Construction method for load decomposition and transmission of special-shaped beam curved template

Abstract

The invention provides a construction method for load decomposition and transmission of a special-shaped beam curved template, and belongs to the technical field of building construction.The load change of each node of the curved template is monitored in real time by establishing a load jump identification matrix, and a supporting system abutting stability matrix is constructed to evaluate the bearing capacity of each abutting point; a graph theory shortest path algorithm is utilized to optimize a load transmission path and form a fluctuation evaluation vector, a prestressed reinforcement tension stability control matrix is constructed to realize symmetrical batch tension, and support system parameters and a tension program are adjusted in real time through a load transmission dynamic balance equation set. And a load transmission effect evaluation vector is established to perform continuity and controllability evaluation on an overall transmission path, and when the transmission efficiency is lower than a set value, the load distribution and path optimization program is executed again, so that the technical problem of instability of a support system caused by uncontrollability of the load transmission path in the construction process of the special-shaped beam curved template is solved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of building construction, and in particular relates to a construction method for load decomposition and transmission of a special-shaped beam bending formwork. BACKGROUND

[0002] In the field of building engineering construction, load transmission control of special-shaped beam bending formwork is a key technology to ensure construction safety and structural quality. In traditional special-shaped beam construction, a uniform distribution of support points is usually used to bear the formwork load, the support spacing and bearing capacity are calculated by empirical formula, and the load is reasonably distributed by combining with the staged tensioning of prestressed reinforcement. This traditional method has been widely used in the construction of regular geometric shape beams, especially in the formwork support system of straight line beam bodies and simple curved beam bodies, and has shown good applicability. However, the traditional technology has obvious defects in dealing with the bending arc segment of special-shaped beams, mainly manifested in the inability to accurately predict the actual transmission path of the load under complex geometric shapes, resulting in over-conservative support system design or safety hazards, and lack of real-time monitoring capability for load mutations and transmission path fluctuations. In the engineering environment of special-shaped beam bending formwork construction with complex geometric shapes and uneven load distribution, the traditional static support design method and empirical tensioning control technology are difficult to adapt to the dynamic change characteristics of the load transmission path, and cannot realize accurate control and dynamic adjustment of the load transmission process. SUMMARY

[0003] Therefore, the present application provides a construction method for load decomposition and transmission of a special-shaped beam bending formwork, which can solve the technical problem of instability of the support system caused by uncontrollable load transmission path during the construction process of the special-shaped beam bending formwork in the prior art.

[0004] The application is implemented in the following manner: the application provides a construction method for load decomposition and transmission of a special-shaped beam bending formwork, which comprises the following steps: establishing a load jump identification matrix for a bending segment of a special-shaped beam, setting load monitoring points for each node of the bending formwork, collecting load data in real time and constructing a three-dimensional load distribution vector, and analyzing the load mutation position and jump amplitude value through the load jump identification matrix; constructing a support system abutment stability matrix based on the three-dimensional load distribution vector, inputting the stiffness coefficient and shear capacity parameter of each connecting node of the support structure into the support system abutment stability matrix, and calculating the load bearing capacity and stability coefficient of each abutment point; establishing a load transmission path fluctuation evaluation vector, abstracting the support system into a graph structure by using a graph theory shortest path algorithm, calculating the fluctuation amplitude value and transmission efficiency of each transmission path, and forming the load transmission path fluctuation evaluation vector; calculating a load control threshold equation set based on the jump amplitude value and the fluctuation amplitude value, obtaining an amplitude threshold and a coefficient threshold, and starting a load redistribution program when the jump amplitude value exceeds the amplitude threshold or the transmission path fluctuation coefficient is greater than the coefficient threshold, so as to adjust the support parameter and the tension distribution; constructing a prestressed steel tendon tension stability control matrix, inputting the tension force, deformation and stress state of each bundle of steel tendons into the prestressed steel tendon tension stability control matrix, and establishing a tension force automatic adjustment system; calculating a load transmission dynamic balance equation set based on the support system abutment stability matrix and the prestressed steel tendon tension stability control matrix, monitoring the stress state of a key transmission node in real time, and dynamically adjusting the support system parameter and the tension program; and establishing a load transmission effect evaluation vector to evaluate the continuity and controllability of the overall load transmission path, and re-executing the load distribution and path optimization program when the load transmission effect evaluation vector shows that the transmission efficiency is lower than 85%.

[0005] The load jump identification matrix is a mathematical model for identifying and quantifying sudden changes in load during transmission, and the position, jump amplitude value and occurrence time of load jump are determined by comparing the load difference and time series change rate of adjacent monitoring points.

[0006] The three-dimensional load distribution vector is a mathematical vector describing the distribution state of the load of each monitoring point in the bending segment of the special-shaped beam in three-dimensional space, and contains load size, direction and action position information.

[0007] The support system abutment stability matrix is a mathematical matrix describing the interaction relationship and stability performance between each connecting node of the support structure, and contains the stiffness coefficient, load bearing capacity and deformation compatibility parameter of each abutment point.

[0008] The load transmission path fluctuation evaluation vector is a mathematical vector representing the fluctuation characteristics and transmission efficiency of the load on different transmission paths, reflecting the energy loss and path stability in the load transmission process, and the fluctuation amplitude value ∈ [0, 1).

[0009] The load control threshold equation set includes amplitude threshold calculation equation and coefficient threshold calculation equation. The amplitude threshold calculation equation is used to determine the critical control value of load jump, and the coefficient threshold calculation equation is used to determine the critical control value of transmission path fluctuation.

[0010] Among them, the load transfer effect evaluation vector is a mathematical vector that comprehensively evaluates the overall performance of the load transfer system, including key performance indicators such as transfer efficiency, stability, and continuity.

[0011] Among them, the prestressed steel tensioning stability control matrix is ​​a mathematical model used to control the coordination of various parameters during the prestressed steel tensioning process, ensuring uniform tension force distribution and stability of the tensioning process.

[0012] The load transfer dynamic equilibrium equation set includes the load distribution equilibrium equation and the support stiffness adjustment equation. The load distribution equilibrium equation is used to calculate the optimal load distribution ratio for each support point, and the support stiffness adjustment equation is used to determine the stiffness adjustment parameters of the support system.

