Installation process of exhibition hall steel structure

Abstract

This invention discloses an installation process for steel structures in exhibition halls, belonging to the field of building construction technology. By constructing a digital twin synchronized with the physical structure and combining it with a sensor network to collect real-time data on the structural posture, stress, and displacement throughout the assembly, lifting, and unloading processes, dynamic adjustment of assembly accuracy and balanced control of lifting load are achieved based on laser scanning point cloud matching and PID control algorithms. This invention realizes intelligent, visual, and precise steel structure installation, significantly improving installation accuracy and construction safety, and is suitable for the rapid installation of large-span exhibition hall steel structures.

Description

Technical Field

[0001] This invention belongs to the field of building construction technology, specifically relating to an installation process for steel structures in exhibition halls. Background Technology

[0002] Due to its large span, heavy weight, and complex shape, the steel structure of the exhibition hall commonly employs a "ground assembly combined with hydraulic synchronous lifting" installation method during construction. While this method achieves the overall hoisting of large components to a certain extent, it still has several significant limitations in actual engineering projects.

[0003] First, assembly accuracy control relies heavily on traditional surveying methods such as total stations for phased testing, making real-time correction during assembly difficult. Errors tend to accumulate, and post-assessment correction is costly. Second, during hydraulic synchronous lifting, load balance at each lifting point depends primarily on system presets and manual experience adjustments, lacking real-time sensing and dynamic adjustment mechanisms, posing risks of localized overload or asynchronous lifting. Third, the entire construction process lacks a systematic and digital monitoring and early warning system; structural stress state and deformation trends are largely judged based on experience, making it difficult to proactively control safety hazards. Furthermore, high-altitude patching and installation procedures are still mainly manual, resulting in difficult positioning, low efficiency, and high operational safety risks.

[0004] Therefore, although existing technologies can complete structural installation, there is still significant room for improvement in terms of construction accuracy, process safety, intelligent control, and efficiency. Summary of the Invention

[0005] To address the aforementioned issues, this invention aims to provide an installation process for steel structures in exhibition halls. By integrating digital twin technology, sensor networks, and intelligent control algorithms, the process of steel structure installation can be monitored in real time, dynamically adjusted, and precisely controlled.

[0006] To achieve the above objectives, the technical solution of the present invention is as follows:

[0007] An installation process for the steel structure of an exhibition hall includes the following steps:

[0008] S1. Establish a building information model of the steel structure of the exhibition hall and construct a digital twin that is synchronized with it;

[0009] S2. Erect an assembly frame on the ground in situ, assemble the roof truss in sections on the side, and deploy sensors at key nodes.

[0010] S3. The point cloud data of the assembly section is collected in real time by a laser scanner and compared with the digital twin. The support height of the jig is dynamically adjusted according to the deviation value.

[0011] S4. After assembly, install the lifting support and hydraulic synchronous lifting system at the designed position of the truss, and deploy pressure, displacement and tilt sensors;

[0012] S5. Start the lifting system, collect load and attitude data of each lifting point in real time, and dynamically adjust the output of each hydraulic cylinder through PID control algorithm to achieve balanced lifting;

[0013] S6. After raising to the design elevation, install the A-type columns, back tie rods, and fitting components;

[0014] S7. During the unloading process, the structural stress and deformation are continuously monitored and compared with the predicted values ​​of the digital twin to ensure unloading safety;

[0015] S8. Remove the lifting support and jig to complete the installation.

[0016] As an improvement of the present invention, the digital twin integrated finite element analysis module in step S1 is used to simulate the structural stress and deformation at each stage of assembly, lifting and unloading.

[0017] As an improvement of the present invention, the sensors mentioned in steps S2 and S4 include strain sensors, tilt sensors, laser displacement sensors and vision sensors, and the data is transmitted to the control center wirelessly.

[0018] As an improvement of the present invention, the deviation control in step S3 uses the following formula to calculate the average deviation:

[0019] ;

[0020] Where Δ is the average deviation, P i M represents the coordinates of the scan point. i Here are the model coordinates, and n is the number of point pairs.

[0021] As an improvement to the present invention, the load balancing control in step S5, the lifting stage, adopts a PID algorithm, and the output force adjustment is:

[0022] ;

[0023] Among them, e j Let k be the load error at point j; p k i k d These are control parameters.

