Deep foundation pit supporting structure construction monitoring method and system

Abstract

The invention discloses a deep foundation pit supporting structure construction monitoring method and system, and relates to the technical field of intelligent monitoring, and the method comprises the steps: deploying a global positioning system (GPS) module, a vehicle attribute module and a vehicle weighing module on a heavy construction vehicle in a deep foundation pit supporting structure construction area, and combining a vehicle-mounted control terminal and a 5G wireless communication module, operation disturbance data can be collected in real time and transmitted to the BIM platform central processing unit in a unified packaging format. The BIM platform constructs a two-dimensional space positioning grid and path trajectory mapping mechanism based on the received data, further generates a vehicle path disturbance heat map reflecting disturbance intensity, divides the vehicle path disturbance heat map into disturbance analysis unit areas, and extracts a disturbance data set based on BIM model soil modeling information. The disturbance intensity is quantitatively output through a disturbance aggregation index D calculation formula which is clear in physical significance and consistent in dimension, and visual display of the disturbance level is achieved in combination with a disturbance evaluation threshold value.

Description

Technical Field

[0001] This invention relates to the field of intelligent monitoring technology, specifically to a method and system for monitoring the construction of deep foundation pit support structures. Background Technology

[0002] With the increasing density of urban construction and the deepening development of underground space, deep foundation pit engineering has become a crucial link in urban infrastructure construction. Especially in complex geological environments or areas where municipal roads intersect, large-scale deep foundation pit projects face increasingly stringent requirements for construction safety and structural stability. Against this backdrop, Building Information Modeling (BIM) technology is gradually being applied throughout the entire project management process, becoming an important tool for improving project safety management and intelligent monitoring capabilities. This invention, based on the BIM platform and integrating construction disturbance modeling and structural risk response mechanisms, proposes a construction safety monitoring and early warning method for deep foundation pit support structures. It focuses on the identification and intelligent hierarchical control of fatigue risks in support structures under the influence of heavy construction vehicle disturbances, constructing a dynamic linkage path between disturbance, structure, and control, thus expanding the application boundaries of the BIM platform in structural health monitoring.

[0003] Currently, in the construction of existing deep foundation pits, the assessment of the safety status of the support structure mostly relies on periodic inspections and single-point strain monitoring by sensors. This approach cannot accurately identify the accumulation of path disturbances caused by repeated passage of construction vehicles. Especially when heavy vehicles are running densely on the support structure's path, the disturbance stress gradually weakens the foundation's bearing capacity and causes fatigue damage to the support structure through frequency superposition and energy accumulation. However, traditional methods lack the spatial distribution identification and temporal superposition analysis of construction path disturbances, making it impossible to detect fatigue risks in advance. Furthermore, existing BIM platforms are usually only used for visual modeling and do not fully integrate vehicle movement disturbance data from the construction site, thus failing to form a closed-loop linkage between disturbance, structure, and early warning.

[0004] Because the disturbance caused by heavy construction vehicles was not dynamically modeled and aggregated for identification, some areas along the construction path were subjected to prolonged high-disturbance, high-frequency vibration stress, leading to a gradual decrease in foundation stiffness and structural fatigue margin. However, management lacked real-time awareness of this change, resulting in several typical anomalies: first, premature cracking or subsidence of the support structure; second, the inability to properly divert heavy vehicles from the construction area, further amplifying disturbances; and third, the failure to issue timely warning signals, delaying structural reinforcement or construction adjustments. These problems could ultimately lead to serious engineering safety accidents such as support instability and excessive foundation pit deformation. Therefore, there is an urgent need for a linked monitoring and early warning method that can deeply integrate vehicle disturbance behavior data with the BIM platform and achieve accurate identification and graded response control of the support structure's fatigue state. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a construction monitoring method and system for deep foundation pit support structures, which solves the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution, comprising the following steps:

[0007] S1. Collect vehicle operation disturbance data and transmit the operation disturbance data to the central processing unit of the BIM platform in real time. Based on the operation disturbance data, generate a vehicle path disturbance heat map. Then, divide the disturbance heat map into several disturbance analysis unit areas and extract the disturbance dataset according to the BIM platform.

[0008] S2. Calculate and output the disturbance clustering index D based on the disturbance dataset, set the disturbance assessment threshold, conduct a preliminary comparative assessment, and classify the disturbance heatmap by color level based on the preliminary comparative assessment results to form a disturbance risk level visual layer.

[0009] S3. For the disturbance analysis unit area with the highest disturbance risk level, trigger the fatigue response mechanism of the support structure, extract the fatigue dataset of the support structure, and construct the fatigue margin index Rf of the support structure.

[0010] S4. Based on the calculation results of the fatigue margin index Rf of the support structure, conduct a structural risk assessment, and output risk control instructions based on the structural risk assessment results.

[0011] Preferably, S1 includes S11;

[0012] S11. On construction vehicles traveling back and forth on the roadbed of the deep foundation pit support structure, a GPS module, a vehicle attribute module, and a vehicle weighing module are installed, and a 5Hz sampling frequency is set to collect the vehicle's running disturbance data in real time; and an interactive interface is established between the GPS module, the vehicle attribute module, and the vehicle weighing module and the vehicle control terminal, and the real-time collected running disturbance data is summarized and transmitted to the vehicle control terminal.

[0013] In the vehicle control terminal, the running disturbance data is encapsulated using a preset encapsulation format. The encapsulation format includes a sampling timestamp, path coordinate field, speed field, vehicle type field, and vehicle load field. The data is then transmitted to the central processing unit of the BIM platform via a wireless data communication module using 5G wireless communication.

[0014] The operational disturbance data includes path coordinates, driving speed, vehicle type, and vehicle load.

[0015] Preferably, S1 further includes S12;

[0016] S12. After receiving the number of operational disturbances, the central processing unit extracts the path coordinates and reconstructs the trajectory sequence according to the sampling time order to obtain the vehicle trajectory sequence of all heavy vehicles.

[0017] Simultaneously, a two-dimensional spatial positioning grid based on GPS path coordinates is constructed in the BIM platform. The two-dimensional spatial positioning grid uses the UTM universal transverse Mercator coordinate system as a reference system, and then maps the collected vehicle trajectory sequence to the reference system. The number of trajectory crossings, vehicle type and load information of all construction vehicles are counted to generate a vehicle path disturbance heat map of disturbance intensity.

[0018] Based on the vehicle path disturbance heat map, the entire deep foundation pit construction area is divided into several equilateral grid units with a set grid side length of 5 meters × 5 meters to obtain the disturbance analysis unit area.

