Construction progress and risk collaborative management system and method for large-span arch bridge in mountainous area

Abstract

The invention relates to the technical field of construction management, and particularly discloses a mountainous area large-span arch bridge construction progress and risk collaborative management system and method. All strong association data items and strong association rules in the mountain terrain and geological data and the large-span arch bridge construction progress data are extracted; the incidence matrix building module is used for building a topographic geology-construction progress incidence matrix of the current mountainous area large-span arch bridge on the basis of collected data of all strong association data items in real-time construction project data of the current mountainous area large-span arch bridge and corresponding strong association rules; the risk value prediction module analyzes risk values of all hidden danger projects based on the topographic geology-construction progress incidence matrix of the large-span arch bridge in the current mountainous area; the risk pre-management module carries out risk pre-intervention based on the risk values of all hidden danger projects; and the safety and controllability of the construction of the large-span arch bridge in the mountainous area are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of construction management, and in particular to a collaborative management system and method for construction progress and risks of long-span arch bridges in mountainous areas. Background Art

[0002] Currently, in the field of transportation infrastructure construction, as my country's transportation network continues to expand into complex terrain areas such as mountainous regions, long-span arch bridges in mountainous areas are widely used due to their ability to effectively cross complex terrain and meet transportation needs. However, the construction of long-span arch bridges in mountainous areas presents unique complexities and challenges. Mountainous areas are characterized by extremely complex topography and geology, with dramatic terrain fluctuations and diverse geological structures. These factors not only directly impact construction progress but also pose various risks, such as damage from natural disasters such as landslides and debris flows, and difficulties in foundation construction caused by complex geology. Accurately monitoring construction progress and effectively managing risks are crucial to ensuring the smooth implementation of long-span arch bridge projects in mountainous areas, guaranteeing project quality and safety, and controlling project costs. Collaborative management of construction progress and risks for long-span arch bridges in mountainous areas can help improve the scientific, systematic, and safe construction of long-span arch bridges in mountainous areas. This approach conforms to the trend of expanding transportation infrastructure construction into complex terrain areas, holds broad application prospects in future mountain bridge construction, and is of great value in promoting the sustainable development of transportation construction in complex terrain.

[0003] However, existing construction management technologies for long-span arch bridges in mountainous areas fail to fully explore the inherent connections between mountainous terrain and geological data and long-span arch bridge construction progress data, failing to clearly present the interrelationship between the two. Consequently, during risk prediction, these inherent connections cannot be used to accurately analyze construction risks, making it difficult to implement effective risk pre-emptive interventions.

[0004] Therefore, the present invention proposes a system and method for collaboratively managing the construction progress and risks of long-span arch bridges in mountainous areas. Summary of the Invention

[0005] The present invention provides a collaborative management system and method for the construction progress and risks of large-span arch bridges in mountainous areas. By analyzing a large amount of historical construction project data, the intrinsic connection between topography and geology and construction progress is explored, and construction risk hazards are accurately analyzed based on the intrinsic connection between the two. This can achieve precise control of construction progress and early warning and effective management of potential risks.

[0006] The present invention provides a system for collaboratively managing the construction progress and risks of long-span arch bridges in mountainous areas, comprising:

[0007] A strongly correlated data item extraction module is used to extract all strongly correlated data items and strong correlation rules between mountain topographic and geological data and long-span arch bridge construction progress data based on a large amount of historical construction project data of long-span arch bridges in mountainous areas;

[0008] The correlation matrix building module is used to construct the topography, geology and construction progress correlation matrix of the current large-span arch bridge in the mountainous area based on the collected data of all strongly correlated data items in the real-time construction project data of the current large-span arch bridge in the mountainous area and the corresponding strong correlation rules;

[0009] The risk value prediction module is used to analyze the risk values ​​of all potential hazards based on the topography, geology, and construction progress correlation matrix of the current long-span arch bridges in mountainous areas;

[0010] The risk prevention management module is used to conduct risk prevention intervention based on the risk values ​​of all potential risk projects.

[0011] Preferably, the strongly associated data item extraction module includes:

[0012] The correlation analysis submodule is used to analyze the historical construction project data of a large number of long-span arch bridges in mountainous areas to obtain the correlation significance value between each topographic and geological core indicator and each key construction process;

[0013] The project association submodule is used to combine the topographic and geological core indicators with association significance values ​​not less than the association significance threshold with the key construction progress processes as strong association data items, and determine the corresponding strong association rules.

[0014] Preferably, the association analysis submodule includes:

[0015] The abnormal progress efficiency record screening unit is used to screen out abnormal progress efficiency record data from the complete historical progress efficiency time series data of each key construction process in the historical construction project data of each large-span arch bridge in the mountainous area;

[0016] A generalized regression fitting unit is used to fit the hypothetical regression equations for each core topographic and geological indicator and each key construction process in each characteristic dimension based on a large number of historical construction project data of long-span arch bridges in mountainous areas;

[0017] The first determining factor calculation unit is used to calculate the first determining factor of each topographic and geological core indicator and each key construction process in each characteristic dimension;

[0018] The second determining factor calculation unit is used to calculate the second determining factor of each topographic and geological core indicator and each key construction process in each characteristic dimension;

[0019] The correlation significance value calculation unit is used to calculate the correlation significance value between each topographic and geological core indicator and each key construction progress process in the corresponding characteristic dimension based on the first determining factor and the second determining factor of each topographic and geological core indicator and each key construction progress process in each characteristic dimension, and obtain the correlation significance value between each topographic and geological core indicator and each key construction progress process.

[0020] Preferably, the abnormal progress efficiency record screening unit includes:

[0021] The first sociality calculation subunit is used to calculate the complete historical progress efficiency time series data of each key construction process in the historical construction project data of each mountainous long-span arch bridge, and calculate the first sociality of the corresponding key construction process of the corresponding mountainous long-span arch bridge at each moment;

[0022] The second sociality calculation subunit is configured to calculate the second sociality of the corresponding key construction progress step of the corresponding mountainous large-span arch bridge at each moment based on the first derivative function of the function expression corresponding to the complete historical progress efficiency time series data of each key construction progress step in the historical construction project data of each mountainous large-span arch bridge;

[0023] A longitudinal grouping degree calculation subunit is used to calculate the longitudinal grouping degree of each key construction process of each mountainous long-span arch bridge at each moment based on the first grouping degree and the second grouping degree of each key construction process of each mountainous long-span arch bridge at each moment;

[0024] The abnormal progress efficiency record screening subunit is used to screen out abnormal progress efficiency record data from the complete historical progress efficiency time series data of each key construction progress process in the historical construction project data of each large-span arch bridge in the mountainous area based on the longitudinal clustering degree of the same key construction progress process of all large-span arch bridges in the mountainous area at all times.

