction

Abstract

The present application relates to the technical field of construction risk identification, and discloses a method and system for bridge construction hazard source identification and risk analysis. The method includes: collecting geological, road and river proximity, and support load parameters to establish a hazard information database; performing three-dimensional mapping to generate an associated risk matrix; extracting key node data to form a stress distribution map; integrating process progress to construct a safety margin curve; calculating critical points to generate a warning threshold table; and quantitatively verifying the prevention and control effects to form a closed-loop optimization strategy chain. The present application establishes a three-dimensional support-load-environment associated risk matrix and a dynamic safety margin index curve to achieve dynamic risk identification, precise warning, and closed-loop optimization of the entire construction process, thereby improving the safety management level of cast-in-place construction of bridge supports and reducing the probability of construction safety accidents.

Description

Technical Field

[0001] The present application relates to the technical field of construction risk identification, and in particular to a method and system for identifying hazard sources and conducting risk analysis in bridge construction. Background Art

[0002] At present, cast-in-place construction of bridge supports is a commonly used construction method in large-scale bridge projects, and is particularly suitable for on-site casting needs under complex terrain conditions. Traditional safety management of cast-in-place construction of bridge supports mainly relies on experience-based judgment and static testing. Common safety management methods include regular inspections, key node monitoring, and periodic acceptance evaluations. During the specific implementation process, engineers usually use design load verification, static analysis, and finite element simulation to evaluate the safety status of the support, while supplemented by monitoring devices such as displacement sensors and strain gauges to collect and record key parameters. With the development of computer technology, some projects have begun to introduce information technology to carry out risk management. For example, the GIS system combined with BIM technology can achieve three-dimensional visualization management of the construction site, which improves the intuitiveness and accuracy of hazard identification.

[0003] However, existing safety management methods for cast-in-place construction of bridge scaffolds generally have problems such as incomplete risk identification, inaccurate early warning mechanisms, and insufficient evaluation of the effectiveness of prevention and control measures. In terms of risk identification, traditional methods focus more on the influence of a single factor and lack a comprehensive analysis of the complex interactions between geological conditions, road and river environments, and scaffold loads, resulting in certain potential risk points being overlooked. In terms of safety early warning, existing methods are mostly based on static threshold judgments, which are difficult to adapt to the dynamic changes in the stress state of the scaffolds throughout the construction process, and are prone to early warning lags or false alarms. In terms of evaluating the effectiveness of the implementation of prevention and control measures, there is a lack of quantitative evaluation standards and closed-loop optimization mechanisms, making it difficult to improve the effectiveness of prevention and control measures in a targeted manner. These shortcomings have led to high safety risks in the scaffold construction process, especially in large-scale bridge projects under special environments such as complex geological conditions, near traffic or water areas, where safety accidents occur from time to time. Summary of the Invention

[0004] This application provides a method and system for identifying and analyzing bridge construction hazards, which is used to establish a three-dimensional correlation risk matrix of support-load-environment and a dynamic safety margin index curve to achieve dynamic risk identification, accurate early warning and closed-loop optimization of the entire construction process, thereby improving the safety management level of cast-in-place construction of bridge supports and reducing the probability of construction safety accidents.

[0005] In the first aspect, the present application provides a method for identifying and analyzing bridge construction hazards, which includes: collecting geological parameters, road and river parameters, and scaffold load parameters of the cast-in-place construction of bridge scaffolds to establish a bridge construction hazard information database; performing three-dimensional mapping of the bridge construction hazard information database to generate a scaffold-load-environment correlation risk matrix; extracting the stress values ​​and deformations of key nodes based on the scaffold-load-environment correlation risk matrix to form a construction node stress distribution map; based on the construction node stress distribution map, integrating the scaffold stress state with the construction process progress to construct a scaffold dynamic safety margin index curve; based on the scaffold dynamic safety margin index curve, calculating the critical instability point and the hazard accumulation area to generate a staged warning threshold table; using the staged warning threshold table to quantitatively verify the implementation effect of prevention and control measures to form a closed-loop optimization prevention and control strategy chain.

[0006] In a second aspect, the present application provides a system for identifying and analyzing bridge construction hazards, the system comprising:

[0007] The acquisition module is used to collect geological parameters, road and river proximity parameters, and support load parameters for cast-in-place construction of bridge supports, and to establish a bridge construction hazard information database;

[0008] A generation module is used to perform three-dimensional mapping on the bridge construction hazard information database to generate a support-load-environment correlation risk matrix;

[0009] An extraction module is used to extract the stress values ​​and deformations of key nodes according to the support-load-environment correlation risk matrix to form a stress distribution diagram of the construction nodes;

[0010] A construction module is used to integrate the support stress state and the construction process progress based on the stress distribution diagram of the construction node to construct a dynamic safety margin index curve of the support;

[0011] A calculation module, configured to calculate the critical instability point and the danger accumulation area based on the dynamic safety margin index curve of the support, and generate a staged warning threshold table;

[0012] The quantification module is used to use the staged warning threshold table to quantitatively verify the effectiveness of the implementation of prevention and control measures, forming a closed-loop optimization prevention and control strategy chain.

[0013] The third aspect of the present invention provides a computer device, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor calls the instructions in the memory so that the computer device executes the above-mentioned method for identifying and analyzing bridge construction hazards.

[0014] A fourth aspect of the present invention provides a computer-readable storage medium having instructions stored therein, which, when executed on a computer, enables the computer to execute the above-mentioned method for identifying and analyzing bridge construction hazards.

[0015] In the technical solution provided by this application, a comprehensive bridge construction hazard information database is established by collecting geological parameters, road and river parameters, and scaffold load parameters of the cast-in-place construction of bridge scaffolds, realizing the systematic collection and integration of multi-dimensional risk factors, and laying a solid data foundation for subsequent risk analysis; the bridge construction hazard information database is three-dimensionally mapped to generate a scaffold-load-environment correlation risk matrix, breaking through the limitations of traditional single-dimensional risk assessment, and establishing a mathematical correlation model between scaffold structure, load distribution and environmental impact, making risk analysis more comprehensive and systematic; according to the scaffold-load-environment correlation risk matrix, the stress values ​​and deformations of key nodes are extracted to form a stress distribution map of construction nodes, realizing the precise positioning and visual expression of risk points, It intuitively reflects the stress state and weak links of the support structure; based on the stress distribution diagram of the construction node, the stress state of the support is integrated with the progress of the construction process, and a dynamic safety margin index curve of the support is constructed. For the first time, static risk analysis is combined with dynamic construction progress, and a dynamic risk assessment of the entire construction process is realized, making the risk warning time-series targeted; based on the dynamic safety margin index curve of the support, the critical instability point and the danger accumulation area are calculated, and a staged warning threshold table is generated, which realizes the quantification and grading of risk warnings and improves the accuracy and timeliness of warnings; using the staged warning threshold table, the implementation effect of prevention and control measures is quantitatively verified, forming a closed-loop optimization prevention and control strategy chain, and establishing a continuous improvement mechanism for prevention and control measures to ensure the effectiveness and adaptability of risk prevention and control. It is particularly noteworthy that this solution utilizes artificial intelligence algorithms in several key areas, including a density clustering algorithm for three-dimensional mapping of the hazard information database, graph data mining for key node extraction, radial basis function interpolation for stress distribution map generation, and a time series data prediction model for dynamic safety margin curve construction. These algorithms contribute significantly to the solution's success: the density clustering algorithm accurately identifies risk clusters in multidimensional data space, improving the accuracy of risk identification; graph data mining intelligently extracts topological relationships within the support structure, enhancing the precision of mechanical analysis; the radial basis function interpolation algorithm optimizes the conversion of discrete point data to continuous fields, improving the accuracy and reliability of stress distribution maps; and the time series data prediction model provides dynamic prediction capabilities for safety margin assessment, making the early warning mechanism more proactive. This represents a significant shift from static, single-point, empirical risk assessment to dynamic, multidimensional, and intelligent risk management, significantly enhancing the safety management level of cast-in-place bridge support construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 This is a schematic diagram of an embodiment of a method for identifying hazard sources and analyzing risks in bridge construction according to an embodiment of the present application;

[0018] Figure 2 This is a schematic diagram of an embodiment of a system for identifying and analyzing bridge construction hazards in an embodiment of the present application;

[0019] Figure 3 It is a schematic block diagram of the structure of a computer device in an embodiment of the present invention. DETAILED DESCRIPTION

[0020] The embodiments of the present application provide a method and system for identifying and analyzing bridge construction hazards. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or that are inherent to these processes, methods, products or devices.