[0013] Among them, the stress state of the key force transmission node refers to the stress distribution at the core force transmission location that has a decisive influence on the overall stability of the load transmission system, including the stress magnitude, direction and trend of change.

[0014] The amplitude threshold calculation equation takes into account jump amplitude, structural safety factor, material strength parameters, bending radius parameters, and environmental load factors, and outputs the amplitude threshold. The coefficient threshold calculation equation takes into account fluctuation amplitude, support stiffness coefficient, transmission distance parameters, stability coefficient, and load distribution coefficient, and outputs the coefficient threshold. The load distribution balance equation takes into account stress state of key force transmission nodes, stiffness coefficient, material elastic modulus, cross-sectional geometric parameters, and load-bearing capacity, and outputs the load distribution coefficient for each support point. The support stiffness adjustment equation takes into account stress state of key force transmission nodes, target stability coefficient, support material performance parameters, shear capacity parameters, and load distribution coefficient, and outputs the support stiffness adjustment coefficient.

[0015] The graph theory shortest path algorithm abstracts the complex support system into a graph structure composed of nodes and edges. Each node represents a key location for load transfer, and edges represent connections and transfer capabilities. By calculating the weight values ​​of each path, it automatically finds the path combination with the minimum load transfer resistance and the highest efficiency. The graph theory shortest path algorithm uses stiffness coefficient, shear capacity parameter, transfer distance parameter, and material elastic modulus as input parameters. It calculates the shortest distance matrix between nodes using Dijkstra's algorithm to obtain the optimal path sequence and transfer efficiency coefficient for load transfer.

[0016] Among them, key performance indicators refer to the core evaluation parameters that have a decisive impact on the overall effect of the load transfer system, including load transfer continuity, support stability, and controllability of the transfer path.

[0017] This invention solves the technical problem of support system instability caused by uncontrollable load transfer paths during the construction of curved beam formwork by establishing a multi-dimensional mathematical model, including a load jump identification matrix, a support system connection stability matrix, and a load transfer path fluctuation evaluation vector. Combined with a graph theory shortest path algorithm and a prestressed steel tensioning stability control system, it addresses this issue. By real-time monitoring of load distribution and constructing a three-dimensional load distribution vector, this invention can promptly identify the location and magnitude of load jumps. Simultaneously, by utilizing the support system connection stability matrix to calculate the bearing capacity and stability coefficient of each connection point, it achieves accurate evaluation and dynamic adjustment of the support system, overcoming the shortcomings of static support design and inaccurate load transfer prediction in traditional technologies. The dynamic equilibrium equations for load transfer established in this invention can calculate the optimal load distribution ratio and support stiffness adjustment parameters in real time. By dynamically adjusting the support system parameters and tensioning program, it ensures that the load transfer path remains controllable, solving the core technical problem of traditional technologies' inability to adapt to dynamic changes in load transfer paths under complex geometries. Attached Figure Description

[0018] Figure 1 This is a flowchart of the method of the present invention.

[0019] Figure 2 This is a schematic diagram of the overall structure of the irregular beam curved template in Example 2.

[0020] Figure 3 This is a schematic diagram of a partial structure for controlling the contact stability of the support system in Example 2.

[0021] Figure 4 This is a distribution diagram of the load monitoring point layout and jump identification results in Example 2.

[0022] Figure 5 This is a stress state monitoring curve during the prestressed steel bar tensioning process in Example 2.

[0023] Figure 6 This is a comparison chart of the efficiency before and after load transfer path optimization in Example 2. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0025] like Figure 1The diagram shown is a flowchart of a construction method for load decomposition and transfer of curved formwork for irregular beams provided by the present invention. This method includes the following steps:

[0026] S01. Establish a load jump identification matrix in the curved section of the irregular beam, set load monitoring points for each node of the curved template, collect load data in real time and construct a three-dimensional load distribution vector, and analyze the load jump identification matrix to determine the location and jump amplitude of load abrupt changes.

[0027] S02. Construct the support system connection stability matrix based on the three-dimensional load distribution vector, input the stiffness coefficient and shear capacity parameters of each connection node of the support structure into the support system connection stability matrix, and calculate the load bearing capacity and stability coefficient of each connection point.

[0028] S03. Establish a load transfer path fluctuation evaluation vector. Use the graph theory shortest path algorithm to abstract the support system into a graph structure, calculate the fluctuation amplitude and transfer efficiency of each transfer path, and form a load transfer path fluctuation evaluation vector.

[0029] S04. Calculate the load control threshold equation set based on the jump amplitude value and the fluctuation amplitude value to obtain the amplitude threshold and coefficient threshold. When the jump amplitude value exceeds the amplitude threshold or the fluctuation coefficient of the transmission path is greater than the coefficient threshold, start the load redistribution program to adjust the support parameters and tension force distribution.

[0030] S05. Construct a prestressed steel tensioning stability control matrix. Using a symmetrical batch tensioning method, input the tension force, deformation, and stress state of each bundle of steel bars into the prestressed steel tensioning stability control matrix to establish an automatic tension force adjustment system.

[0031] S06. Based on the support system's contact stability matrix and the prestressed steel tensioning stability control matrix, calculate the load transfer dynamic equilibrium equation set, monitor the stress state of key force transmission nodes in real time, and dynamically adjust the support system parameters and tensioning program.

[0032] S07. Establish a load transfer effect evaluation vector to assess the continuity and controllability of the overall load transfer path. When the load transfer effect evaluation vector shows that the transfer efficiency is less than 85%, re-execute the load allocation and path optimization program.

[0033] The load control threshold equation set includes amplitude threshold calculation equation and coefficient threshold calculation equation;

[0034] The amplitude threshold calculation equation is used to determine the critical control value of load jump. The inputs include jump amplitude value, structural safety factor, material strength parameter, bending radius parameter, and environmental load factor. The output is the amplitude threshold.

[0035] The coefficient threshold calculation equation is used to determine the critical control value of the transmission path fluctuation. The inputs include the fluctuation amplitude value, support stiffness coefficient, transmission distance parameter, stability coefficient, and load distribution coefficient. The output is the coefficient threshold.

[0036] The load transfer dynamic equilibrium equation set includes load distribution equilibrium equation and support stiffness adjustment equation.

