Multi-layer karst cave area building pile foundation construction risk pre-judgment system

Abstract

The invention provides a multi-layer karst cave area building pile foundation construction risk pre-judgment system. The system comprises a data acquisition layer, a data processing layer, a risk analysis layer, a decision support layer and a system interaction layer. The input end of the data processing layer is wirelessly connected to the output end of the data acquisition layer; the input end of the risk analysis layer is wirelessly connected to the output end of the data processing layer; the input end of the decision support layer is wirelessly connected to the output end of the risk analysis layer; and the input end of the system interaction layer is wirelessly connected to the output end of the decision support layer. According to the multi-layer karst cave area building pile foundation construction risk pre-judgment system, real-time integration of geological data, construction data and environmental data is achieved through multi-source data collection and fusion, the problem that traditional data is single is solved, it is ensured that risk identification is free of omission, risk levels are concrete based on a quantitative evaluation model and a risk database, and the risk level is accurately evaluated. Traditional qualitative judgment is replaced, and the evaluation accuracy is improved.

Description

Technical Field

[0001] This invention relates to the field of risk prediction and intelligent management of building pile foundation construction in multi-layered karst cave areas, and particularly to a risk prediction system for building pile foundation construction in multi-layered karst cave areas. Background Technology

[0002] The geological conditions in areas with multiple karst caves are complex, and the distribution of karst caves is hidden, multi-layered, and irregular, which poses significant risks to pile foundation construction.

[0003] In traditional pile foundation construction, risk prediction relies heavily on manual experience and limited geological survey data, which has the following limitations: First, the data source is singular, making it difficult to integrate real-time parameters and environmental factors during the construction process;

[0004] Second, risk identification is delayed, and the response is often reactive after an accident occurs, making it impossible to predict potential risks such as hole collapse, stuck drill, and water inrush in advance.

[0005] Third, risk assessment lacks quantitative standards and is mostly based on qualitative judgments, resulting in insufficiently targeted response plans;

[0006] Fourth, the adjustment of construction parameters relies on experience, making it difficult to scientifically optimize them based on the dynamic changes of the karst cave.

[0007] These problems can easily lead to construction accidents, not only increasing additional costs but also causing safety hazards, seriously affecting the progress and quality of the project.

[0008] Therefore, it is necessary to provide a risk prediction system for building pile foundation construction in multi-layered karst cave areas to solve the above-mentioned technical problems. Summary of the Invention

[0009] This invention provides a risk prediction system for building pile foundation construction in multi-layered karst cave areas, which solves the problems of lack of systematicness and scientificity in risk prediction during traditional multi-layered karst cave construction: the inability to effectively integrate multi-source data from geological surveys, construction processes, and the environment for real-time analysis, resulting in delayed risk identification; risk assessment relying on qualitative judgment, which is not accurate enough; difficulty in generating targeted response plans based on risk trends in advance; and lack of data support for adjusting construction parameters, ultimately leading to frequent construction accidents, cost overruns, and low decision-making efficiency.

[0010] To address the aforementioned technical problems, this invention provides a risk prediction system for building pile foundation construction in multi-layered karst cave areas, comprising:

[0011] Data acquisition layer, data processing layer, risk analysis layer, decision support layer, and system interaction layer;

[0012] The input end of the data processing layer is wirelessly connected to the output end of the data acquisition layer;

[0013] The input end of the risk analysis layer is wirelessly connected to the output end of the data processing layer;

[0014] The input end of the decision support layer is wirelessly connected to the output end of the risk analysis layer;

[0015] The input end of the system interaction layer is wirelessly connected to the output end of the decision support layer;

[0016] The data acquisition layer includes geological exploration data acquisition, construction process data acquisition, and environmental data acquisition;

[0017] The data processing layer includes data cleaning, data transformation, and data fusion modules;

[0018] The risk analysis layer includes risk identification, risk assessment, and risk prediction;

[0019] The decision support layer includes risk response solution generation, risk assessment and optimization, and suggestions for adjusting construction parameters;

[0020] The system interaction layer includes data visualization, user interface, and information release and early warning.