[0021] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In one embodiment of the present application, a method for identifying and analyzing bridge construction hazards includes:

[0022] Step S101: Collect geological parameters, road and river proximity parameters, and support load parameters for cast-in-place construction of bridge supports, and establish a bridge construction hazard information database;

[0023] Step S102: Perform three-dimensional mapping on the bridge construction hazard information database to generate a support-load-environment correlation risk matrix;

[0024] Step S103: extracting stress values ​​and deformations of key nodes based on the support-load-environment correlation risk matrix to form a stress distribution diagram of construction nodes;

[0025] Step S104: Based on the stress distribution diagram of the construction nodes, the support stress state is integrated with the construction process progress to construct a support dynamic safety margin index curve;

[0026] Step S105: Calculate the critical instability point and the danger accumulation area based on the dynamic safety margin index curve of the support, and generate a staged warning threshold table;

[0027] Step S106: Use the staged warning threshold table to quantitatively verify the effectiveness of the prevention and control measures, forming a closed-loop optimization prevention and control strategy chain.

[0028] It is understandable that the execution subject of this application can be a bridge construction hazard identification and risk analysis system, or a terminal or server, which is not limited here. The embodiment of this application is described by taking the server as the execution subject as an example.

[0029] Specifically, the geological parameters, road and river proximity parameters, and support load parameters for cast-in-place construction of bridge supports are first collected to establish a bridge construction hazard information database. Geological parameter collection includes geological surveys of the construction area and the collection of foundation bearing capacity data. The foundation bearing capacity is required to be no less than 250kPa. Data on the distribution of geological weak layers and groundwater level changes are also collected. Road and river proximity parameter collection involves measuring the distance, elevation difference, and intersection angle between the bridge and adjacent roads and rivers. Support load parameters are calculated based on the design of the bridge box girder structure. The preload calculation needs to consider the concrete density of 26kN / m³, and the formwork load, crowd and equipment load, and vibration load are considered as a combination of 6.5kN / m². These data are divided according to the construction area space to generate a spatial distribution map of bridge construction hazard sources, extract data on areas with concentrated hazard sources, and form a bridge construction hazard information database.

[0030] When mapping the bridge construction hazard database in three dimensions, the data is first categorized by support structure, load distribution, and environmental impact dimensions to generate three-dimensional raw data blocks. The support structure dimension focuses on the support geometry, support material processing, and support form and layout during erection. The load distribution dimension includes the load distribution in the preload zone calculation table. The environmental impact dimension includes geological conditions and surrounding environmental conditions. Data from these three dimensions is converted to a rectangular coordinate system in the support-load-environment space, from which feature points are extracted to form feature point cloud data. Density clustering is performed on the feature point cloud data to identify hazard factor interaction intervals. For example, the lack of edge protection and improperly designed upper and lower steel column access paths are hazard factor interaction points. These hazard factor interaction intervals are mapped to the risk level scale space to generate a risk intensity distribution map. Then, through discretization, the relationship between risk intensity values ​​and location coordinates is extracted to generate a support-load-environment correlation risk matrix.

[0031] Based on the support-load-environment risk matrix, stress values ​​and deformations at key nodes were extracted to generate a stress distribution map for construction nodes. First, support geometry and topology data were extracted from the risk matrix to construct a support node network topology map. The disc-shaped, full-floor support structure serves as the foundation for this topology map. Force analysis was performed on each connection point in the support node network topology map, and the node stress transfer path was calculated. Load transfer proceeds from top to bottom, for example, 2 cm thick bamboo plywood → 10 cm × 10 cm square timber → I14 I-beam → adjustable top support → φ60 × 3.2 mm steel pipe upright → adjustable base → I20a distribution beam → Bailey plate → double 600 × 200 H-section steel beams → steel pipe column → C30 concrete foundation. Based on these stress transfer paths, stress concentration areas were identified and high-stress nodes were marked. Deformation data was collected from high-stress nodes, and a deformation-stress correspondence table was constructed. This data was combined with support geometry information to create a node stress cloud map. Boundary extraction was used to determine the stress jump interval, resulting in a stress distribution map for construction nodes.

[0032] Based on the stress distribution diagram at construction nodes, the support stress state is integrated with the construction process progress to construct a dynamic support safety margin index curve. First, stress state data for key support points is extracted from the stress distribution diagram at construction nodes to generate a support static stress state table. Using the construction schedule, the construction phases of each cast-in-place box girder are mapped to the timeline. The process flow includes "foundation preparation and foundation construction → support installation → support preloading → side formwork installation → bottom and web reinforcement installation → inner formwork installation → top and web reinforcement and formwork installation → concrete pouring → strand threading → tensioning → grouting and end capping → support removal." The support static stress state table is aligned with the process progress node diagram in the time dimension to generate a stress-process correlation dataset. For each process node in the stress-process correlation dataset, the ratio of the support residual bearing capacity to the construction load is calculated to form a safety margin coefficient sequence. Data at key process transition points are selected from the safety margin coefficient sequence to establish a process transition risk marker set. The data points within the process transition risk marker set are connected to form a curve to construct a dynamic support safety margin index curve.

[0033] Based on the dynamic safety margin index curve of the support, critical instability points and hazard accumulation zones are calculated, and a staged warning threshold table is generated. Through inflection point detection, locations where the slope of the curve changes significantly are marked, identifying a set of potential critical points. From this set of potential critical points, the minimum safety margin value points are extracted through derivative analysis to identify critical instability points. Around the critical instability points, the rate of decrease of the safety margin index is measured, the impact range of hazard accumulation is delineated, and the boundaries of the hazard accumulation zone are formed. The critical instability points and the boundaries of the hazard accumulation zone are mapped to construction processes. High-risk processes such as "support preloading" and "prestressing and grouting" are areas that require special attention. A risk-process comparison table is established, and graded warning conditions are set according to importance and urgency. Warning trigger criteria are generated and then arranged in order of construction phases to form a staged warning threshold table.

[0034] Utilize the phased warning threshold table to quantitatively verify the effectiveness of prevention and control measures, forming a closed-loop optimization prevention and control strategy chain. Set corresponding monitoring points based on the phased warning threshold table, collect real-time monitoring data streams, and build a prevention and control data collection network. Compare the data in the prevention and control data collection network with the phased warning threshold table, calculate the threshold deviation, and generate a prevention and control response effectiveness graph. For the low-efficiency areas in the prevention and control response effectiveness graph, extract the characteristics of related prevention and control measures, and establish a prevention and control measure defect traceability table. Sort the items in the prevention and control measure defect traceability table by risk level, determine the prevention and control optimization focus, and form a prevention and control upgrade priority sequence. Convert the prevention and control upgrade priority sequence into specific technical adjustment parameters, and formulate a prevention and control measure adjustment plan. Link the prevention and control measure adjustment plan with the original prevention and control measures into a sequence structure to form an iterative closed-loop optimization prevention and control strategy chain.

[0035] Take Taoyuan No. 1 Bridge as an example. This bridge spans a county road and a river, with a 16-meter-high support structure located on a curve with a radius of 500 meters. On-site geological surveys revealed a foundation bearing capacity of 230 kPa, slightly below the required 250 kPa. Measurements also showed that the intersection angle between the bridge and the rebuilt X078 road was 65 degrees, with a 4-meter elevation difference from the river. Calculations revealed a total load of 924.586 tons for a single-box, double-chamber, cast-in-place box girder with a span of 4 x 30 meters. This data was incorporated into a 3D mapping process, which identified the intersection of the support foundation and the river as the area with the highest risk. Nodal stress analysis revealed that the stress at the connection between the foundation and the column was the highest, reaching 72% of the design allowable value. Comparing this data with the construction process revealed that during the support preloading phase, when the preloading load reached 80%, the safety margin index reached its lowest point, marking a critical instability point. Based on this, a warning threshold table was developed, stipulating that monitoring be initiated when the preloading load reached 60%, and support settlement monitoring points were established. During construction, when the preload reached 60%, the support settlement was observed to be 5mm, below the warning threshold of 8mm, indicating that the prevention and control measures were effective. Subsequently, through optimization of the foundation treatment process, the support settlement was further reduced to 3mm during subsequent construction, verifying the effectiveness of the closed-loop optimization prevention and control strategy chain.

[0036] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0037] (1) Conduct geological surveys in the bridge construction area, collect data on foundation bearing capacity, distribution of weak geological layers, and groundwater level changes, and form a geological parameter dataset;

[0038] (2) By measuring the distance, elevation difference, and intersection angle between the bridge and the adjacent roads and rivers, we can obtain the spatial position relationship data of the adjacent roads and rivers and construct a parameter dataset of the adjacent roads and rivers;

[0039] (3) According to the design drawings of the bridge box girder structure, calculate the concrete deadweight, construction load and preload borne by the support, and establish a support load parameter data set;

[0040] (4) The geological parameter dataset, the adjacent road and river parameter dataset, and the support load parameter dataset are spatially divided according to the construction area to generate a spatial distribution map of bridge construction hazards;

[0041] (5) Based on the spatial distribution map of bridge construction hazards, extract data on hazard-intensive areas and identify high-incidence points of hazards;

[0042] (6) Conduct a preliminary risk assessment of high-risk areas, classify them according to risk levels, and integrate them to generate a bridge construction hazard information database.