[0019] Then, the BIM platform automatically extracts and calculates the disturbance dataset for the subsequent construction of the disturbance clustering index based on the vehicle operation disturbance data within each disturbance analysis unit area.

[0020] The disturbance dataset includes the average daily passing frequency F of vehicle type j within the disturbance analysis unit area. ij The average load W of vehicle type j within the i-th disturbance analysis unit area. ij and the corresponding equivalent foundation stiffness S within the i-th disturbance analysis unit region i .

[0021] Preferably, S2 includes S21;

[0022] S21. Based on the obtained disturbance dataset, the extreme value normalization method is used to eliminate the influence of unit dimensions between parameters in all disturbance datasets, and then the disturbance clustering index D of each disturbance analysis unit region is calculated and output.

[0023] The disturbance clustering index D is calculated and output using the following algorithm formula;

[0024] In the formula, D i P represents the disturbance clustering index within the i-th disturbance analysis unit region, n represents the total number of vehicle types, and P j This represents the vibration impact weight of the j-th vehicle type.

[0025] Preferably, S2 further includes S22;

[0026] S22. Based on the statistical results of the disturbance clustering index D of all disturbances during the construction history period, disturbance assessment thresholds for graded assessment are set according to the cumulative distribution characteristics. The disturbance assessment thresholds include a first disturbance threshold Dth1 and a second disturbance threshold Dth2.

[0027] The disturbance clustering index Di calculated from the disturbance analysis unit region is compared with the disturbance assessment threshold for preliminary evaluation.

[0028] When the disturbance clustering index D in the i-th disturbance analysis unit region i When the disturbance threshold Dth1 is less than the first disturbance threshold, the corresponding disturbance analysis unit area is marked as a normal disturbance area, and the current disturbance analysis unit area is displayed in green.

[0029] When the first disturbance threshold Dth1 ≤ the disturbance clustering index D in the i-th disturbance analysis unit region i When the disturbance threshold Dth2 is less than the second disturbance threshold, the corresponding disturbance analysis unit area is marked as a disturbance interest area, and the current disturbance analysis unit area is displayed in yellow.

[0030] When the disturbance clustering index D in the i-th disturbance analysis unit region i When the value is greater than or equal to the second disturbance threshold Dth2, the corresponding disturbance analysis unit area is marked as a disturbance risk area, and the current disturbance analysis unit area is displayed in red.

[0031] Preferably, S3 includes S31;

[0032] S31. For the disturbance analysis unit area displayed in red, trigger the support structure fatigue response mechanism, which includes support structure data extraction and support fatigue analysis.

[0033] The support structure data extraction is performed through the BIM platform to extract the support structure fatigue dataset, and the extreme value normalization method is used to eliminate the influence of unit dimensions among the parameters in all support structure fatigue datasets.

[0034] The fatigue dataset of the support structure includes the fatigue limit strength PLf of the support structure material and the yield strength QFy of the support structure component.

[0035] Preferably, S3 further includes S32;

[0036] S32. The support fatigue analysis is based on the support structure fatigue dataset and combined with the disturbance aggregation index D of the disturbance analysis unit area shown in red to construct the support structure fatigue margin index Rf, and quantitatively analyze the proportion of the fatigue margin of the support structure to the yield capacity under the current disturbance conditions.

[0037] The fatigue margin index Rf of the support structure is calculated and output using the following algorithm formula;

[0038] In the formula, Rf iThis represents the fatigue margin index of the support structure within the i-th disturbance analysis unit region. This represents the disturbance attenuation coefficient.

[0039] Preferably, S4 includes S41;

[0040] S41. Based on the output results of the fatigue margin index Rf of the support structure in each disturbance analysis unit region, a structural risk assessment is performed, and based on the structural risk assessment results, the structural fatigue risk level of the target disturbance analysis unit region is classified; the specific structural risk assessment content is as follows:

[0041] When the fatigue margin index Rf of the support structure in the i-th disturbance analysis unit region i When the value is greater than 1.2, the current disturbance analysis unit region is determined to be at the structural fatigue safety level;

[0042] When 1.0 < the fatigue margin index Rf of the support structure in the i-th disturbance analysis unit region i When the value is ≤1.2, the current disturbance analysis unit region is determined to be at the structural fatigue warning level;

[0043] When the fatigue margin index Rf of the support structure in the i-th disturbance analysis unit region i When the value is ≤1.0, the current disturbance analysis unit area is determined to be at the structural fatigue risk level.

[0044] Preferably, S4 further includes S42;

[0045] S42. Execute corresponding risk control instructions based on the structural fatigue risk level, as detailed below:

[0046] When the area is determined to be a structural fatigue safety level zone, no analysis control instructions are generated. The existing traffic routes, traffic frequency and construction production rhythm of construction vehicles remain unchanged. The BIM platform only performs real-time fatigue margin updates and area status records, without triggering any construction intervention measures.

[0047] When the structural fatigue warning level is determined, the first risk control instruction is executed. This instruction sends a disturbance reduction control strategy to the construction management terminal via the BIM platform. The disturbance reduction control strategy automatically reduces the average daily frequency F of construction vehicles within the current disturbance analysis unit area by 75% of the original average daily frequency; prohibits vehicles whose average load W exceeds 80% of the upper limit of the support structure's design load from entering the current disturbance analysis unit area; and generates alternative routes by the construction vehicle scheduling module to guide overloaded vehicles to safer passage routes that are farther from the support structure and have lower disturbance sensitivity. At the same time, a reminder message is pushed to the construction management personnel for inspection.

[0048] When the structural fatigue risk level is determined, the second risk control instruction is executed. The second risk control instruction completely prohibits construction vehicles with an average load W exceeding 60% of the upper limit of the support structure's design load from entering the current disturbance analysis unit area, and forcibly reduces the original passage frequency to zero until the risk is eliminated. At this time, an urgent warning is generated and sent to the construction management terminal through the BIM platform, prompting immediate maintenance of the support in the current disturbance analysis unit area.

[0049] A construction monitoring system for deep foundation pit support structures includes a disturbance heat map generation module, a heat map color level classification module, a support fatigue response module, and a risk command triggering module.

[0050] The disturbance heatmap generation module collects vehicle operation disturbance data and transmits the data to the central processing unit of the BIM platform in real time. Based on the disturbance data, it generates a vehicle path disturbance heatmap. The heatmap is then divided into several disturbance analysis unit areas, and the disturbance dataset is extracted from the BIM platform.

