Bridge construction hazard source identification and risk analysis method and system

Abstract

The invention relates to the technical field of construction risk identification, and discloses a bridge construction hazard source identification and risk analysis method and system. The method comprises the following steps: collecting geological, adjacent-channel and adjacent-river and support load parameters to establish a danger information base; performing three-dimensional mapping to generate an associated risk matrix; extracting key node data to form a stress distribution diagram; fusing the process progress to construct a safety margin curve; calculating a critical point to generate an early warning threshold table; and quantitatively verifying the prevention and control effect to form a closed-loop optimization strategy chain. According to the method, the bracket-load-environment three-dimensional association risk matrix and the dynamic safety margin index curve are established, and the risk dynamic identification, accurate early warning and closed-loop optimization of the whole construction process are realized, so that the safety management level of bridge bracket cast-in-place construction is improved, and the probability of construction safety accidents is reduced.

Description

Technical Field

[0001] This application relates to the technical field of construction risk identification, and particularly to a method and system for identifying and analyzing bridge construction hazard sources. Background Art

[0002] At present, cast-in-situ construction with bridge supports is a commonly used construction method in large bridge projects, especially suitable for on-site casting requirements under complex terrain conditions. Traditional safety management for cast-in-situ construction with bridge supports mainly relies on experience judgment and static detection. Common safety management methods include regular patrol inspections, key node monitoring, and stage acceptance evaluations. In the specific implementation process, engineers usually use means such as design load verification, static analysis, and finite element simulation to evaluate the safety status of the supports, and at the same time, 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 means for risk management, such as using GIS systems combined with BIM technology for three-dimensional visualization management of the construction site, improving the intuitiveness and accuracy of hazard source identification.

[0003] However, the existing safety management methods for cast-in-situ construction with bridge supports 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 mostly focus on the influence of single factors and lack comprehensive analysis of the complex interaction between geological conditions, adjacent road and river environments, and support loads, resulting in some potential risk points being ignored. In terms of safety early warning, existing methods are mostly based on static threshold judgment, which is difficult to adapt to the dynamic changes of the support force state during the whole construction process, and it is easy to have the situation of late warning or false alarm. In terms of evaluating the implementation effect of prevention and control measures, there is a lack of quantitative evaluation criteria and a closed-loop optimization mechanism, making it difficult to improve the effectiveness of prevention and control measures targeted. These deficiencies lead to relatively high safety risks during the support construction process, especially in large bridge projects under special environments such as complex geological conditions, adjacent to traffic or water areas, and 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 hazard sources, which is used to establish a three-dimensional correlation risk matrix and a dynamic safety margin index curve of support-load-environment, realize dynamic risk identification, accurate early warning, and closed-loop optimization throughout the construction process, thereby improving the safety management level of cast-in-situ construction with bridge supports and reducing the probability of construction safety accidents.

[0005] In a first aspect, the present application provides a method for identifying and analyzing hazards in bridge construction. The method for identifying and analyzing hazards in bridge construction includes: collecting geological parameters, roadside and riverside parameters, and support load parameters of in-situ cast bridge supports, and establishing 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 key node stress values and deformation amounts according to the support-load-environment correlation risk matrix to form a construction node stress distribution map; based on the construction node stress distribution map, integrating the stress state of the support with the progress of the construction process to construct a dynamic safety margin index curve of the support; calculating the critical instability point and the dangerous accumulation area according to the dynamic safety margin index curve of the support to generate a phased early warning threshold table; and using the phased early warning threshold table to quantitatively verify the implementation effect of the prevention and control measures to form a closed-loop optimized prevention and control strategy chain.

[0006] In a second aspect, the present application provides a system for identifying and analyzing hazards in bridge construction. The system for identifying and analyzing hazards in bridge construction includes: a collection module, configured to collect geological parameters, roadside and riverside parameters, and support load parameters of in-situ cast bridge supports, and establish a bridge construction hazard information database; a generation module, configured to perform three-dimensional mapping on the bridge construction hazard information database to generate a support-load-environment correlation risk matrix; an extraction module, configured to extract key node stress values and deformation amounts according to the support-load-environment correlation risk matrix to form a construction node stress distribution map; a construction module, configured to integrate the stress state of the support with the progress of the construction process based on the construction node stress distribution map to construct a dynamic safety margin index curve of the support; a calculation module, configured to calculate the critical instability point and the dangerous accumulation area according to the dynamic safety margin index curve of the support to generate a phased early warning threshold table; a quantification module, configured to quantitatively verify the implementation effect of the prevention and control measures by using the phased early warning threshold table to form a closed-loop optimized prevention and control strategy chain.

[0007] In a third aspect of the present invention, there is provided a computer device, including: a memory and at least one processor, wherein instructions are stored in the memory; the at least one processor invokes the instructions in the memory so that the computer device executes the above-mentioned method for identifying and analyzing hazards in bridge construction.

[0008] In a fourth aspect of the present invention, there is provided a computer-readable storage medium, wherein instructions are stored in the computer-readable storage medium, and when the instructions are run on a computer, the computer is caused to execute the above-mentioned method for identifying and analyzing hazards in bridge construction.

[0009] In the technical solution provided by this application, by collecting geological parameters, adjacent road and river parameters, and support load parameters during the cast-in-place construction of bridge supports, a comprehensive bridge construction hazard information database is established, realizing the systematic collection and integration of multi-dimensional risk factors, and laying a solid data foundation for subsequent risk analysis; performing three-dimensional mapping on the bridge construction hazard information database to generate a support-load-environment correlation risk matrix, breaking through the limitations of traditional single-dimensional risk assessment, establishing a mathematical correlation model among the support structure, load distribution, and environmental impact, making risk analysis more comprehensive and systematic; according to the support-load-environment correlation risk matrix, extracting the stress values and deformation amounts of key nodes to form a stress distribution map of construction nodes, realizing the precise positioning and visual expression of risk points, and intuitively reflecting the stress state and weak links of the support structure; based on the stress distribution map of construction nodes, integrating the stress state of the support with the progress of the construction process to construct a dynamic safety margin index curve of the support, for the first time combining static risk analysis with dynamic construction progress, realizing the dynamic risk assessment of the entire construction process, and making risk early warning have time-series pertinence; according to the dynamic safety margin index curve of the support, calculating the critical instability point and the dangerous accumulation area, generating a phased early warning threshold table, realizing the quantification and classification of risk early warning, improving the accuracy and timeliness of early warning; using the phased early warning threshold table to quantitatively verify the implementation effect of prevention and control measures, forming a closed-loop optimized prevention and control strategy chain, establishing a continuous improvement mechanism for prevention and control measures, and ensuring the effectiveness and adaptability of risk prevention and control. It is particularly worth emphasizing that artificial intelligence algorithms are applied in multiple key links in this solution, such as the density clustering algorithm used in the three-dimensional mapping process of the hazard information database, the graph data mining technology applied in the extraction of key nodes, the radial basis function interpolation algorithm used in the generation process of the stress distribution map, and the time-series data prediction model applied in the construction of the dynamic safety margin curve. The characteristics of these algorithms have made important contributions to the solution: the density clustering algorithm can accurately identify the risk aggregation areas in the multi-dimensional data space, improving the accuracy of risk identification; the graph data mining technology realizes the intelligent extraction of the topological relationship of the support structure, enhancing the accuracy of mechanical analysis; the radial basis function interpolation algorithm optimizes the conversion process from discrete point data to a continuous field, improving the accuracy and reliability of the stress distribution map; the time-series data prediction model endows the safety margin assessment with dynamic prediction ability, making the early warning mechanism have foresight. It realizes the leap from static, single, and empirical risk assessment to dynamic, multi-dimensional, and intelligent risk management, significantly improving the safety management level of the cast-in-place construction of bridge supports. Description of the Drawings