[0043] Specifically, a comprehensive geological survey of the bridge construction area was conducted. Data on foundation bearing capacity, distribution of weak geological layers, and groundwater level fluctuations were collected through drilling sampling, static penetration testing, and geophysical exploration. Foundation bearing capacity data include the ultimate bearing capacity of geotechnical layers, such as the N value obtained through standard penetration tests, which is then converted into foundation bearing capacity values. Data on distribution of weak geological layers record the thickness, depth, and spatial distribution of weak soil layers. Groundwater level fluctuation data, collected through long-term monitoring wells, capture seasonal groundwater level fluctuations. These data together constitute a geological parameter dataset, which is used to assess the stability risk of the support foundation. Data on the spatial relationship between the bridge and adjacent roads and rivers is collected by precisely measuring the distance, elevation difference, and intersection angle between the bridge and adjacent roads and rivers. Specifically, total stations are used to measure the closest distance between the bridge centerline and the adjacent road edge, the horizontal distance between the bridge foundation and the riverbank, the elevation difference between the bridge deck and the road, and the angle between the bridge axis and the river direction. These data constitute the adjacent road and river parameter dataset, which is used to analyze the potential impacts and constraints of the external environment on bridge construction.

[0044] Based on the design drawings of the bridge box girder structure, the various loads that the support must withstand are calculated. These include the deadweight of the concrete (calculated based on its volume and density), the loads of construction equipment and personnel (determined based on construction specifications and the construction organization design), and the preload (an additional load applied to verify support stability). Using structural mechanics analysis methods, the stress state and pressure distribution at each support node are calculated to form a support load parameter dataset.

[0045] The geological parameter dataset, the adjacent road and river parameter dataset, and the support load parameter dataset were spatially divided according to the construction area. Using geographic information system technology, the information from these three datasets was superimposed into a common spatial coordinate system. A spatial interpolation algorithm was used to perform continuous surface interpolation on the discrete point data to generate a spatial distribution map of bridge construction hazards. This map intuitively illustrates the spatial concentration of various risk factors within the construction area.

[0046] Based on the spatial distribution map of bridge construction hazards, a hotspot analysis algorithm was applied to identify areas where risk factor density exceeded a threshold and identified high-risk areas. This algorithm calculated the cumulative impact of risk factors within each grid cell and compared it with a pre-set safety threshold to identify hotspots where hazard concentrations are high. High-risk areas typically occur in areas with complex geological conditions, concentrated loads, and significant external environmental influences.

[0047] A preliminary risk assessment was conducted on high-risk areas, using the Analytic Hierarchy Process (AHP) to assign weights based on the probability of risk occurrence and the severity of potential consequences. A risk matrix was used to categorize risk levels into low, medium, and high. Areas with similar risks were then consolidated to create a structured bridge construction hazard information database.

[0048] For example, during the construction of a river-crossing bridge, a survey revealed a 2.5-meter-thick soft soil layer near the central pier of the bridge. Standard penetration tests revealed an N value of only 4.5, which translates to a bearing capacity of less than 120 kPa, significantly lower than that of the surrounding strata. Furthermore, the groundwater level in the area experienced seasonal fluctuations of up to 1.8 meters. Measurement data revealed that the pier was only 15 meters from the riverbank, with the bridge axis at a 72-degree angle to the river's direction, creating a risk of erosion by deflected water flow. According to the design drawings, the concrete volume of the upper box girder of the pier reached 280 cubic meters, and the support support would bear a load of 6,720 kN during the concrete pour. After overlaying and analyzing these three sets of data in the same coordinate system, a spatial density algorithm identified the pier as a high-risk location. The calculated risk value reached 0.78 (out of a maximum score of 1), placing it at a high risk level. This prioritized entry into the bridge construction hazard information database and marked it as a key monitoring area.

[0049] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0050] (1) Classify the data in the bridge construction hazard information database according to the support structure dimension, load distribution dimension, and environmental impact dimension to generate a three-dimensional raw data block;

[0051] (2) Perform coordinate system transformation on the original three-dimensional data block to construct the support-load-environment space rectangular coordinate system;

[0052] (3) Extract characteristic points from the rectangular coordinate system of the support-load-environment space to form characteristic point cloud data;

[0053] (4) Perform density clustering on feature point cloud data to identify the interaction intervals of risk factors;

[0054] (5) Mapping the interaction interval of risk factors to the risk level scale space to obtain the risk intensity distribution map;

[0055] (6) Discretize the risk intensity distribution map, extract the relationship between the risk intensity value and the location coordinate, and generate the support-load-environment correlation risk matrix.

[0056] Specifically, the data in the bridge construction hazard information database needs to be categorized according to the support structure, load distribution, and environmental impact dimensions. The support structure dimension includes the support geometry, material parameters, and connection methods; the load distribution dimension includes the distribution of static, dynamic, and accidental loads; and the environmental impact dimension includes external factors such as geological, hydrological, and meteorological conditions. Using data stratification technology, various data types are grouped and categorized according to attribute labels, forming a three-dimensional raw data block with clear boundaries. This data block contains all hazard source information, but a unified spatial reference system has not yet been established. A coordinate system transformation is performed on the three-dimensional raw data block to construct a support-load-environment spatial rectangular coordinate system. This coordinate transformation uses a matrix transformation method to map data from different sources and units into a unified spatial rectangular coordinate system. Specifically, the x-axis represents support structure parameters, including support geometry, node locations, and material properties; the y-axis represents load distribution parameters, including the magnitude, direction, and distribution of various loads; and the z-axis represents environmental impact parameters, including external environmental factors such as geological and hydrological conditions. Through the coordinate transformation matrix, unified expression of data of different dimensions and establishment of spatial correspondence are achieved.

[0057] Feature points are extracted from the rectangular coordinate system of the support-load-environment space to form feature point cloud data. Principal component analysis and key feature point detection algorithms are used to filter out key points with significant impact on hazard identification from massive amounts of data. Specifically, by calculating the impact weight of each data point and retaining those with risk contributions above a preset threshold, a sparse but informative feature point cloud is formed. These feature points represent key risk nodes in the three dimensions of support structure, load distribution, and environmental impact, and serve as the foundation for subsequent risk analysis.

[0058] Density clustering is performed on feature point cloud data to identify risk factor interaction zones. Density clustering utilizes DBSCAN (density-based spatial clustering algorithm) technology. By analyzing the distribution density of feature points in three-dimensional space, it identifies areas with point density exceeding a threshold. These high-density areas represent risk factor interaction zones where multiple risk factors interact and risk overlaps. The DBSCAN algorithm first sets a search radius and a minimum point count threshold, then expands the clusters point by point, ultimately generating multiple risk factor interaction zones with clearly defined boundaries.

[0059] The risk factor interaction interval is mapped to the risk level scale space to obtain the risk intensity distribution map. The risk level scale space is a new metric space in which the risk level is quantified as a value between 0 and 1. The mapping process uses a risk scoring function, which comprehensively considers the severity, probability of occurrence and controllability of the risk factor to convert the risk factor interaction interval into a risk intensity value. Risk scoring function The definition is as follows:

[0060]

[0061] Among them, R represents the risk intensity value, which ranges from 0 to 1; Indicates the probability of a dangerous event, with a value range of 0-1; Indicates the severity of the dangerous event, with a value range of 0-1; Indicates the controllability of the hazard, with a value range of 0-1; 、 、 are the weight coefficients of probability, severity and controllability respectively, and + + = 1. Through the calculation of the risk scoring function, each hazard factor interaction interval is mapped to a risk intensity value, and a risk intensity distribution map is formed in three-dimensional space. The risk intensity distribution map is discretized, and the relationship between the risk intensity value and the position coordinates is extracted to generate a support-load-environment correlation risk matrix. The discretization process uses the grid division method to divide the continuous risk intensity distribution map into a finite number of grid cells, each grid cell corresponding to a risk intensity value. Through sampling and interpolation techniques, the correspondence between the risk intensity value and the spatial position coordinates is established to form a structured support-load-environment correlation risk matrix. This matrix clearly shows the risk distribution under different support positions, different load conditions and different environmental influences, providing a basis for subsequent risk prevention and control.