[0051] The heatmap color level classification module calculates and outputs the disturbance clustering index D based on the disturbance dataset, sets the disturbance assessment threshold, conducts a preliminary comparison assessment, and classifies the disturbance heatmap into color levels based on the preliminary comparison assessment results to form a disturbance risk level visual layer.

[0052] The support fatigue response module triggers the support structure fatigue response mechanism by analyzing the disturbance analysis unit area with the highest disturbance risk level, extracts the support structure fatigue dataset, and constructs the support structure fatigue margin index Rf.

[0053] The risk command triggering module performs structural risk assessment based on the calculation results of the fatigue margin index Rf of the support structure, and outputs risk control commands based on the structural risk assessment results.

[0054] This invention provides a method and system for monitoring the construction of deep foundation pit support structures. It has the following beneficial effects:

[0055] (1) This method deploys GPS modules, vehicle attribute modules, and vehicle weighing modules on heavy construction vehicles in the deep foundation pit support structure construction area. Combined with onboard control terminals and 5G wireless communication modules, it can collect disturbance-related data such as construction vehicle path coordinates, driving speed, vehicle type, and load in real time, and transmit them to the central processing unit of the BIM platform in a unified encapsulation format. The BIM platform constructs a two-dimensional spatial positioning grid and path trajectory mapping mechanism based on the received data, and then generates a vehicle path disturbance heat map reflecting the disturbance intensity. It is then divided into disturbance analysis unit areas, and the disturbance dataset is extracted based on the soil modeling information of the BIM model. Then, the disturbance intensity is quantified and output through the disturbance clustering index D calculation formula with clear physical meaning and consistent dimensions, and the disturbance level is visualized by combining the disturbance assessment threshold. This mechanism not only realizes the digital modeling coupling between construction disturbance behavior and foundation response for the first time, but also improves the refined evaluation and intelligent early warning capabilities of construction disturbance data, and has clear value in construction safety auxiliary judgment.

[0056] (2) When the disturbance clustering index Di exceeds the second disturbance threshold Dth2, the system automatically triggers the "fatigue response mechanism of the support structure", extracts the fatigue ultimate strength PLf and yield strength QFy of the support component material from the BIM platform, and combines the disturbance clustering index D and the disturbance attenuation coefficient. A structural fatigue margin index (Rf) assessment formula is constructed to achieve dynamic assessment and quantitative characterization of the fatigue state of support structures. Based on the Rf value, structural risk levels are classified, and corresponding risk control strategies are formulated, including specific actions such as frequency modulation, load limiting, alternative path guidance, risk-prone access restrictions, and expedited support maintenance. This method achieves intelligent collaboration throughout the entire process from disturbance detection and risk assessment to control response through the automatic closed-loop construction of the disturbance, structure, and control chain, demonstrating high field applicability and engineering safety assurance capabilities.

[0057] (3) This method uses the preliminary assessment result of the disturbance aggregation index D as a trigger signal to effectively link the structural fatigue response analysis process, realizing a dynamic correlation mapping between disturbance intensity and structural safety status. It can not only accurately identify high disturbance risk areas, but also execute matching risk control instructions according to the classification result of Rf, ultimately forming a closed-loop process of "disturbance identification, structural assessment, graded early warning and response control". This mechanism constructs an intelligent decision-making system with closed-loop feedback characteristics, which significantly enhances the adaptability and early warning accuracy of deep foundation pit support structures under complex disturbance conditions, solves the problem of the disconnect between disturbance assessment results and control decisions in the existing technology, and has the system advantages of full-cycle, full-path, and full-information integrated linkage control. Attached Figure Description

[0058] Figure 1This is a schematic diagram illustrating the construction monitoring steps of a deep foundation pit support structure according to the present invention;

[0059] Figure 2 This is a schematic diagram of the construction monitoring system for deep foundation pit support structure according to the present invention;

[0060] Figure 3 This is a schematic diagram showing the heat map of vehicle disturbance paths and the division of disturbance analysis unit areas. Detailed Implementation

[0061] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0062] Example 1

[0063] Please see Figure 1 This invention provides a construction monitoring method for deep foundation pit support structures. To achieve the above objectives, this invention employs the following technical solution, comprising the following steps:

[0064] S1. Collect vehicle operation disturbance data and transmit the operation disturbance data to the central processing unit of the BIM platform in real time. Based on the operation disturbance data, generate a vehicle path disturbance heat map. Then, divide the disturbance heat map into several disturbance analysis unit areas and extract the disturbance dataset according to the BIM platform.

[0065] S2. Calculate and output the disturbance clustering index D based on the disturbance dataset, set the disturbance assessment threshold, conduct a preliminary comparative assessment, and classify the disturbance heatmap by color level based on the preliminary comparative assessment results to form a disturbance risk level visual layer.

[0066] S3. For the disturbance analysis unit area with the highest disturbance risk level, trigger the fatigue response mechanism of the support structure, extract the fatigue dataset of the support structure, and construct the fatigue margin index Rf of the support structure.

[0067] S4. Based on the calculation results of the fatigue margin index Rf of the support structure, conduct a structural risk assessment, and output risk control instructions based on the structural risk assessment results.