[0037] The load distribution balance equation is used to calculate the optimal load distribution ratio for each support point. The inputs include the stress state of key force transmission nodes, stiffness coefficient, material elastic modulus, cross-sectional geometric parameters, and load bearing capacity. The output is the load distribution coefficient for each support point.

[0038] The support stiffness adjustment equation is used to determine the stiffness adjustment parameters of the support system. The inputs include the stress state of key force transmission nodes, target stability coefficient, support material performance parameters, shear capacity parameters, and load distribution coefficient. The output is the support stiffness adjustment coefficient.

[0039] The load jump identification matrix is ​​a mathematical model used to identify and quantify sudden changes in load during transmission. By comparing the load difference and time series change rate between adjacent monitoring points, the location, jump amplitude, and occurrence time of the load jump can be determined.

[0040] Among them, the three-dimensional load distribution vector is a mathematical vector that describes the distribution of loads at each monitoring point in the curved section of the irregular beam in three-dimensional space, including information on load magnitude, direction and location.

[0041] Among them, the support system connection stability matrix is ​​a mathematical matrix that describes the interaction relationship and stability performance between the connection nodes of the support structure, including the stiffness coefficient, load bearing capacity and deformation compatibility parameters of each connection point.

[0042] Among them, the load transfer path fluctuation evaluation vector is a mathematical vector that characterizes the fluctuation characteristics and transfer efficiency of the load on different transfer paths, reflecting the energy loss and path stability during the load transfer process, where the fluctuation amplitude value ∈ [0, 1).

[0043] Among them, the prestressed steel tensioning stability control matrix is ​​a mathematical model used to control the coordination of various parameters during the prestressed steel tensioning process, ensuring uniform tension force distribution and stability of the tensioning process.

[0044] Among them, the stress state of the key force transmission node refers to the stress distribution at the core force transmission location that has a decisive influence on the overall stability of the load transmission system, including the stress magnitude, direction and trend of change.

[0045] Among them, the load transfer effect evaluation vector is a mathematical vector that comprehensively evaluates the overall performance of the load transfer system, including key performance indicators such as transfer efficiency, stability, and continuity.

[0046] The application of graph theory's shortest path algorithm in this construction method demonstrates its effectiveness in accurately identifying the optimal load transfer path, significantly improving load transfer efficiency, and reducing structural deformation. The algorithm abstracts the complex support system into a graph structure composed of nodes and edges. Each node represents a key location for load transfer, and edges represent connections and transfer capabilities. By calculating the weight values ​​of each path, the algorithm automatically finds the path combination with the minimum load transfer resistance and the highest efficiency. In irregularly shaped structures such as curved beam formwork, traditional load transfer analysis methods struggle to accurately predict load flow, while graph theory algorithms can handle arbitrarily complex topologies, making them particularly suitable for the variability and uncertainty of load transfer paths in curved sections. The algorithm uses stiffness coefficients, shear capacity parameters, transfer distance parameters, and material elastic modulus as input parameters. It calculates the shortest distance matrix between nodes using Dijkstra's algorithm, obtaining the optimal load transfer path sequence and transfer efficiency coefficient, providing a scientific basis for the dynamic adjustment of the support system.

[0047] The key performance indicators refer to the core evaluation parameters that have a decisive impact on the overall effect of the load transfer system, including load transfer continuity, support stability and transfer path controllability. These key performance indicators are directly related to construction safety and structural quality.

[0048] The specific implementation methods of the above steps are described in detail below.

[0049] The specific implementation of step S01 is to establish a load jump identification matrix based on multivariate statistical analysis and time series analysis theory. First, load monitoring points are arranged at equal intervals along the longitudinal and transverse directions of the curved section of the irregular beam. The longitudinal spacing is controlled at 1.5–2.0 m, and the transverse spacing is controlled at 1.0–1.5 m, forming a grid-like monitoring network. High-precision strain sensors and pressure sensors are used to collect load data from each monitoring point in real time, with the data acquisition frequency set to 10 Hz. The collected load data is used to establish a three-dimensional load distribution vector according to a three-dimensional coordinate system, including load value, direction of action, and spatial location information. The moving average method is used to preprocess the original load data to eliminate random noise interference. A load jump identification matrix is ​​constructed based on the Z-score standardization method. By calculating the standard deviation and rate of change of the load difference between adjacent monitoring points, the threshold parameters for jump identification are determined. When the rate of change of the load at a monitoring point exceeds 1.2 times the standard deviation or the load difference between adjacent points exceeds 2.5 times the average value, it is determined to be a load jump. This method can accurately identify the location and intensity of abnormal fluctuations during load transfer, providing a data basis for subsequent load control.

[0050] The specific implementation of step S02 involves constructing a support system's contact stability matrix using structural mechanics equilibrium theory and the basic principles of elasticity. The support structure is discretized using finite element analysis, with each connection node considered as an elastic constraint point. The elastic modulus, compressive strength, and shear strength parameters of each support material are determined through material mechanics experiments. A node stiffness matrix is ​​established based on the principle of stiffness superposition, with the linear stiffness coefficient, angular stiffness coefficient, and shear stiffness coefficient of each connection node as matrix elements. The ultimate bearing capacity of each contact point is calculated using Euler's formula and critical load theory from structural stability theory. Node displacement constraint equations are established through deformation compatibility conditions to ensure the deformation compatibility of each support point under load. The stability coefficient is set to a range of 1.5–3.0; when the calculated stability coefficient is lower than 1.8, the support stiffness needs to be increased or the support arrangement adjusted. This matrix can comprehensively evaluate the bearing capacity and stability state of each node in the support system, providing a mechanical basis for optimizing the load transfer path.

[0051] The specific implementation of step S03 involves establishing a load transfer path fluctuation evaluation vector using the shortest path algorithm in graph theory and network flow theory. The complex support system is abstracted as a directed graph structure, with each support node as a vertex and support components as connecting edges. The shortest transfer path between nodes is calculated based on the Dijkstra algorithm, using transfer impedance as the path weight. Transfer impedance comprehensively considers the inverse of support stiffness, component length, and material damping coefficient. The bottleneck location and transfer capacity of the load transfer network are analyzed using the maximum flow minimum cut theorem of network flow. The impact of uncertainties on the transfer path is evaluated using Monte Carlo simulation, and the path fluctuation amplitude is calculated. The fluctuation amplitude is represented by the coefficient of variation, ranging from 0 to 1. When the fluctuation amplitude exceeds 0.15, the transfer path is considered unstable. The transfer efficiency is defined as the ratio of the actual transferred load to the theoretical maximum transfer capacity, and should normally be maintained above 85%. This evaluation vector can quantitatively describe the load transfer characteristics on different transfer paths, providing a quantitative indicator for path optimization and adjustment.