[0062] Taking the cast-in-place construction of a river-crossing bridge support as an example, the collected support structure data (including the coordinates and connection relationships of 60 key nodes), load distribution data (including 15 working conditions such as concrete deadweight and construction loads), and environmental impact data (including eight factors such as geological parameters and hydrological parameters) were first organized into three-dimensional datasets. Through coordinate transformation, these data were mapped into a unified support-load-environment spatial coordinate system, forming an initial point cloud containing 7,200 data points. Principal component analysis was applied to extract the 380 most significant feature points with the greatest impact on risk, forming a feature point cloud. These feature points were density clustered using the DBSCAN algorithm, setting a search radius of 0.15 (normalized units) and a minimum point count threshold of 5. Twelve risk factor interaction intervals were identified. A high-risk interaction interval was identified in the central bridge support region, resulting from the combined effects of support structural instability, excessive concrete pouring load, and weak foundation. Using the risk scoring function, the probability of occurrence (P) in this area was 0.72, the severity (S) was 0.85, the controllability (C) was 0.43, and the weighting coefficients (α) were 0.35, β was 0.45, and γ was 0.2. The resulting risk intensity (R) for this area was 0.67, placing it at a high risk level. Using gridding, the entire three-dimensional space was divided into a 50×40×30 grid, forming 60,000 risk assessment cells. A complete support-load-environment correlation risk matrix was generated. This matrix clearly shows the high-risk distribution in the central support area.

[0063] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0064] (1) Extract the support geometric topology data from the support-load-environment correlation risk matrix and establish the support node network topology map;

[0065] (2) Perform stress analysis on each connection point in the support node network topology diagram and calculate the node stress transfer path;

[0066] (3) Based on the stress transfer path of the node, identify the stress concentration area and mark the high stress nodes;

[0067] (4) Collect deformation data from high stress nodes and establish a deformation-stress correspondence table;

[0068] (5) Combine the data from the deformation-stress correspondence table with the bracket geometric position information to draw a node stress cloud diagram;

[0069] (6) By extracting the boundaries of the node stress cloud map, the stress jump range is determined and the stress distribution map of the construction node is formed.

[0070] Specifically, extracting support geometry and topology data from the support-load-environment correlation risk matrix and establishing a support node network topology diagram are the primary steps in analyzing support stress conditions. Support geometry and topology data refers to a dataset describing the spatial positions and connections of support components, including node coordinates, member connection relationships, and support conditions. The extraction process utilizes graph data mining techniques to separate support structural dimensions from the correlation risk matrix and remove redundant information through data cleaning. When constructing the support node network topology diagram, each support node is represented as a vertex in the graph, and each connecting member is represented as an edge. Attribute values, such as node coordinates, member cross-sectional properties, and material parameters, are assigned to each vertex and edge. This graph-structured representation intuitively displays the spatial relationships and mechanical transmission paths of the support structure. Force analysis is performed on each connection point in the support node network topology diagram, and calculating the node stress transmission paths is a key step in identifying risk points. The node stress transmission path refers to the mechanical propagation path of loads through the support structure to the foundation. The force analysis utilizes the matrix displacement method to establish the structural stiffness equation and calculate the internal forces and deformations at each node. The calculation of the node stress transfer path follows the following mathematical model:

[0071]

[0072] in, Represents the node external force vector, which includes the force components in three directions of all nodes; Represents the overall structural stiffness matrix, reflecting the deformation resistance of each part of the structure; Represents the node displacement vector, which contains the displacement components in three directions of all nodes.

[0073] Furthermore, the stress transfer between nodes can be expressed by the transfer coefficient matrix:

[0074]

[0075] in, represents the stress transfer coefficient from node b to node a; represents the stress value of source node b; It represents the stress increment of target node a caused by source node b. By calculating the stress transfer coefficient matrix of the entire support structure, the complete transfer path of the load from the application point to the foundation is determined.

[0076] Based on the node stress transfer path, identifying stress concentration areas and marking high stress nodes are the basis for risk warning. Stress concentration areas refer to structural parts where the stress value is significantly higher than the surrounding area, which are often the weak links of structural failure. The identification process uses stress gradient analysis to calculate the stress change rate between adjacent nodes. Specifically, the stress gradient vector is calculated for each node. :

[0077]

[0078] The larger the modulus of the stress gradient vector, the higher the rate of stress change at that point, making it more likely to become a stress concentration area. By setting stress and gradient thresholds, we screen out nodes with stress values ​​exceeding 1.5 times the average structural stress and stress gradients exceeding the preset thresholds and mark them as high-stress nodes. These high-stress nodes typically occur in areas directly affected by loads, at sudden changes in component cross-sections, and at locations with varying support conditions.

[0079] Collecting deformation data from high-stress nodes and establishing a deformation-stress correspondence table is an important means of quantifying risk. Deformation data includes physical quantities such as node displacement, rotation, and strain, reflecting the structure's response under load. Data collection utilizes elastic mechanics theory and finite element analysis results to calculate the displacement vector and strain tensor for each high-stress node. When establishing the deformation-stress correspondence table, the stress value of each high-stress node is paired with the corresponding deformation and stored to form a two-dimensional comparison table. This correspondence table reveals the quantitative relationship between structural stress and deformation, providing a basis for assessing structural safety margins.

[0080] Combining the data from the deformation-stress correspondence table with the geometric position information of the stent to draw a node stress cloud diagram is an effective way to intuitively display the risk distribution. The node stress cloud diagram is a visual expression method that maps stress values ​​to colors and can intuitively display the stress distribution state of the stent structure. The drawing process uses an interpolation algorithm to expand the discrete node stress values ​​into a continuous stress field. Specifically, the radial basis function interpolation method is used to calculate the stress value of any point in space with the high stress node as the reference point:

[0081]

[0082] in, Represents the stress value at the spatial position X; represents the position coordinates of the i-th high stress node; represents the weight coefficient; represents radial basis function; Indicates the spatial position X to the node position Through this interpolation method, a continuous stress field covering the entire scaffold structure is generated and displayed intuitively in the form of a chromatogram.

[0083] The final step in risk area delineation is to extract the boundaries of the node stress cloud map, determine the stress jump interval, and form a stress distribution map for the construction node. The stress jump interval refers to the spatial area where the stress value changes significantly, and is often the key area for risk transmission. Boundary extraction uses edge detection technology in image processing to identify areas with drastic color changes in the stress cloud map. Specifically, the continuous stress field is first discretized into grid data, and then the stress gradient of adjacent grid points is calculated. When the gradient value exceeds the preset threshold, the point is marked as a boundary point. By connecting all boundary points, the contour line of the stress jump interval is formed. Finally, the original stress cloud map is superimposed with the boundary line of the stress jump interval to obtain the stress distribution map of the construction node. This map not only shows the distribution state of stress, but also clearly identifies the key areas and boundaries of risk transmission.

[0084] In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0085] (1) Extract the stress state data of key support points from the stress distribution diagram of the construction nodes and generate the static stress state table of the support;

[0086] (2) According to the construction schedule, mark the construction process of each stage of cast-in-situ box girder on the timeline to form a process progress node diagram;

[0087] (3) Align the bracket static stress state table with the process progress node diagram in the time dimension to obtain the stress-process correlation data set;

[0088] (4) For each process node in the stress-process association data set, the ratio of the support residual bearing capacity to the construction load is calculated to form a safety redundancy coefficient sequence;

[0089] (5) Filter the data of key process transition points from the safety redundancy coefficient sequence and establish a process transition risk marker set;

[0090] (6) By connecting the data points in the process transition risk marker set into a curve, the dynamic safety margin index curve of the bracket is constructed in the time-safety redundancy coordinate system.

[0091] Specifically, extracting the stress state data of key support points from the stress distribution diagram of construction nodes and generating a static stress state table of the support are the basic work for building a dynamic safety assessment model. Key support points refer to the nodes that undertake the main load transfer function in the support structure, usually including the intersection of vertical poles and horizontal poles, the connection points between the support and the main structure, and the direct load application points. The extraction process adopts a key node screening algorithm, and comprehensively scores the nodes based on the three dimensions of force magnitude, position importance and failure consequences. The group of nodes with the highest scores are selected as key support points. For each key support point, its coordinate position, current stress value, design allowable stress and stress utilization rate and other parameters are recorded to form a structured static stress state table of the support. This data table reflects the stress condition of the support at a specific moment, but has not yet taken into account the dynamic characteristics of load changes during construction. According to the construction schedule, the construction process of each stage of cast-in-place box girder is marked on the timeline to form a process progress node diagram, which is a key step in introducing the time dimension. The construction of cast-in-place box girders includes major processes such as scaffolding erection, formwork installation, steel bar binding, concrete pouring, maintenance, and scaffolding removal. Each process can be further divided into multiple sub-processes. The critical path method is used to form the process progress node diagram, arranging each process in a logical order and marking the start time, duration, and completion time of each process. Special attention should be paid to key time points where load changes significantly, such as the start time of concrete pouring, the handover time of segmented pouring, and the time when concrete reaches initial strength. These time points are usually turning points where the stress state of the scaffolding changes significantly, and are also periods of time when safety risks are concentrated.