[0025] Preferably, based on the longitudinal cohesion of the same key construction process of all mountainous long-span arch bridges at all times, abnormal progress efficiency record data in the complete historical progress efficiency time series data of each key construction process in the historical construction project data of each mountainous long-span arch bridge is screened out, including:

[0026] All abnormal progress efficiencies are screened out from the complete historical progress efficiency time series data of each key construction process in the historical construction project data of each long-span arch bridge in mountainous areas;

[0027] Calculate the homogeneity of each abnormal progress efficiency of each key construction process of each long-span arch bridge in mountainous area;

[0028] Determine the longitudinal outliers of the same key construction process at all times for all long-span arch bridges in mountainous areas;

[0029] Based on the homogeneity and longitudinal outlier degree, abnormal progress efficiency records are screened out from the complete historical progress efficiency time series data of each key construction process in the historical construction project data of each long-span arch bridge in mountainous area.

[0030] Preferably, the correlation matrix building module includes:

[0031] The conformity evaluation submodule is used to calculate the comprehensive conformity between the collected data of each strongly associated data item in the current real-time construction project data of the large-span arch bridge in the mountainous area and the corresponding strong association rule based on the conformity between the collected data of each strongly associated data item in the current real-time construction project data of the large-span arch bridge in the mountainous area and each hypothetical regression equation under the corresponding strong association rule;

[0032] The association matrix construction submodule is used to construct the topography, geology and construction progress association matrix of the current large-span arch bridge in mountainous area based on the comprehensive conformity of the collected data of all strongly associated data items in the real-time construction project data of the current large-span arch bridge in mountainous area with the corresponding strong association rules.

[0033] Preferably, the risk value prediction module includes:

[0034] The standard matrix construction submodule is used to construct the standard topography, geology and construction progress correlation matrix for long-span arch bridges in mountainous areas;

[0035] The risk value assessment submodule is used to analyze the risk values ​​of all potential danger projects based on the current topography, geology and construction progress correlation matrix of large-span arch bridges in mountainous areas and the standard topography, geology and construction progress correlation matrix of large-span arch bridges in mountainous areas.

[0036] Preferably, the risk value assessment submodule includes:

[0037] A matrix reset unit is used to reassign elements of the standard topography, geology and construction progress correlation matrix based on the current topography, geology and construction progress correlation matrix of the large-span arch bridge in the mountainous area and the standard topography, geology and construction progress correlation matrix of the large-span arch bridge in the mountainous area to obtain a reset matrix;

[0038] The risk value assessment unit is used to analyze the risk values ​​of all potential risk items based on the reset matrix.

[0039] Preferably, the risk pre-management module includes:

[0040] The intervention strategy determination submodule is used to determine the intervention plans for all hidden danger projects whose risk values ​​exceed the intervention threshold based on the preset list of intervention plans for hidden danger projects;

[0041] The risk intervention submodule is used to conduct risk pre-intervention based on the intervention plan for all hidden danger projects whose risk values ​​exceed the intervention threshold.

[0042] The present invention provides a method for collaboratively managing the construction progress and risks of a long-span arch bridge in mountainous areas, comprising:

[0043] Step 1: Based on a large amount of historical construction project data of long-span arch bridges in mountainous areas, all strongly correlated data items and strong correlation rules between mountainous terrain and geological data and long-span arch bridge construction progress data are extracted;

[0044] Step 2: Based on the collected data of all strongly correlated data items in the real-time construction project data of the current large-span arch bridge in the mountainous area and the corresponding strong correlation rules, a topography, geology and construction progress correlation matrix of the current large-span arch bridge in the mountainous area is constructed;

[0045] Step 3: Analyze the risk values ​​of all potential hazards based on the topography, geology, and construction progress correlation matrix of the current long-span arch bridges in mountainous areas;

[0046] Step 4: Conduct risk pre-intervention based on the risk values ​​of all potential risk items.

[0047] The present invention offers the following advantages over existing technologies: The strongly correlated data item extraction module analyzes a large amount of historical construction project data to extract data items and rules that strongly correlate mountainous terrain, geology, and construction progress data. This provides a solid data foundation and logical basis for subsequent analysis, helping to uncover the inherent patterns in the impact of terrain and geology on construction progress. The correlation matrix construction module constructs a terrain, geology, and construction progress correlation matrix based on the collected data and rules of strongly correlated data items in real-time construction projects. This presents complex correlations in an intuitive matrix format, facilitating a systematic, comprehensive, and clear understanding of the connection between terrain, geology, and progress during current construction. The risk value prediction module analyzes the risk value of potential risk projects based on this correlation matrix, accurately identifying potential risks and estimating the risk level in advance, providing managers with a clear understanding of the risk situation. The risk preemptive management module conducts preemptive intervention based on risk values, enabling targeted measures to mitigate risks, ensuring smooth construction progress, reducing delays and losses caused by risks, and improving the safety and controllability of long-span arch bridge construction in mountainous areas.

[0048] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be achieved and obtained through the structures specifically pointed out in this application document.

[0049] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0051] Figure 1 Schematic diagram of a collaborative management system for construction progress and risks of a long-span arch bridge in a mountainous area according to an embodiment of the present invention;

[0052] Figure 2 Schematic diagram of a strongly associated data item extraction module in an embodiment of the present invention;

[0053] Figure 3 Schematic diagram of a risk value prediction module in an embodiment of the present invention. DETAILED DESCRIPTION

[0054] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0055] Example 1:

[0056] The present invention provides a collaborative management system for construction progress and risk of long-span arch bridges in mountainous areas. Figure 1 ,include:

[0057] A strongly correlated data item extraction module is used to extract all strongly correlated data items and strong correlation rules between mountain topographic and geological data and long-span arch bridge construction progress data based on a large amount of historical construction project data of long-span arch bridges in mountainous areas;

[0058] The correlation matrix building module is used to construct the topography, geology and construction progress correlation matrix of the current large-span arch bridge in the mountainous area based on the collected data of all strongly correlated data items in the real-time construction project data of the current large-span arch bridge in the mountainous area and the corresponding strong correlation rules;

[0059] The risk value prediction module is used to analyze the risk values ​​of all potential hazards based on the topography, geology, and construction progress correlation matrix of the current long-span arch bridges in mountainous areas;

[0060] The risk prevention management module is used to conduct risk prevention intervention based on the risk values ​​of all potential risk projects.

