Steel structure anti-falling installation control method based on dynamic load distribution

Abstract

The invention relates to the technical field of steel structure installation safety, and discloses a steel structure anti-falling installation control method based on dynamic load distribution, which comprises the steps of S1, early-stage preparation and model construction, S2, dynamic load real-time monitoring and identification, S3, dynamic load intelligent distribution and anti-falling regulation and control, and S4, full-period optimization and acceptance guarantee. According to the steel structure anti-falling installation control method based on dynamic load distribution, through construction of the load model, real-time monitoring of the dynamic load, intelligent load distribution and full-period optimization acceptance, accurate adaptation of the dynamic load and an anti-falling system is achieved, and the safety and reliability of the steel structure installation process are improved; the technical defects that an existing steel structure anti-falling installation control method cannot effectively adapt to dynamic load changes, load control lags behind, and safety control is incomplete are overcome.

Description

Technical Field

[0001] This invention relates to the field of steel structure installation safety technology, specifically a steel structure anti-fall installation control method based on dynamic load distribution. Background Technology

[0002] Steel structures are widely used in construction engineering due to their high strength and ease of construction, especially in large-scale projects such as high-rise buildings and large-span stadiums. However, the installation of steel structures carries a high risk of falls during component hoisting and high-altitude operations. Accidents can result in serious casualties and property damage. Current fall protection measures for steel structures mostly rely on fixed measures, such as safety nets and lifelines, while depending on the experience of construction personnel for load assessment and installation process planning. However, the loads during steel structure installation have significant dynamic characteristics, such as impact loads during hoisting, wind fluctuation loads at different heights, and variable loads generated by the movement of personnel and equipment. Fixed fall protection measures are difficult to adapt to changes in dynamic loads, which can easily lead to load concentration, causing local component overload deformation and ultimately resulting in the failure of the fall protection structure.

[0003] Furthermore, existing technologies lack real-time monitoring and intelligent allocation mechanisms for dynamic loads, making it impossible to accurately identify load types and trends. When loads exceed safety thresholds, it is difficult to quickly implement effective control measures, resulting in a lag in fall protection safety management. Simultaneously, existing installation control methods lack a full-cycle optimization and acceptance assurance system, hindering continuous optimization of load allocation and making it difficult to fully verify the reliability of fall protection measures. Therefore, addressing the problems of poor dynamic load adaptability, lagging load monitoring and control, and incomplete safety management loops in existing technologies, there is an urgent need for a steel structure fall protection installation control method capable of real-time monitoring of dynamic loads, intelligent load allocation, and full-cycle safety assurance. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a steel structure fall protection installation control method based on dynamic load distribution. This method has the advantages of achieving precise adaptation between dynamic load and fall protection system by constructing a load model, monitoring dynamic load in real time, intelligently distributing load, and optimizing acceptance throughout the entire life cycle, thereby improving the safety and reliability of the steel structure installation process. It also solves the technical defects of existing steel structure fall protection installation control methods, such as inability to effectively adapt to dynamic load changes, lagging load regulation, and imperfect safety management.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for controlling the installation of steel structure anti-fall devices based on dynamic load distribution, comprising the following steps: S1. Preliminary preparation and model building: Based on the parameters of steel structure components and the installation process, a digital model of the steel structure is built. The static load model is built by collecting the self-weight of core components, static load parameters of construction personnel and equipment, and incorporating hoisting moving load and wind dynamic load to generate a hoisting load change model, and clarifying the load bearing range of each load-bearing point; at the same time, the layout of fall protection components is planned and fall protection components that are compatible with the rated load are matched according to the load model. S2. Real-time monitoring and identification of dynamic loads: Multi-dimensional sensing devices are deployed at key nodes of the steel structure to collect load-related signals. The signals are preprocessed by edge computing nodes and the load frequency domain features are extracted by time-frequency conversion. The load "frequency domain fingerprint" is constructed to identify the load type. The load change curves of each load-bearing point are simulated simultaneously, load safety thresholds are set, and real-time early warnings are issued. S3. Dynamic load intelligent distribution and fall protection control: Based on the load change model and the load sorting of each load-bearing point, optimize the installation process and plan the location of auxiliary lifting points to disperse concentrated loads; by adjusting the stiffness of the support structure or adjusting the support state of the actuator array, transfer loads exceeding the safety threshold to redundant support structures; adapt the load-bearing state of fall protection components according to dynamic load changes. S4. Full-cycle optimization and acceptance assurance: Utilize reinforcement learning to optimize load prediction model parameters and load frequency domain fingerprint database, record load distribution effects under different working conditions to form standardized control schemes; conduct dynamic testing and verification of the fall protection system, and combine AI video analysis system to monitor personnel operation procedures, forming a closed loop of full-process safety management.