[0092] Aligning the static stress state table of the bracket with the process progress node diagram in time dimension to obtain the stress-process correlation data set is the core link to realize dynamic analysis. Time dimension alignment uses data mapping technology to calculate the corresponding bracket stress state for each process node. In terms of specific operations, first clarify the influence of each process on the bracket load, such as the increased load of formwork installation, the accumulated load of steel bar binding, the dynamic load generated by concrete pouring, etc. Then, based on the load transfer principle, calculate the stress value of each key support point at each key time point. The stress-process correlation data set generated by this mapping process contains time information, process information and stress state information at the same time, which is the data basis for the dynamic evaluation of bracket safety.

[0093] For each process node in the stress-process association data set, calculating the ratio of the residual bearing capacity of the support to the construction load and forming a safety redundancy coefficient sequence is an important means of quantifying the safety margin. The residual bearing capacity refers to the maximum additional load that the support structure can bear under the current stress state. The calculation method is to subtract the current actual stress from the design allowable stress and then multiply it by the effective cross-sectional area of ​​the component. The construction load refers to the total load acting on the support at a specific process stage, including the dead load of the completed part and the live load of the construction part under construction. The safety redundancy coefficient is calculated using the limit state design method, that is, the ratio of the residual bearing capacity to the construction load. This coefficient reflects the ability of the support to resist additional loads at each construction stage. The larger the coefficient value, the higher the safety margin. A coefficient close to or less than 1 indicates that the structure is in a critical state or has exceeded the safety limit.

[0094] Screening out data on key process transition points from the safety redundancy coefficient sequence and establishing a process transition risk marker set is an effective method for identifying high-risk periods. Key process transition points refer to time nodes during the construction process where a change in the process causes a significant change in the load state or support stress. The screening process uses a rate of change analysis method to calculate the time derivative of the safety redundancy coefficient. When the absolute value of the derivative exceeds the preset threshold, the point is marked as a key process transition point. At the same time, the absolute value of the safety redundancy coefficient must also be considered. When the coefficient is lower than the safety threshold, even if the rate of change is not large, the point should be included in the risk marker set. The process transition risk marker set obtained in this way includes both points where the safety factor changes dramatically and points where the safety factor is relatively low, comprehensively covering potential high-risk periods.

[0095] The final step in visualizing risk trends is to connect the data points in the process conversion risk marker set into a curve and construct a dynamic safety margin index curve for the support in the time-safety redundancy coordinate system. The connection process uses piecewise linear interpolation or spline interpolation to ensure the smoothness of the curve while accurately reflecting the safety factor values ​​at each key point. The constructed safety margin index curve uses time as the horizontal axis and the safety redundancy factor as the vertical axis, intuitively showing the dynamic change trend of the support safety margin throughout the construction process. By analyzing the shape characteristics of the curve, such as steep drop sections, platform sections, and fluctuating sections, different types of risk patterns can be identified, providing a targeted strategic basis for subsequent risk prevention and control.

[0096] Taking the cast-in-place construction of the auxiliary piers of a twin-tower suspension bridge as an example, stress data for 32 key support points were extracted from the stress distribution diagram at construction nodes. The data included the spatial coordinates, current stress values, and design allowable stress values ​​for each point, creating a complete table of the static stress state of the support. Using the construction schedule, the entire cast-in-place process was divided into eight major stages: support erection completion, main beam bottom plate pouring, web reinforcement binding, web pouring, top plate reinforcement binding, top plate pouring, concrete curing, and support removal. A daily progress node diagram was generated. Through mechanical analysis, the load increment corresponding to each process node was calculated and distributed to each key support point. The stress values ​​for each key node at each time point were obtained, forming a correlated dataset encompassing the three dimensions of time, process, and stress. For each process node, the ratio of the residual bearing capacity of each key support point to the construction load at that time was calculated, generating a sequence of safety redundancy factors. The analysis found that during the transition period between the completion of the web pouring and the start of the top plate pouring, the safety redundancy coefficient dropped rapidly from 2.1 to 1.4, with a change rate of -0.35 / day, far exceeding the preset threshold of ±0.2 / day; at the same time, during the top plate pouring process, due to the combined effect of the concrete's own weight and the vibration load, the safety redundancy coefficient further dropped to 1.2, approaching the warning threshold of 1.1. Based on this, the key time points of these two stages were included in the process conversion risk marker set. Finally, all risk marker points were connected by spline interpolation, and a complete dynamic safety margin index curve of the support was drawn in the time-safety redundancy coordinate system. The curve clearly shows the changing trend of the safety margin throughout the construction process, and especially marks two high-risk periods where the safety margin dropped significantly, providing clear time guidance for prevention and control measures such as adding temporary supports, adjusting the pouring sequence, and increasing the monitoring frequency at the construction site.

[0097] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0098] (1) Detect the inflection points of the dynamic safety margin index curve of the bracket, mark the locations where the slope of the curve changes significantly, and determine the potential critical point set;

[0099] (2) Extract the minimum safety margin point from the potential critical point set through derivative analysis and identify the critical instability point;

[0100] (3) Around the critical instability point, measure the rate of decrease of the safety margin index, delineate the impact range of hazard accumulation, and form the boundary of the hazard accumulation zone;

[0101] (4) Align the critical instability points and the boundaries of the hazard accumulation zone with the construction process and establish a risk and process comparison table;

[0102] (5) For the risk points in the risk and process comparison table, set graded warning conditions according to importance and urgency, and generate warning trigger criteria;

[0103] (6) Integrate the warning trigger criteria into the process flow, arrange them in order according to the construction stages, and generate a stage-by-stage warning threshold table.

[0104] Specifically, inflection point detection is performed on the dynamic safety margin index curve of the support, marking locations where the slope of the curve changes significantly. Identifying a set of potential critical points is the first step in safety risk early warning. An inflection point is a location where the derivative of the curve changes significantly, reflecting the turning point in the support's safety status. Inflection point detection uses the curvature extreme value method, calculating the second-order derivative of the curve at each point. When the absolute value of the second-order derivative exceeds a preset threshold, the point is marked as an inflection point. In practice, the safety margin index curve is first numerically differentiated to calculate the first-order derivative (slope) and second-order derivative (slope change rate) at each point. A slope change rate threshold is then set, and points where the absolute value of the second-order derivative exceeds the threshold are selected to form a set of potential critical points. These points typically correspond to transition points in key construction processes, such as the start of pouring, formwork removal, and support adjustment, marking moments when the safety status changes significantly.

[0105] Extracting the minimum safety margin point from the potential critical point set through derivative analysis and identifying the critical instability point is a key step in finding the most dangerous period. The critical instability point refers to the point where the safety margin reaches a local minimum and the first-order derivative changes from negative to positive, representing the moment when the support safety is weakest. The extraction process first calculates the sign of the first-order derivative at each potential critical point and its changes before and after, and identifies the transition point where the derivative sign changes from negative to positive. The safety margin values ​​of these transition points are then compared, and the point with the smallest margin value is selected as the critical instability point. In some cases, there may be multiple local minimum points. At this time, a comprehensive judgment is required based on the absolute value and duration of the safety margin, and the point with the highest risk is selected as the main critical instability point. The critical instability point usually occurs when the load reaches its maximum value or the support performance is temporarily reduced. It is the time node that requires the most key monitoring during the construction process.

[0106] Measuring the rate of decline of the safety margin index around the critical instability point, delineating the hazard accumulation impact range, and forming the hazard accumulation zone boundary are crucial steps in risk area delineation. The rate of decline of the safety margin index is the absolute value of the first-order derivative of the curve during the declining phase, reflecting the speed of risk accumulation. The measurement process utilizes the interval derivative calculation method, tracing back from the critical instability point to calculate the average rate of change of the safety margin from the beginning of its decline to its minimum value. The hazard accumulation impact range is defined as the time interval from the start of a significant decline in the safety margin to the critical instability point, and from the critical instability point to the time when the safety margin recovers to the safety threshold. The delineation method sets a threshold based on the rate of change of the safety margin. When the absolute value of the rate of change exceeds the threshold, the time point is marked as the boundary of the hazard accumulation zone. This complete time range of the hazard accumulation zone is formed, centered at the critical instability point and extending to the boundary points in both directions. This represents the construction phase requiring focused monitoring and management.