[0021] Preferably, the geological survey data acquisition is used to obtain data on the location, size, burial depth and surrounding rock properties of the multi-layered karst cave; the construction process data acquisition is used to collect real-time construction parameters such as drilling speed, mud pressure and borehole water level; and the environmental data acquisition is used to collect environmental information such as precipitation, groundwater level changes and surrounding vibrations.

[0022] Preferably, the data cleaning is used to remove noise, outliers and fill in missing data values, the data conversion is used to standardize data formats and unify units, and the data fusion is used to integrate multi-source data and perform consistency verification and optimization.

[0023] Preferably, the risk identification is based on the constructed risk database and the risk identification, to identify the risk types of hole collapse, stuck drill and water inrush in the pile foundation construction of multi-layer karst areas, the risk assessment is used to determine the risk level and perform quantitative assessment, and the risk prediction is to realize risk trend prediction through trained machine learning / neural network models.

[0024] Preferably, the risk response scheme generation generates preliminary response schemes for high-risk karst areas and construction failure risks; the risk assessment and optimization screens the optimal scheme through feasibility and economic analysis; and the construction parameter adjustment suggestions output dynamic optimization parameters for drilling speed, pressure, mud specific gravity, and borehole water level.

[0025] Preferably, the data visualization display presents the geological structure and karst cave distribution using a three-dimensional model, the user interface supports data query, parameter setting and custom adjustment, and the information release and early warning are achieved through high-risk pop-ups, sound alarms and SMS notifications to provide multi-channel early warning.

[0026] Preferably, the noise outlier removal in the data cleaning process uses statistical analysis and thresholding methods, while missing value filling uses interpolation or a prediction model based on historical data.

[0027] Preferably, the machine learning / neural network model of the risk prediction module includes a BP neural network model and a random forest model, and the model is dynamically trained and optimized using historical construction risk data and real-time collected data.

[0028] Preferably, the parameter adjustment suggestions for the construction parameters are based on real-time changes in the risk level. When the risk level exceeds a preset threshold, emergency adjustment commands such as reducing drilling speed and increasing mud density are automatically triggered.

[0029] Preferably, the warning level of the information release and early warning corresponds to the risk level of the risk assessment, wherein a level 1 warning notifies the construction manager and the supervision unit via SMS, a level 2 warning triggers an on-site audible and visual alarm, and a level 3 warning forcibly suspends construction and pops up a prompt for an emergency handling plan.

[0030] Compared with related technologies, the risk prediction system for building pile foundation construction in multi-layer karst cave areas provided by this invention has the following beneficial effects:

[0031] This invention provides a risk prediction system for building pile foundation construction in multi-layered karst areas. Through multi-source data acquisition and fusion, it achieves real-time integration of geological, construction, and environmental data, solving the problem of single data sources in traditional systems and ensuring comprehensive risk identification. Based on a quantitative assessment model and risk database, it visualizes risk levels, replacing traditional qualitative judgments and improving assessment accuracy. Utilizing machine learning / neural network models, it combines historical and real-time data to predict risk trends, identifying high-risk areas in advance and providing a basis for proactive prevention and control. By generating feasible and economical response plans and dynamic construction parameter suggestions, it reduces additional costs caused by accidents, supports construction teams in making safe and economical decisions, and links multi-channel early warning mechanisms with risk levels to ensure timely response by relevant personnel, reducing the accident rate. Construction parameters are dynamically adjusted according to the risk level; for example, in high-risk situations, it automatically reduces drilling speed and optimizes mud density, improving construction safety and efficiency. Attached Figure Description

[0032] Figure 1 This is a schematic diagram of the first embodiment of a risk prediction system for building pile foundation construction in multi-layer karst cave areas provided by the present invention;

[0033] Figure 2 for Figure 1 The diagram shows the specific modules of the data acquisition layer.