[0006] Preferably, in step S1, the layout of the fall arrestor components is specifically planned as follows: steel hooks are pre-installed on the lower flange of the steel beam, with a hook spacing of 700-800mm, for fixing the safety net; an installation path for the cantilevered net is planned around the steel structure, with the cantilevered net being rotated every 4 floors, and a cantilever width of 2.8-3.2 meters. This layout design ensures the protective coverage of the safety net and the cantilevered net, while avoiding excessive concentration of fall arrestor components leading to excessive local loads.

[0007] Preferably, in step S1, the fall protection component includes a safety rope, a sling, and a lifeline system, wherein the safety rope has a diameter of not less than 9mm and a breaking load of not less than 15kN, ensuring that the fall protection component can withstand the impact of dynamic loads.

[0008] Preferably, in step S2, the multi-dimensional sensing device includes a strain gauge, a weighing device, and an accelerometer; the key nodes include sling connection points, steel beam bearing points, and support connection points; and the load types include hoisting impact loads, wind-induced fluctuation loads, and personnel and equipment movement loads. The multi-dimensional sensing device can comprehensively collect load-related signals, and combined with the load frequency domain fingerprint extracted by time-frequency conversion, it can quickly and accurately identify different types of dynamic loads, providing data support for subsequent load allocation.

[0009] Preferably, in step S3, the specific method for adjusting the stiffness of the support structure is as follows: A heating element and a sensing module are installed on the temperature-sensitive metal strip support structure. A multi-objective optimization algorithm and adaptive PID control are integrated, and the stiffness of the metal strip is changed by adjusting the heating amount of the heating element, so that the resistance value monitored by the strain gauge is maintained within the optimal range of 0.5-2.0Ω. This method enables dynamic adjustment of the stiffness of the support structure, thereby flexibly controlling the load borne by the support structure and avoiding local overload.

[0010] Preferably, in step S3, the specific method for adapting the load-bearing state of the fall arrestor components is as follows: when an increase in wind load is detected, the tension of the horizontal lifeline is tightened to 1.2-1.5 kN, and the connection strength of the safety net hooks is reinforced. By adapting the load-bearing state of the fall arrestor components in real time, the fall arrestor system is ensured to remain stable and reliable under dynamic load changes.

[0011] Preferably, in step S4, the dynamic test of the fall protection system specifically involves: conducting a free-fall test using a 100kg sandbag at a height of 1.5-2.0m, ensuring that the maximum deformation of the safety net does not exceed 50cm, and that there is no damage or detachment. This dynamic test fully verifies the load-bearing capacity and protective effect of the fall protection system.

[0012] Preferably, in step S4, the verification content includes the number of wire rope clamps, the welding strength of the support frame, and the reliability of the connection of the fall protection components. The number of wire rope clamps is no less than three, and the clamp spacing is no less than six times the diameter of the wire rope. A comprehensive verification can promptly identify potential safety hazards in the fall protection system, ensuring the reliability of the fall protection measures.

[0013] Compared with existing technologies, this invention provides a steel structure fall arrest installation control method based on dynamic load distribution, which has the following beneficial effects: 1. The steel structure anti-fall installation control method based on dynamic load distribution, by constructing a static load model and a dynamic load change model, realizes the accurate prediction of various loads during the steel structure installation process, provides a scientific basis for the layout of anti-fall components and load distribution, effectively improves the adaptability of dynamic loads, and avoids the load concentration problem caused by fixed anti-fall measures; 2. This steel structure anti-fall installation control method based on dynamic load distribution adopts multi-dimensional sensing devices combined with edge computing technology to realize real-time monitoring and rapid identification of dynamic loads. Through the load "frequency domain fingerprint", it can accurately distinguish different types of dynamic loads and simultaneously set safety thresholds for real-time early warning, thus solving the problem of lagging load monitoring and control in the existing technology. 3. This steel structure fall arrest installation control method based on dynamic load distribution achieves intelligent distribution of dynamic load by optimizing the installation process, adjusting the stiffness of the support structure and adapting the load-bearing state of the fall arrest components. It can quickly transfer loads exceeding the safety threshold to redundant support structures, ensuring that both the fall arrest system and structural components are in a safe load-bearing state, and significantly improving the safety of the installation process. 4. This steel structure fall protection installation control method based on dynamic load distribution has constructed a full-cycle optimization and acceptance guarantee system. It continuously optimizes the load prediction model with the help of reinforcement learning, verifies the reliability of the fall protection system through dynamic testing and verification, and combines AI video analysis to monitor personnel operation procedures, forming a closed loop of full-process safety management and control, which further ensures the effectiveness and continuity of fall protection installation control. Attached Figure Description