[0107] Mapping critical instability points and hazard accumulation zone boundaries to construction processes and establishing a risk-process comparison table is the foundation for achieving precise risk management. The mapping process uses a time mapping method. First, the specific time coordinates of the critical instability points and hazard accumulation zone boundaries are determined. Then, the construction schedule is searched to determine the specific processes and construction activities corresponding to these time points. The established risk-process comparison table contains at least four columns of information: time coordinates, process name, risk type (such as critical instability points or hazard accumulation zone boundaries), and risk level. This comparison table intuitively shows the correspondence between high-risk time points and specific construction activities, clarifying the key objects and timing of risk management. In actual application, the comparison table can be expanded to include more information, such as specific construction locations, participating personnel, machinery and equipment, and materials, providing a detailed reference for comprehensive risk management.

[0108] For risk points in the risk-process comparison table, setting graded warning conditions based on importance and urgency and generating warning trigger criteria are key steps in establishing a warning mechanism. Warning grading typically adopts a three- or four-tier system, such as red (extremely high risk), orange (high risk), yellow (medium risk), and blue (low risk). This grading is based on two key dimensions: importance and urgency. The importance index reflects the potential severity of the consequences of a risk event and is typically correlated with the absolute value of the safety margin. The urgency index reflects the speed and time urgency of the risk development and is typically correlated with the rate of change of the safety margin. Warning trigger criteria are set using a threshold approach: when a monitored parameter reaches or exceeds a specific threshold, a warning of the corresponding level is triggered. Specifically, for risk points of different levels, corresponding combinations of safety margin thresholds and rate of change thresholds are set to form multi-tiered warning trigger conditions. These criteria consider both static risk levels and dynamic risk trends, enabling comprehensive and accurate risk warnings.

[0109] The final step in implementing the early warning system is to integrate the early warning trigger criteria into the process flow, arrange them in order according to the construction phases, and generate a phased early warning threshold table. The integration process uses a process association mapping method to map the early warning criteria to specific processes and construction phases. The generated phased early warning threshold table contains at least five columns of information: construction phase, specific process, early warning level, trigger threshold, and response measures. The early warning information for each phase is arranged in chronological order according to the construction progress, forming an early warning system synchronized with the construction process. This threshold table not only clarifies the early warning conditions for each stage but also indicates the specific measures to be taken after the early warning is triggered. It is an important guiding document for construction site safety management. In actual application, the threshold table can also be dynamically adjusted based on construction progress and feedback from monitoring data to ensure the continued effectiveness of the early warning mechanism.

[0110] Taking the cantilever cast-in-place construction of a continuous beam bridge as an example, the curvature extreme value method was applied to the dynamic safety margin index curve of the support to detect inflection points. Setting the second-order derivative threshold at 0.05, 15 locations where the slope of the curve changed significantly were identified, forming a set of potential critical points. Derivative analysis revealed that three of these points exhibited the characteristic of a negative-to-positive derivative and a low safety margin. These corresponded to the completion of concrete pouring for block 0, the start of concrete pouring for block 1, and the prestressing of block 3. Comparing the safety margin values ​​at these three points, the safety margin during prestressing for block 3 was the lowest, reaching 1.18, identifying it as the primary critical instability point. Calculating the safety margin decline rate centered on this critical instability point revealed an average decline rate of -0.06 / day from the concrete pouring of block 2 to the prestressing of block 3, exceeding the pre-set threshold of -0.04 / day. Based on this, the period from the beginning of concrete pouring for block 2 to the completion of prestressing for block 3 was designated as a hazard accumulation zone, lasting approximately 12 days. By mapping critical instability points and the boundaries of the hazard accumulation zone to construction processes, a detailed risk-process comparison table was established, clearly showing that high risks are primarily concentrated in specific stages and processes of continuous beam construction. Based on this comparison table, four levels of warning conditions were established: a red warning is triggered when the safety margin is less than 1.2 and the rate of change is less than -0.05 / day; an orange warning is triggered when the safety margin is between 1.2-1.3 or the rate of change is between -0.04 and -0.05 / day; a yellow warning is triggered when the safety margin is between 1.3-1.5 or the rate of change is between -0.03 and -0.04 / day; and a regular blue monitoring status is maintained when the safety margin is greater than 1.5 and the rate of change is greater than -0.03 / day. Ultimately, these early warning trigger criteria were integrated into the process flow according to the construction sequence, forming a phased early warning threshold table, which provided clear implementation standards for on-site monitoring and safety management, and effectively guided the implementation of prevention and control measures such as support reinforcement, load control, and monitoring frequency adjustment during the construction of Blocks 2 to 3.

[0111] In a specific embodiment, the process of executing step S106 may specifically include the following steps:

[0112] (1) Set up corresponding monitoring points based on the staged warning threshold table, collect real-time monitoring data streams, and build a prevention and control data collection network;

[0113] (2) Compare the data in the prevention and control data collection network with the staged warning threshold table, calculate the threshold deviation, and generate a prevention and control response effectiveness graph;

[0114] (3) For the low-efficiency areas in the prevention and control response effectiveness diagram, extract the characteristics of related prevention and control measures and establish a traceability table of prevention and control measure defects;

[0115] (4) Sort the items in the prevention and control measures defect traceability table by risk level, determine the prevention and control optimization focus, and form a prevention and control upgrade priority sequence;

[0116] (5) Convert the priority sequence of prevention and control upgrades into specific technical adjustment parameters and formulate a plan for adjusting prevention and control measures;

[0117] (6) By linking the prevention and control measures adjustment plan with the original prevention and control measures into a sequence structure, a closed-loop optimization prevention and control strategy chain with iteration is formed.

[0118] Specifically, setting up corresponding monitoring points based on the staged warning threshold table, collecting real-time monitoring data streams, and building a prevention and control data collection network are the basic links for achieving real-time risk monitoring. The setting of monitoring points follows the principle of risk coverage, that is, monitoring equipment is preferentially deployed in high-risk areas identified in the warning threshold table. The types of monitoring points include stress monitoring points, deformation monitoring points, inclination monitoring points, and settlement monitoring points. Different types of monitoring points collect data of different physical quantities. The layout of monitoring points adopts a combination of grid distribution and key encryption to form a uniformly distributed basic monitoring network on the entire support structure, and appropriately encrypts the layout of monitoring points in critical instability points and hazard accumulation areas. Data collection uses an automated monitoring system to continuously record data from each monitoring point at a preset frequency, and upload it to the data processing center in real time via wireless transmission or wired network. The constructed prevention and control data collection network is a multi-level, full-coverage data flow channel that ensures real-time visibility of risk status throughout the construction process.

[0119] Comparing the data in the prevention and control data collection network with the staged warning threshold table, calculating the threshold deviation, and generating a prevention and control response effectiveness map are key steps in evaluating the effectiveness of prevention and control measures. The threshold deviation refers to the distance between the measured data and the warning threshold, reflecting the difference between the actual risk state and the expected risk state. The calculation method adopts the normalized difference method, that is, the difference between the measured value and the threshold is divided by the threshold to obtain the relative degree of deviation. For each time series data of each monitoring point, its deviation from the corresponding warning threshold is calculated to form a deviation matrix. Prevention and control response effectiveness refers to the ability of prevention and control measures to reduce risks, which is usually measured by the degree of improvement in threshold deviation. Specifically, the changes in threshold deviation before and after the implementation of prevention and control measures are compared, and the percentage of deviation reduction is calculated as the response effectiveness value. The response effectiveness value is mapped to the spatial coordinate system to form a prevention and control response effectiveness map, which intuitively shows the effectiveness distribution of prevention and control measures in different regions and different process stages.

[0120] Extracting the characteristics of associated prevention and control measures from low-efficiency areas in the prevention and control response effectiveness map and establishing a traceability table for prevention and control measure defects is an effective way to identify prevention and control weaknesses. Low-efficiency areas are time periods or spatial regions where the response effectiveness value falls below a preset threshold, indicating that current prevention and control measures are ineffective in that area. When extracting the characteristics of associated prevention and control measures, the specific processes and construction locations corresponding to the low-efficiency areas are first determined, and then a list of prevention and control measures already implemented in that area is found. For each prevention and control measure, its technical parameters, implementation methods, and coverage are analyzed to identify factors that may have led to low effectiveness. These factors may include insufficient coverage of measures, mismatched technical parameters, inappropriate implementation timing, or poor coordination between measures. These factors are mapped one-to-one to specific prevention and control measures to form a traceability table for prevention and control measure defects. This table clearly displays the causes and specific manifestations of the defects of each low-efficiency prevention and control measure, providing a clear direction for subsequent optimization.