[0061] In this embodiment, historical construction project data refers to data accumulated from a large number of previous long-span arch bridge construction projects in mountainous areas, covering various types of information during the construction process of each project. For example, topographic and geological survey records at different stages of long-span arch bridge projects in different mountainous areas, and actual construction progress, etc.

[0062] In this embodiment, mountainous terrain and geological data primarily involves data on the mountain's topography, geological structure, and rock and soil properties. This data, such as mountain orientation, slope, rock type, and soil bearing capacity, can directly or indirectly impact the construction of long-span arch bridges and is a crucial factor in the study of construction progress.

[0063] In this embodiment, the long-span arch bridge construction progress data records the progress of each key process during the construction of the long-span arch bridge, including the process start time, end time, actual progress efficiency, etc.

[0064] In this embodiment, the real-time construction project data of the current large-span arch bridge in the mountainous area refers to various types of data currently generated by the large-span arch bridge project in the mountainous area under construction, including topographic and geological data collected in real time and the real-time progress of the construction progress.

[0065] In this embodiment, the collected data for strongly correlated data items refers to the actual data collected from the current real-time construction project corresponding to the strongly correlated data items extracted from historical construction project data. For example, if a strongly correlated data item is "specific rock type and foundation construction process," the collected data would be the actual survey data for the specific rock type in the current project, as well as relevant data such as the real-time progress and efficiency of the foundation construction process.

[0066] In this embodiment, a potential hazard project refers to a project or link identified as potentially risky during the construction of a long-span arch bridge in a mountainous area based on a topography, geology, and construction progress correlation matrix analysis, potentially impacting construction progress, quality, or safety. For example, if certain geological conditions could lead to unstable foundations that could affect construction, the foundation construction portion could be identified as a potential hazard project.

[0067] In this embodiment, the risk value of a potential project is a quantitative measure of the potential risk faced by each potential project, assessed through analysis of the topography, geology, and construction progress correlation matrix. A higher risk value indicates a greater likelihood of a potential risk occurring, or a more severe impact if a risk does occur. This value serves as an important basis for determining whether to conduct preemptive risk intervention and what intervention measures to adopt.

[0068] Example 2:

[0069] Based on Example 1, the strongly associated data item extraction module refers to Figure 2 ,include:

[0070] The correlation analysis submodule is used to analyze the values ​​of the same topographic and geological core indicators and the abnormal progress efficiency records of the same key construction process in a large number of historical construction project data of long-span arch bridges in mountainous areas, and obtain the correlation significance value between each topographic and geological core indicator and each key construction process;

[0071] The project association submodule is used to combine the topographic and geological core indicators and the key construction progress processes with correlation significance values ​​not less than the correlation significance threshold as strongly associated data items, and to treat the hypothetical regression equations of the corresponding topographic and geological core indicators and the corresponding key construction progress processes in all characteristic dimensions as the corresponding strong association rules.

[0072] In this embodiment, core topographic and geological indicators refer to parameters within mountainous terrain and geological data that have a significant impact on the construction progress of long-span arch bridges and reflect key topographic and geological characteristics. These parameters, such as rock hardness, groundwater depth, and terrain slope, can significantly affect construction difficulty and progress, making them key factors in analyzing the relationship between topography and construction progress.

[0073] In this embodiment, key construction progress steps refer to specific construction steps or links that play a decisive role in the overall construction progress during the construction of a long-span arch bridge. Examples include foundation pouring and arch ring erection. Delays or problems in these steps can directly lead to delays in the overall construction progress. These steps are therefore key areas of focus when studying the relationship between construction progress and topography and geology.

[0074] In this embodiment, abnormal progress efficiency record data is selected from the historical construction project data of each large-span arch bridge in the mountainous area and the complete historical progress efficiency time series data of each key construction process, and the data records with significant differences from the normal progress efficiency performance are selected.

[0075] In this embodiment, the correlation significance value is a quantitative value obtained through a series of calculations to measure the degree of correlation between each core topographic and geological indicator and each key construction progress step. The higher the value, the more significant the correlation between the two.

[0076] In this embodiment, the correlation significance threshold is a preset standard value, such as 0.8.

[0077] In this embodiment, characteristic dimensions describe or analyze abnormal progress efficiency data from different perspectives. For example, maximum values, minimum values, slopes, etc. Analyzing from multiple characteristic dimensions helps to comprehensively and deeply explore the relationship between topography, geology, and construction progress.

[0078] The beneficial effects of the above technology are as follows: The correlation analysis submodule accurately derives the significance value of the correlation between core topographic and geological indicators and abnormal progress efficiency records of key construction processes in a large number of historical construction project data of long-span arch bridges in mountainous areas, providing a quantitative and objective basis for the screening of strongly correlated data and effectively avoiding subjective misjudgment. The project correlation submodule identifies the combination of indicators and processes with significant correlations as strongly correlated data items, and uses their hypothetical regression equations under various characteristic dimensions as strong correlation rules. While clearly defining the strong correlation relationship, it also constructs a scientific analysis model. These strongly correlated data items and rules provide accurate data support for the subsequent construction of the correlation matrix, making risk value prediction more accurate and risk pre-management more targeted. This significantly improves the scientific nature and effectiveness of the collaborative management system for the construction progress and risk of long-span arch bridges in mountainous areas, ensuring the smooth progress of construction.