[0052] The specific implementation of step S04 is to establish a load control threshold equation set based on control theory and reliability theory. The amplitude threshold calculation equation uses the safety factor method, comprehensively calculating the jump amplitude value with the material safety factor, structural importance factor, and environmental load factor. The material safety factor is set to 1.4–2.0, the structural importance factor is divided into three levels from 1.0 to 1.1 according to the importance of the beam, and the environmental load factor, considering the influence of wind load and temperature load, is set to 1.05–1.15. The amplitude threshold reference value is set to 12%–18% of the design load. The coefficient threshold calculation equation is based on structural dynamics theory, combining the fluctuation amplitude value with the dynamic characteristic parameters of the support system. Considering parameters such as support stiffness coefficient, damping ratio, natural frequency, and load frequency ratio, the coefficient threshold reference value is set to 0.20–0.35. When the monitored parameters exceed the set threshold, a load redistribution program based on the proportional-integral-derivative control algorithm is initiated. The control program uses a feedback adjustment mechanism, achieving load redistribution by adjusting the support preload, changing the support angle, or adding auxiliary supports. This set of threshold equations can promptly identify abnormal load transfer states, ensuring the safety and controllability of the construction process.

[0053] The specific implementation of step S05 involves constructing a prestressed steel bar tensioning stability control matrix using prestressing theory and tensioning control technology. A symmetrical batch tensioning method is adopted, dividing the steel bar bundles into several tensioning batches according to their spatial location and stress characteristics, with the number of steel bars in each batch controlled to be between 25% and 35% of the total. A stress-strain relationship equation for the steel bars is established based on Hooke's law, considering the elastic modulus, cross-sectional area, and initial stress state of the steel bars. Tensioning force control employs a dual-control method, combining stress control and elongation control. The stress control accuracy is required to be within ±5% of the design value, and the elongation control accuracy is required to be within ±6% of the theoretical elongation value. A Kalman filter algorithm is used to filter stress fluctuations during the tensioning process in real time, eliminating measurement noise and environmental interference. An automatic tensioning force adjustment system is established, employing a closed-loop control principle to automatically adjust the loading speed and loading amount of the tensioning equipment based on the real-time monitored stress state. The tensioning speed is controlled between 2 and 5 MPa / min to avoid stress concentration due to excessive loading. This control matrix ensures uniform tension force distribution in each steel bar bundle, maintaining the stability and controllability of the tensioning process.

[0054] The specific implementation of step S06 is to establish a dynamic equilibrium equation set for load transfer based on dynamic equilibrium theory and adaptive control technology. The load distribution equilibrium equation adopts the principle of minimum potential energy, using the minimum total potential energy of the system as the objective function to establish an optimization model for load distribution at each support point. Constraints include the bearing capacity limits of each support point, deformation compatibility conditions, and stability requirements. The optimal load distribution coefficient is solved using the Lagrange multiplier method to ensure a reasonable distribution of load among the support points. The support stiffness adjustment equation is based on structural dynamics theory, achieving stiffness adjustment by adjusting the support preload, support angle, and number of supports. The stress state of key force transmission nodes is monitored in real time, including stress magnitude, stress direction, and stress change rate. When the stress at a key node exceeds 80% of the design value, an early warning mechanism is activated; when it exceeds 90%, an emergency adjustment procedure is initiated. Dynamic adjustment uses a fuzzy control algorithm to determine the adjustment amount based on stress state deviation and change trend. This equilibrium equation set can achieve dynamic optimization of the load transfer system, ensuring the safety and stability of the structure during construction.

[0055] The specific implementation of step S07 involves establishing a load transfer effect evaluation vector using multi-objective optimization theory and fuzzy comprehensive evaluation method. The evaluation vector includes four main indicators: transfer efficiency, transfer continuity, transfer stability, and path controllability. Transfer efficiency is calculated as the ratio of the actual transferred load to the theoretical transfer capacity, with a normal range of 85%–95%. Transfer continuity uses path connectivity analysis, based on graph theory connectivity theory, to evaluate the connectivity of the load transfer network. Transfer stability uses variance analysis to evaluate the fluctuation degree during load transfer, with a stability coefficient greater than 0.85. Path controllability uses controllability matrix analysis in control theory to evaluate the system's response to control inputs. A weight allocation model using the analytic hierarchy process (AHP) is established to determine the weight coefficients of each evaluation indicator. A fuzzy comprehensive evaluation method is used to comprehensively score the overall transfer effect, with a score range of 0–1. When the comprehensive score is below 0.80 or the transfer efficiency is below 85%, the system automatically initiates a load redistribution and path optimization program. The optimization program uses a genetic algorithm to search for the optimal parameter combination, continuously improving the transfer scheme through iterative calculations. This evaluation vector can comprehensively assess the overall performance of the load transfer system and provide a quantitative evaluation basis for construction quality control.

[0056] It should be noted that the first key technical idea of ​​this invention is to establish a real-time monitoring system based on a load jump identification matrix. Traditional load transfer analysis methods mainly rely on static calculations and empirical judgments, making it difficult to detect abnormal changes in the load transfer process in a timely manner. This invention constructs a three-dimensional load distribution vector and a jump identification matrix, and uses time series analysis and statistical methods to achieve real-time identification and quantitative analysis of load changes. This dynamic monitoring mechanism can identify and warn of load anomalies in their early stages, avoiding the risk of structural instability caused by the accumulation of load anomalies in traditional methods.

[0057] The second key technical approach is to optimize load transfer paths using graph theory's shortest path algorithm. Traditional support system design relies primarily on experience and simplified calculations, failing to accurately analyze load transfer patterns under complex geometries. This invention abstracts the support system as a graph structure, employing Dijkstra's algorithm and network flow theory to find the optimal transfer path, capable of handling arbitrarily complex topologies. This algorithmic path optimization method significantly improves load transfer efficiency and reduces structural deformation, making it particularly suitable for load transfer analysis of geometrically irregular structures such as curved sections of irregular beams.