[0068] In this embodiment, the method utilizes GPS modules, vehicle attribute modules, and weighing modules installed on heavy construction vehicles traveling along the construction path to achieve real-time acquisition of operational disturbance data such as vehicle path coordinates, speed, load, and type at a sampling frequency of 5Hz. The encapsulated disturbance data is then transmitted to the central processing unit of the BIM platform via a 5G communication module. The high-frequency real-time sampling is chosen because the soil response changes significantly under instantaneous disturbance behaviors such as vehicle turning, acceleration, and braking. If the sampling frequency is too low, key disturbance behaviors will be missed, leading to an underestimation of the disturbance assessment results. Stable transmission of the high-frequency data stream ensures the spatiotemporal accuracy of trajectory reconstruction and disturbance heatmap construction, accurately reflecting the spatial clustering characteristics of disturbances at the construction site. In the central processing unit, vehicle trajectory data is mapped to a two-dimensional grid system constructed using the UTM coordinate system, further generating a vehicle path disturbance heatmap, which is divided into 5m×5m disturbance analysis unit areas. The grid scale was chosen because 5m is a commonly used design scale for the spacing of most support piles. This scale is sufficient to capture the local variation trend of disturbance energy distribution without generating excessive computational redundancy, ensuring spatial focusing effect. The construction of the disturbance aggregation index D models the energy-resistance ratio of vehicle disturbance sources (type, frequency, load) and foundation disturbance resistance parameters (equivalent stiffness), reflecting the dynamic balance between "external disturbance accumulation and soil energy dissipation capacity". This design can effectively avoid misjudgment caused by the excessively high value of a certain disturbance index, thus achieving the scientific nature of multi-factor comprehensive evaluation. The disturbance assessment threshold is dynamically set based on the distribution characteristics of historical disturbance statistics, avoiding the bias caused by subjective human limitations, making the early warning mechanism more objective and universal. Furthermore, only when the disturbance aggregation index Di of a certain area exceeds the high threshold Dth2 is the structural fatigue response mechanism triggered, the Rf index extracted, and graded control implemented. This setting aims to precisely trigger and intervene in stages. For example, at the structural fatigue warning level, only the frequency of passage is reduced and the entry of heavy-load vehicles is restricted to avoid overreaction affecting construction efficiency. When the structural fatigue index Rf drops below 1.0, the path is forcibly closed and risk instructions are urgently pushed to ensure that the structure does not enter the critical state of fatigue failure. In summary, this implementation method achieves precise tracking of disturbance behavior and timely early warning and control of the safety status of the support structure through a chain-like closed loop of "disturbance identification - risk quantification - structural response - intelligent intervention". This greatly improves the risk response speed, structural safety margin, and adaptation accuracy of construction path intervention for deep foundation pit support structures, effectively avoiding the engineering risk of support structure instability caused by uncontrolled disturbances.

[0069] Example 2

[0070] Please see Figure 1 and Figure 3 Specifically: S1 includes S11;

[0071] S11. On construction vehicles traveling back and forth on the roadbed of the deep foundation pit support structure, a GPS module, a vehicle attribute module, and a vehicle weighing module are installed, and a 5Hz sampling frequency is set to collect the vehicle's running disturbance data in real time; and an interactive interface is established between the GPS module, the vehicle attribute module, and the vehicle weighing module and the vehicle control terminal, and the real-time collected running disturbance data is summarized and transmitted to the vehicle control terminal.

[0072] In the vehicle control terminal, the running disturbance data is encapsulated using a preset encapsulation format. The encapsulation format includes sampling timestamp, path coordinate field, speed field, vehicle type field, and vehicle load field. The data is then transmitted to the central processing unit of the BIM platform via 5G wireless communication through the wireless data communication module.

[0073] Operational disturbance data includes path coordinates, driving speed, vehicle type, and vehicle load.

[0074] The path coordinates and driving speed are collected in real time by the GPS module at a set sampling frequency of 5Hz. The vehicle's two-dimensional path coordinate information is collected in real time, and the instantaneous driving speed of the vehicle is calculated based on the track difference algorithm inside the GPS module.

[0075] The vehicle type is determined by the vehicle attribute module based on the vehicle's internal bus system, which obtains the vehicle's current operating mode and vehicle attribute code.

[0076] The vehicle load is measured in real time by the vehicle weighing module using strain gauge load cells installed on the vehicle axle, and the measured load data is transmitted to the vehicle control terminal.

[0077] The BIM platform integrates data from the IFC-BIM model and the geographic information system of the deep foundation pit construction area. It constructs an integrated three-dimensional information model that includes information on support structure components, support system, soil layer information, and construction terrain boundary information. The information model is pre-loaded with geological environmental data such as geological category, soil layer thickness, foundation bearing capacity parameters, and groundwater level corresponding to different spatial locations, thereby enabling the modeling of soil properties in the path area of ​​the deep foundation pit support structure.

[0078] S1 also includes S12;

[0079] S12. After receiving the number of operational disturbances, the central processing unit extracts the path coordinates and reconstructs the trajectory sequence according to the sampling time order to obtain the vehicle trajectory sequence of all heavy vehicles.

[0080] Simultaneously, a two-dimensional spatial positioning grid based on GPS path coordinates is constructed in the BIM platform. The two-dimensional spatial positioning grid uses the UTM universal transverse Mercator coordinate system as a reference system, and then maps the collected vehicle trajectory sequence to the reference system. The number of trajectory crossings, vehicle type and load information of all construction vehicles are counted to generate a vehicle path disturbance heat map of disturbance intensity.

[0081] Based on the vehicle path disturbance heat map, the entire deep foundation pit construction area is divided into several equilateral grid units with a set grid side length of 5 meters × 5 meters to obtain the disturbance analysis unit area.

[0082] Then, the BIM platform automatically extracts and calculates the disturbance dataset for the subsequent construction of the disturbance clustering index based on the vehicle operation disturbance data within each disturbance analysis unit area.

[0083] The disturbance dataset includes the average daily passage frequency F of vehicle type j within the disturbance analysis unit region i. ij The average load W of vehicle type j within the i-th disturbance analysis unit area. ij and the corresponding equivalent foundation stiffness S within the i-th disturbance analysis unit region i ;

[0084] Wherein, the corresponding equivalent foundation stiffness S within the i-th disturbance analysis unit region i Soil properties are extracted using a BIM platform based on an integrated 3D information model.

[0085] In this embodiment, the method uses a GPS module, a vehicle attribute module, and a weighing module on the construction vehicle, sampling at a high frequency of 5Hz to collect operational disturbance data in real time. This ensures the capture of transient disturbance behaviors such as sharp turns and braking, avoiding distortion or omissions in disturbance assessment. Simultaneously, key fields are encapsulated and transmitted at high speed to the central processing unit of the BIM platform via 5G communication, effectively improving the real-time performance and stability of data transmission. The use of a trajectory differential algorithm to accurately calculate instantaneous velocity avoids speed misjudgments caused by "jump points" in positioning data under adverse environments such as multipath reflections or satellite obstruction, enhancing the physical accuracy of the velocity data. The generation of trajectory heatmaps and the 5m grid division based on UTM coordinates make the disturbance clustering patterns more intuitive and detailed in the spatial dimension, reflecting localized areas of concentrated disturbance rather than averaged errors, providing a clear "landing point" for subsequent risk warnings. Setting the grid side length to 5m is a general spacing consideration for the piles of the support structure near the construction path. If the setting is too large, it will "dilute" the disturbance hotspots; if the setting is too small, there is a risk of frequent vehicle disturbances crossing the grid and causing interference. Therefore, this scale achieves a good balance between engineering feasibility and analytical performance. The BIM platform integrates the IFC model with the GIS geographic information system, "binding" the disturbance data with multi-source heterogeneous data such as the support structure, soil layer, and foundation into the same spatial model. This ensures that the disturbance data not only has a "location" but also a "structural response background," providing basic soil stiffness parameters for subsequent disturbance clustering index and structural fatigue margin analysis.