[0121] Sorting the items in the prevention and control measures defect traceability table according to the degree of risk, determining the focus of prevention and control optimization, and forming a priority sequence for prevention and control upgrades are important links in resource optimization allocation. The sorting process adopts a multi-factor comprehensive scoring method, which comprehensively considers the three dimensions of risk level, impact range, and governance difficulty caused by defects. The risk level is based on the safety margin deviation assessment. The greater the deviation, the higher the risk; the impact range is based on the construction area and number of processes involved in the defect. The wider the scope, the greater the impact; the governance difficulty is based on the technical complexity and resource demand assessment. The higher the difficulty, the greater the cost. The comprehensive score of each defect is calculated through weighted summation or hierarchical analysis method, and sorted from high to low by score to form a priority sequence for prevention and control upgrades. This sequence clarifies the order of optimization of prevention and control measures and ensures that limited resources can be used most efficiently at the most critical risk points.

[0122] Converting the priority sequence of prevention and control upgrades into specific technical adjustment parameters and formulating adjustment plans for prevention and control measures are the core steps to achieve precise optimization. Technical adjustment parameters include multiple dimensions such as support structure parameters, construction process parameters, and monitoring and control parameters. The conversion process uses a parameter mapping method to determine the specific parameters and target values ​​that need to be adjusted based on the defect type and risk characteristics. For example, for defects with insufficient support strength, it may be necessary to adjust the support density, rod cross-section, or material strength; for defects with incomplete monitoring coverage, it may be necessary to adjust the monitoring point layout, sampling frequency, or warning threshold. The formulated prevention and control measure adjustment plan is a set of systematic technical documents that detail the specific adjustment content, adjustment range, implementation method, and verification method for each prevention and control measure that needs to be optimized, providing clear guidance for on-site implementation.

[0123] The final step to achieving continuous improvement is to link the prevention and control measures adjustment plan with the original prevention and control measures into a sequence structure to form an iterative closed-loop optimization prevention and control strategy chain. The sequence structure refers to arranging the prevention and control measures before and after the adjustment in chronological order to form an evolutionary chain of prevention and control versions. The linking process adopts a progressive integration method, which not only retains the original effective prevention and control measures, but also adds new optimization measures, while setting clear version identification and effect evaluation points. Closed-loop optimization means that after each round of prevention and control measures is implemented, it returns to the data collection and comparison link to evaluate the effectiveness of the new measures, and conducts the next round of optimization based on the evaluation results, forming a continuous improvement cycle. This closed-loop structure ensures that the prevention and control system can dynamically adapt to changes in the construction process and continuously improve the accuracy and effectiveness of risk prevention and control.

[0124] Taking the steel box girder installation of a cross-river bridge as an example, 42 monitoring points were set up in the steel box girder installation area based on a phased early warning threshold table. These included 24 stress monitoring points, 12 deformation monitoring points, and 6 inclination monitoring points. These points collected data on the support stress state in real time, establishing a comprehensive prevention and control data collection network. During the 14-day installation process, the system collected data hourly, generating a total of 14,112 data points. Comparing these data with the early warning threshold table revealed that the threshold deviations of three stress monitoring points in the central support area continued to increase between days 8 and 10, reaching a maximum of 0.78. However, the temporary reinforcement measures implemented only reduced the deviations by 0.13, resulting in a response efficiency value of only 16.7%, far below the expected 65% efficiency. Consequently, the area was marked as low-efficiency. Analysis of the temporary reinforcement measures implemented in this area revealed three major deficiencies: insufficient support density due to insufficient reinforcement rod placement, poor stress transfer due to poorly designed rod connection nodes, and excessive local loads due to improper construction sequencing. These deficiencies were mapped to specific control measures, and a traceability table for control measure defects was established. Using a multi-factor scoring method, each defect was comprehensively assessed based on its risk level (weight 0.5), impact (weight 0.3), and difficulty of remediation (weight 0.2). Stress transfer issues caused by poor connection node design received the highest score, placing them at the top of the priority list for control and prevention upgrades. Based on this, a specific adjustment plan for control and prevention measures was developed, including technical measures such as adding stiffeners to connection nodes, improving node connection methods, and optimizing load transfer paths. The implementation steps and verification methods for each adjustment were also clearly defined. Ultimately, the adjustment plan was integrated with the original control and prevention measures into a sequential structure, forming a complete closed-loop optimization control and prevention strategy chain. In a post-implementation evaluation, the response efficiency of the central support area increased to 78.5%, effectively addressing the inefficiency of the original control and prevention measures.

[0125] The above describes the method for identifying and analyzing bridge construction hazards in the embodiment of the present application. The following describes the system for identifying and analyzing bridge construction hazards in the embodiment of the present application. Figure 2 In one embodiment of the present application, a system for identifying and analyzing bridge construction hazards includes:

[0126] The acquisition module is used to collect geological parameters, road and river proximity parameters, and support load parameters for cast-in-place construction of bridge supports, and to establish a bridge construction hazard information database;

[0127] A generation module is used to perform three-dimensional mapping of the bridge construction hazard information database and generate a support-load-environment correlation risk matrix;

[0128] An extraction module is used to extract the stress values ​​and deformations of key nodes according to the support-load-environment correlation risk matrix to form a stress distribution diagram of the construction nodes;

[0129] A construction module is used to integrate the support stress state and the construction process progress based on the stress distribution diagram of the construction node to construct a dynamic safety margin index curve of the support;

[0130] A calculation module, configured to calculate the critical instability point and the danger accumulation area based on the dynamic safety margin index curve of the support, and generate a staged warning threshold table;

[0131] The quantification module is used to use the staged warning threshold table to quantitatively verify the effectiveness of the implementation of prevention and control measures, forming a closed-loop optimization prevention and control strategy chain.

[0132] Through the collaborative cooperation of the above-mentioned components, a comprehensive bridge construction hazard information database was established by collecting geological parameters, road and river parameters, and scaffold load parameters of the cast-in-place construction of bridge scaffolds, realizing the systematic collection and integration of multi-dimensional risk factors, and laying a solid data foundation for subsequent risk analysis; the bridge construction hazard information database was three-dimensionally mapped to generate a scaffold-load-environment correlation risk matrix, breaking through the limitations of traditional single-dimensional risk assessment, and establishing a mathematical correlation model between scaffold structure, load distribution and environmental impact, making risk analysis more comprehensive and systematic; based on the scaffold-load-environment correlation risk matrix, the stress values ​​and deformations of key nodes were extracted to form a stress distribution map of construction nodes, realizing the precise positioning and visualization of risk points. It directly reflects the stress state and weak links of the support structure; based on the stress distribution diagram of the construction node, the stress state of the support is integrated with the progress of the construction process, and the dynamic safety margin index curve of the support is constructed. For the first time, the static risk analysis is combined with the dynamic construction progress, and the dynamic risk assessment of the whole construction process is realized, so that the risk warning has time-series pertinence; according to the dynamic safety margin index curve of the support, the critical instability point and the danger accumulation area are calculated, and a staged warning threshold table is generated, which realizes the quantification and grading of risk warning and improves the accuracy and timeliness of the warning; using the staged warning threshold table, the implementation effect of the prevention and control measures is quantitatively verified, forming a closed-loop optimization prevention and control strategy chain, and establishing a continuous improvement mechanism for prevention and control measures to ensure the effectiveness and adaptability of risk prevention and control. It is particularly noteworthy that this solution utilizes artificial intelligence algorithms in several key areas, including a density clustering algorithm for three-dimensional mapping of the hazard information database, graph data mining for key node extraction, radial basis function interpolation for stress distribution map generation, and a time series data prediction model for dynamic safety margin curve construction. These algorithms contribute significantly to the solution's success: the density clustering algorithm accurately identifies risk clusters in multidimensional data space, improving the accuracy of risk identification; graph data mining intelligently extracts topological relationships within the support structure, enhancing the precision of mechanical analysis; the radial basis function interpolation algorithm optimizes the conversion of discrete point data to continuous fields, improving the accuracy and reliability of stress distribution maps; and the time series data prediction model provides dynamic prediction capabilities for safety margin assessment, making the early warning mechanism more proactive. This represents a significant shift from static, single-point, empirical risk assessment to dynamic, multidimensional, and intelligent risk management, significantly enhancing the safety management level of cast-in-place bridge support construction.

[0133] Reference Figure 3 In an embodiment of the present invention, a computer device is also provided. The computer device may be a server, and its internal structure may be as follows: Figure 3As shown. The computer device includes a processor, memory, display screen, input device, network interface and database connected via a system bus. The processor of the computer design is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the above method is implemented.

[0134] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0135] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which implements the above-described method when executed by a processor. It is understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0136] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware using a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the above-described method embodiments. Any reference to memory, storage, database, or other media provided herein and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM.

