A wharf car dumper room deep foundation pit construction process simulation and multi-stage early warning method and system

Abstract

The present application relates to the field of engineering construction monitoring and digital twin technology, in particular to a wharf car dumper room deep foundation pit construction process simulation and multi-level early warning method and system. It builds a digital twin 3D model through component unit modeling and multi-level LOD expression, associates and binds the construction process nodes and monitoring sensors with the three-dimensional components; establishes the mapping relationship between the construction process and the three-dimensional components, dynamically controls the component state using the incremental state reconstruction algorithm, and simulates the continuous evolution of the construction process combined with the parameterized behavior function, supports the dynamic advancement and historical backtracking of the construction process; carries out finite element simulation on the discrete working conditions of the construction process and its internal key control nodes, generates a dynamic early warning benchmark curve that changes continuously with the construction process using a three-order spline interpolation, and builds a multi-level early warning threshold system. The present application can intuitively reflect the correlation evolution of construction progress and structural safety state, significantly improve the visualization control ability and early warning accuracy of deep foundation pit construction.

Description

Technical Field

[0001] This invention relates to the field of engineering construction monitoring and digital twin technology, specifically to a method and system for simulating the construction process and providing multi-level early warning for deep foundation pits at wharf tippler rooms. Background Technology

[0002] With the continuous increase in port throughput, the construction of large-scale specialized terminals is constantly developing towards deeper water and larger scale. As a key node for unloading and transshipment at the terminal, the foundation pit engineering of tippler houses generally has significant characteristics such as large excavation depth, complex surrounding environment, and high construction risks. Under the combined influence of construction loads, dynamic hydrogeological conditions, and multiple overlapping operations, the deep foundation pit structure system is subjected to asymmetric and unsteady mechanical forces for a long time, easily leading to gradual deformation accumulation in stress concentration areas. If not identified and controlled in time, it may cause catastrophic accidents such as failure of the support system or even overall instability of the foundation pit. Therefore, achieving real-time monitoring, dynamic simulation, and timely early warning of the deformation state of the foundation pit system during construction has become a core and urgent requirement for the construction safety management of deep foundation pit engineering for tippler houses.

[0003] Currently, most deep foundation pit construction process simulations involve static decomposition or linear description of the construction process. Existing simulation methods typically employ pre-rendered 3D animation sequences, controlling the playback, pause, and transitions of animation segments via a linear timeline. However, this method solidifies each construction process into an independent animation, disconnecting the animation content from the safety control logic, making it difficult to intuitively reflect changes in safety status during construction. Because construction procedures and key safety management points cannot be dynamically linked, this method struggles to support the deduction and analysis of the evolution of construction risks, thus diminishing the practical value of simulation in safety pre-control.

[0004] In terms of safety early warning, existing technologies mainly rely on threshold triggering mechanisms based on a single monitoring indicator. This method collects monitoring data in real time, compares the measured values ​​with standard limits or empirical thresholds, and triggers corresponding early warning information. However, due to limitations in site conditions and cost, monitoring sensors are usually only deployed at a limited number of measuring points in key parts of the building envelope, resulting in typical discreteness in the monitoring data. More importantly, existing early warning logic fails to effectively link with construction progress information. The structural stress state and risk sensitivity change significantly at different construction stages, and a single fixed threshold cannot reflect this dynamic evolution, easily leading to delayed early warnings at high-risk stages and frequent false alarms at relatively safe stages, reducing the reliability and comprehensiveness of the early warning results. Summary of the Invention

[0005] The purpose of this invention is to propose a method and system for simulating the construction process and providing multi-level early warning for deep foundation pits at wharf tipper rooms, thereby achieving visualization, traceability, and early warning of the entire construction process and improving the safety and controllability of deep foundation pit construction.

[0006] According to a first aspect of the present disclosure, a method for simulating the construction process and providing multi-level early warning for deep foundation pits of wharf tippler rooms is provided, comprising the following steps: A digital twin 3D model of the deep foundation pit project of the tippler room was established by adopting the component unit modeling and multi-level level of detail (LOD) expression method, and the construction process nodes and on-site monitoring sensor data were associated and bound with the three-dimensional components in the model. Based on the digital twin 3D model, the construction process is discretized into a set of construction processes, and a mapping relationship between the construction processes and three-dimensional components is established. The state of the three-dimensional components is dynamically controlled through an incremental state reconstruction algorithm, and a behavior function is predefined for each three-dimensional component to simulate the continuous evolution of the construction process. At the same time, dynamic advancement and historical backtracking are achieved by combining the historical sequence of the construction process. Based on the construction process and its key internal control nodes, a continuous dynamic early warning benchmark curve covering the entire construction period is generated using finite element simulation and third-order spline interpolation methods. A multi-level early warning threshold system that continuously evolves with the construction process is constructed, and safety level is determined by combining real-time monitoring data.

[0007] In one embodiment, the specific method for establishing a digital twin 3D model of the deep foundation pit project for the tippler room is as follows: For each of the three-dimensional components of the tippler room project, four levels of modular models, namely LOD100, LOD200, LOD300 and LOD400, were constructed. LOD100 represents the geometric outline and spatial position, LOD200 adds size and shape features, LOD300 refines the structural details and material properties, and LOD400 deepens the internal structure and construction parameters. Based on the spatial reference relationships and engineering subordination relationships between the various three-dimensional components, a tree-like hierarchical structure is constructed for the overall model; Each 3D component is bound to a unique identifier ID, and the construction process number and monitoring sensors are associated with this ID, enabling centralized management and collaborative access to construction process information, real-time monitoring data, and multi-level LOD models.