[0024] As an improvement of the present invention, the installation of the patching component in step S6 adopts a mobile installation robot with vision recognition and robotic arm, which locates the installation position through image recognition.

[0025] As an improvement of the present invention, step S7 further includes performing a three-dimensional laser scan on the overall structure after unloading to generate a completed digital model and comparing it with the design model for compliance.

[0026] As an improvement of the present invention, the control center in step S8 is equipped with a human-machine interface that displays the installation progress, deviation warning, stress cloud diagram and operation log in real time.

[0027] The beneficial effects of this invention are as follows:

[0028] This invention achieves full-element digital mapping of the construction process by combining digital twins and sensor networks. Aspects that traditionally rely on manual measurement and experience-based judgment, such as assembly deviations, lifting synchronization, and structural stress states, are now visualized and monitored through real-time data acquisition and model comparison. This significantly reduces human error and improves construction accuracy and consistency.

[0029] During the assembly stage, the system can automatically identify deviations and prompt adjustments through laser scanning and point cloud matching technology, avoiding delays and increased costs caused by post-construction correction. During the lifting stage, the system achieves real-time load balancing at each lifting point through multi-sensor fusion and PID control algorithms, preventing local overload and making the lifting process more stable and safe.

[0030] This invention enhances the automation level of high-altitude operations and patching installations through intelligent equipment and robotic assistance. The installation robot, combining visual recognition and a robotic arm, can accurately locate the installation position, reducing the risks of manual high-altitude work and improving installation efficiency and accuracy. Furthermore, post-completion 3D scanning and model comparison provide objective and complete digital evidence for project quality acceptance. Attached Figure Description

[0031] Figure 1 This is a system architecture diagram of the present invention;

[0032] Figure 2 This is a process flow diagram of the present invention. Detailed Implementation

[0033] The present invention will be further illustrated below with reference to the accompanying drawings and specific embodiments. It should be understood that the following specific embodiments are for illustrative purposes only and are not intended to limit the scope of the invention.

[0034] Example 1

[0035] This embodiment provides an intelligent exhibition hall steel structure installation process system that integrates digital twins and real-time monitoring. For example... Figure 1 As shown, the system consists of a field perception layer, an edge processing layer, a platform decision-making layer, and an execution control layer. Each layer works collaboratively through wired or wireless networks to achieve intelligent monitoring and closed-loop control of the entire process of steel structure assembly, lifting, and unloading.

[0036] The on-site sensing layer consists of various sensors deployed at key structural locations. Laser rangefinders are installed at each support point of the assembly jig to monitor elevation changes at the top of the jig. Strain sensors are attached to key sections such as chord joints and mid-span of each truss to monitor stress states during assembly and lifting. Inclination sensors are placed at both ends and mid-span of the trusses to monitor the overall attitude of the trusses in real time. High-precision pressure and displacement sensors are installed at each lifting point of the hydraulic lifter to monitor lifting force and vertical displacement, respectively. All raw data collected by the sensors is aggregated and initially packaged through an IoT gateway.

[0037] The edge processing layer is implemented by edge computing gateways deployed at the construction site. The gateways have built-in microprocessors responsible for real-time preprocessing, local diagnostics, and anomaly alerts of the massive amounts of raw data uploaded from the perception layer. Its core processing flow includes:

[0038] (1) Data filtering and noise reduction: Real-time filtering is performed on the raw sensor data (such as stress and displacement) to reduce the impact of environmental noise and instantaneous interference. For parameters with relatively gradual changes, such as stress, a first-order low-pass filtering algorithm can be used, as shown in the following formula:

[0039] ;

[0040] Among them, S t S represents the filtered stress value at the current moment. t-1 S represents the filtered stress value from the previous moment. raw,t This represents the original stress value collected at the current moment. The filter coefficient can be selected between 0.85 and 0.95 depending on the signal characteristics.