[0014] Figure 1 This is a flowchart illustrating the implementation steps of the fall protection installation control method of the present invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] Please see Figure 1 A method for controlling the installation of anti-fall devices on steel structures based on dynamic load distribution includes the following steps: S1. Preliminary preparation and model building: Based on the parameters of steel structure components and the installation process, a digital model of the steel structure is built. The static load model is built by collecting the self-weight of core components, static load parameters of construction personnel and equipment, and incorporating hoisting moving load and wind dynamic load to generate a hoisting load change model, and clarifying the load bearing range of each load-bearing point; at the same time, the layout of fall protection components is planned and fall protection components that are compatible with the rated load are matched according to the load model. S2. Real-time monitoring and identification of dynamic loads: Multi-dimensional sensing devices are deployed at key nodes of the steel structure to collect load-related signals. The signals are preprocessed by edge computing nodes and the load frequency domain features are extracted by time-frequency conversion. The load "frequency domain fingerprint" is constructed to identify the load type. The load change curves of each load-bearing point are simulated simultaneously, load safety thresholds are set, and real-time early warnings are issued. S3. Dynamic load intelligent distribution and fall protection control: Based on the load change model and the load sorting of each load-bearing point, optimize the installation process and plan the location of auxiliary lifting points to disperse concentrated loads; by adjusting the stiffness of the support structure or adjusting the support state of the actuator array, transfer loads exceeding the safety threshold to redundant support structures; adapt the load-bearing state of fall protection components according to dynamic load changes. S4. Full-cycle optimization and acceptance assurance: Utilize reinforcement learning to optimize load prediction model parameters and load frequency domain fingerprint database, record load distribution effects under different working conditions to form standardized control schemes; conduct dynamic testing and verification of the fall protection system, and combine AI video analysis system to monitor personnel operation procedures, forming a closed loop of full-process safety management.

[0017] This embodiment focuses on the steel structure installation project of a high-rise building, and adopts the steel structure fall prevention installation control method based on dynamic load distribution of the present invention. The specific steps are as follows: S1. Preliminary Preparations and Model Building: First, parameters (such as model, dimensions, and material) of core components of the high-rise building's steel structure, such as steel columns and beams, were collected. Combined with a pre-defined installation process, a digital model of the steel structure was constructed using BIM technology. The self-weight data of core components such as steel columns (12t each) and steel beams (3t each) were collected using weighing devices. Simultaneously, static load parameters such as those of construction workers (80kg each) and welding equipment (300kg each) were calculated. A static load model was then built using ANSYS software.

[0018] Subsequently, the hoisting moving load (maximum impact load coefficient is taken as 1.2) is determined by combining parameters such as the moving trajectory and hoisting speed of the hoisting equipment. Wind data for different installation heights are obtained from the local meteorological department (maximum wind speed of 25m / s at 100m height, corresponding to wind load of 0.5kN / m²). The above dynamic factors are incorporated into the static load model to generate a hoisting load change model, and the load bearing range of each sling connection point and steel beam bearing point is clarified (maximum allowable load of sling connection point is 15t, and maximum allowable load of steel beam bearing point is 8t).

[0019] Based on the load model, the layout of the fall protection components is planned as follows: 20mm diameter steel hooks are pre-installed on the lower flange of the steel beam, spaced 750mm apart, to secure the high-strength safety net (made of polyester, with a breaking strength of 5kN); an external cantilever net installation path is planned around the steel structure, constructed using Φ48×3.5mm steel pipes, rotated every 4 floors, with a cantilever width of 3 meters. Simultaneously, fall protection components are matched: 9mm diameter steel wire ropes are selected as safety ropes (breaking load 18kN), high-strength synthetic fiber slings with a rated load of 20t are used, and the lifeline system uses Φ12mm steel strand.