[0008] In one embodiment, the state of the three-dimensional component is dynamically controlled through an incremental state reconstruction algorithm, specifically as follows: Construct a standardized 3D component attribute data structure ,in, Each component has a unique identification number. Assign a construction process number to which this component belongs. This indicates the current construction status of the component. Get the currently identified construction progress number Update it to the current system process variable. ; The construction progress status of each three-dimensional component can be obtained using the following formula: Based on the state variables of the three-dimensional components Control the display in the 3D scene: 3D components with state 2 are displayed with normal materials, 3D components with state 1 are displayed with highlighted dynamic materials, and 3D components with state 0 are hidden.

[0009] In one embodiment, the predefined behavioral functions of the three-dimensional component include at least: Behavior: When the construction process begins, the corresponding 3D component switches from a hidden state to a visible state; Growth behavior: used to simulate a continuous construction process, so that three-dimensional components are displayed step by step according to a preset growth pattern, including the growth of the pile from the bottom up and the expansion of the concrete from one side to the other. Assembly behavior: used to simulate the hoisting, positioning and connection process of prefabricated 3D components, including moving steel supports from the stacking position to the design position and displaying the node structure; Demolition behavior: When the construction process involves the demolition of three-dimensional components, the three-dimensional components switch from a visible state to a hidden state, and the demolition process is simulated by fade-out or translation removal effects.

[0010] In one embodiment, the dynamic advancement and historical backtracking are implemented as follows: Establish a construction process history sequence ,in, Indicates the timestamp recorded by the system. This indicates the construction progress at that moment; When you need to query historical moments When the construction status is active, search for records that meet the time conditions in the historical sequence H and obtain the corresponding process number. ; System process variables Updated to And redetermine the construction progress status of each three-dimensional component, thereby realizing the forward advancement (incrementing process number) and reverse backtracking (decreasing process number) of the construction process.

[0011] In one embodiment, a continuous dynamic early warning baseline curve covering the entire construction period is generated using finite element simulation and third-order spline interpolation methods, specifically: For each construction process Finite element numerical simulations were performed on the key control nodes (including start nodes, key process nodes, and completion nodes) to obtain the planned time and simulated displacement values ​​of each node, forming discrete data points. , in, Represents a process The planned start time and corresponding displacement value; Represents a process Internal m Planned time and simulated displacement values ​​for each key node; Represents a process The planned completion time and simulated displacement values; Using the aforementioned discrete data points as nodes, a piecewise cubic spline interpolation function is constructed for any interval between adjacent nodes. Internally constructed cubic polynomial: in, The coefficients to be determined are: apply the following conditions to the above equation: equal function values, first-order continuity, second-order continuity, and natural boundary conditions. The continuous cumulative displacement prediction function for the entire construction period is obtained by solving the problem. and displacement change rate prediction function .

[0012] In one embodiment, a multi-level early warning threshold system that continuously evolves with the construction process is constructed, specifically as follows: set up , To monitor the obtained cumulative displacement value and rate of change, , From , The predicted value read , The design value is as required by the specification; The following decision function is used. Determine the security level: Among them, when When indicates a safe state; when When it indicates a warning status; when When indicates the alarm status; when This indicates a dangerous situation.

[0013] According to a second aspect of the present disclosure, a construction progress simulation and multi-level early warning system for deep foundation pits of wharf tippler rooms is provided, comprising: The 3D multi-level model building module adopts the component unit modeling and multi-level detail level LOD expression method to establish a digital twin 3D model of the deep foundation pit project of the tippler room, and associates and binds the construction process nodes and on-site monitoring sensor data with the three-dimensional components in the model. The construction process simulation module, based on the digital twin 3D model, discretizes the construction process into a set of construction processes, establishes a mapping relationship between the construction processes and three-dimensional components, dynamically controls the state of the three-dimensional components through an incremental state reconstruction algorithm, and predefines a behavior function for each three-dimensional component to simulate the continuous evolution of the construction process. At the same time, it combines the historical sequence of the construction process to realize dynamic advancement and historical backtracking. The construction early warning module, based on the construction process and its internal key control nodes, uses finite element simulation and third-order spline interpolation to generate a continuous dynamic early warning benchmark curve covering the entire construction period, constructs a multi-level early warning threshold system that continuously evolves with the construction process, and combines real-time monitoring data to determine the safety level.

[0014] According to a third aspect of the present disclosure, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and running on the memory. When the processor executes the program, it implements the method for simulating the construction process and providing multi-level early warning for a deep foundation pit of a wharf tippler room.

[0015] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the aforementioned method for simulating the construction process and providing multi-level early warning for a deep foundation pit of a wharf tippler room.

[0016] The advantages of the above technical solutions adopted in this invention compared with the prior art are as follows: 1. This invention establishes a mapping relationship between construction progress and three-dimensional components, employing an incremental state reconstruction algorithm and parameterized behavior functions to enable the three-dimensional scene to automatically update with the actual construction progress, realistically reflecting the correlation and evolution process between construction progress and structural state. Simultaneously, by leveraging the historical sequence of the construction process, it supports state playback and tracing at any historical moment, providing intuitive and reliable visualization support for construction simulation, progress verification, and risk source analysis.