[0010] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0011] Figure 1 It is a schematic diagram of an embodiment of the method for identifying and analyzing bridge construction hazard sources in the embodiments of the present application; Figure 2 It is a schematic diagram of an embodiment of the system for identifying and analyzing bridge construction hazard sources in the embodiments of the present application; Figure 3 It is a structural schematic block diagram of a computer device in the embodiments of the present invention. Detailed implementation manners

[0012] The embodiments of the present application provide a method and a system for identifying and analyzing bridge construction hazard sources. The terms "first", "second", "third", "fourth", etc. (if any) in the specification, claims and above-mentioned drawings of the present application are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments described here can be implemented in an order other than that illustrated or described here. In addition, the term "comprising" or "having" and any variation thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0013] For ease of understanding, the following describes the specific process of the embodiments of the present application. Please refer to Figure 1 , an embodiment of the method for identifying and analyzing bridge construction hazard sources in the embodiments of the present application includes: Step S101: Collect geological parameters, adjacent road and river parameters, and support load parameters of the cast-in-place construction of the bridge support, and establish a bridge construction hazard information database; Step S102: Perform three-dimensional mapping on the bridge construction hazard information database to generate a support-load-environment correlation risk matrix; Step S103: According to the support-load-environment correlation risk matrix, extract the stress values and deformation amounts of key nodes to form a construction node stress distribution diagram; Step S104: Based on the construction node stress distribution diagram, fuse the stress state of the support with the progress of the construction process to construct a support dynamic safety margin index curve; Step S105: Calculate the critical instability point and the dangerous accumulation area based on the dynamic safety margin index curve of the support, and generate a phased early warning threshold table. Step S106: Use the phased early warning threshold table to quantitatively verify the implementation effect of the prevention and control measures, and form a closed-loop optimized prevention and control strategy chain.

[0014] It can be understood that the execution entity of this application can be a system for identifying and analyzing the hazards of bridge construction, or it can also be a terminal or a server, and specific limitations are not made here. In this embodiment of the application, the server is used as the execution entity for illustration.

[0015] Specifically, first, collect the geological parameters, parameters adjacent to roads and rivers, and support load parameters of the in-situ cast bridge support construction, and establish a bridge construction hazard information database. The collection of geological parameters includes conducting a geological survey of the construction area, collecting data on the bearing capacity of the foundation, and the "requirement for the bearing capacity of the foundation is not less than 250 kPa" clearly stated in the disclosure letter. It is also necessary to collect data on the distribution of geological soft layers and the change data of the groundwater level. The collection of parameters adjacent to roads and rivers involves measuring the distance, height difference, and intersection angle between the bridge and the adjacent roads and rivers. The disclosure letter mentions that "Taoyuan No. 1 Bridge is designed to cross roads and rivers", and the distance and height difference from the X078 rebuilt road need to be recorded. The support load parameters are calculated based on the structural design of the bridge box girder. The disclosure letter mentions that the preloading load calculation needs to consider that "the unit weight of concrete is 26 kN / m³, and the combined load of formwork load, personnel and equipment load, and vibration load is 6.5 kN / m²". These data are divided according to the spatial division of the construction area to generate a spatial distribution map of bridge construction hazards, extract data on densely populated hazard areas, and form a bridge construction hazard information database.

[0016] When performing three-dimensional mapping on the bridge construction hazard information database, first classify the data according to the support structure dimension, load distribution dimension, and environmental impact dimension to generate three-dimensional original data blocks. The support structure dimension focuses on the geometric configuration of the support, the support form and layout in the "support material processing" and "support erection" described in the disclosure letter; the load distribution dimension includes the load distribution in the preloading load sub-region layout calculation table; the environmental impact dimension includes geological conditions and the surrounding environmental conditions. Convert the data in these three dimensions to the support-load-environment space rectangular coordinate system, extract characteristic points from it, and form characteristic point cloud data. Perform density clustering on the characteristic point cloud data to identify the interaction intervals of risk factors. "Lack of edge protection" and "irregular setting of the operation passage for the upper and lower steel columns" in the disclosure letter are typical interaction points of risk factors. Map these interaction intervals of risk factors to the risk level scale space to obtain a risk intensity distribution map, and then through discretization processing, extract the relationship between the risk intensity value and the position coordinates to generate a support-load-environment correlation risk matrix.

[0017] According to the support-load-environment correlation risk matrix, the stress values ​​and deformations of key nodes are extracted to form a stress distribution diagram of construction nodes. First, the support geometric topological structure data is extracted from the risk matrix to establish the support node network topological diagram. The "disk-type full-floor support" structure mentioned in the briefing book is the basis of this topological diagram. The force analysis of each connection point in the support node network topological diagram is carried out to calculate the node stress transfer path. The briefing book mentions the path of "load transfer from top to bottom", such as "2cm thick bamboo plywood → 10cm×10cm square wood → I14 I-beam → adjustable top support → φ60×3.2mm steel pipe vertical pole → adjustable base → I20a distribution beam → Bailey plate → double 600×200H steel beam → steel pipe column → C30 concrete foundation". According to these stress transfer paths, stress concentration areas are identified and high stress nodes are marked. Deformation data is collected from high-stress nodes, a deformation-stress correspondence table is established, the data is combined with the support geometric position information, a node stress cloud map is drawn, the stress jump range is determined by boundary extraction, and a stress distribution map of the construction node is formed.

[0018] 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 the dynamic safety margin index curve of the bracket. First, the stress state data of the key support points are extracted from the stress distribution diagram of the construction nodes to generate the static stress state table of the bracket. According to the construction schedule, the processes of each stage of the cast-in-place box girder construction are marked on the timeline. The process flow mentioned in the briefing book includes "foundation treatment and foundation construction → bracket installation → bracket preloading → installation of side formwork → installation of bottom and web reinforcement → installation of inner formwork → installation of top and web reinforcement and formwork → pouring concrete → threading steel strands → tensioning → grouting and end sealing → bracket removal". The static stress state table of the bracket is aligned with the process progress node diagram in the time dimension to obtain the stress-process association data set. For each process node in the stress-process association data set, the ratio of the bracket's residual bearing capacity to the construction load is calculated to form a safety redundancy coefficient sequence. The data of the key process conversion points are selected from the safety redundancy coefficient sequence, and a process conversion risk marker set is established. The data points in the process conversion risk marker set are connected into a curve to construct the dynamic safety margin index curve of the bracket.

[0019] According to the dynamic safety margin index curve of the support, calculate the critical instability point and the dangerous accumulation area, and generate a phased early warning threshold table. Through inflection point detection, mark the positions with significant changes in the curve slope to determine the set of potential critical points. Extract the minimum safety margin point from the set of potential critical points through derivative analysis to identify the critical instability point. Around the critical instability point, measure the decline rate of the safety margin index, delimit the scope of the dangerous accumulation impact, and form the boundary of the dangerous accumulation area. Correlate the critical instability point and the boundary of the dangerous accumulation area to the construction processes. The processes with higher risks mentioned in the instruction manual, such as "support preloading" and "prestressing tensioning and grouting", are the key links that need to be focused on. Establish a risk-process comparison table, set hierarchical early warning conditions according to importance and urgency, generate early warning trigger criteria, and then arrange them in the order of construction stages to form a phased early warning threshold table.

[0020] Use the phased early warning threshold table to quantitatively verify the implementation effect of the prevention and control measures, and form a closed-loop optimized prevention and control strategy chain. Set corresponding monitoring points based on the phased early warning threshold table, collect real-time monitoring data streams, and construct a prevention and control data acquisition network. Compare the data in the prevention and control data acquisition network with the phased early warning threshold table, calculate the threshold deviation degree, and generate a prevention and control response efficiency graph. For the low-efficiency areas in the prevention and control response efficiency graph, extract the characteristics of the associated 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 according to the risk level, determine the key points for prevention and control optimization, and form a priority sequence for prevention and control upgrade. 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 a cyclic iterative closed-loop optimized prevention and control strategy chain.