[0079] Example 3:

[0080] Based on Example 2, the association analysis submodule, refer to Figure 2 ,include:

[0081] The abnormal progress efficiency record screening unit is used to screen out abnormal progress efficiency record data from the complete historical progress efficiency time series data of each key construction process in the historical construction project data of each large-span arch bridge in the mountainous area;

[0082] The generalized regression fitting unit is used to fit a hypothetical regression equation between the corresponding topographic and geological core indicator and the corresponding key construction process in the corresponding characteristic dimension based on the mean, covariance, and variance of all indicator values ​​of the same topographic and geological core indicator in historical construction project data of a large number of long-span arch bridges in mountainous areas, as well as the mean, covariance, and variance of all eigenvalues ​​of the abnormal progress efficiency records of the same key construction process in the same characteristic dimension. This unit collects all measured values ​​of the same topographic and geological core indicator (such as rock hardness) from historical construction projects of large-span arch bridges in mountainous areas, calculates its mean, covariance, and variance, and simultaneously obtains the mean, covariance, and variance of all eigenvalues ​​of the abnormal progress efficiency records of the same key construction process (such as bridge erection) in a certain characteristic dimension (such as erection speed). Using these statistics, regression analysis is used to attempt to identify the possible mathematical relationship between the topographic and geological core indicator and the key construction process in this characteristic dimension, thereby fitting a hypothetical regression equation. This equation is used to describe how topographic and geological factors affect the abnormal progress efficiency of key construction progress processes under this characteristic dimension. For example, an equation of the form y = a + bx may be obtained, where y represents the abnormal progress efficiency of key construction progress processes, x represents the core topographic and geological indicators, and a and b are coefficients determined by calculation (a is the ratio of the covariance to the variance of all indicator values, and b is the value obtained by subtracting the product of a and the mean of all indicator values ​​from the mean of all characteristic values).

[0083] The first determinant calculation unit is used to calculate the average of the differences between all characteristic values ​​of the abnormal progress efficiency record data for each key construction process in each characteristic dimension and the calculated index value of each hypothetical regression equation under the corresponding topographic and geological core indicator, and use this as the first determinant between the corresponding topographic and geological core indicator and the key construction process in the corresponding characteristic dimension. For each key construction process's abnormal progress efficiency record data, in each specific characteristic dimension, all characteristic values ​​(e.g., the individual erection speed values ​​in the abnormal progress efficiency record data for the bridge erection process) are subtracted from the index value calculated based on the previously fitted hypothetical regression equation under the corresponding topographic and geological core indicator value (i.e., the theoretical erection speed calculated based on the hypothetical regression equation by substituting the corresponding rock hardness value) and then the average of these differences is calculated. This average value is the first determinant between the corresponding topographic and geological core indicator and the key construction process in that characteristic dimension. It reflects the average degree of deviation between the actual abnormal progress efficiency characteristic value and the theoretical value calculated based on the hypothetical regression equation, helping to determine the close correlation between the topographic and geological core indicator and the key construction process in that characteristic dimension.

[0084] The second determining factor calculation unit is used to calculate the average of the differences between all eigenvalues ​​and the mean of the abnormal progress efficiency record data of each key construction progress process in each characteristic dimension, as the second determining factor of the corresponding topographic geological core indicator and the corresponding key construction progress process in the corresponding characteristic dimension: for the abnormal progress efficiency record data of each key construction progress process in each characteristic dimension, first calculate the average value of all eigenvalues. Then, subtract this average value from each eigenvalue to obtain the difference between each eigenvalue and the mean, and then calculate the average value of these differences. This average value is the second determining factor of the corresponding topographic geological core indicator and the corresponding key construction progress process in this characteristic dimension. This factor measures the degree of dispersion of each eigenvalue of the abnormal progress efficiency record data in this characteristic dimension relative to its own mean, and reflects the closeness of the relationship between the topographic geological core indicator and the key construction progress process in this characteristic dimension from another perspective;

[0085] The correlation significance value calculation unit is used to treat the quotient of the first determinant and the second determinant of each topographic and geological core indicator and each key construction progress process in each characteristic dimension as the correlation significance value of each topographic and geological core indicator and each key construction progress process in the corresponding characteristic dimension, and based on the correlation significance value of each topographic and geological core indicator and each key construction progress process in all characteristic dimensions, obtain the correlation significance value of each topographic and geological core indicator and each key construction progress process.

[0086] In this embodiment, the complete historical progress efficiency time series data of the key construction process refers to the complete progress efficiency data sequence of each key construction process recorded in chronological order in the historical construction projects of a large number of large-span arch bridges in mountainous areas. These data reflect the actual progress efficiency of the key process at different time points and include information on efficiency changes from the beginning to the end of the process. For example, in the key process of pouring piers of a large-span arch bridge in a certain mountainous area, the data such as the concrete pouring volume and construction time recorded every day, after being sorted out, form a progress efficiency sequence that changes over time, which is the complete historical progress efficiency time series data of the process.

[0087] In this embodiment, a correlation significance value is obtained for each topographic and geological core indicator and each key construction process step based on the correlation significance values ​​across all characteristic dimensions. The correlation significance values ​​across these different characteristic dimensions are combined, perhaps by averaging or other comprehensive calculation methods, to obtain a single value representing the overall correlation significance between the topographic and geological core indicator and the key construction process step. This final correlation significance value more comprehensively reflects the closeness of the association between the two and is used to subsequently determine whether they constitute strongly correlated data items.

[0088] The beneficial effects of the above technologies are as follows: the abnormal progress efficiency record screening unit accurately screens the abnormal progress efficiency record data of key processes, focuses on key information, and provides core data support for subsequent analysis. The generalized regression fitting unit fits the hypothesized regression equation based on multiple data features, constructs a relationship model between topography, geology and construction progress, and enhances the scientific nature of the analysis. The first and second determining factor calculation units respectively calculate the mean of the difference and the mean of the difference between the characteristic value and the mean, and measure the closeness of the relationship between the two from different angles. The correlation significance value calculation unit determines the correlation significance value by the quotient of the two, comprehensively integrates information from various dimensions, and the resulting correlation significance value more accurately reflects the degree of correlation between the core indicators of topography and geology and the key processes of construction progress. This series of operations makes the extraction of strongly correlated data items more accurate, provides a more reliable basis for subsequent collaborative risk management, and improves the construction management level of large-span arch bridges in mountainous areas.