[0017] 2. This invention uses the construction process and its key control nodes as discrete working conditions for finite element simulation, and combines third-order spline interpolation to generate a baseline curve of cumulative displacement and rate of change that continuously varies with the construction process. Based on this, a four-level safety early warning threshold system is constructed, comparing the dynamic benchmark with real-time monitoring data and standard limits from multiple dimensions, effectively overcoming the shortcomings of traditional fixed threshold methods such as delayed early warning and high false alarm rate. Simultaneously, by using different colors and highlighting methods in a three-dimensional scene for real-time spatial positioning of risk sources, the intelligent level and response efficiency of safety risk management for deep foundation pit construction of tippler rooms are significantly improved. Attached Figure Description

[0018] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments of this application and their descriptions are used to explain this application and do not constitute an undue limitation of this application.

[0019] Figure 1 Architecture diagram of a method and system for simulating the construction process and providing multi-level early warning for the deep foundation pit of the dock tipper room; Figure 2 Displaying images for digital twin 3D models; Figure 3 This is a diagram showing the progress of the foundation pit excavation from -2.5m to -6.9m and the construction of the capping beam; Figure 4 A 3D scene visualization and location map for early warning and risk sources. Detailed Implementation

[0020] The present disclosure will be further described below with reference to the accompanying drawings and embodiments.

[0021] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0022] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0023] It should be noted that the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods and systems according to various embodiments of this disclosure. It should be noted that each block in a flowchart or block diagram may represent a module, segment, or portion of code, which may include one or more executable instructions for implementing the logical functions specified in the various embodiments. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutively represented blocks may actually be executed substantially in parallel, or they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, may be implemented using a dedicated hardware-based system that performs the specified functions or operations, or using a combination of dedicated hardware and computer instructions.

[0024] Example 1: like Figure 1 As shown, this embodiment provides a method for simulating the construction process and providing multi-level early warning for deep foundation pits at wharf tippler rooms, including the following steps: S1. A digital twin 3D model of the deep foundation pit project of the tippler room was established by adopting the component unit modeling and multi-level detail level LOD expression method, and the construction process nodes and on-site monitoring sensor data were associated and bound with the three-dimensional components in the model; This embodiment constructs a digital twin 3D model of the deep foundation pit project for the tippler room through "component unit modeling - multi-level LOD expression - construction process node binding - monitoring data association". First, a multi-level unit model from LOD100 to LOD400 is established. Then, the model state is bound to construction process nodes. Finally, monitoring sensors such as displacement and stress are bound to specific component locations, achieving centralized management and collaborative access to construction progress, monitoring information, and multi-level geometric models. This model can simulate deformation during deep foundation pit construction, accurately identify construction risks, and simultaneously simulate actual changes in the foundation pit's state, effectively improving the efficiency and quality of construction monitoring. Specific details are as follows: Step 11: For the components of the dock tipper room project, pre-construct multi-level modular models to express the geometric contours, precise dimensions, structural details and parametric material properties of the components at different LOD levels.

[0025] For the same component, pre-build modular models at LOD100 to LOD400 levels.

[0026] The LOD100 model is used to characterize the geometric information of the components of the wharf tipper building, covering the basic geometric shapes and overall building volume of components such as retaining piles, horizontal supports, and main wall panels. It can accurately reflect their spatial location and scope in the entire project. In terms of texture representation, LOD100 uses a single color fill, such as dark gray for retaining piles, sky blue for steel supports, and light gray for concrete structures, so as to distinguish component types and express structural outlines at the overall level.

[0027] The LOD200 model, building upon LOD100, adds dimensional information and shape features to components, clearly expressing key geometric parameters such as retaining pile diameter and length, support cross-sectional dimensions, and wall thickness, and explicitly distinguishing major material types like concrete and steel. Texture representation uses basic, universal textures, such as low-resolution maps simulating concrete surfaces and anti-rust paint on steel structures, thus intuitively differentiating between various engineering material categories like concrete and steel.

[0028] The LOD300 model further refines the geometric and engineering information of components, including key geometric shapes and structural relationships at the construction stage, such as the reinforcement range of support piles, the construction of support nodes, and the locations of wall panel openings and embedded parts. It can also add major engineering attributes such as material strength grades and design specifications. Texture representation uses standard, realistic material textures, such as realistic textures depicting concrete pouring formwork marks and steel surface weld marks, significantly improving visual realism and facilitating the identification of structural details.

[0029] The LOD400 model, building upon LOD300, deepens the internal structure and construction details of components, including details such as the arrangement of reinforcing cages within retaining piles, high-strength bolt connections in steel supports, and embedded sleeves and waterstops in the main structure. It also integrates complete construction parameters and engineering attributes, such as rebar type, concrete mix proportions, and bolt torque. Its textures utilize high-precision, realistic mapping to accurately represent microscopic visual features such as color differences after concrete curing, the gloss of anti-rust paint on steel, and traces of construction surface treatment, achieving a high-precision visual and informational reproduction of the engineering entity.

[0030] The details are shown in Table 1.

[0031] Table 1 Component LOD Level Step 12: Based on the unitized model, construct the hierarchical structure and spatial relationship of the overall model of the tippler room project, bind key construction process nodes, and accurately associate multi-level LOD models.