[0020] S2. Real-time monitoring and identification of dynamic loads: Strain gauges (model: BX120-3AA), load cells (model: YZC-320), and accelerometers (model: ADXL345) were installed at key nodes such as the sling connection points, steel beam bearing points, and support connection points of the steel structure to build a multimodal sensor network. The sensor data sampling frequency was set to 100Hz.

[0021] The collected strain, weight, and acceleration signals are preprocessed (noise removal and signal amplification) using edge computing nodes. Fourier transform is then used for time-frequency conversion to extract frequency domain features corresponding to different loads, constructing a load "frequency domain fingerprint" database (e.g., the frequency domain features of hoisting impact loads are 10-20Hz, and the frequency domain features of wind-driven wave loads are 0.5-5Hz). By comparing the real-time extracted frequency domain features with the fingerprint database, the load type is quickly identified.

[0022] Simultaneously, the tensile force variation curves of each bearing point are simulated using MATLAB software. The load safety threshold for the sling connection point is set to 12t (corresponding to a strain gauge resistance of 2.0Ω), and the load safety threshold for the steel beam bearing point is set to 6.4t. When the load is detected to be close to 90% of the safety threshold, an audible and visual warning is activated.

[0023] S3. Dynamic load intelligent distribution and fall protection control: Based on the load variation model and the load order of each load-bearing point (load at sling connection point > load at steel beam load-bearing point > load at support connection point), the installation process is optimized: the core steel column (with strong load-bearing capacity) is installed first, followed by the main beam, secondary beam and other auxiliary components; during the steel beam hoisting process, two auxiliary hoisting points are planned (located at 1 / 3 of both ends of the steel beam), and the concentrated load is distributed to multiple hoisting points by adjusting the lifting speed of the main hoisting point and the auxiliary hoisting point, so as to avoid overloading of a single hoisting point.

[0024] A heating element (model: SRY2-220 / 1) and a sensing module are added to the temperature-sensitive metal strip support structure. Integrating a multi-objective optimization algorithm (NSGA-Ⅲ) and adaptive PID control, when the strain gauge detects a resistance value exceeding 1.8Ω (approaching the safety threshold), the heating power of the heating element (heating temperature range 20-80℃) is adjusted to change the stiffness of the metal strip, maintaining the resistance value within the optimal range of 0.5-2.0Ω. Simultaneously, control commands are sent to the actuator array (model: MTS810) to adjust the support state in real time, transferring loads exceeding the safety threshold to redundant support structures (such as temporary support steel frames).

[0025] When an increase in wind load is detected (wind speed exceeds 20m / s), the tension of the horizontal lifeline is tightened to 1.4kN using an electric tensioner. At the same time, the connection of the safety net hook is reinforced with double nuts to improve the load-bearing stability of the fall arrestor.

[0026] S4. Full-cycle optimization and acceptance assurance: By using a reinforcement learning algorithm (DQN algorithm) to compare actual load data with predicted data, the parameters of the spatiotemporal graph neural network prediction model and the load frequency domain fingerprint database are continuously optimized to improve the accuracy of load prediction and identification. The load distribution effect under different hoisting heights and wind conditions is recorded to form a standardized control scheme adapted to the steel structure of this high-rise building.

[0027] Dynamic testing of the fall protection system was conducted: a 100kg sandbag was dropped from a height of 1.8m for free fall. The maximum deformation of the safety net was 35cm, with no damage or detachment, meeting safety standards. The number of wire rope clips (4 were installed), the welding strength of the bracket (ultrasonic flaw detection was used, and no welding defects were found), and the reliability of the fall protection component connections were reviewed to ensure compliance with specifications.

[0028] During the operation, the AI ​​video analysis system (using the YOLOv8 algorithm) monitors the personnel's operating procedures, such as whether they wear safety protective equipment correctly and whether they cross guardrails in violation of regulations. Combined with load data and personnel behavior data, a closed loop of safety control is formed for the entire process of "load monitoring-control-acceptance-personnel management".