[0089] Example 4:

[0090] Based on Example 3, the abnormal progress efficiency record screening unit refers to Figure 2 ,include:

[0091] The first sociality calculation subunit is used to calculate the similarity between the value of the complete historical progress efficiency time series data of each key construction progress process in the historical construction project data of each mountainous large-span arch bridge at each moment in the corresponding complete historical period and the value of the complete historical progress efficiency time series data of the same key construction progress time series in the construction project data of all mountainous large-span arch bridges except the current mountainous large-span arch bridge at the same moment in the corresponding complete historical period, and use it as the first sociality of the corresponding key construction progress process of the corresponding mountainous large-span arch bridge at the corresponding moment;

[0092] The second sociality calculation subunit is used to calculate the function value of the first derivative function of the function expression corresponding to the complete historical progress efficiency time series data of each key construction progress process in the historical construction project data of each mountainous large-span arch bridge at each moment in the corresponding complete historical period, and the similarity between the function value of the first derivative function of the function expression corresponding to the complete historical progress efficiency time series data of the same key construction progress sequence in the construction project data of all mountainous large-span arch bridges except the current mountainous large-span arch bridge at the same moment in the corresponding complete historical period, and use it as the second sociality of the corresponding key construction progress process of the corresponding mountainous large-span arch bridge at the corresponding moment;

[0093] A longitudinal grouping degree calculation subunit is used to calculate the longitudinal grouping degree of each key construction process of each mountainous long-span arch bridge at each moment based on the first grouping degree and the second grouping degree of each key construction process of each mountainous long-span arch bridge at each moment;

[0094] The abnormal progress efficiency record screening subunit is used to screen out abnormal progress efficiency record data from the complete historical progress efficiency time series data of each key construction progress process in the historical construction project data of each large-span arch bridge in the mountainous area based on the longitudinal clustering degree of the same key construction progress process of all large-span arch bridges in the mountainous area at all times.

[0095] In this embodiment, the similarity calculation may adopt various methods, such as calculating the ratio of the smaller value to the larger value of two values.

[0096] In this embodiment, the longitudinal cohesion of each key construction process of each mountainous long-span arch bridge at each moment is calculated based on the first cohesion and second cohesion of each key construction process at each moment. This can be achieved by calculating the first cohesion and second cohesion at each moment using a pre-set calculation rule, such as a weighted summation method (giving different weights to the first cohesion and second cohesion, such as 0.5 each), to obtain the longitudinal cohesion of each key construction process of each mountainous long-span arch bridge at each moment. A higher longitudinal cohesion indicates that the process is more similar to the same process of other mountainous long-span arch bridges in terms of progress efficiency values ​​and temporal trends; conversely, a lower longitudinal cohesion indicates a greater difference.

[0097] The beneficial effects of the above technologies are as follows: First, the second grouping calculation subunit calculates grouping from the progress efficiency value and the time series change trend (derivative), respectively, taking a comprehensive approach. The vertical grouping calculation subunit integrates the two to obtain the vertical grouping, comprehensively evaluating the similarity between a specific construction process and other projects. The abnormal progress efficiency record screening subunit filters abnormal records based on the vertical grouping, accurately identifying data that deviates from the normal progress efficiency, providing more targeted data for subsequent strong correlation analysis, and making the coordinated management of construction progress and risks of long-span arch bridges in mountainous areas more accurate and scientific.

[0098] Example 5:

[0099] On the basis of Example 4, based on the longitudinal cohesion of the same key construction process of all mountainous long-span arch bridges at all times, the abnormal progress efficiency record data in the complete historical progress efficiency time series data of each key construction process in the historical construction project data of each mountainous long-span arch bridge was screened out, and the abnormal progress efficiency record data was used as a reference. Figure 2 ,include:

[0100] In the complete historical progress efficiency time series data of each key construction process in the historical construction project data of each mountainous long-span arch bridge, all progress efficiencies with a longitudinal sociality not less than the sociality threshold are calibrated as all abnormal progress efficiencies of the corresponding key construction process of the corresponding mountainous long-span arch bridge;

[0101] The ratio of the interval between each abnormal progress efficiency of each key construction process of each mountainous long-span arch bridge and all the remaining abnormal progress efficiencies of the corresponding key construction process of the mountainous long-span arch bridge except the current abnormal progress efficiency to the corresponding complete historical period is regarded as the same-class outlier of the corresponding abnormal progress efficiency;

[0102] Normalize the difference between 1 and the similar outlier degree of all abnormal progress efficiency of each key construction process of each mountainous long-span arch bridge to calculate the similarity degree of each abnormal progress efficiency of each key construction process of each mountainous long-span arch bridge;

[0103] The difference between 1 and the longitudinal grouping degree of the same key construction process of each mountainous long-span arch bridge at each moment is regarded as the longitudinal outlier degree of the same key construction process of each mountainous long-span arch bridge at each moment;

[0104] The complete historical progress efficiency time series data of each key construction process in the historical construction project data of each large-span arch bridge in mountainous areas, from the earliest moment to the latest moment where the square root of the sum of the square of the same-sex sociability and the vertical outlier is not less than the preset threshold, are used as abnormal progress efficiency record data.

[0105] In this embodiment, the sociability threshold is a pre-set standard value used to determine whether the progress efficiency of key construction processes is abnormal. For example, if the sociability threshold is set to 0.6, a progress efficiency with a vertical sociability greater than or equal to 0.6 may be considered to deviate from the normal level and enter the subsequent abnormality analysis process.

[0106] In this embodiment, the preset threshold is also a preset numerical standard, for example, 0.6.

[0107] The beneficial effects of the above technology are as follows: first, abnormal progress efficiency is calibrated using a sociability threshold to preliminarily locate suspicious data. By calculating the outlier degree and sociability of the same category, the degree of dispersion and similarity between abnormal data are measured. The vertical outlier degree is determined and, combined with the sociability of the same category, a comprehensive consideration is given to the overall deviation and clustering of the data. Finally, based on the square root of the sum of the squares of the two and a preset threshold, abnormal progress efficiency records are accurately screened. This makes the screening criteria more comprehensive and scientific, providing more reliable data for subsequent strong correlation analysis, and significantly improving the accuracy of coordinated progress and risk management for the construction of long-span arch bridges in mountainous areas.