[0034] Figure 3 for Figure 1 A schematic diagram of the specific modules of the data processing layer is shown below.

[0035] Figure 4 for Figure 1 The diagram shows the specific modules of the risk analysis layer.

[0036] Figure 5 for Figure 1 The diagram shows the specific modules of the decision support layer.

[0037] Figure 6 for Figure 1 The diagram shows the specific modules of the system interaction layer.

[0038] Figure 7 This is a schematic diagram of the second embodiment of a risk prediction system for building pile foundation construction in multi-layer karst cave areas provided by the present invention;

[0039] Figure 8 for Figure 7 A schematic diagram of part A shown;

[0040] Figure 9 This is a schematic diagram of the data acquisition device.

[0041] The diagram shows: 1. Main body of the data collector; 2. Handle;

[0042] 3. Support components; 31. Mounting head; 32. Support telescopic rod;

[0043] 4. Rotating assembly; 41. Rotating frame; 42. Circular block; 43. Handle;

[0044] 5. Limiting component; 51. Arc-shaped plate; 52. Limiting bolt; 53. Limiting hole;

[0045] 6. Protective components; 61. Fixing ring; 62. Rotating seat; 63. Fixing rod; 64. Movable sleeve; 65. Protective cover; 66. Fixing bolt; 67. Stop block. Detailed Implementation

[0046] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0047] First Embodiment

[0048] Please refer to the following: Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 and Figure 6 ,in, Figure 1 This is a schematic diagram of a preferred embodiment of a construction risk prediction system for building pile foundations in multi-layered karst cave areas provided by the present invention; Figure 2 for Figure 1 The diagram shows the specific modules of the data acquisition layer. Figure 3 for Figure 1 A schematic diagram of the specific modules of the data processing layer is shown below. Figure 4 for Figure 1 The diagram shows the specific modules of the risk analysis layer. Figure 5 for Figure 1 The diagram shows the specific modules of the decision support layer. Figure 6 for Figure 1 The diagram shows the specific modules of the system interaction layer. A risk prediction system for building pile foundation construction in multi-layered karst cave areas includes:

[0049] Data acquisition layer, data processing layer, risk analysis layer, decision support layer, and system interaction layer;

[0050] The input end of the data processing layer is wirelessly connected to the output end of the data acquisition layer;

[0051] The input end of the risk analysis layer is wirelessly connected to the output end of the data processing layer;

[0052] The input end of the decision support layer is wirelessly connected to the output end of the risk analysis layer;

[0053] The input end of the system interaction layer is wirelessly connected to the output end of the decision support layer;

[0054] The data acquisition layer includes geological exploration data acquisition, construction process data acquisition, and environmental data acquisition;

[0055] The data processing layer includes data cleaning, data transformation, and data fusion modules;

[0056] The risk analysis layer includes risk identification, risk assessment, and risk prediction;

[0057] The decision support layer includes risk response solution generation, risk assessment and optimization, and suggestions for adjusting construction parameters;

[0058] The system interaction layer includes data visualization, user interface, and information release and early warning.

[0059] Data acquisition layer: Geological exploration data includes the depth of karst caves, the thickness of the roof and the filling material; construction process data includes drilling rig torque, borehole verticality, mud flow rate, etc.; environmental data includes hourly precipitation, daily changes in groundwater level, settlement values ​​of surrounding buildings, etc., to ensure that the data dimensions cover the entire scenario of "geology-construction-environment".

[0060] Risk analysis layer: The risk identification model combines expert rules with machine learning classification algorithms; the risk prediction model adopts a rolling prediction mechanism, updating the prediction results every hour to ensure the timeliness of trend prediction.

[0061] Decision support layer: When the real-time risk level rises above the predicted value, the system automatically reassesses the response plan and generates new construction parameter suggestions within a specified time; the feasibility analysis of the plan includes dimensions such as equipment adaptability and personnel skill matching.