[0032] First, parent-child node relationships are established for various components in the deep foundation pit project of the tippler room. Based on the spatial reference and subordinate relationships between components, and combined with the division of the underground structure project of the tippler room into sections and items (such as foundation pit excavation, underground continuous walls, ring beams and vertical rib support as section-level components, and their subordinate steel bars, formwork, concrete, etc. as item-level components), a unified tree-like hierarchical structure is constructed. This realizes the inheritance and unified management of the spatial position of components. The parent-child node division is shown in Table 2.

[0033] Table 2 Parent-Child Node Relationships Based on the construction design data, spatial position, scaling, and rotation information are set for each unit model, and an identification ID is assigned to it. Each unit model with an ID serves as the basic unit for binding construction process simulation and monitoring data. Construction process nodes are bound to each model, and multi-level geometric information from LOD100 to LOD400 is associated with the model ID. Real-time monitoring data collected by various sensors deployed at the construction site, such as displacement, settlement, stress, strain, and water level sensors, are transmitted to the backend database for storage and management through data acquisition terminals. Each type of sensor is bound to the corresponding component model (ID). Finally, the component ID is used as an index to achieve centralized management and collaborative access to construction process information, real-time monitoring data, and multi-level LOD models.

[0034] S2. Based on the digital twin 3D model, the construction process is discretized into a set of construction processes, and a mapping relationship between the construction processes and three-dimensional components is established. The state of the three-dimensional components is dynamically controlled through the incremental state reconstruction algorithm, and a behavior function is predefined for each three-dimensional component to simulate the continuous evolution of the construction process. At the same time, dynamic advancement and historical backtracking are achieved by combining the historical sequence of the construction process. This embodiment discretizes the deep foundation pit construction process into a set of construction processes, associates these processes with three-dimensional components through mapping relationships, and dynamically controls the component states using an incremental state reconstruction algorithm. Parameterized behavior functions (such as growth and assembly) are predefined for each component, driving its continuous evolution over time when a process is activated. Simultaneously, by combining the historical sequence of construction processes, dynamic advancement and reversible historical backtracking of the construction process are achieved. The specific details are as follows: Step 21: Subdivide the construction process of the tippler room into construction steps and construct a set of construction steps { , ,….}, constructing the construction process Association mapping relationship with components ; Based on the construction organization design plan, the construction process is divided into several construction processes with independent construction significance, and their logical order and successive relationships are determined. Each construction process is assigned a number. In the deep foundation pit project of the tippler house, the overall construction process includes deep foundation pit excavation and tippler house main structure construction. Deep foundation pit excavation includes the construction of retaining structures, water-stopping structures, dewatering wells and dewatering operation, layered and zoned earthwork excavation, and the installation and removal of internal supports or retaining structures. The tippler house main structure construction includes the construction of the foundation layer, the binding of the bottom slab reinforcement and concrete pouring, the construction of the side walls and frame structure, the construction of the roof slab, and subsequent structural improvements. The specific divisions are shown in Table 3. Table 3 Construction Progress Division A standardized component attribute data structure is constructed based on component IDs to store basic component attribute information and full-cycle stage control information. Its formal expression is as follows: ,in, Each component has a unique identification number. Assign a construction process number to which this component belongs. This indicates the current construction status of the component. It is based on the component's attribute structure. SIDi The field serves as the grouping basis, categorizing the component sets by process. For any construction process... Construct the corresponding component number set : Based on this, a construction phase mapping table is further constructed: That is, based on the construction progress number As a key value ( Key ), based on the component number set corresponding to this stage ( The mapping content (Value) establishes a one-to-one correspondence between process IDs and component sets. This mapping table is organized in memory using a key-value structure (such as a hash table or indexed dictionary).

[0035] Step 22: By comparing the process number to which the component belongs with the current system process number, dynamically set and update the construction status of the component, thereby controlling its display method in the 3D scene.

[0036] During system operation, the system continuously outputs the currently identified construction process stage (number). Upon receiving a new process number, the system updates it to the current construction process control variable. : .in, This is the current identification result; These are process-driven variables within the system, used to drive the update of the 3D model's state. When the system process variable... After the update, the system executes an incremental state reconstruction algorithm to partially update the component set related to the current construction process and the component set of the previous construction process. Other historically completed components retain their existing state and are not recalculated. (Utilizing formula...) The construction progress status of the components is obtained as follows: The system is based on the component state variables Simulates the control of the construction process. The state is... The components represent structural entities that were completed prior to this construction process and are displayed with normal materials in the 3D scene; the status is... The components represent structural components currently under construction, displayed in the scene using highlighted dynamic or construction-state materials; the status is... The components represent future process components that have not yet been constructed; they are shown or hidden.

[0037] Step 23: Accurately simulate the construction process by using a predefined behavior function that changes over time, and record the historical sequence to achieve historical backtracking.

[0038] Pre-define corresponding behavior functions for each component, and synchronously call these behaviors when the "under construction" state is activated; When the construction process is in the "under construction" state, the component construction behavior is defined as a behavior function (Type, Params, Timeline, Interpolation) that evolves continuously over time: Type: Appearance, Growth, Assembly, Dismantling; Params: Details that control behavior, such as growth direction, assembly start coordinates, etc. Timeline: Maps process time to specific progress (0% to 100%). Interpolation: Defines how the progress changes (e.g., linear, gradual, stepwise).

