BIM deepening-based steel reinforced concrete composite column ultra-large-span corridor construction method

Abstract

The invention relates to the technical field of building construction, and discloses a BIM (Building Information Modeling) deepening-based steel reinforced concrete composite column ultra-large-span corridor construction method, which comprises the following steps of: constructing a multi-modal sensing building information model, and integrating a fiber bragg grating and a distributed acoustic sensor on a steel member to form an intelligent member; establishing a data link between the intelligent component and the sensing BIM center and calibrating the data link; dynamic optimization control based on strain feedback is carried out on collaborative hoisting, and internal quality monitoring based on multi-modal sensing fusion is carried out on concrete pouring; after construction is completed, digital twin baseline calibration is carried out, and a data packet containing a structure initial state vector is generated. According to the method, the multi-modal sensing system and the BIM are deeply fused, and the whole construction process is penetrated, so that real-time, closed-loop and quantitative control of key procedures is realized, the safety, accuracy and quality of super-large complex structure construction are remarkably improved, and a high-fidelity digital twinning baseline is established for structural health monitoring of the whole life cycle of a building.

Description

Technical Field

[0001] This invention relates to the field of building construction technology, specifically to a construction method for ultra-large span connecting corridors using steel-concrete composite columns based on BIM (Building Information Modeling). Background Technology

[0002] With the development of modern building technology, structural situations are becoming increasingly complex, especially for ultra-large span, heavy steel structure corridors built between high-rise buildings, which face enormous challenges in terms of safety and precision control during construction. Existing construction methods typically rely on pre-established construction plans, visual disclosure using BIM technology, and phased configuration monitoring based on surveying equipment such as total stations and GPS. In key stages such as collaborative hoisting, coordination is mainly achieved by controlling cable tension and lifting speed at each hoisting point, combined with manual observation and communication. For the concrete pouring inside the steel columns, quality control relies more on standardized construction processes and workers' operational experience, with subsequent inspections conducted through non-destructive testing or core sampling.

[0003] However, this traditional construction control model has inherent limitations. It largely separates the digital design model from the physical construction site, creating information silos and resulting in a lack of real-time control and internal physical condition basis. In the dynamic and complex hoisting process, relying solely on discrete external measurement data makes it impossible to comprehensively and in real-time grasp the complex stress distribution within the hoisted components and their temporary support systems. This leaves the entire construction system constantly exposed to unpredictable stress concentration and deformation risks, making control accuracy and safety highly dependent on conservative preset parameters and the experience-based judgment of on-site personnel.

[0004] Similarly, in concrete pouring operations, because the internal flow and filling state cannot be visualized during the pouring process, the quality of construction depends entirely on indirect control methods. Potential internal defects such as voids and segregation cannot be detected and remedied in a timely manner, and can only be passively and locally verified after the structure has formed. This not only may lead to high repair costs but also creates long-term safety hazards for the structure. Therefore, there is an urgent need for a method that can deeply integrate real-time feedback of the internal state of the structure with active control of the construction process to address the challenges of constructing complex structures. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a construction method for ultra-large span connecting corridors using steel-concrete composite columns based on BIM (Building Information Modeling). This method solves the problem of the disconnect between digital models and the physical site in traditional construction methods, which leads to a lack of real-time, quantitative internal state perception and closed-loop control for key construction stages.

[0006] To achieve the above objectives, this invention provides the following technical solution: a construction method for ultra-large span connecting corridors using steel-concrete composite columns based on BIM (Building Information Modeling), comprising the following steps:

[0007] a. Construct a multimodal sensing building information model, in which sensors are planned and deployed, including fiber optic grating sensors and distributed acoustic sensing optical cables, and preset stress control thresholds and hydration heat reference models;

[0008] b. Based on the multimodal sensing building information model, a smart component integrating the fiber optic grating sensor and the distributed acoustic sensing optical cable is prefabricated in the factory;

[0009] c. At the construction site, the intelligent component is connected to a sensing BIM hub, which collects data from the sensors in real time and performs at least one of the following control operations:

[0010] Based on the strain data from the fiber grating sensor, the operating parameters of the collaborative hoisting are dynamically optimized so that the internal stress state of the temporary structural system composed of the hoisted component and the supporting component approaches a preset target.