[0021] Taking Taoyuan No. 1 Bridge as an example, the bridge spans a county road and a river channel. The height of the support is up to 16m, and it is on a curve section with a curve radius of 500m. Through on-site geological exploration, it is found that the bearing capacity of the foundation geology is 230kPa, slightly lower than the required 250kPa. At the same time, the intersection angle between the bridge and the reconstructed road X078 is measured to be 65 degrees, and the height difference from the river channel is 4m. It is calculated that the total load of the single-cell double-chamber cast-in-place box girder with a span of 4×30m is 924.586 tons. These data enter the three-dimensional mapping process, and it is identified that the junction between the support foundation and the river channel is the area with the highest risk coefficient. Through node stress analysis, it is found that the stress at the connection between the foundation and the column is the largest, and its value reaches 72% of the design allowable value. Comparing this data with the construction process, it is found that during the preloading stage of the support, when the preloading load reaches 80%, the safety margin index drops to the lowest point, which is marked as the critical instability point. Based on this, an early warning threshold table is formulated, stipulating that monitoring is started when the preloading load reaches 60%, and support settlement monitoring points are set. During the construction, when the preloading load reaches 60%, the settlement of the support is observed to be 5mm, lower than the early warning threshold of 8mm, indicating that the prevention and control measures are effective. Subsequently, through the optimization of the foundation treatment link, the settlement of the support is further reduced to 3mm during the subsequent construction, verifying the effectiveness of the closed-loop optimization prevention and control strategy chain.

[0022] In a specific embodiment, the process of executing step S101 may specifically include the following steps: (1) Conduct geological exploration on the bridge construction area, collect data on the bearing capacity of the foundation, the distribution of geological soft layers, and the change data of the groundwater level, and form a geological parameter data set; (2) By measuring the distances, height differences, and intersection angles between the bridge and adjacent roads and rivers, obtain the spatial position relationship data of the adjacent roads and rivers, and construct an adjacent road and river parameter data set; (3) According to the structural design drawings of the bridge box girder, calculate the self-weight of the concrete, construction load, and preloading load borne by the support, and establish a support load parameter data set; (4) Divide the geological parameter data set, adjacent road and river parameter data set, and support load parameter data set spatially according to the construction area, and generate a spatial distribution map of bridge construction hazard sources; (5) Based on the spatial distribution map of bridge construction hazard sources, extract the data of the hazard source intensive areas and determine the high-incidence points of hazard sources; (6) Conduct a preliminary risk assessment on the high-incidence points of hazard sources, classify them according to the risk level, and integrate them to generate a bridge construction hazard information database.

[0023] Specifically, a comprehensive geological survey is carried out on the bridge construction area, and data on foundation bearing capacity, distribution of geological soft layers, and changes in groundwater levels are collected through means such as drilling and sampling, static cone penetration testing, and geophysical prospecting. The foundation bearing capacity data includes the ultimate bearing capacity values of rock and soil layers. For example, the N value of the soil layer is obtained through standard penetration tests and then converted into the foundation bearing capacity value. The distribution data of geological soft layers records the thickness, depth, and spatial distribution range of soft soil layers. The data on changes in groundwater levels is obtained through long-term monitoring wells to obtain seasonal groundwater level fluctuations. These data together constitute a geological parameter dataset, which is used to evaluate the stability risk of the support foundation. By accurately measuring the distances, height differences, and intersection angles between the bridge and adjacent roads and rivers, data on the spatial position relationship with the road and river is obtained. Specifically, it includes data such as the shortest distance between the bridge centerline and the edge of the adjacent road measured by a total station, the horizontal distance between the bridge foundation and the river bank, the height difference between the bridge deck and the road, and the angle between the bridge axis and the river direction. These data constitute a parameter dataset for the road and river adjacent to the bridge, which is used to analyze the potential impacts and constraint conditions of the external environment on bridge construction.

[0024] According to the design drawings of the bridge box girder structure, various loads that the support needs to bear are calculated. Specifically, it includes the self-weight of concrete (calculated based on the volume and density of concrete), the loads of construction equipment and personnel (determined based on construction specifications and construction organization design), the preloading load (an additional load applied to verify the stability of the support), etc. Through structural mechanics analysis methods, the stress state and pressure distribution of each support node are calculated to form a support load parameter dataset.

[0025] The geological parameter dataset, the parameter dataset for the road and river adjacent to the bridge, and the support load parameter dataset are spatially divided according to the construction area, and the information of these three datasets is superimposed onto the same spatial coordinate system using geographic information system technology. Through spatial interpolation algorithms, continuous surface interpolation is performed on discrete point data to generate a spatial distribution map of bridge construction hazard sources. This distribution map visually shows the spatial concentration of various risk factors within the construction area.

[0026] Based on the spatial distribution map of bridge construction hazard sources, a hot spot analysis algorithm is applied to extract the areas where the density of risk factors exceeds the threshold to determine the high-incidence points of hazard sources. This algorithm calculates the cumulative impact value of risk factors within each grid cell and compares it with the set safety threshold to identify the hot spot areas where hazard sources are concentrated. High-incidence points of hazard sources usually appear in areas with complex geological conditions, concentrated loads, and significant impacts from the external environment.

[0027] A preliminary risk assessment is carried out on the high-incidence points of hazard sources, and weight assignments are made using the analytic hierarchy process according to the probability of risk occurrence and the severity of possible consequences. Through the risk matrix method, the risk levels are divided into three levels: low, medium, and high, and areas with similar risks are integrated. Finally, a structured bridge construction hazard information database is generated.

[0028] Taking the construction of a river-crossing bridge as an example, it is found through investigation that there is a soft soil layer with a thickness of 2.5 meters near the middle pier of the bridge. The N value measured at this location through the standard penetration test is only 4.5, and the bearing capacity after conversion is less than 120 kPa, which is significantly lower than the surrounding strata. At the same time, the seasonal fluctuation range of the groundwater level in this area reaches 1.8 meters. The measurement data shows that the pier is only 15 meters away from the river bank edge, and the included angle between the bridge axis and the river direction is 72 degrees, forming a risk of skewed water flow scouring. According to the design drawings, the concrete volume of the upper box girder of the pier reaches 280 cubic meters, and the load borne by the support during concrete pouring will reach 6720 kN. After superimposing and analyzing these three groups of data in the same coordinate system, the location of the pier is identified as a high-risk point through the spatial density algorithm, and the calculated risk value reaches 0.78 (with a full score of 1), belonging to the high-risk level. It is preferentially entered into the bridge construction hazard information database and marked as a key monitoring area.

[0029] In a specific embodiment, the process of executing step S102 may specifically include the following steps: (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 original data block; (2) Perform coordinate system conversion on the three-dimensional original data block to construct a support-load-environment space rectangular coordinate system; (3) Extract feature points from the support-load-environment space rectangular coordinate system to form feature point cloud data; (4) Perform density clustering on the feature point cloud data to identify the interaction interval of risk factors; (5) Map the interaction interval of risk factors to the risk level scale space to obtain a risk intensity distribution map; (6) Perform discretization processing on the risk intensity distribution map, extract the relationship between the risk intensity value and the position coordinate, and generate a support-load-environment correlation risk matrix.