[0058] The third key technological approach is to construct a multi-matrix collaborative dynamic equilibrium control system. Traditional construction control methods often employ single-parameter control or simple threshold judgments, lacking a systematic coordination and control mechanism. This invention establishes multiple control matrices, including a load jump identification matrix, a support system contact stability matrix, and a prestressed steel tensioning stability control matrix, forming a multi-parameter collaborative control system. These matrices interact and coordinate parameters through a set of dynamic equilibrium equations, enabling precise control and real-time adjustment of complex construction processes.

[0059] The synergistic effect of these key technological approaches is reflected in the formation of a complete closed-loop control system. The load jump identification matrix provides real-time monitoring data, graph theory algorithms optimize the transmission path, and the multi-matrix control system achieves dynamic adjustment. These three elements work together to form a complete technological chain from monitoring to analysis to control. Compared with traditional open-loop control methods, this collaborative control mechanism can adaptively cope with various uncertainties during construction, significantly improving construction safety and structural quality. The entire system possesses self-learning and optimization capabilities, continuously adjusting control strategies according to actual construction conditions, achieving a technological leap from passive to active control.

[0060] It should be noted that this invention also solves the following technical problems: In the construction of curved beam formwork, the lack of coordinated control during the prestressing steel tensioning process leads to uneven stress distribution. Traditional prestressing steel tensioning typically employs a batch-by-batch, step-by-step tensioning method. However, due to the geometrical uniqueness of the curved section of the curved beam, the tensioning path length and radius of curvature of each bundle of steel bars differ, resulting in mutual influence and stress redistribution among the bundles during tensioning. This invention constructs a prestressing steel tensioning stability control matrix, using the tension force, deformation, and stress state of each bundle of steel bars as input parameters to establish an automatic tension force adjustment system. A symmetrical batch tensioning method ensures the coordination of the tensioning process. This matrix can calculate the mutual influence coefficient between each bundle of steel bars in real time, dynamically adjusting the tensioning program and tension force distribution, avoiding the problems of local stress concentration and uneven overall structural deformation caused by uncoordinated tensioning in traditional methods. The lack of quantitative standards for load transfer efficiency assessment in the construction of curved beam formwork also presents a technical problem, making construction quality control difficult. In the construction of structures with complex geometries, traditional techniques mainly rely on experience and qualitative analysis to evaluate load transfer effectiveness, lacking accurate quantitative evaluation methods. This leads to vague construction quality control standards and makes it difficult to promptly identify and correct problems in load transfer. This invention establishes a load transfer effectiveness evaluation vector, including key performance indicators such as transfer efficiency, stability, and continuity. A clear quantitative control standard is established by setting an 85% transfer efficiency threshold. When the evaluation vector shows that the transfer efficiency is below the threshold, the system automatically initiates load redistribution and path optimization procedures, ensuring that the overall load transfer system always maintains a highly efficient and stable working state. This solves the problem of traditional techniques relying on subjective judgment for load transfer quality control.

[0061] Specifically, the principle of this invention is as follows: The fundamental principle behind this invention's ability to solve the problem of uncontrollable load transfer paths in the construction of curved beam formwork lies in the construction of a complete closed-loop control system for load transfer state perception, path analysis, and dynamic adjustment. Due to the complex geometry of curved beam segments, loads exhibit phenomena such as path bifurcation, stress concentration, and changes in transfer efficiency during the transfer process. Traditional methods cannot accurately grasp these dynamic changes. This invention establishes a precise perception mechanism for load abrupt changes through a load jump identification matrix. This matrix, by comparing the load difference and time series change rate between adjacent monitoring points, can promptly detect abnormal changes in the load transfer path, providing an accurate data foundation for subsequent path adjustment. The support system connection stability matrix quantifies the interaction relationships between each connection node from a structural mechanics perspective. By inputting stiffness coefficients and shear capacity parameters, it can accurately assess the actual bearing capacity and stability state of each support point. The application of the graph theory shortest path algorithm is the technological innovation of this invention. This algorithm abstracts the complex support system into a mathematical graph structure, automatically finding the path combination with the minimum load transfer resistance and highest efficiency by calculating weight values, solving the problem that traditional methods struggle to handle complex topological structures. The synergistic effect of the prestressed steel tensioning stability control matrix and the load transfer dynamic equilibrium equations ensures the dynamic balance of the entire system. When fluctuations in the load transfer path or a decrease in transfer efficiency are detected, the system can automatically adjust the support parameters and tensioning program to redirect the load transfer path back to the optimal state. This multi-level, multi-dimensional control mechanism ensures that the entire load transfer process is always monitorable and adjustable, thereby achieving precise control of the load transfer path of the curved beam formwork.

[0062] The following provides a specific embodiment 1 of the present invention, and the specific implementation of each step in this embodiment 1 is described in detail below.

[0063] The specific implementation of step S01 is to establish a load jump identification matrix, which is specifically represented as follows:

[0064]

[0065] In the formula, H jump For load jump identification matrix; h ij Let be the load jump coefficient for the monitoring point in the i-th row and j-th column; m be the number of rows of monitoring points; and n be the number of columns of monitoring points. The three-dimensional load distribution vector is represented as:

[0066]

[0067] In the formula, L is the three-dimensional load distribution vector at time t; x (t), L y (t), L z(t) represent the load components in the x, y, and z directions, respectively. The formula for calculating the load jump amplitude is:

[0068]

[0069] In the formula, ΔL jump This represents the load jump amplitude, in kN; L k σ represents the load component in the k-th direction, in kN; t is the time variable, in seconds; L σ represents the standard deviation of the load, in kN; ε1 is the measurement error term, dimensionless, ranging from 0.02 to 0.05. L The load data was obtained experimentally, including step 1: continuously monitoring load data for 30 working cycles; and step 2: calculating the standard deviation of the load data as σ. L The value of .