[0062] System Interaction Layer: The 3D model supports scaling and rotation, allowing for a direct view of the spatial relationship between the karst cave and the pile foundation. Early warning information includes risk type, impact range, and suggested response time, improving decision-making efficiency.

[0063] The geological survey data acquisition is used to obtain data on the location, size, burial depth and surrounding rock properties of the multi-layered karst caves. The construction process data acquisition is used to collect real-time construction parameters such as drilling speed, mud pressure and borehole water level. The environmental data acquisition is used to collect environmental information such as precipitation, groundwater level changes and surrounding vibrations.

[0064] The data cleaning is used to remove noise, outliers and fill in missing data values; the data transformation is used to standardize data formats and unify units; and the data fusion is used to integrate multi-source data and perform consistency verification and optimization.

[0065] The risk identification is based on the constructed risk database and the risk identification itself, and identifies the risk types of hole collapse, stuck drill and water inrush in the pile foundation construction of multi-layer karst areas. The risk assessment is used to determine the risk level and perform quantitative assessment. The risk prediction is achieved by predicting risk trends through a trained machine learning / neural network model.

[0066] The risk response plan generation generates preliminary response plans for high-risk karst areas and construction failure risks. The risk assessment and optimization screens the optimal plan through feasibility and economic analysis. The construction parameter adjustment suggestions output dynamic optimization parameters for drilling speed, pressure, mud specific gravity and borehole water level.

[0067] The data visualization displays geological structures and cave distribution using a 3D model. The user interface supports data querying, parameter setting, and custom adjustments. Information dissemination and early warning are achieved through multiple channels, including high-risk pop-ups, sound alarms, and SMS notifications.

[0068] The data cleaning process involves removing outliers using statistical analysis and thresholding methods, while filling in missing values ​​using interpolation or prediction models based on historical data.

[0069] The machine learning / neural network models of the risk prediction module include BP neural network models and random forest models, and the models are dynamically trained and optimized using historical construction risk data and real-time collected data.

[0070] The proposed adjustment of construction parameters is based on real-time changes in risk level. When the risk level exceeds a preset threshold, emergency adjustment commands such as reducing drilling speed and increasing mud density are automatically triggered.

[0071] The warning levels of the information release and early warning correspond to the risk levels of the risk assessment. The first-level warning notifies the construction manager and the supervision unit via SMS. The second-level warning triggers an on-site audible and visual alarm. The third-level warning forces a halt to construction and displays an emergency response plan in a pop-up window.

[0072] The risk assessment and optimization include: technical feasibility assessment and economic credibility assessment.

[0073] The aforementioned technical feasibility assessment is used to evaluate the suitability of the construction equipment, construction process, and personnel skills required for the risk response plan.

[0074] The technical feasibility assessment includes the following steps:

[0075] Construction equipment compatibility: Verify whether the grouting pump pressure meets the requirements of the plan and whether the steel casing size is compatible with the construction scenario;

[0076] Construction process adaptability: Determine whether the on-site construction conditions support the implementation of processes such as segmented grouting and pipe roof support;

[0077] Personnel skill suitability: Verify whether the construction personnel have the qualifications to perform operations such as high-pressure grouting and steel casing sinking.

[0078] The economic credibility assessment is used to calculate the direct and indirect costs of implementing the plan, compare the cost input of different plans, and select the plan with reasonable cost.

[0079] The economic credibility assessment includes the following steps:

[0080] Direct costs: Include material costs, equipment rental costs, and labor costs;

[0081] Indirect costs: accounting for losses due to project delays, environmental remediation costs, and costs associated with handling potential risks.

[0082] For multiple options that have passed the feasibility analysis, a comprehensive comparison is conducted from the dimensions of risk control effectiveness, construction efficiency, and cost input, including the following methods:

[0083] Construct a scheme comparison matrix and set weights: risk control effectiveness weight, construction efficiency weight, and cost input weight;

[0084] Risk control effectiveness assessment: Through simulated construction or historical case comparison, assess the extent to which the risk level is reduced after the implementation of the plan (e.g., from high risk to low risk is excellent, and from low risk to medium risk is good).