[0041] (2) Real-time calculation of assembly deviation: During the assembly stage, after the mobile 3D laser scanner completes the scanning of an assembly segment, the edge gateway receives its point cloud data and performs rapid registration and comparison with the theoretical coordinates of the segment's BIM model issued from the platform decision layer. The deviation calculation uses the root mean square error formula to evaluate the overall assembly accuracy:

[0042] ;

[0043] Where Δ is the average positional deviation of the segment (unit: mm), Pi represents the 3D coordinates of the i-th point in the scanned point cloud, Mi represents the 3D coordinates of the i-th theoretical point paired with it in the BIM model, and n is the number of successfully paired feature points. If Δ exceeds the preset tolerance threshold (e.g., 3 mm), the edge gateway will immediately generate a local warning and prompt the adjustment of the corresponding jig lifting device.

[0044] (3) Improved Synchronization Diagnosis: During the hydraulic lifting stage, the edge gateway calculates the lifting speed v of each lifting point in real time. j and load ratio The preset rules include: determining whether the speed difference between any two suspension points exceeds a threshold. Determine whether the deviation between the load at any suspension point and the average load exceeds a set percentage (e.g., ±15%). Once the rule is triggered, a local alarm is generated immediately.

[0045] The platform's decision-making layer is the digital twin intelligent monitoring platform deployed in the project command center. This platform integrates a complete BIM model of the exhibition hall structure, a finite element analysis module, and a construction process simulation engine. Its main functions include:

[0046] (1) Data fusion and visualization: Receive and store all preprocessed data from the edge processing layer. In the three-dimensional visualization interface of the platform, the measured stress, displacement and attitude data are superimposed on the BIM model in the form of dynamic cloud map or numerical label to realize the synchronous mapping of physical construction and digital model.

[0047] (2) Simulation Prediction and Decision Support: Based on the current measured state, the finite element model is driven to perform real-time or near-real-time analysis to predict the impact of the next construction operation (such as the next lifting step or unloading) on ​​the internal forces and deformation of the structure. For the load balancing problem in the lifting process, the platform uses a PID control algorithm to dynamically calculate the output adjustment of each hydraulic cylinder. The core formula is as follows:

[0048] ;

[0049] in, For sending the output force adjustment command (kN) to hydraulic lift j; The current load at the j-th lifting point and the average load of all lifting points. The relative error; , , These are the proportional, integral, and derivative coefficients, which need to be tuned according to the response characteristics of the hydraulic system. The platform sends this sequence of instructions to the execution control layer.

[0050] The execution control layer receives control commands from the platform decision layer and drives the field devices to execute them. This mainly includes:

[0051] Intelligent hydraulic pump station: Receives data from the platform. The command precisely adjusts the oil pressure of the corresponding hydraulic lifter through the proportional servo valve to achieve dynamic balance of the output force at each lifting point.

[0052] The jig fine-tuning and lifting device: During the assembly stage, it receives correction commands from the platform or edge gateway and performs millimeter-level precision fine-tuning of the height of the designated jig support points.

[0053] Installation robot: During the patching installation phase, the robot control system receives the installation path and coordinates generated based on the BIM model and visual positioning, and guides the robotic arm to complete the gripping, positioning and temporary fixation of the high-altitude patching rod.

[0054] Example 2

[0055] This embodiment provides an intelligent installation method for exhibition hall steel structures based on the above system, the process of which is as follows: Figure 2 As shown, the specific steps are as follows:

[0056] S1. Pre-construction Digital Modeling and Planning: Before construction begins, a detailed BIM model of the exhibition hall's steel structure is created, clearly marking the theoretical placement locations of all sensors, support points of the formwork, lifting points, and locations of insert components. Based on the model, a full-process construction simulation is conducted to preliminarily determine key process parameters such as assembly segmentation, lifting sequence, and unloading scheme.

[0057] S2. On-site Hardware Deployment and System Integration: Assemble the assembly jig and lifting support on-site according to the plan, and install various sensors and edge computing gateways at designated locations, connecting them to the hydraulic synchronous lifting system and intelligent pump station. Connect all on-site equipment to the digital twin monitoring platform for system communication integration and initial calibration.

[0058] S3. Digital Twin-Driven Assembly Operation: Ground assembly begins. Each completed segment is scanned using a 3D laser scanner. The edge gateway calculates the assembly deviation Δ; if it exceeds the deviation, it prompts for adjustment of the jig until it meets the requirements. During assembly, the platform continuously displays a real-time structural stress cloud map to ensure that welding and other operations are performed within safe stress limits.