[0011] The temperature field data stream from the fiber optic grating sensor and the acoustic signal data stream from the distributed acoustic sensing optical cable are collected synchronously, and the temperature field data stream and the acoustic signal data stream are fused to generate a three-dimensional quality index in real time that characterizes the concrete filling state inside the steel column.

[0012] d. After construction is completed and temporary loads are removed, the sensing BIM hub performs a synchronous data acquisition on all the sensors to obtain an initial state vector and generate a digital twin baseline data package, which records the initial physical state of the super-large span corridor upon completion.

[0013] In one specific embodiment, the stress control threshold is preset based on the material's yield strength and the corresponding safety factor.

[0014] In one specific embodiment, the distributed acoustic sensing optical cable is laid out along the entire length of the cavity inside the steel column.

[0015] In one specific embodiment, the step of prefabricating and integrating the fiber Bragg grating sensor and the distributed acoustic sensing optical cable includes: embedding or laying the fiber Bragg grating sensor at key mechanical locations of the steel column and the connecting corridor steel components by means of surface grooving or high-strength epoxy resin bonding.

[0016] In one specific embodiment, the following steps are also included: after the intelligent component arrives at the site, the sensing BIM hub performs sensor access testing and initial state calibration.

[0017] Preferably, in the step of dynamically optimizing the operating parameters of the collaborative hoisting, the sensing BIM hub synchronously and in real time collects strain data from the fiber optic grating sensors on the hoisted component and the supporting component, respectively.

[0018] Furthermore, the sensing BIM hub generates the dynamic optimization adjustment command by solving and minimizing a system comprehensive strain deviation objective function in real time. The system comprehensive strain deviation objective function is determined based on the weighted sum of squared deviations between the measured strain values ​​and the reference strain values ​​of all sensors participating in the coordinated control. The system comprehensive strain deviation objective function can be expressed by the following formula:

[0019] ;

[0020] in, The system's comprehensive strain deviation objective function at a specific time represents... and specific operation parameter vector The output value is a scalar whose magnitude quantifies the overall deviation of the temporary structural system from the ideal stress state under its current condition. The goal of dynamic optimization is to find the operating parameter vector that minimizes this function value. ; Indicates the specific moment or point in time at which the calculation was performed; This represents the operational parameter vector for coordinated hoisting, which includes multiple controllable operational variables that affect the stress state of the structural system, such as the speed, acceleration, and cable tension of each hoisting device. The sensor index is a number from 1 to 1. An integer used to uniquely identify each sensor participating in the coordinated control; This indicates the total number of sensors participating in the coordinated control. Indicates the first The preset weighting coefficient for the first sensor. This is a dimensionless numerical value used to characterize the first sensor. The importance of individual sensor measurements in the overall deviation calculation. For example, sensors deployed in critical stress concentration areas can be assigned a higher weighting factor; Indicates the first Each sensor has a preset reference strain value or target strain value. This is an ideal strain expectation value, pre-calculated and determined by the structural design model; Indicating in the operation parameter vector Under the influence of time The collected number The measured strain value of each sensor. This value changes dynamically with time and operating parameters; This represents the summation operator, which means to sum all... The weighted sum of squared biases of each sensor.

[0021] In one specific embodiment, the step of generating a three-dimensional quality index characterizing the concrete filling state inside the steel column in real time includes:

[0022] The deviation between the temperature field data stream and the preset hydration heat reference model is calculated to obtain a temperature anomaly degree;

[0023] The acoustic signal data stream is processed and its features are extracted to obtain an acoustic anomaly degree.

[0024] The three-dimensional quality index is obtained by fusing the temperature anomaly and the acoustic anomaly.

[0025] In one specific embodiment, the method further includes the following step: performing three-dimensional visualization rendering of the three-dimensional quality index on the multimodal sensing building information model.

[0026] In one specific embodiment, the digital twin baseline data package includes the completed multimodal sensing building information model, a list of information for all sensors, and the initial state vector.

[0027] This invention provides a construction method for ultra-large span connecting corridors using steel-concrete composite columns based on BIM (Building Information Modeling). It offers the following advantages:

[0028] 1. This invention utilizes fiber optic grating sensors prefabricated and integrated on intelligent components to acquire real-time strain data of the suspended and supporting components during the hoisting process, and dynamically optimizes the operational parameters for coordinated hoisting based on this data. This closed-loop feedback control method allows the internal stress state of the temporary structural system to be actively controlled and approach a preset safety target, effectively avoiding stress overload caused by improper operation or sudden changes in working conditions, and significantly improving the safety and construction accuracy of complex hoisting operations.