[0108] Example 6:

[0109] Based on Example 1, the correlation matrix building module includes:

[0110] The conformity assessment submodule is used to analyze the conformity between the collected data of each strongly correlated data item in the current real-time construction project data of the long-span arch bridge in the mountainous area and each hypothetical regression equation under the corresponding strong correlation rule. Based on the conformity between the collected data of each strongly correlated data item in the current real-time construction project data of the long-span arch bridge in the mountainous area and each hypothetical regression equation under the corresponding strong correlation rule, the comprehensive conformity of the collected data of each strongly correlated data item in the current real-time construction project data of the long-span arch bridge in the mountainous area and the corresponding strong correlation rule is calculated. This means that the actual collected data, such as the progress data of a construction process under specific terrain and geological conditions, is substituted into the hypothetical regression equation previously fitted based on historical data to see how well the actual data fits the equation's prediction results. Then, based on these individual conformities, a comprehensive conformity is calculated using a certain algorithm (such as weighted averaging, which may assign different weights based on the importance of different hypothetical regression equations or strongly correlated data items). This value comprehensively reflects the overall degree of match between the collected data of each strongly correlated data item and the corresponding strong correlation rule.

[0111] The association matrix construction submodule is used to construct a topography, geology, and construction progress association matrix for long-span arch bridges in mountainous areas based on the comprehensive conformance of all strongly correlated data items collected from the real-time construction project data of these bridges with the corresponding strong association rules. In this matrix, rows and columns may represent different topographic and geological factors and key construction progress steps, and the matrix elements represent the corresponding comprehensive conformance.

[0112] The beneficial effects of these technologies are as follows: The compliance assessment submodule analyzes the conformity of real-time construction data with the hypothesized regression equation, calculates the overall conformity, and accurately measures the degree of match between real-time data and strong correlation rules, providing a quantitative basis for subsequent matrix construction. The correlation matrix construction submodule constructs a topography-geology-construction progress correlation matrix based on the overall conformity, visualizing the relationship between the two. This process improves the accuracy and scientific nature of the correlation matrix, provides a more reliable foundation for risk value prediction, helps managers clearly understand the relationship between topography, geology, and construction progress during construction, and effectively improves the efficiency of collaborative management of construction progress and risks for long-span arch bridges in mountainous areas.

[0113] Example 7:

[0114] Based on Example 1, the risk value prediction module, refer to Figure 3 ,include:

[0115] The standard matrix construction submodule is used to construct the standard topography, geology and construction progress correlation matrix for long-span arch bridges in mountainous areas;

[0116] The risk value assessment submodule is used to analyze the risk values ​​of all potential danger projects based on the current topography, geology and construction progress correlation matrix of large-span arch bridges in mountainous areas and the standard topography, geology and construction progress correlation matrix of large-span arch bridges in mountainous areas.

[0117] In this embodiment, a standard topography and geology-construction progress correlation matrix for long-span arch bridges in mountainous areas is constructed to establish a universal, reference correlation model for the construction of long-span arch bridges in mountainous areas. The construction process includes:

[0118] The degree of conformity between a large number of previously obtained historical construction project data of large-span arch bridges in mountainous areas and each hypothesized regression equation under the corresponding strong association rule was calculated, and the comprehensive conformity between the historical data of each strongly associated data item in the historical construction project data of all large-span arch bridges in mountainous areas and the corresponding strong association rule was averaged. Based on the same matrix construction method, the average value of the comprehensive conformity was used as the element of the corresponding position in the matrix to obtain the standard topography, geology and construction progress correlation matrix.

[0119] The beneficial effects of these technologies are as follows: First, the standard matrix construction submodule constructs a standard topography, geology, and construction progress correlation matrix, providing a unified reference standard for risk assessment and a clear comparison benchmark. Second, the risk value assessment submodule analyzes the risk value of potential hazards based on the current and standard correlation matrices. By comparing the two, it can keenly detect discrepancies between the current construction status and the standard, thereby accurately predicting risks. This helps to identify potential risk points in advance, providing strong data support for pre-emptive risk management, making risk management and control of long-span arch bridge construction in mountainous areas more targeted and effective, and ensuring construction safety and progress.

[0120] Example 8:

[0121] Based on Example 7, the risk value assessment submodule, refer to Figure 3 ,include:

[0122] A matrix reset unit is used to reassign elements of the standard topography, geology and construction progress association matrix based on the deviation between the corresponding column vectors and the deviation between the corresponding row vectors in the current topography, geology and construction progress association matrix of the long-span arch bridge in the mountainous area and the standard topography, geology and construction progress association matrix for the long-span arch bridge in the mountainous area, to obtain a reset matrix;

[0123] The risk value assessment unit is used to analyze the risk values ​​of all potential risk items based on the reset matrix.

[0124] In this embodiment, the column vector deviation degree is as follows: the vectors in the same column position in the two matrices are compared (each column may represent the impact of a certain type of topographic and geological factors on different key construction progress processes), and the degree of deviation between them is calculated through a specific algorithm (such as calculating the sum of the absolute values ​​of the corresponding element differences, Euclidean distance, etc.).

[0125] Row vector deviation: Similarly, the deviation is calculated for vectors in the same row position (each row may represent a key process of the construction progress affected by different topographical and geological factors).

[0126] Reassign elements: According to the calculated column vector and row vector deviation degrees: set the element value whose row vector deviation degree and column vector deviation degree are both less than the deviation threshold value (for example, 0.7) to 1; set the element value whose row vector deviation degree and column vector deviation degree are both greater than the deviation threshold value (for example, 0.7) to -1; otherwise, set it to 0.

[0127] In this embodiment, the risk values ​​of all potential risk items are analyzed based on the reset matrix:

[0128] Based on the preset weight list, determine the 1 element weight value, 0 element weight value, and -1 element weight value of each hidden danger item in each key process of the construction progress. For example, determine that the weight of the element with an element value of 1 in the reset matrix is ​​0.6, the weight of the element with an element value of 0 is 0.3, and the weight of the element with an element value of -1 is 0.1;

[0129] Based on the above weights, all elements in each column of the reset matrix are weighted summed to obtain the risk value of the corresponding hidden danger item under the key construction progress process corresponding to the corresponding column;

[0130] The risk values ​​of each hidden danger project in all key construction progress processes are weighted summed up (still with pre-determined weights, for example, the weight of the landslide hidden danger project in the arch seat excavation process is 0.35) to obtain the risk value of each hidden danger project.