[0059] S4. Real-time Sensing-Based Synchronous Lifting: After assembly and acceptance, the hydraulic synchronous lifting system is activated. Based on real-time data collected from each lifting point, the platform's decision-making layer executes a PID control algorithm, continuously sending fine-tuning commands to the hydraulic pump station. This ensures a synchronized and smooth lifting process. Operators can monitor the overall truss posture, loads at various points, and comparisons with the theoretical trajectory in real time through the platform interface.

[0060] S5. Data Verification and Unloading: After the truss is lifted to the design elevation, main components such as A-type columns and back tie rods are installed. For the interlocking components such as web members reserved in the area traversed by the lifting support, a robot is used for high-precision installation. Before unloading the overall structure, the platform performs a final state finite element analysis based on the current measured data; unloading can only proceed after safety is confirmed. During unloading, the monitoring system continuously operates to verify the consistency between the actual stress on the structure and the model predictions.

[0061] S6. Generation of As-Built Digital Archives: After all installation procedures are completed, a final 3D laser scan of the overall structure is performed to generate an as-built point cloud model. This model is compared with the original design BIM model to generate an installation accuracy inspection report. All monitoring data, early warning logs, control commands, and video images from the entire construction process are linked to the construction procedures to form a complete and traceable digital twin archive of the construction process.

[0062] It should be noted that the above are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. For those skilled in the art, several improvements and modifications can be made on the basis of the above embodiments without departing from the principle of the present invention, and all such improvements and modifications fall within the scope of protection of the claims of the present invention.

Claims

1. A steel structure installation process for an exhibition hall, characterized in that, Includes the following steps: S1. Establish a building information model of the steel structure of the exhibition hall and construct a digital twin that is synchronized with it; S2. Erect an assembly frame on the ground in situ, assemble the roof truss in sections on the side, and deploy sensors at key nodes. S3. The point cloud data of the assembly section is collected in real time by a laser scanner and compared with the digital twin. The support height of the jig is dynamically adjusted according to the deviation value. S4. After assembly, install the lifting support and hydraulic synchronous lifting system at the designed position of the truss, and deploy pressure, displacement and tilt sensors; S5. Start the lifting system, collect load and attitude data of each lifting point in real time, and dynamically adjust the output of each hydraulic cylinder through PID control algorithm to achieve balanced lifting; S6. After raising to the design elevation, install the A-type columns, back tie rods, and fitting components; S7. During the unloading process, the structural stress and deformation are continuously monitored and compared with the predicted values ​​of the digital twin to ensure unloading safety; S8. Remove the lifting support and jig to complete the installation.

2. The installation process for the steel structure of the exhibition hall according to claim 1, characterized in that, The digital twin integrated finite element analysis module mentioned in step S1 is used to simulate the structural stress and deformation at each stage of assembly, lifting, and unloading.

3. The installation process for the steel structure of the exhibition hall according to claim 1, characterized in that: The sensors mentioned in steps S2 and S4 include strain sensors, tilt sensors, laser displacement sensors, and vision sensors, and the data is transmitted wirelessly to the control center.

4. The installation process for the steel structure of the exhibition hall according to claim 1, characterized in that, Step S3 deviation control uses the following formula to calculate the average deviation: ； Where Δ is the average deviation, P i M represents the coordinates of the scan point. i Here are the model coordinates, and n is the number of point pairs.

5. The installation process for the steel structure of the exhibition hall according to claim 1, characterized in that, The load balancing control in step S5, the lifting stage, adopts a PID algorithm, and the output force adjustment is: ； Among them, e j Let k be the load error at point j; p k i k d These are control parameters.

6. The installation process for the steel structure of the exhibition hall according to claim 1, characterized in that, The installation of the patching component in step S6 is carried out using a mobile installation robot equipped with vision recognition and a robotic arm, which locates the installation position through image recognition.

7. The installation process for the steel structure of the exhibition hall according to claim 1, characterized in that, Step S7 also includes performing a three-dimensional laser scan of the overall structure after unloading to generate an as-built digital model and comparing it with the design model for compliance.

8. The installation process for the steel structure of the exhibition hall according to claim 1, characterized in that, The control center mentioned in step S8 is equipped with a human-machine interface that displays the installation progress, deviation warning, stress cloud diagram and operation log in real time.

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