[0085] Construction efficiency assessment: Compare the time required to implement the plan; the shorter the time, the higher the efficiency.

[0086] Cost input assessment: Calculate the proportion of the proposed cost to the total cost of the project's pile foundation construction;

[0087] The comprehensive score of each scheme is calculated using a matrix, and the scheme with the highest score is selected as the optimal scheme.

[0088] During the implementation of the plan, construction data and risk changes are collected in real time to trigger optimization processes and adjust the plan, including the following methods:

[0089] Triggering conditions: If the risk level does not decrease as expected, or if new risk factors emerge, the optimization process will be initiated immediately;

[0090] Re-analyze the causes of the risks and adjust the plan accordingly:

[0091] Parameter adjustments: such as increasing grouting pressure or changing grouting materials;

[0092] Change of scheme type: such as changing from grouting scheme to steel casing follow-up scheme;

[0093] Continuously monitor the implementation effect of the adjusted plan to ensure that risks are effectively controlled.

[0094] The working principle of the risk prediction system for building pile foundation construction in multi-layer karst cave areas provided by this invention is as follows:

[0095] Data Acquisition Layer: The geological exploration data acquisition module obtains basic data such as the location and scale of karst caves through methods such as ground-penetrating radar and drilling; the construction process data acquisition module collects parameters such as drilling speed, mud pressure, and hole depth in real time through sensors; the environmental data acquisition module monitors environmental information such as precipitation, groundwater level, and surrounding vibration. All three types of data are transmitted to the data processing layer in real time.

[0096] Data processing layer: The data cleaning module removes outliers and fills in missing values; the data conversion module standardizes data of different formats and units; the data fusion module integrates multi-source data, verifies consistency, and generates standardized datasets.

[0097] Risk Analysis Layer: The risk identification module calls the risk database and combines the identification model to identify risk types such as hole collapse and stuck drill; the risk assessment module calculates the risk value through a quantitative algorithm to determine the low, medium and high risk levels; the risk prediction module uses a trained BP neural network model.

[0098] Decision support layer: Based on the risk analysis results, the risk response solution generation module outputs preliminary response plans; the risk assessment and optimization module selects the optimal solution based on feasibility and economy; and the construction parameter adjustment suggestion module outputs specific parameters according to the risk level.

[0099] System Interaction Layer: The data visualization module presents the distribution of karst caves and risk heat maps using a 3D model; the user interface supports data query and parameter customization; the information release and early warning module triggers corresponding early warnings based on the risk level, enabling efficient transmission of risk information and decision-making response.

[0100] Compared with related technologies, the risk prediction system for building pile foundation construction in multi-layer karst cave areas provided by this invention has the following beneficial effects:

[0101] This invention provides a risk prediction system for building pile foundation construction in multi-layered karst areas. Through multi-source data acquisition and fusion, it achieves real-time integration of geological, construction, and environmental data, solving the problem of single data sources in traditional systems and ensuring comprehensive risk identification. Based on a quantitative assessment model and risk database, it visualizes risk levels, replacing traditional qualitative judgments and improving assessment accuracy. Utilizing machine learning / neural network models, it combines historical and real-time data to predict risk trends, identifying high-risk areas in advance and providing a basis for proactive prevention and control. By generating feasible and economical response plans and dynamic construction parameter suggestions, it reduces additional costs caused by accidents, supports construction teams in making safe and economical decisions, and links multi-channel early warning mechanisms with risk levels to ensure timely response by relevant personnel, reducing the accident rate. Construction parameters are dynamically adjusted according to the risk level; for example, in high-risk situations, it automatically reduces drilling speed and optimizes mud density, improving construction safety and efficiency.