[0030] Specifically, the data in the bridge construction hazard information database need to be classified according to the dimensions of the support structure, load distribution, and environmental impact. The support structure dimension includes the geometric layout of the support, material parameters, and connection methods; the load distribution dimension includes the distribution of static loads, dynamic loads, and accidental loads; and the environmental impact dimension includes external factors such as geological conditions, hydrological conditions, and meteorological conditions. Through data stratification processing technology, various types of data are grouped and classified according to attribute tags to form three-dimensional raw data blocks with clear boundaries. This data block contains all hazard source information but has not yet established a unified spatial reference system. Perform coordinate system transformation on the three-dimensional raw data block to construct a support-load-environment space rectangular coordinate system. The coordinate transformation uses the matrix transformation method to map data from different sources and units into a unified space rectangular coordinate system. Specifically, the x-axis represents the support structure parameters, including the geometric dimensions of the support, node positions, and material properties; the y-axis represents the load distribution parameters, including the magnitude, direction, and distribution form of various loads; and the z-axis represents the environmental impact parameters, including external environmental factors such as geological conditions and hydrological conditions. Through the coordinate transformation matrix, the unified expression of data in different dimensions and the establishment of spatial correspondence relationships are achieved.

[0031] Extract feature points from the support-load-environment space rectangular coordinate system to form feature point cloud data. The feature point extraction uses the principal component analysis method and the key feature point detection algorithm to screen out key points from the massive data that have a significant impact on hazard source identification. Specifically, by calculating the influence weights of each data point, those data points with a risk contribution degree higher than the preset threshold are retained to form a sparse but information-rich feature point cloud. These feature points represent the key risk nodes in the three dimensions of the support structure, load distribution, and environmental impact, and are the basis for subsequent risk analysis.

[0032] Perform density clustering on the feature point cloud data to identify the interaction intervals of risk factors. The density clustering uses the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) technology. By analyzing the distribution density of feature points in three-dimensional space, regions with a point density higher than the threshold are identified. These high-density regions represent the interaction intervals of risk factors where multiple risk factors interact and risks are superimposed. The DBSCAN algorithm first sets the search radius and the minimum number of points threshold, and then expands point by point to form clustering regions, and finally obtains multiple interaction intervals of risk factors with clear boundaries.

[0033] Map the interaction intervals of risk factors to the risk level scale space to obtain the risk intensity distribution map. The risk level scale space is a new metric space, where the risk level is quantified according to the values between 0 and 1. The mapping process uses a risk scoring function, which comprehensively considers the severity, occurrence probability, and controllability of risk factors to convert the interaction intervals of risk factors into risk intensity values. The risk scoring function is defined as follows:

[0034] Among them, R represents the risk intensity value, and the value range is 0 - 1; represents the probability of the occurrence of a hazardous event, and the value range is 0 - 1; represents the severity of the hazardous event, and the value range is 0 - 1; represents the controllability of the hazard, and the value range is 0 - 1; , , are the weight coefficients of probability, severity, and controllability respectively, and + + = 1. Through the calculation of the risk scoring function, each interaction interval of risk factors 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, the relationship between the risk intensity value and the position coordinates is extracted, and a support-load-environment correlation risk matrix is generated. The discretization process uses the grid division method to divide the continuous risk intensity distribution map into a finite number of grid cells, and each grid cell corresponds to a risk intensity value. Through sampling and interpolation techniques, the corresponding relationship 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 impacts, providing a basis for subsequent risk prevention and control.

[0035] Taking the cast-in-situ construction of a river-crossing bridge support as an example, first, the collected support structure data (including the coordinates and connection relationships of 60 key nodes), load distribution data (including 15 working conditions such as self-weight of concrete and construction load), and environmental impact data (including 8 types of factors such as geological parameters and hydrological parameters) are respectively sorted into three-dimensional data sets. Through coordinate transformation, these data are mapped into a unified support-load-environment space coordinate system to form an initial point cloud containing 7,200 data points. Using the principal component analysis method, 380 feature points that have the most significant impact on risks are extracted to form feature point cloud data. The DBSCAN algorithm is used to perform density clustering on these feature points, setting the search radius to 0.15 (standardized unit) and the minimum number of points threshold to 5, and 12 risk factor interaction intervals are identified. Among them, a high-risk interaction interval jointly affected by unstable support structure, excessive concrete pouring load, and soft foundation is formed in the middle support area of the bridge. Through risk scoring function calculation, the occurrence probability P of this area is 0.72, the severity S is 0.85, the controllability C is 0.43, the weight coefficients α is 0.35, β is 0.45, γ is 0.2, and the risk intensity value R of this area is 0.67, belonging to the high-risk level. Through grid processing, the entire three-dimensional space is divided into a grid of 50×40×30, forming 60,000 risk assessment units, and a complete support-load-environment associated risk matrix is generated. This matrix clearly shows the high-risk distribution in the middle support area.

[0036] In a specific embodiment, the process of executing step S103 may specifically include the following steps: (1) Extract the support geometric topology structure data from the support-load-environment associated risk matrix and establish a support node network topology graph; (2) Conduct a force analysis on each connection point in the support node network topology graph and calculate the node stress transfer path; (3) Based on the node stress transfer path, identify the stress concentration area and mark the high-stress nodes; (4) Collect deformation data from the high-stress nodes and establish a deformation-stress correspondence table; (5) Combine the data in the deformation-stress correspondence table with the support geometric position information to draw a node stress nephogram; (6) Determine the stress jump interval by extracting the boundary of the node stress nephogram to form a construction node stress distribution diagram.

[0037] Specifically, extracting the geometric topology data of the support from the support-load-environment correlation risk matrix and establishing the support node network topology graph are the primary steps in analyzing the stress state of the support. The geometric topology data of the support refers to the data set that describes the spatial positions and connection relationships of the various components of the support, including node coordinates, member connection relationships, and support conditions, etc. The extraction process uses graph data mining techniques to separate the data of the support structure dimension from the correlation risk matrix and removes redundant information through data cleaning. When establishing the support node network topology graph, each support node is represented as a vertex in the graph, and the connecting members are represented as edges in the graph. At the same time, attribute values are assigned to each vertex and edge, such as node coordinates, member cross-section characteristics, and material parameters, etc. This graph-structured expression intuitively shows the spatial relationship and mechanical transmission path of the support structure. Conducting a stress analysis on each connection point in the support node network topology graph and calculating the node stress transmission path are the key links in identifying risk points. The node stress transmission path refers to the mechanical propagation route by which the load is transmitted through the support structure to the foundation. The stress analysis uses the matrix displacement method to establish the structural stiffness equation and calculate the internal forces and deformations of each node. The calculation of the node stress transmission path follows the following mathematical model:

[0038] where, represents the node external force vector, which contains the force components in three directions of all nodes; represents the overall structural stiffness matrix, which reflects the anti-deformation ability of each part of the structure; represents the node displacement vector, which contains the displacement components in three directions of all nodes.

[0039] Furthermore, the stress transmission between nodes can be represented by the transmission coefficient matrix:

[0040] where, represents the stress transmission coefficient from node b to node a; represents the stress value of the source node b; represents the stress increment of the target node a caused by the source node b. By calculating the stress transmission coefficient matrix of the entire support structure, the complete transmission path of the load from the application point to the foundation is determined.

[0041] Based on the node stress transmission path, identifying the stress concentration area and marking the high-stress nodes are the basis for risk warning. The stress concentration area refers to the structural part where the stress value is significantly higher than the surrounding area, and it is often the weak link of structural failure. The identification process uses the stress gradient analysis method to calculate the stress change rate between adjacent nodes. Specifically, for each node, calculate its stress gradient vector :

[0042] The larger the modulus value of the stress gradient vector, the higher the stress change rate at that point, and the more likely it is to become a stress concentration area. By setting stress thresholds and gradient thresholds, nodes with stress values exceeding 1.5 times the average stress of the structure and stress gradients exceeding the preset threshold are screened out and marked as high-stress nodes. These high-stress nodes usually appear in the areas directly affected by loads, at the sudden changes in member cross-sections, and at the changes in support conditions.