[0137] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0138] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0139] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for identifying and analyzing bridge construction hazards, characterized in that: The method for identifying and analyzing bridge construction hazards includes: Collect geological parameters, road and river proximity parameters, and support load parameters for cast-in-place construction of bridge supports to establish a bridge construction hazard information database; Performing three-dimensional mapping on the bridge construction hazard information database to generate a support-load-environment correlation risk matrix; Extracting stress values ​​and deformations of key nodes based on the support-load-environment correlation risk matrix to form a stress distribution diagram of construction nodes; Based on the stress distribution diagram of the construction nodes, the stress state of the bracket is integrated with the progress of the construction process to construct a dynamic safety margin index curve of the bracket, including: extracting the stress state data of the key support points from the stress distribution diagram of the construction nodes to generate a static stress state table of the bracket; marking the processes of each stage of the cast-in-place box girder construction on the time axis by comparing with the construction schedule to form a process progress node diagram; aligning the static stress state table of the bracket with the process progress node diagram in the time dimension to obtain a stress-process association data set; for each process node in the stress-process association data set, calculating the ratio of the bracket residual bearing capacity to the construction load to form a safety redundancy coefficient sequence; screening the data of the key process conversion points from the safety redundancy coefficient sequence to establish a process conversion risk marker set; by connecting the data points in the process conversion risk marker set into a curve, constructing a dynamic safety margin index curve of the bracket in the time-safety redundancy coordinate system; According to the dynamic safety margin index curve of the support, the critical instability point and the danger accumulation area are calculated, and a staged warning threshold table is generated; The staged warning threshold table is used to quantitatively verify the effectiveness of the implementation of prevention and control measures, forming a closed-loop optimization prevention and control strategy chain.

2. The method for identifying and analyzing bridge construction hazards according to claim 1 is characterized in that: The collection of geological parameters, road and river proximity parameters, and support load parameters for cast-in-place construction of bridge supports to establish a bridge construction hazard information database includes: Conduct geological surveys of the bridge construction area to collect data on foundation bearing capacity, distribution of weak geological layers, and groundwater level changes to form a geological parameter dataset; By measuring the distance, elevation difference, and intersection angle between the bridge and adjacent roads and rivers, we can obtain spatial positional relationship data of adjacent roads and rivers and construct a parameter dataset of adjacent roads and rivers. According to the design drawings of the bridge box girder structure, calculate the concrete deadweight, construction load and preload borne by the support, and establish a support load parameter data set; The geological parameter data set, the adjacent road and river parameter data set, and the support load parameter data set are spatially divided according to the construction area to generate a spatial distribution map of bridge construction hazard sources; Based on the spatial distribution map of bridge construction hazards, extract data on hazard-intensive areas and determine high-incidence points of hazards; Conduct a preliminary risk assessment on the high-incidence points of the hazard sources, classify them according to the risk level, and integrate them to generate a bridge construction hazard information database.

3. The method for identifying and analyzing bridge construction hazards according to claim 1 is characterized in that: The three-dimensional mapping of the bridge construction hazard information database to generate a support-load-environment correlation risk matrix includes: The data in the bridge construction hazard information database is classified according to the support structure dimension, load distribution dimension and environmental impact dimension to generate a three-dimensional original data block; Performing coordinate system conversion on the three-dimensional original data block to construct a support-load-environment space rectangular coordinate system; Extracting characteristic points from the support-load-environment space rectangular coordinate system to form characteristic point cloud data; Performing density clustering on the feature point cloud data to identify interaction intervals of risk factors; Mapping the risk factor interaction interval to the risk level scale space to obtain a risk intensity distribution map; The risk intensity distribution map is discretized, the relationship between the risk intensity value and the position coordinate is extracted, and a support-load-environment correlation risk matrix is ​​generated.

4. The method for identifying and analyzing bridge construction hazards according to claim 1, characterized in that: The step of extracting stress values ​​and deformations of key nodes based on the support-load-environment correlation risk matrix to form a stress distribution diagram of construction nodes includes: Extract the support geometric topology data from the support-load-environment correlation risk matrix and establish the support node network topology diagram; Performing stress analysis on each connection point in the support node network topology diagram and calculating the node stress transfer path; Based on the node stress transfer path, identifying stress concentration areas and marking high stress nodes; Collect deformation data from the high stress nodes and establish a deformation-stress correspondence table; Combining the data in the deformation-stress correspondence table with the bracket geometric position information to draw a node stress cloud diagram; By extracting the boundaries of the node stress cloud map, the stress jump interval is determined to form a construction node stress distribution map.

5. The method for identifying and analyzing bridge construction hazards according to claim 1 is characterized in that: The method of calculating the critical instability point and the danger accumulation area based on the dynamic safety margin index curve of the bracket and generating a staged warning threshold table includes: Perform inflection point detection on the dynamic safety margin index curve of the bracket, mark the location where the slope of the curve changes significantly, and determine the potential critical point set; Extracting the minimum safety margin point from the potential critical point set through derivative analysis to identify the critical instability point; Around the critical instability point, the rate of decrease of the safety margin index is measured, the impact range of the hazard accumulation is delineated, and the boundary of the hazard accumulation zone is formed; Corresponding the critical instability points and the boundaries of the hazard accumulation zone to the construction process, and establishing a risk and process comparison table; For the risk points in the risk and process comparison table, set graded warning conditions according to importance and urgency, and generate warning trigger criteria; The warning trigger criteria are integrated into the process flow, arranged in order of construction stages, and a stage-by-stage warning threshold table is generated.

6. The method for identifying and analyzing bridge construction hazards according to claim 1 is characterized in that: The use of the staged warning threshold table to quantitatively verify the effectiveness of the prevention and control measures to form a closed-loop optimization prevention and control strategy chain includes: Set up corresponding monitoring points based on the staged warning threshold table, collect real-time monitoring data streams, and build a prevention and control data collection network; Comparing the data in the prevention and control data collection network with the staged warning threshold table, calculating the threshold deviation, and generating a prevention and control response effectiveness graph; For the low-efficiency areas in the control response effectiveness graph, extract the characteristics of related control measures and establish a control measure defect traceability table; Sort the items in the prevention and control measures defect traceability table by risk level, determine the prevention and control optimization focus, and form a prevention and control upgrade priority sequence; Convert the priority sequence of prevention and control upgrades into specific technical adjustment parameters and formulate a plan for adjusting prevention and control measures; By linking the prevention and control measures adjustment plan with the original prevention and control measures into a sequence structure, a cyclic iterative closed-loop optimization prevention and control strategy chain is formed.

7. A system for identifying and analyzing bridge construction hazards, for implementing the method for identifying and analyzing bridge construction hazards as claimed in any one of claims 1 to 6, characterized in that: The bridge construction hazard identification and risk analysis system includes: The acquisition module is used to collect geological parameters, road and river proximity parameters, and support load parameters for cast-in-place construction of bridge supports, and to establish a bridge construction hazard information database; A generation module is used to perform three-dimensional mapping on the bridge construction hazard information database to generate a support-load-environment correlation risk matrix; An extraction module is used to extract the stress values ​​and deformations of key nodes according to the support-load-environment correlation risk matrix to form a stress distribution diagram of the construction nodes; A construction module is used to integrate the support stress state and the construction process progress based on the construction node stress distribution diagram to construct a dynamic safety margin index curve of the support, including: extracting the stress state data of the key support points from the construction node stress distribution diagram to generate a static stress state table of the support; marking the processes of each stage of cast-in-place box girder construction on the time axis by comparing with the construction schedule to form a process progress node diagram; aligning the static stress state table of the support with the process progress node diagram in the time dimension to obtain a stress-process association data set; for each process node in the stress-process association data set, calculating the ratio of the support residual bearing capacity to the construction load to form a safety redundancy coefficient sequence; screening the data of the key process conversion points from the safety redundancy coefficient sequence to establish a process conversion risk marker set; and constructing a dynamic safety margin index curve of the support in the time-safety redundancy coordinate system by connecting the data points in the process conversion risk marker set into a curve; A calculation module, configured to calculate the critical instability point and the danger accumulation area based on the dynamic safety margin index curve of the support, and generate a staged warning threshold table; The quantification module is used to use the staged warning threshold table to quantitatively verify the effectiveness of the implementation of prevention and control measures, forming a closed-loop optimization prevention and control strategy chain.

8. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and is characterized in that when the processor executes the computer program, it implements the method for identifying and analyzing bridge construction hazards as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that A computer program is stored thereon, and when the computer program is executed by a processor, the processor is enabled to execute the method for identifying and analyzing bridge construction hazards as described in any one of claims 1 to 6.

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