[0029] 2. This invention simultaneously acquires temperature field data streams from fiber optic grating sensors and acoustic signal data streams from distributed acoustic sensing optical cables, and fuses these two different physical modes of data to generate a three-dimensional quality index characterizing the internal filling state of concrete. This method transforms the invisible internal pouring process in traditional construction into a data-driven, visualized quality control process, capable of identifying and locating potential internal defects such as voids and segregation in real time, fundamentally ensuring the construction quality of core load-bearing components.

[0030] 3. After construction is completed and temporary loads are removed, this invention obtains an initial state vector that accurately reflects the zero-stress state of the structure after completion by synchronously acquiring data from all sensors. This initial state vector, together with the as-built model and sensor information, constitutes a digital twin baseline data package, providing a true and reliable initial state reference for subsequent long-term structural health monitoring, performance evaluation, and maintenance decisions, thereby improving the scientific rigor and predictability of building lifecycle management. Attached Figure Description

[0031] Figure 1 This is a schematic diagram of the process of the present invention;

[0032] Figure 2 This is a schematic diagram showing the connection between the construction site sensing BIM hub and the intelligent components of the present invention;

[0033] Figure 3 This is a logic block diagram of the real-time closed-loop control of the key construction process of the present invention.

[0034] Figure 4 This is a schematic diagram illustrating the structure of the digital twin baseline data packet of the present invention.

[0035] Among them, 100 is the sensing BIM hub; 110 is the demodulator module; 120 is the high-performance computing module; 130 is the interface module; 210 is the communication optical cable bundle; 310 is the fiber optic grating sensor; 320 is the distributed acoustic sensing optical cable; and 400 is the intelligent component. Detailed Implementation

[0036] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] See attached document Figure 1 This invention provides a construction method for ultra-large span connecting corridors using steel-concrete composite columns based on BIM, including a detailed description of the following steps.

[0038] In one specific embodiment, the first stage of the method is systematic preliminary planning and prefabrication. This stage first requires the construction of a multimodal sensing building information model. This model is based on a conventional building information model, but deeply integrates the unified planning information of the multimodal sensing system. The construction of the model includes planning the sensor deployment strategy and presetting key control parameters.

[0039] For sensor deployment strategy planning, different deployment principles are adopted for different types of sensors. For fiber optic grating sensors 310, the deployment locations are determined based on the key structural mechanical locations obtained from finite element analysis, such as the column bases and tops of steel columns, and the beam ends, mid-spans, and key connection nodes of connecting corridor steel components. These locations are areas of structural stress concentration or significant strain response. For distributed acoustic sensing optical cables 320, the deployment scheme is to carry out full-length coverage along the height direction of the internal cavity of the steel column, with the aim of ensuring comprehensive monitoring of acoustic dynamics during the concrete pouring process.

[0040] The preset parameters for key control parameters include stress control thresholds and a hydration heat reference model. The stress control threshold provides a clear, quantified safety boundary for dynamic optimization control during collaborative hoisting. This threshold is determined based on the yield strength of the component material and a preset safety factor. For example, the stress control threshold for a specific monitoring point i... It can be determined using the following formula:

[0041] ;

[0042] in: This is the yield strength of the steel, an inherent property value determined by material standards; This is a preset safety factor, the value of which is determined according to relevant construction specifications.

[0043] The hydration heat reference model is a three-dimensional spatial and temporal dataset used to characterize the spatiotemporal evolution of the temperature field caused by the cement hydration heat reaction during the setting and hardening process of concrete inside a steel column under standard working conditions. This model is generated through thermodynamic simulation calculations, and the input parameters include the concrete mix proportions, the geometric dimensions of the steel column, the initial pouring temperature, and the ambient temperature.

[0044] After the construction of the multimodal sensing building information model is completed, the factory prefabrication stage of intelligent component 400 begins. In this stage, based on the precise information contained in the model, fiber optic grating sensors 310 and distributed acoustic sensing optical cables 320 are integrated with steel components in the factory to form intelligent component 400.