[0043] Collecting deformation data at high-stress nodes and establishing a deformation-stress correspondence table is an important means to quantify the risk level. The deformation data includes physical quantities such as the displacement, rotation angle, and strain of the nodes, reflecting the response degree of the structure under the action of loads. The data collection uses the theory of elasticity and the results of finite element analysis to calculate the displacement vector and strain tensor of each high-stress node. When establishing the deformation-stress correspondence table, the stress values of each high-stress node are paired and stored with the corresponding deformation amounts to form a two-dimensional correspondence table. This correspondence table reveals the quantitative relationship between the structure stress and deformation, providing a basis for evaluating the structural safety margin.

[0044] Combining the data of the deformation-stress correspondence table with the geometric position information of the support, drawing a node stress nephogram is an effective method to visually display the risk distribution. A node stress nephogram is a visual expression method that maps stress values to colors, capable of intuitively showing the stress distribution state of the support 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, with high-stress nodes as reference points, to calculate the stress value at any point in space:

[0045] where represents the stress value at spatial position X; represents the position coordinates of the i-th high-stress node; represents the weight coefficient; represents the radial basis function; represents the Euclidean distance from spatial position X to the node position . Through this interpolation method, a continuous stress field covering the entire support structure is generated and visually displayed in the form of a chromatogram.

[0046] The last step in risk area demarcation is to extract the boundaries of the node stress cloud map, determine the stress jump interval, and form a stress distribution map of the construction node. The stress jump interval refers to the spatial area where the stress value changes significantly, which 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. In specific operations, 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.

[0047] In a specific embodiment, the process of executing step S104 may specifically include the following steps: (1) Extract the stress state data of key support points from the stress distribution diagram of the construction nodes and generate a static stress state table of the support; (2) According to the construction schedule, mark the construction process of each stage of cast-in-place box girder on the timeline to form a process progress node diagram; (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; (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; (5) Filter out the data of key process transition points from the safety redundancy coefficient sequence and establish a process transition risk marker set; (6) By connecting the data points in the process transition risk marker set into a curve, a dynamic safety margin index curve of the bracket is constructed in the time-safety redundancy coordinate system.

[0048] Specifically, extracting the stress state data of key support points from the construction node stress distribution diagram and generating the static stress state table of the support is the basic work for constructing the dynamic safety assessment model. Key support points refer to the nodes in the support structure that undertake the main load transfer function, usually including the intersection points of vertical poles and horizontal bars, the connection points between supports and the main structure, and the direct load application points, etc. The extraction process uses the key node screening algorithm, which comprehensively scores based on three dimensions: the magnitude of the force on the node, the importance of the position, and the consequences of failure, and selects the group of nodes with the highest score as the key support points. For each key support point, record parameters such as its coordinate position, current stress value, design allowable stress, and stress utilization rate, etc., 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 does not yet consider the dynamic characteristics of the load change during the construction process. Comparing with the construction schedule, marking each stage process of the cast-in-place box girder construction on the time axis to form the process progress node diagram is the key step to introduce the time dimension. The cast-in-place box girder construction includes main processes such as support erection, formwork installation, steel bar binding, concrete pouring, curing, and support removal, and each process can be further divided into multiple sub-processes. The formation of the process progress node diagram uses the critical path method, arranges each process in logical order, and marks the start time, duration, and completion time of each process. Special attention needs to be paid to the key time points with significant load changes, such as the start time of concrete pouring, the handover time of segmented pouring, the time when the concrete reaches the initial strength, etc. These time points are usually the turning points where the stress state of the support changes significantly and are also the periods when safety risks are concentrated.

[0049] Aligning the static stress state table of the support with the process progress node diagram in the time dimension to obtain the stress-process correlation dataset is the core link for realizing dynamic analysis. The time dimension alignment uses data mapping technology to calculate the corresponding stress state of the support for each process node. Specifically, first clarify the influence law of each process on the support load, such as the load increased by formwork installation, the cumulative 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 dataset 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 safety assessment of the support.

[0050] 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 to quantify 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 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.

[0051] Screening out the data of key process transition points from the safety redundancy coefficient sequence and establishing a process transition risk marker set is an effective method to identify high-risk periods. Key process transition points refer to the time nodes when the process changes during the construction process, resulting in significant changes in the load state or the force of the support. The screening process uses the change rate 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, fully covering potential high-risk periods.

[0052] The last step in visualizing risk trends is to connect the data points in the process conversion risk tag 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 accurately reflect the safety factor values ​​at each key point while ensuring the smoothness of the curve. The constructed safety margin index curve uses time as the horizontal coordinate and the safety redundancy coefficient as the vertical coordinate, which intuitively shows the dynamic change trend of the support safety margin during the entire construction process. By analyzing the shape characteristics of the curve, such as steep drop sections, platform sections, and fluctuation sections, different types of risk patterns can be identified, providing targeted strategic basis for subsequent risk prevention and control.

[0053] Taking the in-situ construction of the auxiliary piers of a certain double-tower suspension bridge as an example, stress data of 32 key support points were extracted from the stress distribution diagram of the construction nodes, including the spatial coordinates, current stress values, and design allowable stress values of each point, forming a complete table of the static stress state of the support. Comparing with the construction schedule, the entire in-situ casting process was divided into 8 main process stages: completion of support erection, casting of the main girder bottom slab, binding of web reinforcement, casting of the web, binding of top slab reinforcement, casting of the top slab, concrete curing, and removal of the support, forming a process progress node diagram with days as the unit. Through mechanical analysis, the load increment corresponding to each process node was calculated, and these loads were distributed to each key support point to obtain the stress values of each key node at each time point, forming a correlation dataset containing three dimensions of time, process, and stress. For each process node, the ratio of the remaining bearing capacity of each key support point to the current construction load was calculated, generating a sequence of safety redundancy coefficients. It was found through analysis that during the transition stage between the completion of web casting and the start of top slab casting, the safety redundancy coefficient rapidly decreased from 2.1 to 1.4, with a change rate reaching -0.35 / day, far exceeding the preset threshold of ±0.2 / day; at the same time, during the top slab casting process, due to the combined action of the self-weight of the concrete and the vibration load, the safety redundancy coefficient further decreased to 1.2, approaching the warning threshold of 1.1. Accordingly, the key time points of these two stages were included in the process conversion risk marking set. Finally, all risk marking points were connected by the spline interpolation method, and a complete dynamic safety margin index curve of the support was drawn in the time-safety redundancy coordinate system. This curve clearly shows the change trend of the safety margin during the entire construction process, especially marking two high-risk periods with significant decreases in the safety margin, providing clear time guidance for prevention and control measures such as adding temporary supports, adjusting the casting sequence, and strengthening the monitoring frequency at the construction site.

[0054] In a specific embodiment, the process of executing step S105 may specifically include the following steps: (1) Detect the inflection points of the dynamic safety margin index curve of the support, mark the positions with significant changes in the curve slope, and determine the set of potential critical points; (2) Extract the minimum safety margin point from the set of potential critical points through derivative analysis to identify the critical instability point; (3) Around the critical instability point, measure the decreasing rate of the safety margin index, delimit the range of the cumulative influence of danger, and form the boundary of the cumulative danger area; (4) Correlate the critical instability point and the boundary of the cumulative danger area to the construction process to establish a risk-process comparison table; (5) For the risk points in the risk-process comparison table, set grading warning conditions according to importance and urgency to generate warning trigger criteria; (6) Integrate the warning trigger criteria into the process flow, arrange them in the order of construction stages, and generate a sub-stage warning threshold table.

[0055] Specifically, the first step of safety risk warning is to detect the inflection points of the dynamic safety margin index curve of the support, mark the positions where the curve slope changes significantly, and determine the set of potential critical points. An inflection point refers to the position where the derivative of the curve changes significantly, reflecting the turning point of the support safety state. The inflection point detection adopts the curvature extreme value method. By calculating the second derivative of the curve at each point, when the absolute value of the second derivative exceeds the preset threshold, this point is marked as an inflection point. In actual operation, first perform numerical differentiation on the safety margin index curve, calculate the first derivative (slope) and the second derivative (slope change rate) at each point. Then set the slope change rate threshold, and screen out the points where the absolute value of the second derivative exceeds the threshold to form a set of potential critical points. These points usually correspond to the conversion points of some key processes during construction, such as the start of pouring, form removal, support adjustment, etc., which are the moments when the safety state changes significantly.