[0039] 1) Behavior: When the construction process begins, the components in the component set belonging to that process switch from the "hidden" state to the "visible" state, which may be accompanied by effects such as gradation and displacement.

[0040] 2) Growth Behavior: For components requiring continuous construction (such as concrete pouring), the component gradually appears according to a preset growth pattern during the process duration. For example: retaining pile concrete pouring: the pile grows from the bottom upwards, simulating the concrete pouring process; base slab concrete pouring: the concrete expands from one side to the other, simulating layered continuous pouring.

[0041] 3) Assembly Behavior: For prefabricated components (such as steel support installation), simulate the hoisting, positioning, and connection processes of the components. For example, steel support installation: the support components are moved from the site stacking location to the design location, while displaying the details of bolted connections, welding, and other node structures.

[0042] 4) Demolition behavior: When the construction process involves the demolition of components (such as the removal of supports), the components switch from the "visible" state to the "hidden" state, and the demolition process is simulated through effects such as fade-out, breaking or translation removal.

[0043] Behavioral functions can be configured with time parameters, motion trajectories, deformation curves, etc., to accurately simulate actual construction processes. For example: Retaining piles: In the drilling process, call the drilling behavior function to display the drilling position and depth; in the rebar cage hoisting process, call the hoisting behavior function to display the lowering of the rebar cage; in the concrete pouring process, call the pouring behavior function to display the concrete filling.

[0044] Steel support: During the installation of the first layer of steel support, the assembly behavior function is called to translate from the initial stacking position to the design position, rotate to the design angle, and display the end connecting plate and bolts.

[0045] Dewatering well: Calls the well formation behavior function to display the well formation process; calls the water level change behavior function during the dewatering operation to display the water level drop; continuously displays the running status in subsequent processes.

[0046] Step 24: Establish a historical sequence consisting of construction process numbers and timestamps, and use the component state incremental reconstruction algorithm and serialization display to realize the dynamic advancement and historical backtracking of the construction process.

[0047] Each time the construction progress is updated, the time and construction progress number are recorded simultaneously to form a historical sequence of the construction progress: ,in, Indicates the timestamp recorded by the system. This indicates the construction progress at that specific moment. When a user needs to query a historical time... When in the construction state, it can be viewed in the historical sequence. H Search for records that meet the time criteria and obtain the corresponding process ID. Then, based on the construction process number sequence and the state increment reconstruction algorithm, the process status and related displays of the construction 3D model are serialized and updated, enabling intuitive historical backtracking of the construction process. ; ).

[0048] As the process number increments, it indicates that the construction process is progressing in the positive direction. When the process number decreases, it indicates that the construction process history is being replayed or rolled back. Each update of the construction process number triggers an incremental process reconstruction calculation and updates the 3D model state. Through a timeline recording mechanism and a phase-based incremental reconstruction algorithm, the system enables real-time progress tracking, historical replay, and timeline playback of the construction process.

[0049] S3. Based on the construction process and its internal key control nodes, a continuous dynamic early warning benchmark curve covering the entire construction period is generated using finite element simulation and third-order spline interpolation methods. A multi-level early warning threshold system that continuously evolves with the construction process is constructed, and safety level is determined by combining real-time monitoring data.

[0050] Based on the construction progress ( SID The system and its key internal control nodes are treated as discrete working conditions. Finite element numerical simulations are performed to obtain the predicted displacement values ​​of each key node, forming discrete data points. Subsequently, third-order spline interpolation technology is used to generate continuous and smooth cumulative displacement and rate of change prediction curves covering the entire construction period based on these discrete points. and This system implements dynamic early warning thresholds that evolve continuously with the construction progress, enabling refined and continuous early warning of construction safety conditions. Specific details are as follows: Step 31: For key control nodes in different construction processes, use finite element simulation to calculate their displacement. Regarding the construction progress { , Based on the consideration of the completion nodes of the process, key control nodes with clear engineering significance are further selected within the process. These nodes are determined according to the characteristics of construction technology, load variation characteristics, and risk distribution patterns: 1) Starting node: process The starting moment corresponds to the initial state of the commencement of construction activities.

[0051] 2) Key process nodes: Within the process, the completion time of the process that significantly changes the stress state of the structure. For example: for the first layer of earthwork excavation, the excavation to 1 / 3 depth and the excavation to 2 / 3 depth can be selected as internal key nodes; for the first layer of steel support installation, the completion of the first support installation and the completion of half of the supports installation can be selected as internal key nodes.

[0052] 3) Completion node: process The moment of completion, i.e., the simulation point.

[0053] Simulation calculation of each construction process The cumulative displacement of the support structure at each key control node (including the starting point, internal key processes, and completion nodes). Assume each process selects... k For each internal key node (excluding start and finish nodes), then for N Each construction process will yield a set of discrete data points: , in, Represents a process The planned start time and corresponding displacement value; Represents a process Internal m Planned time and simulated displacement values ​​for each key node; Represents a process The planned completion time and simulated displacement values; Using the aforementioned discrete data point set as nodes, a continuous threshold function is constructed using third-order spline interpolation technology. This function is smoother and better reflects the details of deformation development within the construction process.