[0086] Ultimately, this implementation method not only enables the perception and reconstruction of construction disturbance behavior across the entire process, all elements, and all spatial dimensions, but also establishes a precise correlation between disturbance and structural response, effectively improving the accuracy of subsequent disturbance assessment and structural early warning, as well as the value of engineering implementation.

[0087] Example 3

[0088] Please see Figure 1 Specifically: S2 includes S21;

[0089] S21. Based on the obtained disturbance dataset, the extreme value normalization method is used to eliminate the influence of unit dimensions between parameters in all disturbance datasets. Then, the disturbance clustering index D of each disturbance analysis unit area is calculated and output. The disturbance contribution value of the first to nth types of construction vehicles in the analysis unit is calculated and summed to obtain the disturbance clustering index D that reflects the magnitude of vehicle disturbance intensity in the disturbance analysis unit area.

[0090] The disturbance clustering index D is calculated and output using the following algorithm formula;

[0091] In the formula, D iP represents the disturbance clustering index within the i-th disturbance analysis unit region, n represents the total number of vehicle types, and P j The value represents the vibration impact weight of vehicle type j, indicating the contribution of different vehicle types to foundation disturbance. The value is set according to the vehicle grounding structure, vibration mode and load characteristics, and the value range is 0.5-1.2. Among them, tracked vehicle > dump truck > concrete mixer truck > medium-sized engineering vehicle (pump truck, etc.) > small transport vehicle.

[0092] In this formula, to achieve a quantitative assessment of the disturbance effect caused by vehicle traffic within the path area of ​​the deep foundation pit support structure, a disturbance clustering index Di calculated based on a disturbance dataset is proposed. This index is used to characterize the total disturbance intensity caused by different types of construction vehicles at different times in a specific area along the construction path, and serves as a key triggering parameter for subsequent structural fatigue response determination and risk level early warning. This disturbance clustering index model originates from traffic load disturbance theory and foundation bearing capacity evaluation principles, combining vehicle dynamic disturbance and soil disturbance resistance parameters.

[0093] The formula is structurally represented by the disturbance source term (P). j ·F ij ·W ij ) and the disturbance rejection factor (S) ij The sum of the ratios of the disturbance source and the resistance factor conforms to the physical modeling logic of "accumulated disturbance energy ÷ resistance attenuation". The disturbance source term comprehensively considers vehicle type (excitation characteristics), load (disturbance intensity) and frequency (disturbance superposition effect), while the resistance factor reflects the dynamic absorption and attenuation capacity of the soil itself.

[0094] From the perspective of dimensional consistency, the perturbation dataset consists of dimensionless units after processing by the extreme value normalization method, so the output of the perturbation clustering index D is also dimensionless.

[0095] S2 also includes S22;

[0096] S22. Based on the statistical results of the disturbance clustering index D of all disturbances during the construction history period, disturbance assessment thresholds for graded assessment are set according to the cumulative distribution characteristics. The disturbance assessment thresholds include a first disturbance threshold Dth1 and a second disturbance threshold Dth2. The disturbance clustering index D value in the 50th quantile of the statistical distribution is set as the first disturbance threshold Dth1 to distinguish between normal disturbance areas and low-level concern areas. The disturbance clustering index D value in the 85th quantile of the statistical distribution is set as the second disturbance threshold Dth2 to distinguish between concern areas and high-disturbance-risk areas.

[0097] The disturbance clustering index Di calculated from the disturbance analysis unit region is compared with the disturbance assessment threshold for preliminary evaluation.

[0098] When the disturbance clustering index D in the i-th disturbance analysis unit region i When the disturbance threshold Dth1 is less than the first disturbance threshold, the corresponding disturbance analysis unit area is marked as a normal disturbance area, and the current disturbance analysis unit area is displayed in green.

[0099] When the first disturbance threshold Dth1 ≤ the disturbance clustering index D in the i-th disturbance analysis unit region i When the disturbance threshold Dth2 is less than the second disturbance threshold, the corresponding disturbance analysis unit area is marked as a disturbance interest area, and the current disturbance analysis unit area is displayed in yellow.

[0100] When the disturbance clustering index D in the i-th disturbance analysis unit region i When the value is greater than or equal to the second disturbance threshold Dth2, the corresponding disturbance analysis unit area is marked as a disturbance risk area, and the current disturbance analysis unit area is displayed in red.

[0101] In this embodiment, the method employs extreme value normalization to perform dimensionless processing on the disturbance dataset parameters. This addresses the issue of different units and large differences in numerical scales among vehicle frequency, load, and foundation stiffness. Without eliminating the influence of units, a single parameter would dominate the disturbance clustering index D, distorting the evaluation results and making it difficult to objectively reflect the true degree of disturbance. The disturbance clustering index D incorporates a vehicle type vibration influence weight P to account for the true differences in the disturbance to the foundation caused by different vehicles. For example, tracked vehicles, due to their large ground contact area and continuous load distribution, cause strong ground disturbance. Treating them the same as small transport vehicles would underestimate the impact of such heavy vehicles on the support structure. Simultaneously, by setting disturbance evaluation thresholds—a first disturbance threshold Dth1 and a second disturbance threshold Dth2—and performing graded comparison and evaluation to construct a color-coded visual layer, the method aims to quickly identify key high-incidence areas of disturbance. For instance, without graded labeling, in a vast construction area, managers would find it difficult to immediately identify potential risk locations, delaying early warning responses. The physical essence of this method is to quantify the dynamic relationship between "energy excitation" (vehicle behavior) and "disturbance resistance" (foundation response), thereby achieving precise location and early intervention of disturbance risks and providing stable input for subsequent structural fatigue analysis. Through this mechanism, the accuracy and response efficiency of risk area identification are significantly improved, and a good foundation for visualization and management is provided.

[0102] Example 4

[0103] Please see Figure 1 Specifically: S3 includes S31;

[0104] S31. For the disturbance analysis unit area displayed in red, trigger the support structure fatigue response mechanism. The support structure fatigue response mechanism includes support structure data extraction and support fatigue analysis.

[0105] The support structure data extraction is carried out by extracting the support structure fatigue dataset through the BIM platform, and the extreme value normalization method is used to eliminate the influence of unit dimensions among the parameters in all support structure fatigue datasets.