[0029] In this embodiment, by adopting the control method of the present invention, no problems of component deformation and fall prevention failure caused by load concentration occurred during the steel structure installation process. The dynamic load control response time is ≤0.5s, and the early warning accuracy rate reaches 98%, which significantly improves the safety and reliability of the installation process.

[0030] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for controlling the installation of anti-fall devices on steel structures based on dynamic load distribution, characterized in that, Includes the following steps: S1. Preliminary preparation and model building: Based on the parameters of steel structure components and the installation process, a digital model of the steel structure is built. The static load model is built by collecting the self-weight of core components, static load parameters of construction personnel and equipment, and incorporating hoisting moving load and wind dynamic load to generate a hoisting load change model, and clarifying the load bearing range of each load-bearing point; at the same time, the layout of fall protection components is planned and fall protection components that are compatible with the rated load are matched according to the load model. S2. Real-time monitoring and identification of dynamic loads: Multi-dimensional sensing devices are deployed at key nodes of the steel structure to collect load-related signals. The signals are preprocessed by edge computing nodes and the load frequency domain features are extracted by time-frequency conversion. The load "frequency domain fingerprint" is constructed to identify the load type. The load change curves of each load-bearing point are simulated simultaneously, load safety thresholds are set, and real-time early warnings are issued. S3. Dynamic load intelligent distribution and fall prevention control: Based on the load change model and the load sorting of each load-bearing point, optimize the installation process and plan the location of auxiliary lifting points to disperse concentrated loads; by adjusting the stiffness of the support structure or adjusting the support state of the actuator array, loads exceeding the safety threshold are transferred to redundant support structures. Adapt the load-bearing status of the fall arrestor components according to dynamic load changes; S4. Full-cycle optimization and acceptance assurance: Utilize reinforcement learning to optimize load prediction model parameters and load frequency domain fingerprint database, record load distribution effects under different working conditions to form standardized control schemes; conduct dynamic testing and verification of the fall protection system, and combine AI video analysis system to monitor personnel operation procedures, forming a closed loop of full-process safety management.

2. The method for controlling the installation of steel structures for fall protection based on dynamic load distribution according to claim 1, characterized in that: In step S1, the layout planning of the fall protection components is as follows: steel bar hooks are pre-set on the lower flange of the steel beam, with a hook interval of 700-800mm, for fixing the safety net; an installation path for the cantilevered net is planned on the periphery of the steel structure, with the cantilevered net being rotated every 4 floors, and the cantilever width being 2.8-3.2 meters. In step S1, the fall protection components include a safety rope, sling, and lifeline system, wherein the diameter of the safety rope is not less than 9mm, and the breaking load is not less than 15kN.

3. The steel structure fall protection installation control method based on dynamic load distribution according to claim 1, characterized in that: In step S2, the multi-dimensional sensing device includes a strain gauge, a weighing device, and an accelerometer; the key nodes include sling connection points, steel beam bearing points, and support connection points; and the load types include hoisting impact loads, wind-driven fluctuation loads, and personnel and equipment movement loads.

4. The method for controlling the installation of steel structures for fall protection based on dynamic load distribution according to claim 1, characterized in that: In step S3, the specific method for adjusting the stiffness of the support structure is as follows: a heating element and a sensing module are added to the temperature-sensitive metal strip support structure, and a multi-objective optimization algorithm and adaptive PID control are integrated. The stiffness of the metal strip is changed by adjusting the heating amount of the heating element, so that the resistance value monitored by the strain gauge is maintained in the optimal range of 0.5-2.0Ω. In step S3, the specific method for adapting the load-bearing state of the fall arrestor is as follows: when the dynamic wind load is detected to increase, the tension of the horizontal lifeline is tightened to 1.2-1.5kN, and the connection strength of the safety net hook is reinforced.

5. The steel structure fall protection installation control method based on dynamic load distribution according to claim 1, characterized in that: In step S4, the dynamic test of the fall protection system specifically involves: using a 100kg sandbag for a free fall test at a height of 1.5-2.0m to ensure that the maximum deformation of the safety net does not exceed 50cm and that there is no damage or detachment. In step S4, the verification content includes the number of wire rope clips, the welding strength of the bracket, and the reliability of the connection of the fall protection components. The number of wire rope clips shall not be less than 3, and the clip spacing shall not be less than 6 times the diameter of the wire rope.

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