[0054] Step 32: Generate a continuous curve using third-order spline interpolation. This embodiment uses the discrete data points described in step 31 as nodes to construct a piecewise cubic spline interpolation function to generate a continuous displacement prediction curve covering the entire construction time domain. Within the interval formed by any two adjacent key nodes... ( i Within the range (=0,...,.n), construct a piecewise cubic spline polynomial: in, The coefficients are to be determined. The spline function must satisfy the following constraints to ensure the curve is continuous and smooth: Function values ​​are equal: The first derivative is continuous: The second derivative is continuous: Natural boundary conditions: By solving the above system of constraint equations, the coefficients of the piecewise polynomial are determined. ), to obtain the cubic spline polynomial function for each segment. Thus, the cumulative displacement prediction function for the entire construction period is obtained. By differentiating this function, we can obtain the displacement rate prediction function at any given time. : thus, and Together, they form a dynamic early warning baseline curve that changes continuously with the construction progress; Step 33: Based on the standard requirements, and combined with the simulation calculation results and design requirements, construct a multi-level early warning threshold for safety risks driven by the construction process of the tippler room, so as to realize dynamic multi-level early warning for the deep foundation pit construction of the tippler room.

[0055] set up To monitor the cumulative displacement value, To monitor the rate of change of the obtained displacement, and These are the cumulative displacement value and the rate of change, respectively. and These are the design requirements for displacement and rate of change specified in the "Technical Standard for Monitoring of Building Foundation Pit Engineering".

[0056] During construction, based on the currently identified construction progress status (by... (Driver) determines the current time t and from continuous prediction curves and Above, read the predicted value corresponding to time t. and This serves as a dynamic benchmark for safety assessment at that moment. The safety status of the tippler room is divided into four levels, with its assessment function... Represented as: in, () indicates time t The security level. When When indicates a safe state; when When it indicates a warning status; when When indicates the alarm status; when This indicates a dangerous situation.

[0057] This embodiment uses the construction of a deep foundation pit for a tippler room at an ore terminal as an engineering background to design an experimental scheme. The foundation pit is a circular structure, and the support adopts a combination system of diaphragm wall + cap beam / ring beam + vertical rib. The inner diameter of the diaphragm wall is 37m, and the area of ​​the foundation pit is 4300m². 2 The excavation depth reached 15m. The geology of the 30m wide area outside the circular diaphragm wall was treated with cement mixing piles. The cement mixing piles had a diameter of 600mm, a spacing of 1.45m, and a reinforcement depth of 8.5m, arranged in a quincunx pattern. The model data covers all elements of the project, including the retaining structure, main structure, earthwork layers, surrounding pipelines, and monitoring facilities.

[0058] The above scheme provides a solution for construction process simulation and multi-level early warning, the main effects of which can be summarized in the following three aspects: 1) It achieved efficient display of BIM information for complex deep foundation pit construction of the tippler room. Employing near-field and far-field multi-level LOD model technology, it effectively improved the precision and efficiency of the 3D model of the wharf tippler room construction information, such as... Figure 2 As shown.

[0059] 2) Real-time simulation and backtracking of construction progress have been achieved. Real-time construction simulation can be performed based on the construction progress, thus intuitively reflecting the construction process and its corresponding structural status, and supporting backtracking of the construction history. This successfully solves the problem of the difficulty in intuitively reflecting construction progress and the correlation between construction progress and the evolution of structural safety status, realizing dynamic advancement of the construction process and reversible historical backtracking, effectively improving the safety management level of the wharf tippler room construction, such as... Figure 3 As shown.

[0060] 3) Dynamic multi-level safety early warning and rapid risk source location were achieved during the construction of the wharf tippler room. Based on finite element simulation and third-order spline interpolation to generate deformation reference curves, a dynamic multi-level risk assessment mechanism was constructed to complete dynamic multi-level early warning during the construction process. Furthermore, it can identify the location of risky components based on risk assessment, and with the help of multi-threaded collaborative rendering technology, quickly locate the location and risk level (yellow / orange / red) of the early warning risk source in the 3D scene, improving the ability to identify and manage safety risks during the construction of the wharf tippler room. Figure 4 As shown.

[0061] Example 2: like Figure 1 As shown, this embodiment provides a simulation and multi-level early warning system for the construction process of a deep foundation pit for a wharf tippler room, including: The 3D multi-level model building module adopts the component unit modeling and multi-level detail level LOD expression method to establish a digital twin 3D model of the deep foundation pit project of the tippler room, and associates and binds the construction process nodes and on-site monitoring sensor data with the three-dimensional components in the model. The construction process simulation module, based on the digital twin 3D model, discretizes the construction process into a set of construction processes, establishes a mapping relationship between the construction processes and three-dimensional components, dynamically controls the state of the three-dimensional components through an incremental state reconstruction algorithm, and predefines a behavior function for each three-dimensional component to simulate the continuous evolution of the construction process. At the same time, it combines the historical sequence of the construction process to realize dynamic advancement and historical backtracking. The construction early warning module, based on the construction process and its internal key control nodes, uses finite element simulation and third-order spline interpolation to generate a continuous dynamic early warning benchmark curve covering the entire construction period, constructs a multi-level early warning threshold system that continuously evolves with the construction process, and combines real-time monitoring data to determine the safety level.