[0106] The fatigue dataset for support structures includes the fatigue limit strength PLf of the support structure material and the yield strength QFy of the support structure components;

[0107] The fatigue limit strength PLf of the support structure material is extracted from the support structure component information in the BIM platform. The support structure component information records the material type used in different structural components and their corresponding fatigue limit values.

[0108] The yield strength QFy of the support structure component is obtained by extracting the yield strength information of the support structure component from the BIM platform.

[0109] S3 also includes S32;

[0110] S32. Support fatigue analysis is conducted by constructing the support structure fatigue margin index Rf based on the support structure fatigue dataset and the disturbance aggregation index D of the disturbance analysis unit area shown in red. This quantitatively analyzes the proportion of the fatigue margin of the support structure to the yield capacity under the current disturbance conditions.

[0111] The fatigue margin index Rf of the support structure is calculated and output using the following algorithm formula;

[0112] In the formula, Rf i This represents the fatigue margin index of the support structure within the i-th disturbance analysis unit region. This represents the disturbance attenuation coefficient, used to simulate the disturbance energy generated when a vehicle travels on the path of the foundation pit support structure. It is a quantitative coefficient that represents the attenuation of the disturbance energy due to the propagation loss of the soil medium during the process of transmission from the disturbance source (i.e., the vehicle path) to the support structure location. The value range is 0.3-0.8.

[0113] In this formula, to accurately assess the fatigue safety status of deep foundation pit support structures under continuous vehicle disturbance, a calculation model for the structural fatigue margin index Rf is proposed. This model characterizes the ratio between the fatigue response intensity triggered by the disturbance and the remaining structural resistance, and is a core judgment indicator in the early warning control system. This formula originates from the basic theories of fatigue limit and bearing capacity assessment in mechanics of materials. In classical structural mechanics models, the fatigue safety status of a component is usually determined by the difference between the "bearable fatigue strength" and the "current disturbance stress".

[0114] This formula introduces a perturbation propagation correction mechanism and combines the modeling results of the perturbation clustering index D. In the formula, • Di represents the equivalent disturbance intensity actually transmitted to the surface of the support structure, taking into account the attenuation effect of the disturbance during propagation; PLf- Di represents the remaining fatigue strength of the structure; by dividing by the structural yield strength QFy, the fatigue margin index Rf of the structure is made to allow for a lateral comparison between structures of different materials or different regions.

[0115] In this embodiment, the method is configured to trigger the fatigue response mechanism of the support structure only for the red-level disturbance analysis unit area. The main purpose is to achieve "precise response" under risk classification, avoid redundant calculations for the entire structure, and improve processing efficiency. Triggering the structural response for all areas would not only waste computational resources but could also disrupt the construction schedule due to frequent alarms. A disturbance attenuation coefficient is introduced. This is based on the physical propagation path between the disturbance source and the support structure. For example, if the construction vehicle is only 3 meters away from the support structure, the disturbance energy has virtually no attenuation. The value should approach the upper limit; however, if the path is more than 10 meters away, the foundation medium has a significant buffering effect on the disturbance. The value should be reduced. This can be achieved through dynamic adjustment. The value ensures that the impact of the disturbance concentration index D is truly reflected in the structural response, avoiding "false high warnings from afar." The core of introducing the fatigue margin index Rf for support structures lies in quantifying the ratio difference between the fatigue stress induced by disturbance and the material resistance. This ratio index is more dynamically adaptable than traditional single loads or safety factors. For example, even if PLf is high, frequent and concentrated vehicle disturbances... When D is large, Rf will decrease significantly, reflecting that the structure is approaching the critical state of fatigue. This implementation process ultimately realizes the quantitative prediction and comparable assessment of structural fatigue risk, significantly improves the safety monitoring accuracy and response timeliness of deep foundation pit support systems, and effectively avoids the risk of missing "local high disturbance - low resistance" hidden danger points by traditional experience-based inspections.

[0116] Example 5

[0117] Please see Figure 1 Specifically: S4 includes S41;

[0118] S41. Based on the output results of the fatigue margin index Rf of the support structure in each disturbance analysis unit region, a structural risk assessment is performed, and based on the structural risk assessment results, the structural fatigue risk level of the target disturbance analysis unit region is classified; the specific structural risk assessment content is as follows:

[0119] When the fatigue margin index Rf of the support structure in the i-th disturbance analysis unit region iWhen the value is greater than 1.2, the current disturbance analysis unit region is determined to be at the structural fatigue safety level;

[0120] When 1.0 < the fatigue margin index Rf of the support structure in the i-th disturbance analysis unit region i When the value is ≤1.2, the current disturbance analysis unit region is determined to be at the structural fatigue warning level;

[0121] When the fatigue margin index Rf of the support structure in the i-th disturbance analysis unit region i When the value is ≤1.0, the current disturbance analysis unit area is determined to be at the structural fatigue risk level.

[0122] S4 also includes S42;

[0123] S42. Execute corresponding risk control instructions based on the structural fatigue risk level, as detailed below:

[0124] When the area is determined to be a structural fatigue safety level zone, no analysis control instructions are generated. The existing traffic routes, traffic frequency and construction production rhythm of construction vehicles remain unchanged. The BIM platform only performs real-time fatigue margin updates and area status records, without triggering any construction intervention measures.

[0125] When the structural fatigue warning level is determined, the first risk control instruction is executed. This instruction sends a disturbance reduction control strategy to the construction management terminal via the BIM platform. The disturbance reduction control strategy automatically reduces the average daily frequency F of construction vehicles within the current disturbance analysis unit area by 75% of the original average daily frequency. That is, if the original frequency is F, it is controlled to 0.75·F. Vehicles with an average load W exceeding 80% of the upper limit of the support structure's design load are prohibited from entering the current disturbance analysis unit area. The construction vehicle scheduling module generates alternative routes to guide overloaded vehicles to safer routes that are farther from the support structure and have lower disturbance sensitivity. At the same time, a reminder message is pushed to the construction management personnel for inspection.

[0126] When the structural fatigue risk level is determined, the second risk control instruction is executed. The second risk control instruction completely prohibits construction vehicles with an average load W exceeding 60% of the upper limit of the support structure's design load from entering the current disturbance analysis unit area, and forcibly reduces the original passage frequency to zero until the risk is eliminated. At this time, an urgent warning is generated and sent to the construction management terminal through the BIM platform, prompting immediate maintenance of the support in the current disturbance analysis unit area.