[0131] The beneficial effects of the above technology are as follows: the matrix reset unit reassigns the standard matrix elements to a reset matrix based on the deviation between the row and column vectors of the current association matrix and the standard. This process fully considers the differences between the current construction characteristics and the standard, making the reset matrix more consistent with the actual construction situation. The risk value assessment unit analyzes the risk value of the hidden danger project based on the reset matrix, which can more accurately reflect the current risk situation and improve the accuracy of risk prediction. This provides a more reliable basis for pre-risk management, facilitates the timely formulation of effective response measures, ensures the smooth progress of long-span arch bridge construction in mountainous areas, and reduces the probability of risk occurrence.

[0132] Example 9:

[0133] Based on Example 1, the risk pre-management module includes:

[0134] An intervention strategy determination submodule is used to retrieve a preset list of intervention plans for hidden danger projects based on the risk values ​​of all hidden danger projects whose risk values ​​exceed the intervention threshold, and determine the intervention plans for all hidden danger projects whose risk values ​​exceed the intervention threshold;

[0135] The risk intervention submodule is used to conduct risk pre-intervention based on the intervention plan for all hidden danger projects whose risk values ​​exceed the intervention threshold.

[0136] In this embodiment, a preset list of intervention plans for hidden danger projects: This is a pre-established list that brings together countermeasures for various potential hidden danger projects that may arise during the construction of large-span arch bridges in mountainous areas. It is formed based on past construction experience, industry standards, and analysis of potential risks. The list records in detail the specific intervention methods corresponding to different types of hidden danger projects. For example, for a hidden danger project with unstable foundation due to special terrain and geology, there may be a detailed foundation reinforcement plan in the list, including what kind of reinforcement materials and construction technology to use. This list provides a strategic reserve for risk pre-management. After identifying the hidden danger project and its risk value, the applicable intervention plan can be quickly retrieved from it.

[0137] In this embodiment, the intervention plan for hidden danger projects refers to a specific response method selected from a preset list of hidden danger project intervention plans for a specific hidden danger project. It is determined based on factors such as the characteristics of the hidden danger project, the size of the risk value, and the actual construction situation. For example, if a hidden danger project affects the progress of bridge erection due to a large terrain slope, its intervention plan may be to adjust the construction sequence, carry out some auxiliary terrain modification work first, or adopt construction equipment and technology that is more suitable for large slope operations. The purpose is to reduce the risks brought by the hidden danger project, ensure the smooth progress of construction, and ensure construction safety and quality.

[0138] The beneficial effects of these technologies are as follows: The intervention strategy determination submodule retrieves a list of pre-set solutions based on the risk value of the hidden danger project exceeding the intervention threshold, identifying an intervention plan. This process is fast and targeted, leveraging existing experience and strategies to address risks. The risk intervention submodule conducts preemptive intervention based on the identified plan, ensuring measures are taken at the nascent stage of risk, mitigating the impact of risks on construction progress and quality. This effectively ensures the smooth construction of long-span arch bridges in mountainous areas, enhances project management's risk response capabilities, and reduces potential losses.

[0139] Example 10:

[0140] The present invention provides a method for collaboratively managing the construction progress and risks of a long-span arch bridge in mountainous areas, comprising:

[0141] Step 1: Based on a large amount of historical construction project data of long-span arch bridges in mountainous areas, all strongly correlated data items and strong correlation rules between mountainous terrain and geological data and long-span arch bridge construction progress data are extracted;

[0142] Step 2: Based on the collected data of all strongly correlated data items in the real-time construction project data of the current large-span arch bridge in the mountainous area and the corresponding strong correlation rules, a topography, geology and construction progress correlation matrix of the current large-span arch bridge in the mountainous area is constructed;

[0143] Step 3: Analyze the risk values ​​of all potential hazards based on the topography, geology, and construction progress correlation matrix of the current long-span arch bridges in mountainous areas;

[0144] Step 4: Conduct risk pre-intervention based on the risk values ​​of all potential risk items.

[0145] The beneficial effects of the above technology are as follows: First, step one analyzes historical data to extract strongly correlated data items and rules, providing a solid data foundation and logical support for subsequent management, and exploring the inherent connection between topography, geology, and construction progress. Second, step two constructs a correlation matrix based on real-time data and rules, visually presenting the relationship between the two and facilitating overall control. Furthermore, step three analyzes risk values ​​based on the matrix to accurately identify potential risks. Finally, step four conducts preemptive intervention based on risk values ​​to prevent risks in advance, ensure construction progress, reduce losses, and enhance the scientific and safe management of long-span arch bridge construction in mountainous areas.

[0146] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the present invention and its equivalents, the present invention is intended to include these modifications and variations.

Claims

1. The construction progress and risk collaborative management system for long-span arch bridges in mountainous areas is characterized by: include: A strongly correlated data item extraction module is used to extract all strongly correlated data items and strong correlation rules between mountain topographic and geological data and long-span arch bridge construction progress data based on a large amount of historical construction project data of long-span arch bridges in mountainous areas; The correlation matrix building module is used to construct the topography, geology and construction progress correlation matrix of the current large-span arch bridge in the mountainous area based on the collected data of all strongly correlated data items in the real-time construction project data of the current large-span arch bridge in the mountainous area and the corresponding strong correlation rules; The risk value prediction module is used to analyze the risk values ​​of all potential hazards based on the topography, geology, and construction progress correlation matrix of the current long-span arch bridges in mountainous areas; The risk prevention management module is used to conduct risk prevention intervention based on the risk values ​​of all potential risk projects.

2. The system for collaborative management of construction progress and risk of long-span arch bridges in mountainous areas according to claim 1 is characterized in that: Strongly associated data item extraction module, including: The correlation analysis submodule is used to analyze the historical construction project data of a large number of long-span arch bridges in mountainous areas to obtain the correlation significance value between each topographic and geological core indicator and each key construction process; The project association submodule is used to combine the topographic and geological core indicators with association significance values ​​not less than the association significance threshold with the key construction progress processes as strong association data items, and determine the corresponding strong association rules.