[0062] Preferably, it also includes a 3D visualization module: this module is responsible for the dynamic 3D visualization of all construction information. During the rendering loop, it reads the construction progress and early warning results in real time, loads a model of appropriate precision based on the Level of Depth (LOD), and reads the current viewpoint position. Calculate the Euclidean distance between each associated component and the viewpoint; Based on the current position of the viewpoint Obtain viewpoint and components ） Euclidean distance between D i : Based on the distance between the viewpoint and the model The LOD level of the model loading is determined by comparing it with the distance threshold. The distance threshold can be set according to the line of sight of the construction area, the complexity of the model, and the user's operating habits.

[0063] The monitored displacement data is used as vertex offsets to display structural deformation, and component positioning and warnings are issued based on the warning level. By decoupling from the background calculation thread and combining it with a dynamic LOD mechanism, the smoothness and real-time performance of 3D visualization in complex deep foundation pit scenarios are ensured. The LOD-based method for constructing and displaying large-scale 3D models effectively solves the problems of slow loading speed and untimely visualization response in large-scale 3D scenes.

[0064] The above modules can be deployed on the same device or distributed devices; the division of modules is only a functional logic description and does not limit the specific physical boundaries or implementation order.

[0065] Example 3: An electronic device is provided for running the aforementioned "Simulation and Multi-level Early Warning Method for Deep Foundation Pit Construction Process of Dock Tilting Machine Room". The electronic device includes: a processor, a memory, and optional communication interfaces / display devices / input devices, etc.; the memory stores a computer program that can run on the processor, and when the processor executes the program, it implements steps S1 to S3 of the method described in Embodiment 1, specifically including but not limited to: S1. A digital twin 3D model of the deep foundation pit project of the tippler room was established by adopting the component unit modeling and multi-level detail level LOD expression method, and the construction process nodes and on-site monitoring sensor data were associated and bound with the three-dimensional components in the model; S2. Based on the digital twin 3D model, the construction process is discretized into a set of construction processes, and a mapping relationship between the construction processes and three-dimensional components is established. The state of the three-dimensional components is dynamically controlled through the incremental state reconstruction algorithm, and a behavior function is predefined for each three-dimensional component to simulate the continuous evolution of the construction process. At the same time, dynamic advancement and historical backtracking are achieved by combining the historical sequence of the construction process. S3. Based on the construction process and its internal key control nodes, a continuous dynamic early warning benchmark curve covering the entire construction period is generated using finite element simulation and third-order spline interpolation methods. A multi-level early warning threshold system that continuously evolves with the construction process is constructed, and safety level is determined by combining real-time monitoring data.

[0066] The electronic device hardware can be one of a server, personal computer, workstation, industrial controller, edge computing device, or mobile terminal; the processor can be a general-purpose CPU, GPU, NPU, FPGA, or a combination thereof; the memory can be RAM, ROM, flash memory, or disk array. The device can interact with local / remote data storage (acquiring observation data and outputting inversion results) through a communication interface. The above hardware configuration does not constitute a limitation of the present invention.

[0067] Example 4: A computer-readable storage medium storing a computer program, which, when run on a processor of an electronic device, causes the program to execute the method steps S1 to S3 described in Embodiment 1; the storage medium may be a disk, optical disk, flash memory, solid-state drive, read-only memory, random access memory, or any combination of the above media.

[0068] Those skilled in the art will understand that the modules or steps described above can be implemented using general-purpose computer devices. Optionally, they can be implemented using computer-executable program code, which can then be stored in a storage device for execution by a computer device. Alternatively, they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. This disclosure is not limited to any particular combination of hardware and software.

[0069] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0070] While the specific embodiments of this disclosure have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of this disclosure. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of this disclosure are still within the scope of protection of this disclosure.

Claims

1. A method for simulating the construction process and providing multi-level early warning for deep foundation pits at wharf tipper rooms, characterized in that, Includes the following steps: A digital twin 3D model of the deep foundation pit project of the tippler room was established by adopting the component unit modeling and multi-level level of detail (LOD) expression method, and the construction process nodes and on-site monitoring sensor data were associated and bound with the three-dimensional components in the model. Based on the digital twin 3D model, the construction process is discretized into a set of construction processes, and a mapping relationship between the construction processes and three-dimensional components is established. The state of the three-dimensional components is dynamically controlled through an incremental state reconstruction algorithm, and a behavior function is predefined for each three-dimensional component to simulate the continuous evolution of the construction process. At the same time, dynamic advancement and historical backtracking are achieved by combining the historical sequence of the construction process. Based on the construction process and its key internal control nodes, a continuous dynamic early warning benchmark curve covering the entire construction period is generated using finite element simulation and third-order spline interpolation methods. A multi-level early warning threshold system that continuously evolves with the construction process is constructed, and safety level is determined by combining real-time monitoring data.

2. The method for simulating the construction process and providing multi-level early warning for the deep foundation pit of a wharf tippler room according to claim 1, characterized in that, The specific method for establishing a digital twin 3D model of the deep foundation pit project for the tippler room is as follows: For each of the three-dimensional components of the tippler room project, four levels of modular models, namely LOD100, LOD200, LOD300 and LOD400, were constructed. LOD100 represents the geometric outline and spatial position, LOD200 adds size and shape features, LOD300 refines the structural details and material properties, and LOD400 deepens the internal structure and construction parameters. Based on the spatial reference relationships and engineering subordinate relationships between the various three-dimensional components, a tree-like hierarchical structure is constructed for the overall model; Each 3D component is bound to a unique identifier ID, and the construction process number and monitoring sensors are associated with this ID, enabling centralized management and collaborative access to construction process information, real-time monitoring data, and multi-level LOD models.