[0127] In this embodiment, the method uses the fatigue margin index Rf of the support structure to classify the risk level of the disturbance analysis unit area. The core purpose is to achieve "dynamic risk identification based on response indicators." Compared with traditional methods based on empirical thresholds or equivalent static load estimation, the fatigue margin index Rf of the support structure reflects the true ratio between the remaining resistance of the structure and the disturbance intensity, and has stronger adaptability and accuracy. For example, although the frequency of construction vehicle traffic in a certain area is not high, if the soil stiffness is low and the disturbance attenuation capacity is weak, the Rf value may drop rapidly to the warning or risk level. At this time, if the construction rhythm is maintained according to the empirical frequency, it is very easy to induce structural fatigue cracks or local yield failure without warning. Therefore, classifying the fatigue margin index Rf of the support structure into three levels—>1.2, 1.0~1.2, and ≤1.0—can accurately identify the true degree of structural safety redundancy under the current disturbance state. Furthermore, setting a 75% reduction in traffic frequency and an 80% load limit as the first risk control instruction under the warning level is based on the principle of "controllability" of the disturbance source intensity. Especially in path areas within 5m of the support structure, the energy of vehicle disturbance in the soil has almost no attenuation, and the disturbance transmission intensity increases significantly. If the load is not reduced and the frequency is not limited in time, the fatigue margin index Rf of the support structure will drop below the risk threshold in a short period of time. Ultimately, the risk classification and corresponding control strategy driven by the fatigue margin index Rf of the support structure effectively improves the operational toughness of the support structure and the timeliness of early warning intervention, avoiding sudden structural instability or excessive maintenance caused by "risk oversight".

[0128] Example 6

[0129] Please see Figure 1 and Figure 2 A construction monitoring system for deep foundation pit support structures includes a disturbance heat map generation module, a heat map color level classification module, a support fatigue response module, and a risk command triggering module.

[0130] The disturbance heatmap generation module collects vehicle operation disturbance data and transmits the data to the central processing unit of the BIM platform in real time. Based on the disturbance data, it generates a vehicle path disturbance heatmap. The heatmap is then divided into several disturbance analysis unit areas, and the disturbance dataset is extracted from the BIM platform.

[0131] The heatmap color level classification module calculates and outputs the disturbance clustering index D based on the disturbance dataset, sets the disturbance assessment threshold, conducts a preliminary comparative assessment, and classifies the disturbance heatmap into color levels based on the preliminary comparative assessment results, forming a disturbance risk level visual layer.

[0132] The support fatigue response module triggers the support structure fatigue response mechanism by analyzing the disturbance analysis unit area with the highest disturbance risk level, extracts the support structure fatigue dataset, and constructs the support structure fatigue margin index Rf.

[0133] The risk command triggering module performs structural risk assessment based on the calculation results of the fatigue margin index Rf of the support structure, and outputs risk control commands based on the structural risk assessment results.

[0134] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention.

Claims

1. A method of monitoring the construction of a deep foundation pit support structure, characterized by: Comprise the following steps: S1, collect vehicle running disturbance data, and transmit the running disturbance data to the central processing unit of the BIM platform in real time, and form a vehicle path disturbance heat map based on the running disturbance data; then divide the disturbance heat map into several disturbance analysis unit regions, and extract the disturbance data set according to the BIM platform; S2, based on the disturbance data set, calculate the disturbance aggregation index D, set the disturbance evaluation threshold, carry out preliminary comparative evaluation, divide the disturbance heat map according to the color level based on the preliminary comparative evaluation result, and form a disturbance risk level visual layer; S3, for the disturbance analysis unit region with the highest risk of disturbance risk level, trigger the supporting structure fatigue response mechanism, extract the supporting structure fatigue data set, and construct the supporting structure fatigue margin index Rf; S4, based on the calculation result of the supporting structure fatigue margin index Rf, carry out structure risk assessment, and output risk control instruction based on the structure risk assessment result.

2. The method according to claim 1, wherein: The S1 comprises S11; S11, a global positioning system GPS module, a vehicle attribute module and a vehicle weighing module are arranged on the construction vehicle running on the deep foundation pit supporting structure subgrade, and the running disturbance data of the vehicle is collected in real time with a sampling frequency of 5Hz; and an interactive interface is established between the global positioning system GPS module, the vehicle attribute module and the vehicle weighing module and the vehicle control terminal, and the real-time collected running disturbance data is transmitted to the vehicle control terminal; The running disturbance data is encapsulated in the vehicle control terminal using a preset encapsulation format, and the encapsulation format includes sampling timestamp, path coordinate field, speed field, vehicle type field and vehicle load field; and the wireless data communication module sends the data to the central processing unit of the BIM platform in a 5G wireless communication mode; The running disturbance data includes path coordinates, driving speed, vehicle type and vehicle load.

3. The method for monitoring the construction of a deep foundation pit support structure according to claim 2, characterized in that: The S1 further comprises S12; S12, after receiving the running disturbance data, the central processing unit extracts the path coordinates and reconstructs the trajectory sequence in the order of sampling time to obtain the vehicle trajectory sequence of all heavy vehicles; At the same time, a two-dimensional spatial positioning grid based on GPS path coordinates is constructed in the BIM platform, the two-dimensional spatial positioning grid is referenced by the UTM universal transverse Mercator coordinate system, and then the collected vehicle trajectory sequence is mapped into the reference system to count the trajectory crossing times, vehicle type and load information of all construction vehicles, and generate a vehicle path disturbance heat map of disturbance intensity; Based on the vehicle path disturbance heat map, the entire deep foundation pit construction area is divided into several equilateral grid units with a set grid length of 5m*5m to obtain the disturbance analysis unit region; Then, the BIM platform automatically extracts and calculates the disturbance data set for subsequent disturbance aggregation index construction in each disturbance analysis unit region according to the vehicle running disturbance data; said perturbation dataset comprises a daily average frequency of passage F of a jth vehicle type within an ith perturbation analysis cell region ij , an average load W of a jth vehicle type within an ith perturbation analysis cell region ij , and a corresponding ground equivalent stiffness S within an ith perturbation analysis cell region i .

4. The method for monitoring the construction of a deep foundation pit support structure according to claim 3, characterized in that: The S2 comprises S21; S21, using the extreme value normalization method based on the obtained disturbance data set, eliminating the unit dimension influence between the parameters in all disturbance data sets, and then calculating and outputting the disturbance aggregation index D of each disturbance analysis unit area; The disturbance aggregation index D is calculated and output by the following algorithm formula; ; wherein D i represents the disturbance aggregation index in the i-th disturbance analysis unit region, n represents the total number of vehicle types, P j represents the vibration influence weight of the j-th vehicle type.