[0070] The specific implementation of step S02 is to construct a support system abutment stability matrix, which is represented as follows:

[0071]

[0072] In the formula, S stability To support the stability matrix of the system; k ij Let be the stiffness coefficient between the i-th and j-th support points; p is the total number of support points. The formula for calculating the load-bearing capacity of each contact point is:

[0073]

[0074] In the formula, P bear，i E represents the load-bearing capacity of the i-th contact point, in kN. i Let A be the elastic modulus of the i-th supporting material, in GPa; i The cross-sectional area of ​​the i-th support is expressed in m². 2 L i F represents the length of the i-th support, in meters. safety The safety factor is dimensionless and ranges from 1.4 to 2.0; β i σ is a dimensionless correction factor for material properties, ranging from 0.85 to 1.15; yield，i Let be the yield strength of the i-th supporting material, in MPa. The stability coefficient is calculated using the following formula:

[0075]

[0076] In the formula, K stability，i P is the stability coefficient of the i-th contact point, dimensionless; actual，i λ represents the actual load borne by the i-th contact point, in kN; modify∈3 represents the stability correction factor, with a value ranging from 0.9 to 1.1; ∈3 represents the calculation error, with a value ranging from 0.01 to 0.02.

[0077] The specific implementation of step S03 is to establish a load transfer path fluctuation evaluation vector, which is represented as:

[0078]

[0079] In the formula, p is the load transfer path fluctuation assessment vector; i Let be the fluctuation coefficient of the i-th transmission path; q is the total number of transmission paths. The shortest path weight matrix of Dijkstra's algorithm is:

[0080]

[0081] In the formula, W is the weight matrix; w ij Let be the transmission impedance from node i to node j; r be the total number of supporting nodes. The formula for calculating the transmission impedance is:

[0082]

[0083] In the formula, α, γ, and δ are weighting coefficients, dimensionless, and their default values ​​are 0.4, 0.3, and 0.3, respectively; d ij ξ is the distance from node i to node j, in meters; ij The material damping coefficient is dimensionless and ranges from 0.02 to 0.08. The formula for calculating the fluctuation amplitude is:

[0084]

[0085] In the formula, The average transmission impedance of the i-th path, in units of W. ij Same; v i η is the fluctuation amplification factor, dimensionless, ranging from 1.0 to 1.5; i This is a random disturbance term, with a value range of 0.01 to 0.03.

[0086] The specific implementation of step S04 is to establish a set of load control threshold equations, and the amplitude threshold calculation equation is as follows:

[0087] T amplitude =ΔL jump ·K structure ·K material ·K radius ·K environment +ε2;

[0088] In the formula, T amplitude This is the amplitude threshold, in kN; K structureK is the structural safety factor, dimensionless, ranging from 1.2 to 1.8. material K is a dimensionless material strength parameter, ranging from 0.8 to 1.2. radius This is the radius parameter for the bend, in meters (m). -1 ;K environment ε0 is the environmental load factor, dimensionless, ranging from 1.05 to 1.15; ε2 is the calculation error term, dimensionless, ranging from 0.01 to 0.03. Where K... radius The calculation formula is:

[0089]

[0090] In the formula, R arc ψ is the radius of curvature, in meters (m). curvature The curvature influence coefficient is dimensionless and ranges from 0.5 to 1.5. The equation for calculating the coefficient threshold is:

[0091]

[0092] In the formula, T coefficient K represents the coefficient threshold, which is dimensionless. stiffness The stiffness coefficient is expressed in kN / m; K distance To transmit distance parameters, the unit is meters. -1 ;K stability K is the stability coefficient, dimensionless, ranging from 1.5 to 3.0. distribution The load distribution factor is dimensionless and ranges from 0.7 to 1.3. The formula for calculating the transfer distance parameter is as follows:

[0093]

[0094] In the formula, L transfer，avg χ represents the average transmission distance in meters. distance This is the distance correction factor, dimensionless, with a value ranging from 0.8 to 1.2.

[0095] The specific implementation of step S05 is to construct a prestressed steel tension stability control matrix, which is represented as follows:

[0096]

[0097] In the formula, T control This is the control matrix for the tensioning stability of prestressed steel bars; t ij Let be the tension compatibility coefficient between the i-th and j-th reinforcement bundles; s is the total number of reinforcement bundles. The formula for calculating the tension force is:

[0098] F tension，i =E steel ·A steel，i ·ε desigh，i·η loss +ζ i ;

[0099] In the formula, F tension，i E represents the tension force of the i-th reinforcing bar, expressed in kN. steel This refers to the elastic modulus of steel reinforcement, expressed in GPa, with a typical value of 200; A steel，i The cross-sectional area of ​​the i-th reinforcement bundle is in mm. 2 ;ε design，i Let η be the design strain of the i-th reinforcement bundle, dimensionless, and ranging from 0.006 to 0.008; loss ζ is the prestress loss coefficient, dimensionless, with a value ranging from 0.85 to 0.95; i This is the tensioning error term, expressed in kN, with a value range of ±3% of the design tension force. The control equation for the automatic tension adjustment system is:

[0100]

[0101] In the formula, F adjust，i (t+1) represents the tension force of the i-th steel reinforcement after adjustment at the next moment, in kN; e i (t) represents the stress deviation of the i-th reinforcement bundle at time t, in MPa; K p K i K d These are the proportional, integral, and derivative control parameters, with values ​​ranging from 0.5 to 1.5, 0.1 to 0.5, and 0.05 to 0.2, respectively.

[0102] The specific implementation of step S06 is to calculate the dynamic equilibrium equations for load transfer. The load distribution equilibrium equations are as follows:

[0103]

[0104] In the formula, λ i P is the load distribution factor for the i-th support point; total For total load; μ balance The equilibrium coefficient is given. The equation for adjusting the support stiffness is:

[0105]

[0106] In the formula, k adjust，i Adjust the stiffness of the i-th support point; k initial，i Let ρ be the initial stiffness of the i-th support point; i σ is the stiffness adjustment coefficient. actual，i σ represents the actual stress at the i-th support point; target，i φ is the target stress at the i-th support point; i This is a material property adjustment factor.

[0107] The specific implementation of step S07 is to establish a load transfer effect evaluation vector, which is represented as:

[0108]

[0109] In the formula, E is the load transfer effect evaluation vector. efficiency For transmission efficiency; E continuity To convey continuity; E stability For the purpose of transmitting stability; E controllability For path controllability. The formula for calculating transmission efficiency is:

[0110]

[0111] In the formula, P actual For the actual transmitted load; P theoretical For theoretical transmission capability; θ deviation ω is the transmission direction deviation angle; ω is the efficiency correction term.