[0045] The sensor integration employs one of two specific processes: surface grooving embedding or high-strength epoxy resin bonding. When using the surface grooving embedding method, a groove with a cross-sectional dimension slightly larger than the sensor's outer diameter is first machined at a designated location on the steel component surface. After the sensor is placed into the groove, it is then filled and sealed with a specified covering material. When using the high-strength epoxy resin bonding method, the bonding area of ​​the steel component is first surface-treated, including rust removal, degreasing, and roughening. Then, epoxy resin is applied to the treated surface, and the sensor is laid on the resin. After the resin cures, a strong connection is formed.

[0046] During the prefabrication process, each smart component 400 undergoes quality inspection after sensor integration. The inspection includes testing the connectivity and signal response of each integrated sensor to ensure their proper functioning. Qualified smart components 400 are assigned a unique identification code, which is then associated with the corresponding digital component in the multimodal sensing building information model, thus achieving a one-to-one correspondence between the physical component and the digital model.

[0047] See attached document Figure 2 The second phase of implementation involves deployment and initialization at the construction site. During this phase, the prefabricated intelligent components 400 are transported to the construction site and establish physical connections and data links with the core control system's sensing BIM hub 100.

[0048] After the intelligent component 400 arrives on site, the first step is to deploy the on-site sensor network system. During transportation, the integrated fiber optic grating sensor 310 and distributed acoustic sensing optical cable 320 of the intelligent component 400 have pre-installed fiber optic connectors or communication ports at their ends. Before or during on-site installation, these connectors are connected to the sensor BIM hub 100 via the on-site laid communication optical cable bundle 210. The sensor BIM hub 100 is a comprehensive control platform integrating a fiber optic sensor signal demodulator, data acquisition unit, high-performance computing server, and on-site network interface.

[0049] After the physical connection is completed, system initialization and state calibration are performed. This includes two key operations: sensor access testing and initial state calibration.

[0050] Sensor access testing is a step to ensure the integrity of the sensing link and data quality. The sensor BIM hub 100 transmits and receives test signals to all connected fiber Bragg grating sensors 310 and distributed acoustic sensing optical cables 320. For the fiber Bragg grating sensors 310, the hub records and analyzes the sensor's initial wavelength or reflection spectrum to ensure that all embedded sensors can be stably identified and that the signal strength meets predetermined standards. For the distributed acoustic sensing optical cables 320, the hub performs optical time domain reflectance (OTDR) testing and preliminary acoustic excitation testing to verify the physical integrity of the optical cable and its response to ambient acoustic signals. Any sensor that fails the test will be marked and troubleshooted or replaced by on-site technicians.

[0051] Initial state calibration involves obtaining the structural reference state of a component after installation but before it bears the main construction load. The specific calibration procedure is as follows: After the intelligent component 400 arrives on site and is rigidly connected (e.g., temporarily fixed), the sensor BIM hub 100 performs a synchronous data acquisition on all fiber optic grating sensors 310 under static, no temporary load conditions. The strain data obtained in this acquisition serves as the zero-strain reference for the component on site. All subsequent strain measurements will use this zero-strain reference as a reference point for difference calculations, thereby accurately eliminating inherent deviations caused by initial sensor installation stress, temperature changes, and the component's own weight, ensuring the accuracy of subsequent hoisting control data.

[0052] The Sensor BIM Hub 100 is the core component for realizing all real-time control operations. On the hardware side, the Sensor BIM Hub 100 is equipped with a dedicated demodulator module 110 for high-speed acquisition of fiber optic sensor data, a high-performance computing module 120 for data processing and algorithm execution, and an interface module 130 for network communication and data storage. On the software side, the Sensor BIM Hub 100 runs structural analysis models, dynamic optimization algorithms, and multimodal data fusion algorithms. All data streams acquired by the sensors are received in real-time by the interface module 130 of the Sensor BIM Hub 100 via fiber optic or network interfaces and then transmitted to the high-performance computing module 120 for millisecond-level real-time calculations.

[0053] See attached document Figure 3 The third phase of implementation involves real-time closed-loop control of key construction processes. In this phase, the on-site sensor BIM hub 100 utilizes real-time sensor data from the intelligent component 400 to proactively intervene and monitor the quality of the two core construction steps: collaborative hoisting and concrete pouring.