[0102] Please refer to the following: Figure 7 , Figure 8 and Figure 9 ,in, Figure 7 This is a schematic diagram of the second embodiment of a risk prediction system for building pile foundation construction in multi-layer karst cave areas provided by the present invention; Figure 8 for Figure 7 A schematic diagram of part A shown; Figure 9 This is a schematic diagram of the data acquisition device. A risk prediction system for building pile foundation construction in multi-layered karst cave areas is described. The data acquisition process utilizes a data acquisition device, which includes a main body 1, a handle 2, a support assembly 3, and a rotating assembly 4.

[0103] The rotating assembly 4 is disposed between the collector body 1 and the handle 2. The rotating assembly 4 includes a rotating frame 41, a circular block 42 and a plurality of handles 43. The rotating frame 41 is connected between the handle 2 and the collector body 1. The circular block 42 is connected to the surface of the rotating frame 41. The plurality of handles 43 are respectively connected to the side of the circular block 42.

[0104] The support component 3 is disposed at the bottom of the handle 2. The support component 3 includes a mounting head 31 and a support telescopic rod 32. The mounting head 31 is plugged into the bottom of the handle 2, and the support telescopic rod 32 is connected to the bottom of the mounting head 31.

[0105] A mounting hole adapted to the mounting head 31 is provided at the bottom of the handle 2. The use of the rotating base 41 allows the collector body 1 to be rotated to adapt to various angles. The circular block 42 with multiple handles 43 can assist the rotating frame 41 in rotating the collector body 1. When in use, when the collector body 1 is rotated, the multiple handles 43 are pushed to drive the circular block 42 to rotate. When the circular block 42 rotates, the rotating frame 41 drives the collector body 1 to rotate.

[0106] A limiting component 5 is provided between the collector body 1 and the handle 2. The limiting component 5 includes an arc plate 51, a limiting bolt 52 and a plurality of limiting holes 53. The limiting plate 51 is connected to the bottom of the collector body 1. The limiting bolt 52 is disposed between the arc plate 51 and the handle 2. The plurality of limiting holes 53 are respectively opened on the surface of the handle 2.

[0107] The use of the arc plate 51 and the limiting bolt 52 makes it easy to fix the collector body 1 to the handle 2 after it is rotated to the specified angle. After the collector body 1 is rotated to the specified angle, the limiting bolt 52 can be passed through the arc plate 51 and connected to the limiting hole 53 on the surface of the handle 2.

[0108] The handle 2 is provided with a protective component 6. The protective component 6 includes a fixing ring 61, a rotating seat 62, a fixing rod 63, a movable sleeve 64, a protective cover 65, a fixing bolt 66, and a stop block 67. The fixing ring 61 is sleeved on the surface of the handle 2. The rotating seat 62 is connected to the surface of the fixing ring 61. The fixing rod 63 is connected to one side of the rotating seat 62. The movable sleeve 64 is sleeved on the surface of the fixing rod 63. The protective cover 65 is connected to one side of the movable sleeve 64. The fixing bolt 66 is disposed between the movable sleeve 64 and the fixing rod 63. The stop block 67 is connected to one end of the fixing rod 63.

[0109] In use, when the main body 1 of the collector is not in use, the protective cover 65 is first rotated upward by the rotating seat 62 and the fixed rod 63. After the protective cover 65 is rotated above the main body 1 of the collector, it is then pulled downward by the movable sleeve 64 and the fixed rod 63 until the protective cover 65 is fitted onto the surface of the main body 1 of the collector. After the protective cover 65 is fitted onto the surface of the main body 1 of the collector, the fixing bolt 66 is passed through the movable sleeve 64 and connected to the fixed rod 63. When the rotating seat 62 is used, the protective cover 65 can be rotated to one side to prevent it from blocking the operation of the main body 1 of the collector. The fixed rod 63 and the movable sleeve 64 can adjust the position of the protective cover 65. The fixed rod 63 has a fixing hole that matches the fixing bolt 66. The stop block 67 can prevent the movable sleeve 64 from falling off the surface of the fixed rod 63.