[0112] It should be explained that the load jump identification matrix achieves precise location and quantitative analysis of load anomalies by establishing a multi-dimensional monitoring network. Compared with traditional single-point monitoring methods, it can comprehensively grasp the spatial variation law of load distribution, significantly improving the accuracy and timeliness of anomaly identification. The three-dimensional load distribution vector describes the spatial distribution characteristics of the load in vector form, and can simultaneously consider the magnitude, direction, and location of the load, providing a complete data foundation for subsequent load transfer analysis. Compared with traditional two-dimensional analysis methods, it can more accurately reflect the complex load state in actual engineering. The support system connection stability matrix describes the interaction relationship between support nodes in matrix form, considering stiffness coupling effect and deformation coordination conditions. It can accurately assess the overall stability of the support system, and can better reflect the collaborative working mechanism of the support system compared with traditional independent analysis methods of individual support points. The load transfer path fluctuation evaluation vector, combined with graph theory algorithms, realizes the quantitative analysis of complex transfer networks, can identify transfer bottlenecks and optimize transfer paths, and has higher scientificity and accuracy than traditional experience-based judgment methods. The load control threshold equations establish scientific control standards by comprehensively considering various factors such as structural safety, material properties, geometric characteristics, and environmental influences. This enables precise control of the construction process and offers better adaptability and reliability compared to traditional single-parameter control methods. The prestressed steel tensioning stability control matrix ensures uniform stress distribution across all steel bundles by establishing a tensioning coordination mechanism, avoiding stress concentration caused by uneven tensioning. This method better guarantees tensioning quality and construction safety compared to traditional bundle-by-bundle tensioning. The load transfer dynamic equilibrium equations achieve adaptive control of load distribution and stiffness adjustment through a dynamic optimization model. This allows for real-time adjustment of control strategies based on actual construction conditions, offering better dynamic response and control accuracy compared to traditional static control methods. The load transfer effect evaluation vector establishes a multi-dimensional evaluation system to comprehensively assess the transfer effect. It fully reflects the performance status of the transfer system, providing a scientific evaluation basis for construction quality control. This method offers greater comprehensiveness and objectivity compared to traditional single-index evaluation methods.

[0113] To better understand and implement this invention, the following is a specific application scenario of the invention, Example 2: First, the technical team established a load jump identification matrix on the curved section of an irregular beam. Monitoring points were arranged longitudinally along the beam at 1.8m intervals, totaling 48 longitudinal monitoring points. Laterally, they were arranged at 1.2m intervals, with 3 lateral monitoring points at each section, forming a grid-like monitoring network of 144 monitoring points. Strain and pressure sensors with an accuracy of 0.1% were used, and the data acquisition frequency was set to 10Hz. After monitoring began, the system collected load data at each point in real time and constructed a three-dimensional load distribution vector. After preprocessing using the moving average method and Z-score standardization, the system identified 17 potential load jump locations, of which 8 locations had jump amplitude values ​​exceeding 1.2 times the standard deviation, and the maximum jump amplitude value reached 2.3 times the standard deviation. Figure 4 As shown.

[0114] Next, the stability matrix of the support system is constructed based on the three-dimensional load distribution vector. For example... Figure 2 As shown, the support system uses steel pipe and coupler scaffolding, with a total of 156 support nodes, each with a load-bearing capacity of 25 kN. Material mechanics tests determined the elastic modulus of the supporting steel pipes to be 206,000 MPa, the compressive strength to be 235 MPa, and the shear strength to be 136 MPa. Figure 3 As shown in Table 1, after establishing the node stiffness matrix, the bearing capacity distribution of each contact point is calculated.

[0115] Table 1 Distribution of Carrying Capacity of Major Supporting Nodes

[0116] Node number Bearing capacity (kN) Stability coefficient Stiffness coefficient (kN / mm) A1-A12 23.5 2.1 145 B1-B18 24.8 2.3 158 C1-C15 22.3 1.9 132 D1-D21 25.0 2.4 162 E1-E9 21.7 1.8 128

[0117] The stability coefficient calculation results show that the stability coefficient of 75 nodes is above 2.0, which meets the design requirements, but the stability coefficient of 12 nodes is below 1.8, and the support stiffness needs to be increased.

[0118] Subsequently, a load transfer path fluctuation evaluation vector was established. The support system was abstracted as a directed graph structure containing 156 nodes and 312 edges, and Dijkstra's algorithm was used to calculate the shortest transfer path between nodes. Using the reciprocal of the support stiffness, member length, and material damping coefficient as path weights, 42 main transfer paths were calculated. The path fluctuation characteristics were evaluated through 1000 Monte Carlo simulations. The results showed that the fluctuation amplitude of 38 paths was within the range of 0.08–0.14, while the fluctuation amplitude of 4 paths exceeded 0.15, requiring path optimization. The calculated transfer efficiency was 87.3%, meeting the requirement of over 85%.

[0119] The load control threshold equations were calculated based on the jump amplitude and fluctuation amplitude values. The material safety factor was set to 1.6, the structural importance factor to 1.1, and the environmental load factor (considering wind load influence) to 1.12. The calculated amplitude threshold was 15.2% of the design load. The support stiffness coefficient was 158 kN / mm, the damping ratio was 0.045, the natural frequency was 3.2 Hz, and the load frequency ratio was 0.28. The calculated coefficient threshold was 0.28. During monitoring, jump amplitude values ​​at six locations exceeded the amplitude threshold, and fluctuation coefficients along three transmission paths exceeded the coefficient threshold. The system immediately initiated a load redistribution procedure, adjusting the preload of 18 support points and the angle of 12 support points.

[0120] In the stage of constructing the prestressed steel tensioning stability control matrix, a total of 32 prestressed steel bundles were set in the beam, with a design tension force of 580 kN per bundle. A symmetrical batch tensioning method was adopted, divided into 4 batches, with 8 bundles of steel in each batch. The elastic modulus of the steel was 195,000 MPa, and the cross-sectional area of ​​a single bundle was 140 mm². 2 The tensioning process employs a dual-control method, with stress control accuracy of ±4.8% and elongation control accuracy of ±5.7%. Figure 5 As shown, stress state monitoring during the tensioning process revealed that the tension force distribution in each bundle of steel bars was uniform, with a maximum deviation of 3.2%. The Kalman filter algorithm effectively eliminated measurement noise, and the automatic tension adjustment system automatically adjusted the loading speed within the range of 2.5–4.8 MPa / min based on the real-time stress state.