[0054] When performing collaborative hoisting operations on ultra-long span connecting corridors, this method implements dynamic optimization control based on strain feedback. The sensing BIM hub 100, through a dedicated demodulator module 110, synchronously and continuously acquires strain data from fiber optic grating sensors 310 deployed on the hoisted component and all related supporting components at a preset sampling frequency (e.g., 100Hz). This data is timestamped, forming a multi-channel real-time strain data stream, and is transmitted to the high-performance computing module 120.

[0055] In the high-performance computing module 120, a dynamic optimization calculation is performed once in each calculation cycle (e.g., every 100 milliseconds). This calculation first substitutes the collected strain values ​​from each sensor into a preset system comprehensive strain deviation objective function to calculate the overall strain deviation of the temporary structural system at the current moment.

[0056] The system's overall strain deviation objective function can be expressed by the following formula:

[0057] ;

[0058] in, The system's comprehensive strain deviation objective function at a specific time represents... and specific operation parameter vector The output value is a scalar whose magnitude quantifies the overall deviation of the temporary structural system from the ideal stress state under its current condition. The goal of dynamic optimization is to find the operating parameter vector that minimizes this function value. ; Indicate the specific moment or point in time at which the calculation was performed; This represents the operational parameter vector for coordinated hoisting, which includes multiple controllable operational variables that affect the stress state of the structural system, such as the speed, acceleration, and cable tension of each hoisting device. The sensor index is a number from 1 to 1. An integer used to uniquely identify each sensor participating in the coordinated control; This indicates the total number of sensors participating in the coordinated control. Indicates the first The preset weighting coefficient for the first sensor. This is a dimensionless numerical value used to characterize the first sensor. The importance of individual sensor measurements in the overall deviation calculation. For example, sensors deployed in critical stress concentration areas can be assigned a higher weighting factor; Indicates the first Each sensor has a preset reference strain value or target strain value. This is an ideal strain expectation value, pre-calculated and determined by the structural design model; Indicating in the operation parameter vector Under the influence of time The collected number The measured strain value of each sensor. This value changes dynamically with time and operating parameters; This represents the summation operator, which means to sum all... The weighted sum of squared biases of each sensor.

[0059] Subsequently, the high-performance computing module 120 runs an optimization algorithm, such as gradient descent or particle swarm optimization, to... The objective is to minimize the collaborative hoisting operation parameter vector, which is then solved in real time. An adjustment amount The adjustment amount It contains specific adjustment values ​​for the operating parameters (such as lifting speed, cable tension, and horizontal displacement rate) of each hoisting device (such as multiple cranes or hydraulic jacks). These adjustment values ​​are encapsulated into control commands and sent to the underlying control system (such as a PLC) of each hoisting device on the construction site through the interface module 130, thereby completing a closed-loop feedback control.

[0060] During the concrete pouring operation inside the steel column, this method implements pouring quality monitoring based on multimodal sensor fusion. During this period, the sensor BIM hub 100 concurrently acquires two independent data streams:

[0061] First, the temperature field data stream acquired by the fiber optic grating sensor 310;

[0062] Secondly, acoustic signal data streams are collected through distributed acoustic sensing optical cables 320 laid along the entire length of the steel column.

[0063] The high-performance computing module 120 processes these two data streams to generate a three-dimensional quality index characterizing the concrete filling state. First, the high-performance computing module 120 compares the real-time acquired temperature field data with a preset four-dimensional hydration heat reference model to calculate the temperature deviation at each monitoring point at the current moment; this deviation constitutes the temperature anomaly degree. In one specific embodiment, located in spatial coordinates The monitoring points are at all times Temperature anomaly It can be calculated using the following formula:

[0064] ;

[0065] in: This indicates the real-time temperature value collected at this point; This represents the reference temperature value of the hydration heat reference model in this spatiotemporal coordinate.

[0066] Secondly, the high-performance computing module 120 applies a series of signal processing algorithms, including filtering and time-frequency analysis, to the original acoustic signal data stream, extracting key features such as signal energy and spectral entropy. These features are compared with a preset acoustic feature baseline representing a normal casting state; the differences constitute the acoustic anomaly degree. For example, along the height of the steel column... Location in time period Acoustic anomaly within It can be determined based on signal energy deviation:

[0067] ;

[0068] in, Indicates position At any time The original acoustic signal amplitude; Indicates the time period Real-time signal energy calculated internally; Indicates position The instantaneous power of the acoustic signal; This represents the reference energy value pre-calibrated or given by the model under normal pouring conditions; Indicates to In time period The summation and accumulation within this time period yields the total energy of the signal during that period. This indicates that under normal pouring conditions, along the height The acoustic signal energy reference value is either pre-calibrated at the location or given by the model. This value represents the ideal, defect-free state of concrete pouring.