[0056] Extract the minimum safety margin point from the set of potential critical points through derivative analysis. Identifying the critical instability point is the key step to find the most dangerous period. The critical instability point refers to the point where the safety margin reaches the local minimum and the first 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 derivative at each potential critical point and its change before and after, and identifies the turning point where the derivative sign changes from negative to positive. Then compare the safety margin values of these turning points, and select the point with the minimum margin value as the critical instability point. In some cases, there may be multiple local minimum points. At this time, it is necessary to make a comprehensive judgment by combining the absolute value and duration of the safety margin, and select the point with the highest risk as the main critical instability point. The critical instability point usually appears at the moment when the load reaches the maximum or the support performance temporarily decreases, which is the time node that needs to be focused on monitoring during construction.

[0057] Around the critical instability point, measure the decline rate of the safety margin index, delimit the scope of the cumulative risk impact, and form the boundary of the cumulative risk area, which is an important part of the risk area division. The decline rate of the safety margin index refers to the absolute value of the first derivative of the curve in the descending stage, reflecting the speed of risk accumulation. The measurement process adopts the interval derivative calculation method. Taking the critical instability point as the center, trace back forward to calculate the average change rate of the safety margin from the start of decline to the minimum value. The scope of the cumulative risk impact refers to the time interval from when the safety margin starts to decline significantly to the critical instability point, and the time interval from the critical instability point to when the safety margin returns to the safety threshold. The delimitation method is to set a threshold based on the change rate of the safety margin. When the absolute value of the change rate exceeds the threshold, mark this time point as the boundary point of the cumulative risk area. In this way, with the critical instability point as the center, extend to both sides to the boundary points to form the complete time range of the cumulative risk area, representing the construction stages that need to be focused on monitoring and management.

[0058] Corresponding the critical instability point and the boundary of the risk accumulation area to the construction process and establishing a risk-process comparison table is the basis for achieving precise risk control. The corresponding process uses the time mapping method. First, determine the specific time coordinates of the critical instability point and the boundary of the risk accumulation area, and then search the construction schedule to determine the specific processes and construction activities corresponding to these time points. The established risk-process comparison table should at least contain four columns of information: time coordinates, process name, risk type (such as critical instability point or boundary of the risk accumulation area), and risk level. This comparison table visually shows the correspondence between high-risk time points and specific construction activities, and clarifies the key objects and timing of risk control. In practical applications, the comparison table can also be extended to include more information, such as specific construction locations, participants, construction machinery and equipment, and materials, providing a detailed reference for comprehensive risk control.

[0059] Setting grading warning conditions according to the importance and urgency of the risk points in the risk-process comparison table and generating warning trigger criteria are the core steps in establishing a warning mechanism. Warning grading usually adopts a three-level or four-level system, such as red (extremely high risk), orange (high risk), yellow (medium risk), and blue (low risk). The grading basis mainly considers two dimensions: importance index and urgency index. The importance index reflects the severity of the consequences that a risk event may cause, usually related to the absolute value of the safety margin; the urgency index reflects the speed and time urgency of risk development, usually related to the change rate of the safety margin. The warning trigger criteria are set using the threshold method, that is, when the monitored parameter reaches or exceeds a specific threshold, the corresponding level of warning is triggered. Specifically, for risk points of different levels, set corresponding combinations of safety margin thresholds and change rate thresholds to form the trigger conditions for multi-level warnings. These criteria consider both the static risk level and the dynamic risk development trend, enabling comprehensive and precise risk warnings.

[0060] Integrating the warning trigger criteria into the process flow, arranging them in the order of construction stages, and generating a stage-based warning threshold table is the last step in implementing the warning system. The integration process uses the process correlation mapping method to correspond the warning criteria to specific processes and construction stages one by one. The generated stage-based warning threshold table should at least contain five columns of information: construction stage, specific process, warning level, trigger threshold, and countermeasures. Arrange the warning information of each stage in the chronological order of construction progress to form a warning system synchronized with the construction process. This threshold table not only clarifies the warning conditions for each stage but also indicates the specific measures to be taken after the warning is triggered, and is an important guiding document for on-site safety management. In practical applications, the threshold table can also be dynamically adjusted according to the construction progress and the feedback of monitoring data to ensure the continuous effectiveness of the warning mechanism.

[0061] Taking the cast-in-place cantilever construction of a continuous beam bridge as an example, the curvature extreme value method is applied to detect the inflection points of the dynamic safety margin index curve of the support. The second derivative threshold is set to 0.05, and 15 positions with significant changes in the curve slope are identified, forming a set of potential critical points. Through derivative analysis, it is found that 3 of these points meet the characteristics that the derivative changes from negative to positive and the safety margin is low, corresponding to the completion of concrete pouring of the 0th block, the start of concrete pouring of the 1st block, and the prestress tensioning of the 3rd block respectively. Comparing the safety margin values of these three points, the safety margin at the time of prestress tensioning of the 3rd block is the lowest, reaching 1.18, and it is identified as the main critical instability point. Taking this critical instability point as the center, the safety margin decline rate is calculated. It is found that during the period from the concrete pouring of the 2nd block to the prestress tensioning of the 3rd block, the average decline rate reaches -0.06 / day, exceeding the preset threshold of -0.04 / day. Based on this, the time range from the start of concrete pouring of the 2nd block to the completion of prestress tensioning of the 3rd block is defined as the dangerous accumulation area, with a total duration of about 12 days. Corresponding the critical instability point and the boundary points of the dangerous accumulation area to the construction processes, a detailed risk and process comparison table is established, clearly showing that the high risks are mainly concentrated in specific stages and processes of continuous beam construction. Based on this comparison table, four-level early warning conditions are set: a red early warning is triggered when the safety margin is lower than 1.2 and the change rate is less than -0.05 / day; an orange early warning is triggered when the safety margin is between 1.2 and 1.3 or the change rate is between -0.04 and -0.05 / day; a yellow early warning is triggered when the safety margin is between 1.3 and 1.5 or the change rate is between -0.03 and -0.04 / day; a normal blue monitoring state is maintained when the safety margin is higher than 1.5 and the change rate is greater than -0.03 / day. Finally, these early warning trigger criteria are integrated into the process flow according to the construction sequence, forming a phased early warning threshold table, providing a clear implementation standard for on-site monitoring and safety control, and effectively guiding the implementation of prevention and control measures such as support reinforcement, load control, and monitoring frequency adjustment during the construction of the 2nd block to the 3rd block.

[0062] In a specific embodiment, the process of executing step S106 may specifically include the following steps: (1) Set corresponding monitoring points based on the phased early warning threshold table, collect real-time monitoring data streams, and construct a prevention and control data acquisition network; (2) Compare the data in the prevention and control data acquisition network with the phased early warning threshold table, calculate the threshold deviation degree, and generate a prevention and control response efficiency map; (3) For the low-efficiency areas in the prevention and control response efficiency map, extract the associated prevention and control measure characteristics, and establish a prevention and control measure defect traceability table; (4) Sort the items in the prevention and control measure defect traceability table according to the risk level, determine the key points for prevention and control optimization, and form a prevention and control upgrade priority sequence; (5) Convert the prevention and control upgrade priority sequence into specific technical adjustment parameters, and formulate a prevention and control measure adjustment plan; (6) By linking the prevention and control measure adjustment plan with the original prevention and control measures into a sequence structure, form a cyclic iterative closed-loop optimized prevention and control strategy chain.