[0110] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A risk prediction system for building pile foundation construction in multi-layered karst cave areas, characterized in that, include: Data acquisition layer, data processing layer, risk analysis layer, decision support layer, and system interaction layer; The input end of the data processing layer is wirelessly connected to the output end of the data acquisition layer; The input end of the risk analysis layer is wirelessly connected to the output end of the data processing layer; The input end of the decision support layer is wirelessly connected to the output end of the risk analysis layer; The input end of the system interaction layer is wirelessly connected to the output end of the decision support layer; The data acquisition layer includes geological exploration data acquisition, construction process data acquisition, and environmental data acquisition; The data processing layer includes data cleaning, data transformation, and data fusion modules; The risk analysis layer includes risk identification, risk assessment, and risk prediction; The decision support layer includes risk response solution generation, risk assessment and optimization, and suggestions for adjusting construction parameters; The system interaction layer includes data visualization, user interface, and information release and early warning.

2. The risk prediction system for building pile foundation construction in multi-layered karst cave areas according to claim 1, characterized in that, The geological survey data acquisition is used to obtain data on the location, size, burial depth and surrounding rock properties of the multi-layered karst caves. The construction process data acquisition is used to collect real-time construction parameters such as drilling speed, mud pressure and borehole water level. The environmental data acquisition is used to collect environmental information such as precipitation, groundwater level changes and surrounding vibrations.

3. The risk prediction system for building pile foundation construction in multi-layered karst cave areas according to claim 1, characterized in that, The data cleaning is used to remove noise, outliers and fill in missing data values; the data transformation is used to standardize data formats and unify units; and the data fusion is used to integrate multi-source data and perform consistency verification and optimization.

4. The risk prediction system for pile foundation construction in multi-layered karst cave areas according to claim 1, characterized in that, The risk identification is based on the constructed risk database and the risk identification itself, and identifies the risk types of hole collapse, stuck drill and water inrush in the pile foundation construction of multi-layer karst areas. The risk assessment is used to determine the risk level and perform quantitative assessment. The risk prediction is achieved by predicting risk trends through a trained machine learning / neural network model.

5. The risk prediction system for building pile foundation construction in multi-layered karst cave areas according to claim 1, characterized in that, The risk response plan generation generates preliminary response plans for high-risk karst areas and construction failure risks. The risk assessment and optimization screens the optimal plan through feasibility and economic analysis. The construction parameter adjustment suggestions output dynamic optimization parameters for drilling speed, pressure, mud specific gravity and borehole water level.

6. The risk prediction system for building pile foundation construction in multi-layered karst cave areas according to claim 1, characterized in that, The data visualization displays geological structures and cave distribution using a 3D model. The user interface supports data querying, parameter setting, and custom adjustments. Information dissemination and early warning are achieved through multiple channels, including high-risk pop-ups, sound alarms, and SMS notifications.

7. The risk prediction system for pile foundation construction in multi-layered karst cave areas according to claim 3, characterized in that, The data cleaning process involves removing outliers using statistical analysis and thresholding methods, while filling in missing values ​​using interpolation or prediction models based on historical data.

8. The risk prediction system for pile foundation construction in multi-layered karst cave areas according to claim 4, characterized in that, The machine learning / neural network models of the risk prediction module include BP neural network models and random forest models, and the models are dynamically trained and optimized using historical construction risk data and real-time collected data.

9. The risk prediction system for pile foundation construction in multi-layered karst cave areas according to claim 5, characterized in that, The proposed adjustment of construction parameters is based on real-time changes in risk level. When the risk level exceeds a preset threshold, emergency adjustment commands such as reducing drilling speed and increasing mud density are automatically triggered.

10. The risk prediction system for building pile foundation construction in multi-layered karst cave areas according to claim 6, characterized in that, The warning levels of the information release and early warning correspond to the risk levels of the risk assessment. The first-level warning notifies the construction manager and the supervision unit via SMS. The second-level warning triggers an on-site audible and visual alarm. The third-level warning forces a halt to construction and displays an emergency response plan in a pop-up window.

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