3. The method for simulating the construction process and providing multi-level early warning for the deep foundation pit of a wharf tippler room according to claim 1, characterized in that, The state of 3D components is dynamically controlled through an incremental state reconstruction algorithm, specifically as follows: Construct a standardized 3D component attribute data structure ,in, Each component has a unique identification number. Assign a construction process number to which this component belongs. This indicates the current construction status of the component. Get the currently identified construction progress number Update it to the current system process variable. ; The construction progress status of each three-dimensional component can be obtained using the following formula: Based on the state variables of the three-dimensional components Control the display in the 3D scene: 3D components with state 2 are displayed with normal materials, 3D components with state 1 are displayed with highlighted dynamic materials, and 3D components with state 0 are hidden.

4. The method for simulating the construction process and providing multi-level early warning for the deep foundation pit of a wharf tippler room according to claim 1, characterized in that, The predefined behavioral functions of a 3D component include at least the following: Behavior: When the construction process begins, the corresponding 3D component switches from a hidden state to a visible state; Growth behavior: used to simulate a continuous construction process, so that three-dimensional components are displayed step by step according to a preset growth pattern, including the growth of piles from the bottom up and the expansion of concrete from one side to the other. Assembly behavior: used to simulate the hoisting, positioning and connection process of prefabricated 3D components, including moving steel supports from the stacking position to the design position and displaying the node structure; Demolition behavior: When the construction process involves the demolition of three-dimensional components, the three-dimensional components switch from a visible state to a hidden state, and the demolition process is simulated by fade-out or translation removal effects.

5. The method for simulating the construction process and providing multi-level early warning for the deep foundation pit of a wharf tippler room according to claim 1, characterized in that, The specific steps to achieve dynamic advancement and historical retrospection are as follows: Establish a construction process history sequence ,in, Indicates the timestamp recorded by the system. This indicates the construction progress at that moment; When you need to query historical moments When the construction status is active, search for records that meet the time conditions in the historical sequence H and obtain the corresponding process number. ; System process variables Updated to This allows for the re-determination of the construction progress status of each three-dimensional component, thereby enabling both forward advancement and reverse backtracking of the construction process.

6. The method for simulating the construction process and providing multi-level early warning for the deep foundation pit of a wharf tippler room according to claim 1, characterized in that, A continuous dynamic early warning baseline curve covering the entire construction period is generated using finite element simulation and third-order spline interpolation methods, specifically as follows: For each construction process Finite element numerical simulations were performed on the key control nodes and their internal components to obtain the planned time and simulated displacement values ​​of each node, forming discrete data points: , in, Represents a process The planned start time and corresponding displacement value; Represents a process Internal m Planned time and simulated displacement values ​​for each key node; Represents a process The planned completion time and simulated displacement values; Using the aforementioned discrete data points as nodes, a piecewise cubic spline interpolation function is constructed for any interval between adjacent nodes. Internally constructed cubic polynomial: in, The coefficients to be determined are: apply the following conditions to the above equation: equal function values, first-order continuity, second-order continuity, and natural boundary conditions. The continuous cumulative displacement prediction function for the entire construction period is obtained by solving the problem. and displacement change rate prediction function .

7. The method for simulating the construction process and providing multi-level early warning for the deep foundation pit of a wharf tippler room according to claim 1, characterized in that, Construct a multi-level early warning threshold system that evolves continuously with the construction progress, specifically as follows: Set , To monitor the obtained cumulative displacement value and rate of change, , From , The predicted value read , The design value is as required by the specification; The following decision function is used. Determine the security level: Among them, when When indicates a safe state; when When it indicates a warning status; when When indicates the alarm status; when This indicates a dangerous situation.

8. A simulation and multi-level early warning system for the construction process of a deep foundation pit for a wharf tippler room, characterized in that, include: The 3D multi-level model building module adopts the component unit modeling and multi-level detail level LOD expression method to establish a digital twin 3D model of the deep foundation pit project of the tippler room, and associates and binds the construction process nodes and on-site monitoring sensor data with the three-dimensional components in the model. The construction process simulation module, based on the digital twin 3D model, discretizes the construction process into a set of construction processes, establishes a mapping relationship between the construction processes and three-dimensional components, dynamically controls the state of the three-dimensional components through an incremental state reconstruction algorithm, and predefines a behavior function for each three-dimensional component to simulate the continuous evolution of the construction process. At the same time, it combines the historical sequence of the construction process to realize dynamic advancement and historical backtracking. The construction early warning module, based on the construction process and its internal key control nodes, uses finite element simulation and third-order spline interpolation to generate a continuous dynamic early warning benchmark curve covering the entire construction period, constructs a multi-level early warning threshold system that continuously evolves with the construction process, and combines real-time monitoring data to determine the safety level.

9. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and running thereon, characterized in that, When the processor executes the program, it implements the method for simulating the construction process and providing multi-level early warning for the deep foundation pit of the wharf tippler room as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the method for simulating the construction process and providing multi-level early warning for the deep foundation pit of the wharf tippler room as described in any one of claims 1-7.

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