[0063] Specifically, based on the phased warning threshold table, set corresponding monitoring points, collect real-time monitoring data streams, and constructing a prevention and control data acquisition network is the basic link to achieve real-time risk monitoring. The setting of monitoring points follows the risk coverage principle, that is, priority is given to deploying monitoring equipment in the high-risk areas marked in the warning threshold table. The types of monitoring points include stress monitoring points, deformation monitoring points, inclination monitoring points, and settlement monitoring points, etc. Different types of monitoring points collect data of different physical quantities. The layout of monitoring points adopts a combination of grid distribution and key point densification, forming a uniformly distributed basic monitoring network on the entire support structure, and appropriately densifying the layout of monitoring points at the critical instability points and in the danger accumulation areas. Data acquisition uses an automated monitoring system to continuously record the data of each monitoring point at a preset frequency and upload it to the data processing center in real time through wireless transmission or wired network. The constructed prevention and control data acquisition network is a multi-level and full-coverage data stream channel, ensuring the real-time visibility of the risk status during the entire construction process.

[0064] Comparing the data in the prevention and control data acquisition network with the phased warning threshold table, calculating the threshold deviation degree, and generating a prevention and control response effectiveness map are the key steps to evaluate the effectiveness of prevention and control measures. The threshold deviation degree refers to the distance between the measured data and the warning threshold, reflecting the difference between the actual risk status and the expected risk status. The calculation method uses the normalized difference method, that is, dividing the difference between the measured value and the threshold by the threshold to obtain the relative deviation degree. For each time series data of each monitoring point, calculate its deviation degree from the corresponding warning threshold to form a deviation degree matrix. The prevention and control response effectiveness refers to the ability of prevention and control measures to reduce risks, usually measured by the improvement degree of the threshold deviation degree. Specifically, compare the changes in the threshold deviation degree before and after the implementation of prevention and control measures, and calculate the percentage of the reduction in the deviation degree as the response effectiveness value. Map the response effectiveness value to the spatial coordinate system to form a prevention and control response effectiveness map, which visually shows the effectiveness distribution of prevention and control measures in different regions and different process stages.

[0065] For the low - efficiency regions in the prevention and control response efficiency map, extracting the associated prevention and control measure characteristics and establishing a defect traceability table for prevention and control measures are effective ways to identify prevention and control weaknesses. The low - efficiency region refers to the time period or spatial area where the response efficiency value is lower than the preset threshold, indicating that the current prevention and control measures have poor effects in this region. When extracting the associated prevention and control measure characteristics, first determine the specific processes and construction parts corresponding to the low - efficiency region, and then find the list of prevention and control measures implemented in this region. For each prevention and control measure, analyze its technical parameters, implementation methods, coverage scope and other characteristics to identify the factors that may lead to low efficiency. These factors may include insufficient measure coverage, mismatched technical parameters, improper implementation timing, or poor synergy among measures. Correlate these factors with specific prevention and control measures one by one to form a defect traceability table for prevention and control measures. This table clearly shows the defect causes and specific manifestations of each low - efficiency prevention and control measure, providing a clear direction for subsequent optimization.

[0066] Sorting the items in the defect traceability table for prevention and control measures according to the risk level, determining the key points for prevention and control optimization, and forming a priority sequence for prevention and control upgrade are important links in the optimal allocation of resources. The sorting process uses a multi - factor comprehensive scoring method, considering three dimensions: the risk level caused by the defect, the influence scope, and the difficulty of governance. The risk level is evaluated based on the deviation degree of the safety margin. The greater the deviation degree, the higher the risk; the influence scope is evaluated based on the number of construction areas and processes involved in the defect. The wider the scope, the greater the influence; the difficulty of governance is evaluated based on technical complexity and resource requirements. The higher the difficulty, the greater the cost. By weighted summation or the analytic hierarchy process, calculate the comprehensive score of each defect and sort them from high to low to form a priority sequence for prevention and control upgrade. This sequence clarifies the order of priority for optimizing prevention and control measures, ensuring that limited resources can be used most efficiently for the most critical risk points.

[0067] Converting the priority sequence for prevention and control upgrade into specific technical adjustment parameters and formulating a prevention and control measure adjustment plan 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 the parameter mapping method. According to the defect type and risk characteristics, determine the specific parameters to be adjusted and their target values. For example, for the defect of insufficient support strength, it may be necessary to adjust the support density, cross - section of the member, or material strength; for the defect of incomplete monitoring coverage, it may be necessary to adjust the layout of monitoring points, sampling frequency, or warning threshold. The formulated prevention and control measure adjustment plan is a set of systematic technical documents, which details the specific adjustment content, adjustment range, implementation method, and verification method for each prevention and control measure to be optimized, providing clear guidance for on - site implementation.

[0068] The final step to achieve continuous improvement is to form a cyclic iterative closed-loop optimized prevention and control strategy chain by linking the prevention and control measure adjustment plan with the original prevention and control measures into a sequential structure. The sequential structure refers to arranging the prevention and control measures before and after adjustment in chronological order to form an evolution 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 optimized measures, while setting clear version identifiers 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, evaluates the effect of the new measures, and based on the evaluation results, conducts the next round of optimization, forming a continuous improvement cycle process. This closed-loop structure ensures that the prevention and control system can dynamically adapt to the changes in the construction process and continuously improve the accuracy and effectiveness of risk prevention and control.

[0069] Taking the steel box girder hoisting construction of a cross-river bridge as an example, based on the phased early warning threshold table, 42 monitoring points were set in the steel box girder hoisting area, including 24 stress monitoring points, 12 deformation monitoring points and 6 inclination monitoring points, to collect the data of the stress state of the support in real time and construct a full-coverage prevention and control data collection network. During the 14-day hoisting process, the system collected data once an hour, generating a total of 14,112 data points. By comparing these data with the early warning threshold table, it was found that during the period from the 8th day to the 10th day, the threshold deviation of 3 stress monitoring points in the middle support area continued to increase, reaching a maximum of 0.78. The implemented temporary reinforcement measures only reduced the deviation by 0.13, and the response efficiency value was only 16.7%, far lower than the expected 65% efficiency, so it was marked as a low-efficiency area. For this area, it was found that there were mainly three defects in the implemented temporary reinforcement measures: insufficient layout of reinforcement members resulting in insufficient support density, unreasonable design of the connection nodes of the members resulting in poor stress transfer, and improper adjustment of the construction sequence resulting in excessive local load. Corresponding these defects to the specific prevention and control measures, a defect traceability table of prevention and control measures was established. Using the multi-factor scoring method, comprehensively evaluating the risk degree (weight 0.5), influence range (weight 0.3) and treatment difficulty (weight 0.2) of each defect, it was found that the stress transfer problem caused by the unreasonable design of the connection nodes scored the highest and ranked first in the priority sequence of prevention and control upgrade. Accordingly, a specific prevention and control measure adjustment plan was formulated, including specific technical measures such as adding stiffening plates to the connection nodes, improving the node connection method, and optimizing the load transfer path, and clarifying the implementation steps and verification methods of each adjustment. Finally, the adjustment plan was integrated with the original prevention and control measures into a sequential structure to form a complete closed-loop optimized prevention and control strategy chain. In the effect evaluation after implementation, the response efficiency in the middle support area was increased to 78.5%, effectively solving the low-efficiency problem of the original prevention and control measures.

[0070] The method for identifying and analyzing bridge construction hazard sources in the embodiments of the present application has been described above. Next, the system for identifying and analyzing bridge construction hazard sources in the embodiments of the present application will be described. Please refer to Figure 2 , an embodiment of the system for identifying and analyzing bridge construction hazard sources in the embodiments of the present application includes: A collection module, configured to collect geological parameters, adjacent road and river parameters, and support load parameters of in-situ cast bridge supports, and establish a bridge construction hazard information database; A generation module, configured to perform three-dimensional mapping on the bridge construction hazard information database to generate a support-load-environment correlation risk matrix; An extraction module, configured to extract key node stress values and deformation amounts according to the support-load-environment correlation risk matrix to form a construction node stress distribution map; A construction module, configured to integrate the stress state of the support with the progress of the construction process based on the construction node stress distribution map to construct a dynamic safety margin index curve of the support; A calculation module, configured to calculate the critical instability point and the dangerous accumulation area according to the dynamic safety margin index curve of the support, and generate a phased early warning threshold table; A quantification module, configured to quantitatively verify the implementation effect of the prevention and control measures by using the phased early warning threshold table to form a closed-loop optimized prevention and control strategy chain.