5. The method for monitoring the construction of a deep foundation pit support structure according to claim 4, characterized in that: The S2 further includes S22; S22, based on the statistical results of the disturbance aggregation index D of all disturbances in the construction history period, setting the disturbance evaluation threshold for hierarchical evaluation according to the cumulative distribution characteristics, the disturbance evaluation threshold including a first disturbance threshold Dth1 and a second disturbance threshold Dth2; The disturbance aggregation index Di calculated by the disturbance analysis unit area is compared with the disturbance evaluation threshold in sequence, When the disturbance aggregation index D in the i-th disturbance analysis unit region is less than the first disturbance threshold Dth1 i When the disturbance aggregation index D in the i-th disturbance analysis unit region is less than the first disturbance threshold Dth1 When the disturbance aggregation index D in the i-th disturbance analysis unit region is less than the first disturbance threshold Dth1 When the first disturbance threshold Dth1 ≤ the disturbance aggregation index D in the i-th disturbance analysis unit region i When the second disturbance threshold Dth2 is reached, the corresponding disturbance analysis unit region is marked as a disturbance attention region, and the current disturbance analysis unit region is displayed in yellow. When the disturbance aggregation index D in the i-th disturbance analysis unit region i ≥ the second disturbance threshold Dth2, the corresponding disturbance analysis unit region is marked as a disturbance risk region, and the current disturbance analysis unit region is displayed in a red level.

6. The method for monitoring the construction of a deep foundation pit support structure according to claim 5, characterized in that: The S3 includes S31; S31, triggering the support structure fatigue response mechanism for the disturbance analysis unit area displayed in red level, the support structure fatigue response mechanism including support structure data extraction and support fatigue analysis; The support structure data extraction extracts the support structure fatigue data set through the BIM platform, and uses the extreme value normalization method to eliminate the unit dimension influence between the parameters in all support structure fatigue data sets; The support structure fatigue data set includes the fatigue limit strength PLf of the support structure material and the yield strength QFy of the support structure member.

7. The method for monitoring the construction of a deep foundation pit support structure according to claim 6, characterized in that: The S3 further includes S32; S32, the support fatigue analysis constructs the support structure fatigue margin index Rf by combining the disturbance aggregation index D of the disturbance analysis unit area displayed in red level based on the support structure fatigue data set, and quantitatively analyzes the proportion of the fatigue margin of the support structure to the yield capacity under the current disturbance condition; The support structure fatigue margin index Rf is calculated and output by the following algorithm formula; wherein Rf i represents a support structure fatigue margin index in the i-th perturbation analysis unit region, represents a perturbation decay coefficient.

8. The method for monitoring the construction of a deep foundation pit support structure according to claim 7, characterized in that: The S4 includes S41; S41, based on the output results of the support structure fatigue margin index Rf of each disturbance analysis unit area, performing structure risk assessment, and based on the structure risk assessment results, dividing the structure fatigue of the target disturbance analysis unit area into risk levels; The specific structure risk assessment content is as follows: When the fatigue margin index Rf of the support structure in the i-th disturbance analysis unit region i When the value is greater than 1.2, the current disturbance analysis unit region is determined to be at the structural fatigue safety level. When 1.0 < the support structure fatigue margin index Rf in the i-th disturbance analysis unit region i ≤ 1.2, the current disturbance analysis unit region is determined as a structure fatigue warning level; When the support structure fatigue margin index Rf within the i-th perturbation analysis unit region i ≤ 1.0, the current perturbation analysis unit region is determined to be a structure fatigue risk level.

9. The method for monitoring the construction of a deep foundation pit support structure according to claim 8, characterized in that: The S4 further includes S42; S42, executing the corresponding risk control instruction based on the structure fatigue risk level, and the specific content is as follows: When it is determined that the structure fatigue safety level area, no analysis control instruction is generated at this time, the existing traffic path, traffic frequency and construction production rhythm of the construction vehicle are maintained, the BIM platform only updates the real-time fatigue margin and records the area state, and does not trigger any construction intervention measures; When the structure fatigue warning level is determined, the first risk control instruction is executed, which sends a disturbance load reduction control strategy to the construction management terminal through the BIM platform. The disturbance load reduction control strategy automatically reduces the daily average passing frequency F of the construction vehicle in the current disturbance analysis unit area by 75% of the original daily average frequency; vehicles with an average load W exceeding 80% of the upper limit of the design load of the supporting structure are prohibited from entering the current disturbance analysis unit area; and the construction vehicle scheduling module generates an alternative path to guide the vehicles exceeding the limit to a safe passage path that is farther away from the supporting structure and has lower disturbance sensitivity; at the same time, prompt information is pushed to the construction management personnel for inspection; When the structure fatigue risk level is determined, the second risk control instruction is executed, which completely prohibits construction vehicles with an average load W exceeding 60% of the upper limit of the design load of the supporting structure from entering the current disturbance analysis unit area, and forces the original passing frequency to zero until the risk is removed. At this time, an urgent warning is generated and sent to the construction management terminal through the BIM platform, prompting immediate maintenance of the supporting structure in the current disturbance analysis unit area.

10. A deep foundation pit support structure construction monitoring system applied to the deep foundation pit support structure construction monitoring method of any one of claims 1-9, characterized in that: It includes a disturbance heat map generation module, a heat map color level division module, a supporting fatigue response module, and a risk instruction triggering module. The disturbance heat map generation module collects vehicle operation disturbance data and transmits it to the central processing unit of the BIM platform in real time, and forms a vehicle path disturbance heat map based on the operation disturbance data. Then, the disturbance heat map is divided into several disturbance analysis unit areas, and the disturbance data set is extracted based on the BIM platform; The heat map color level division module calculates and outputs the disturbance aggregation index D based on the disturbance data set, sets a disturbance evaluation threshold, and performs preliminary comparative evaluation. Based on the preliminary comparative evaluation result, the disturbance heat map is divided into color levels to form a disturbance risk level visual layer. The supporting fatigue response module triggers the supporting structure fatigue response mechanism for the disturbance analysis unit area with the highest risk level, extracts the supporting structure fatigue data set, and constructs the supporting structure fatigue margin index Rf. The risk instruction triggering module performs structure risk assessment based on the calculation result of the supporting structure fatigue margin index Rf, and outputs the risk control instruction based on the structure risk assessment result.

Images