[0069] Finally, the high-performance computing module 120 executes a data fusion algorithm to weight and fuse the spatially registered temperature anomalies and acoustic anomalies, generating a three-dimensional meshed quality index matrix. A specific fusion method is linear weighted fusion, resulting in the final three-dimensional quality index... It can be given by the following formula:

[0070] ;

[0071] in, and These are preset weighting coefficients used for adjustment. and The relative importance in the assessment, and the satisfaction of + =1.

[0072] The generated three-dimensional quality index matrix is ​​sent to the visualization interface of the Sensing BIM Hub 100 in real time, and is overlaid on the geometric model of the steel column corresponding to the multimodal sensing building information model in a three-dimensional volume rendering manner, realizing intuitive and real-time monitoring of the internal filling quality of concrete.

[0073] See attached document Figure 4 The fourth phase of implementation involves the calibration of the digital twin baseline after construction is completed. This phase aims to accurately capture the initial physical state of the structure after completion and before it enters long-term service, and to solidify it as a benchmark reference for the digital twin model.

[0074] This stage occurs after the main structure construction is completed and all temporary loads affecting the structure have been removed. This state includes, but is not limited to: all scaffolding, temporary support systems, and large construction equipment have been removed, and the structure is only subjected to a constant self-weight load. Under this specific condition, the Sensing BIM Hub 100 performs a synchronous data acquisition operation of all system sensors.

[0075] The synchronous data acquisition operation involves the sensor BIM hub 100 sending a precise synchronization trigger command to all demodulator modules 110, ensuring that strain and temperature readings from all fiber Bragg grating sensors 310 are acquired simultaneously (e.g., within the same millisecond). The dataset obtained from this acquisition constitutes an initial state vector. In a specific embodiment, this initial state vector... It can be represented as:

[0076] ;

[0077] in, Indicates the first The strain values ​​acquired by a fiber Bragg grating sensor 310 in the initial state; Indicates the first Temperature values ​​collected by a fiber Bragg grating sensor 310 in the initial state; This indicates the total number of fiber Bragg grating sensors 310 in the system; This refers to the temperature at the sensor's location. Since the readings of the fiber Bragg grating sensor 310 are affected by both strain and temperature, the temperature value must be recorded simultaneously for temperature compensation in subsequent analysis to obtain strain purely caused by stress; subscript It is an integer variable whose value ranges from 1 to 1. Its function is to uniquely identify a particular sensor in the system. For example, =1 represents the first sensor. =2 represents the second sensor, and so on; It is a dimensionless quantity that describes the degree of relative deformation of an object under the action of an external force.

[0078] The initial state vector The internal strain and temperature field distributions of the structure under its own weight and without any external temporary loads were accurately recorded.

[0079] After obtaining the initial state vector, the sensor BIM hub 100 generates a digital twin baseline data package. This data package is a structured, tamper-proof collection of data, and its generation process involves encapsulating and archiving the following three core data components.

[0080] The first component is the post-construction multimodal sensing building information model. This model is an as-built model that has been updated and corrected based on the construction phase model and actual construction results. It accurately reflects the final geometry and material properties of the building and includes the final spatial location and identification information of all sensors in the structure.

[0081] The second component is a list of information for all sensors. This is a detailed metadata table that records each sensor's unique identification code, sensor type, precise three-dimensional spatial coordinates, installation date, calibration coefficients during the prefabrication stage, the structural component number to which it is attached, and its current operating status.

[0082] The third component is the initial state vector acquired in the preceding steps. .

[0083] These three components are encapsulated together to form a digital twin baseline data package. This data package provides a unique, high-fidelity initial state reference for subsequent structural health monitoring throughout the building's entire lifecycle. After the building is put into use, sensor data collected at any time can be compared with the initial state vector in this baseline data package to accurately calculate strain increments and state changes caused by service loads, environmental changes, or structural damage.