[0071] Through the collaborative cooperation of the above-mentioned various components, by collecting geological parameters, roadside and riverside parameters, and support load parameters during the cast-in-place construction of bridge supports, a comprehensive bridge construction risk information database has been established, realizing the systematic collection and integration of multi-dimensional risk factors, laying a solid data foundation for subsequent risk analysis; performing three-dimensional mapping on the bridge construction risk information database to generate a support-load-environment correlation risk matrix, breaking through the limitations of traditional single-dimensional risk assessment, establishing a mathematical correlation model among the support structure, load distribution, and environmental impact, making risk analysis more comprehensive and systematic; according to the support-load-environment correlation risk matrix, extracting the stress values and deformation amounts of key nodes to form a stress distribution map of construction nodes, realizing the precise positioning and visual expression of risk points, and intuitively reflecting the stress state and weak links of the support structure; based on the stress distribution map of construction nodes, integrating the stress state of the support with the progress of the construction process, constructing a dynamic safety margin index curve of the support, for the first time combining static risk analysis with dynamic construction progress, realizing the dynamic risk assessment of the entire construction process, making risk early warning have time-series pertinence; according to the dynamic safety margin index curve of the support, calculating the critical instability point and the dangerous accumulation area, generating a phased early warning threshold table, realizing the quantification and classification of risk early warning, improving the accuracy and timeliness of early warning; using the phased early warning threshold table to quantitatively verify the implementation effect of prevention and control measures, forming a closed-loop optimized prevention and control strategy chain, establishing a continuous improvement mechanism for prevention and control measures, and ensuring the effectiveness and adaptability of risk prevention and control. It is particularly worth emphasizing that this solution applies artificial intelligence algorithms in multiple key links, such as the density clustering algorithm used in the three-dimensional mapping process of the risk information database, the graph data mining technology applied in the extraction of key nodes, the radial basis function interpolation algorithm used in the generation process of the stress distribution map, and the time-series data prediction model applied in the construction of the dynamic safety margin curve. The characteristics of these algorithms have made important contributions to the solution: the density clustering algorithm can accurately identify the risk aggregation areas in the multi-dimensional data space, improving the accuracy of risk identification; the graph data mining technology realizes the intelligent extraction of the topological relationship of the support structure, enhancing the accuracy of mechanical analysis; the radial basis function interpolation algorithm optimizes the conversion process from discrete point data to a continuous field, improving the accuracy and reliability of the stress distribution map; the time-series data prediction model endows the safety margin assessment with dynamic prediction capabilities, making the early warning mechanism have foresight. It has achieved a leap from static, single, and empirical risk assessment to dynamic, multi-dimensional, and intelligent risk management, significantly improving the safety management level of the cast-in-place construction of bridge supports.

[0072] Referring to Figure 3 , in the embodiment of the present invention, a computer device is further provided. This computer device can be a server, and its internal structure can be as Figure 3As shown in the figure. The computer device includes a processor, a memory, a display screen, an input device, a network interface, and a database connected through a system bus. Among them, 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 the 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 through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0073] Those skilled in the art can understand that Figure 3 the structure shown in the figure is only a block diagram of some structures 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.

[0074] An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the above method is implemented. It can be understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0075] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium provided by the present invention and used in the embodiments can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or an external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM, etc.

[0076] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the systems, systems and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0077] If the integrated unit is implemented in the form of 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, in essence, or the part that contributes to the prior art, or all or part of this 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 causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0078] The above is the case. The above embodiments are only used to illustrate the technical solutions of the present application and are not intended to limit them. Although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments or perform equivalent replacements for some of the technical features. These modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present application.

Claims

1. A method for identifying and analyzing bridge construction hazards, characterized in that: The method for bridge construction hazard identification and risk analysis includes: Collect geological parameters, road and river parameters, and support load parameters for cast-in-place construction of bridge supports, and 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; According to the support-load-environment correlation risk matrix, the stress values ​​and deformations of key nodes are extracted to form a stress distribution diagram of the construction nodes; Based on the stress distribution diagram of the construction nodes, the stress state of the support is integrated with the progress of the construction process to construct a dynamic safety margin index curve of the support; 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 bridge construction hazard identification and risk analysis according to claim 1 is characterized in that: The collection of geological parameters, road and river 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 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 data set; By measuring the distance, height difference and intersection angle between the bridge and the adjacent roads and rivers, the spatial position relationship data of the adjacent roads and rivers is obtained, and the parameter data set of the adjacent roads and rivers is constructed; 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 the 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 the data of hazard-intensive areas to determine the high-incidence points of hazards; Conduct a preliminary risk assessment on the high-risk 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 bridge construction hazard identification and risk analysis 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 the interaction interval of the 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 bridge construction hazard identification and risk analysis according to claim 1 is characterized in that: The step of extracting stress values ​​and deformations of key nodes according to the support-load-environment correlation risk matrix to form a stress distribution diagram of construction nodes includes: Extract the support geometric topological structure 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 boundary of the node stress cloud diagram, the stress jump interval is determined to form a construction node stress distribution diagram.

5. The method for bridge construction hazard identification and risk analysis according to claim 1 is characterized in that: Based on the stress distribution diagram of the construction node, the support stress state is integrated with the construction process progress to construct a dynamic safety margin index curve of the support, including: Extract stress state data of key support points from stress distribution diagram of construction nodes and generate static stress state table of support; According to the construction schedule, mark the construction process of each stage of cast-in-place box girder on the timeline to form a process progress node diagram; Aligning the bracket static stress state table with the process progress node diagram in time dimension to obtain a stress-process correlation data set; 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; Filtering data of 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 mark set into a curve, a dynamic safety margin index curve of the bracket is constructed in a time-safety redundancy coordinate system.

6. The method for bridge construction hazard identification and risk analysis 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 value point of safety margin from the potential critical point set by 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; Correspond the critical instability point and the boundary of the danger accumulation zone to the construction process, and establish 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 staged warning threshold table is generated.

7. The method for bridge construction hazard identification and risk analysis according to claim 1 is characterized in that: The use of the staged warning threshold table to quantitatively verify the implementation effect of the prevention and control measures forms a closed-loop optimization prevention and control strategy chain, including: 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; 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; For the low-efficiency area in the control response effectiveness diagram, extract the characteristics of related control measures and establish a control measure defect traceability table; Sort the items in the defect traceability table of the prevention and control measures by risk level, determine the focus of prevention and control optimization, and form a priority sequence for prevention and control upgrades; 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.

8. A system for identifying and analyzing bridge construction hazards, used to implement the method for identifying and analyzing bridge construction hazards as described in any one of claims 1 to 7, characterized in that: The bridge construction hazard source identification and risk analysis system includes: The collection module is used to collect geological parameters, road and river 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, used to extract the stress values ​​and deformations of key nodes according to the support-load-environment association risk matrix, and 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; A calculation module, used to calculate the critical instability point and the danger accumulation area according to 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 implementation effect of the prevention and control measures, forming a closed-loop optimization prevention and control strategy chain.

9. A computer device, characterized in that: It 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, the method for identifying and analyzing bridge construction hazards as described in any one of claims 1 to 7 is implemented.

10. 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 executes the method for identifying and analyzing bridge construction hazards as described in any one of claims 1 to 7.

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