3. The system for collaborative management of construction progress and risk of long-span arch bridges in mountainous areas according to claim 2 is characterized in that: Association analysis submodule, including: The abnormal progress efficiency record screening unit is used to screen out abnormal progress efficiency record data from the complete historical progress efficiency time series data of each key construction process in the historical construction project data of each large-span arch bridge in the mountainous area; A generalized regression fitting unit is used to fit the hypothetical regression equations for each core topographic and geological indicator and each key construction process in each characteristic dimension based on a large number of historical construction project data of long-span arch bridges in mountainous areas; The first determining factor calculation unit is used to calculate the first determining factor of each topographic and geological core indicator and each key construction process in each characteristic dimension; The second determining factor calculation unit is used to calculate the second determining factor of each topographic and geological core indicator and each key construction process in each characteristic dimension; The correlation significance value calculation unit is used to calculate the correlation significance value between each topographic and geological core indicator and each key construction progress process in the corresponding characteristic dimension based on the first determining factor and the second determining factor of each topographic and geological core indicator and each key construction progress process in each characteristic dimension, and obtain the correlation significance value between each topographic and geological core indicator and each key construction progress process.

4. The system for collaborative management of construction progress and risks of long-span arch bridges in mountainous areas according to claim 3 is characterized in that: Abnormal progress efficiency record screening unit, including: The first sociality calculation subunit is used to calculate the complete historical progress efficiency time series data of each key construction process in the historical construction project data of each mountainous long-span arch bridge, and calculate the first sociality of the corresponding key construction process of the corresponding mountainous long-span arch bridge at each moment; The second sociality calculation subunit is configured to calculate the second sociality of the corresponding key construction progress step of the corresponding mountainous large-span arch bridge at each moment based on the first derivative function of the function expression corresponding to the complete historical progress efficiency time series data of each key construction progress step in the historical construction project data of each mountainous large-span arch bridge; A longitudinal grouping degree calculation subunit is used to calculate the longitudinal grouping degree of each key construction process of each mountainous long-span arch bridge at each moment based on the first grouping degree and the second grouping degree of each key construction process of each mountainous long-span arch bridge at each moment; The abnormal progress efficiency record screening subunit is used to screen out abnormal progress efficiency record data from the complete historical progress efficiency time series data of each key construction progress process in the historical construction project data of each large-span arch bridge in the mountainous area based on the longitudinal clustering degree of the same key construction progress process of all large-span arch bridges in the mountainous area at all times.

5. The system for collaborative management of construction progress and risk of long-span arch bridges in mountainous areas according to claim 4 is characterized in that: Based on the longitudinal clustering degree of the same key construction process at all times for all mountainous long-span arch bridges, the abnormal progress efficiency records in the complete historical progress efficiency time series data of each key construction process in the historical construction project data of each mountainous long-span arch bridge are screened out, including: All abnormal progress efficiencies are screened out from the complete historical progress efficiency time series data of each key construction process in the historical construction project data of each long-span arch bridge in mountainous areas; Calculate the homogeneity of each abnormal progress efficiency of each key construction process of each long-span arch bridge in mountainous area; Determine the longitudinal outliers of the same key construction process at all times for all long-span arch bridges in mountainous areas; Based on the homogeneity and longitudinal outlier degree, abnormal progress efficiency records are screened out from the complete historical progress efficiency time series data of each key construction process in the historical construction project data of each long-span arch bridge in mountainous area.

6. The system for collaborative management of construction progress and risk of long-span arch bridges in mountainous areas according to claim 1 is characterized in that: Correlation matrix building module, including: The conformity evaluation submodule is used to calculate the comprehensive conformity between the collected data of each strongly associated data item in the current real-time construction project data of the large-span arch bridge in the mountainous area and the corresponding strong association rule based on the conformity between the collected data of each strongly associated data item in the current real-time construction project data of the large-span arch bridge in the mountainous area and each hypothetical regression equation under the corresponding strong association rule; The association matrix construction submodule is used to construct the topography, geology and construction progress association matrix of the current large-span arch bridge in mountainous area based on the comprehensive conformity of the collected data of all strongly associated data items in the real-time construction project data of the current large-span arch bridge in mountainous area with the corresponding strong association rules.

7. The system for collaborative management of construction progress and risks of long-span arch bridges in mountainous areas according to claim 1 is characterized in that: The risk value prediction module includes: The standard matrix construction submodule is used to construct the standard topography, geology and construction progress correlation matrix for long-span arch bridges in mountainous areas; The risk value assessment submodule is used to analyze the risk values ​​of all potential danger projects based on the current topography, geology and construction progress correlation matrix of large-span arch bridges in mountainous areas and the standard topography, geology and construction progress correlation matrix of large-span arch bridges in mountainous areas.

8. The system for collaborative management of construction progress and risks of long-span arch bridges in mountainous areas according to claim 7 is characterized in that: The risk value assessment submodule includes: A matrix reset unit is used to reassign elements of the standard topography, geology and construction progress correlation matrix based on the current topography, geology and construction progress correlation matrix of the large-span arch bridge in the mountainous area and the standard topography, geology and construction progress correlation matrix of the large-span arch bridge in the mountainous area to obtain a reset matrix; The risk value assessment unit is used to analyze the risk values ​​of all potential risk items based on the reset matrix.

9. The system for collaborative management of construction progress and risks of long-span arch bridges in mountainous areas according to claim 1 is characterized in that: Risk pre-management module, including: The intervention strategy determination submodule is used to determine the intervention plans for all hidden danger projects whose risk values ​​exceed the intervention threshold based on the preset list of intervention plans for hidden danger projects; The risk intervention submodule is used to conduct risk pre-intervention based on the intervention plan for all hidden danger projects whose risk values ​​exceed the intervention threshold.

10. A collaborative management method for construction progress and risks of long-span arch bridges in mountainous areas, characterized by: include: Step 1: Based on a large amount of historical construction project data of long-span arch bridges in mountainous areas, all strongly correlated data items and strong correlation rules between mountainous terrain and geological data and long-span arch bridge construction progress data are extracted; Step 2: Based on the collected data of all strongly correlated data items in the real-time construction project data of the current large-span arch bridge in the mountainous area and the corresponding strong correlation rules, a topography, geology and construction progress correlation matrix of the current large-span arch bridge in the mountainous area is constructed; Step 3: Analyze the risk values ​​of all potential hazards based on the topography, geology, and construction progress correlation matrix of the current long-span arch bridges in mountainous areas; Step 4: Conduct risk pre-intervention based on the risk values ​​of all potential risk items.

Images