Claims

1. A construction method for ultra-large span connecting corridors using steel-concrete composite columns based on BIM (Building Information Modeling), characterized in that... Includes the following steps: a. Construct a multimodal sensing building information model, in which sensors are planned and deployed, including fiber optic grating sensors (310) and distributed acoustic sensing optical cables (320), and preset stress control thresholds and hydration heat reference models; b. Based on the multimodal sensing building information model, a smart component (400) integrating the fiber optic grating sensor (310) and the distributed acoustic sensing optical cable (320) is prefabricated in the factory. c. At the construction site, the intelligent component (400) is connected to the sensing BIM hub (100), which collects data from the sensor in real time and performs at least one of the following control operations: Based on the strain data from the fiber optic grating sensor (310), the operating parameters of the collaborative hoisting are dynamically optimized so that the internal stress state of the temporary structural system composed of the hoisted component and the supporting component approaches the preset target. The temperature field data stream from the fiber optic grating sensor (310) and the acoustic signal data stream from the distributed acoustic sensing optical cable (320) are collected synchronously, and the temperature field data stream and the acoustic signal data stream are fused to generate a three-dimensional quality index characterizing the concrete filling state inside the steel column in real time. d. After construction is completed and temporary loads are removed, the sensing BIM hub (100) performs a synchronous data acquisition on all the sensors to obtain an initial state vector and generate a digital twin baseline data package, which records the initial physical state of the super-large span corridor at the time of completion.

2. The construction method for ultra-large span connecting corridors using BIM-based steel-concrete composite columns according to claim 1, characterized in that, In the step of dynamically optimizing the operating parameters of the collaborative hoisting, the sensing BIM hub (100) synchronously and in real time collects strain data from the fiber optic grating sensors (310) on the hoisted component and the supporting component respectively.

3. The construction method for ultra-large span connecting corridors using BIM-based steel-concrete composite columns according to claim 2, characterized in that, The sensing BIM hub (100) generates the dynamic optimization adjustment command by solving and minimizing the system comprehensive strain deviation objective function in real time; the system comprehensive strain deviation objective function is determined based on the weighted sum of squared deviations between the measured strain values ​​and the reference strain values ​​of all sensors participating in the coordinated control.

4. The construction method for ultra-large span connecting corridors using steel-concrete composite columns based on BIM detailing as described in claim 1, characterized in that... The step of generating a three-dimensional quality index characterizing the concrete filling state inside the steel column in real time includes: The deviation between the temperature field data stream and the preset hydration heat reference model is calculated to obtain the temperature anomaly degree; The acoustic signal data stream is processed and its features are extracted to obtain the acoustic anomaly degree. The three-dimensional quality index is obtained by fusing the temperature anomaly and the acoustic anomaly.

5. The construction method for ultra-large span connecting corridors using BIM-based steel-concrete composite columns according to claim 4, characterized in that, It also includes the following steps: The three-dimensional quality indicators are then rendered in three dimensions on the multimodal sensing building information model.

6. The construction method for ultra-large span connecting corridors using steel-concrete composite columns based on BIM detailing as described in claim 1, characterized in that... The stress control threshold is preset based on the material's yield strength and the corresponding safety factor.

7. The construction method for ultra-large span connecting corridors using steel-concrete composite columns based on BIM detailing as described in claim 1, characterized in that... The digital twin baseline data package includes the completed multimodal sensing building information model, a list of sensor information, and the initial state vector.

8. The construction method for ultra-large span connecting corridors using steel-concrete composite columns based on BIM detailing as described in claim 1, characterized in that... The step of prefabricating and integrating the fiber optic grating sensor (310) and the distributed acoustic sensing optical cable (320) includes: embedding or laying the fiber optic grating sensor (310) at key mechanical locations of the steel column and the connecting corridor steel components by means of surface grooving or high-strength epoxy resin bonding.

9. The construction method for ultra-large span connecting corridors using steel-concrete composite columns based on BIM detailing as described in claim 1, characterized in that... The distributed acoustic sensing optical cable (320) is laid out along the height direction of the internal cavity of the steel column in a full-length coverage manner.

10. The construction method for ultra-large span connecting corridors using steel-concrete composite columns based on BIM detailing as described in claim 1, characterized in that, It also includes the following steps: After the intelligent component (400) arrives at the site, the sensor BIM hub (100) performs sensor access testing and initial state calibration.

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