[0121] Based on the support system's contact stability matrix and the prestressed steel tensioning stability control matrix, the dynamic equilibrium equations for load transfer were calculated. The optimal load distribution coefficient was solved using the Lagrange multiplier method, with the goal of minimizing the system's total potential energy. The calculation results showed that the load distribution coefficient at the support points in region A was 0.28, in region B it was 0.32, in region C it was 0.24, and in region D it was 0.16. Key force transmission nodes monitored in real time included the center point of the curved section, the connection points at both ends, and the section with the maximum bending moment, totaling 18 key nodes. During monitoring, stress at three key nodes exceeded 80% of the design value. The system activated its early warning mechanism, and by adjusting the support preload and adding two auxiliary support points, the stress level was controlled below 75%.

[0122] A load transfer effectiveness evaluation vector was established to comprehensively assess the overall load transfer path. The transfer efficiency was calculated as 87.3% by the ratio of the actual transferred load of 589 kN to the theoretical transfer capacity of 675 kN. Transfer continuity was assessed using graph theory connectivity analysis, with a connectivity coefficient of 0.92. Transfer stability was assessed using analysis of variance, with a fluctuation coefficient of 0.13 and a stability coefficient of 0.87. Path controllability was assessed using a controllability matrix analysis, with a controllability index of 0.89. The analytic hierarchy process (AHP) was used to determine the weights of each indicator: transfer efficiency 0.35, transfer continuity 0.25, transfer stability 0.25, and path controllability 0.15. The fuzzy comprehensive evaluation yielded an overall score of 0.86, higher than the required standard of 0.80.

[0123] During construction, the system continuously monitored the load transfer status. On the 15th day, monitoring revealed that the transfer efficiency had dropped to 83.2%, below the control standard of 85%, and the system automatically initiated a load redistribution program. Using a genetic algorithm for optimization, after 127 iterations, a new optimal parameter combination was found: the preload of 24 support points was adjusted, 15 transfer paths were optimized, and 6 auxiliary support points were added. Figure 6 As shown, the optimized transmission efficiency increased to 88.7%, and all indicators met the design requirements.

[0124] The entire construction process lasted 28 days, involving three load redistributions, two optimizations of the load transfer path, and one adjustment of the support system parameters. The final beam achieved good quality, with geometric dimensional accuracy controlled within ±8mm, surface flatness within ±3mm, and prestressed tension uniformity reaching 96.5%.

[0125] Compared to traditional construction methods, this invention achieves real-time monitoring and dynamic adjustment of the load transfer process by establishing a mathematical model and control matrix. Traditional methods mainly rely on experience and static calculations, making it difficult to accurately predict the load flow direction and transfer characteristics under complex geometries, and easily leading to local stress concentration and support instability. This invention employs a graph theory shortest path algorithm to abstract the complex support system into a mathematical model, which can automatically identify the optimal transfer path, significantly improving the efficiency and stability of load transfer. The application of the prestressed steel tensioning stability control matrix ensures the uniformity and controllability of the tensioning process, avoiding the stress unevenness problem caused by improper tensioning sequence in traditional methods. The establishment of a dynamic equilibrium equation set enables the system to automatically adjust control parameters based on real-time monitoring data, realizing a shift from passive to active control, and significantly improving construction safety and structural quality.

[0126] It should be noted that the variables involved in this invention are explained in detail in Tables 2 and 3.

[0127] Table 2. Variable Explanation Table (Part 1)

[0128]

[0129]

[0130] Table 3. Variable Explanation Table (Part Two)

[0131]

[0132]

[0133] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A construction method for load decomposition and transfer of curved formwork for irregularly shaped beams, characterized in that, include: A load jump identification matrix is ​​established for the curved section of the irregular beam. Load monitoring points are set at each node of the curved template to collect load data in real time and construct a three-dimensional load distribution vector. The load jump identification matrix is ​​used to analyze the location and magnitude of load jumps. Based on the three-dimensional load distribution vector, a support system connection stability matrix is ​​constructed. The stiffness coefficient and shear capacity parameters of each connection node of the support structure are input into the support system connection stability matrix to calculate the load bearing capacity and stability coefficient of each connection point. A load transfer path fluctuation evaluation vector is established. The support system is abstracted into a graph structure using a graph theory shortest path algorithm. The fluctuation amplitude and transfer efficiency of each transfer path are calculated to form the load transfer path fluctuation evaluation vector. Based on the jump amplitude and fluctuation amplitude values, a load control threshold equation set is calculated to obtain the amplitude threshold and coefficient threshold. When the jump amplitude exceeds the amplitude threshold or the fluctuation coefficient of the transfer path is greater than the coefficient threshold, a load redistribution procedure is initiated to adjust the support parameters and tension distribution. A prestressed steel tensioning stability control matrix is ​​constructed. Using a symmetrical batch tensioning method, the tension force, deformation, and stress state of each steel bundle are input into the prestressed steel tensioning stability control matrix to establish an automatic tension adjustment system. Based on the support system's contact stability matrix and the prestressed steel tensioning stability control matrix, the dynamic equilibrium equations for load transfer are calculated. The stress state of key force transmission nodes is monitored in real time, and the support system parameters and tensioning program are dynamically adjusted. A load transfer effect evaluation vector is established to assess the continuity and controllability of the overall load transfer path. When the load transfer effect evaluation vector shows that the transfer efficiency is less than 85%, the load allocation and path optimization program is re-executed.

2. The construction method for load decomposition and transfer of irregular beam curved formwork according to claim 1, characterized in that, The load jump identification matrix is ​​a mathematical model used to identify and quantify sudden changes in load during transmission. By comparing the load difference and time series change rate between adjacent monitoring points, the location, jump amplitude, and occurrence time of the load jump can be determined.

3. The construction method for load decomposition and transfer of irregular beam curved formwork according to claim 2, characterized in that, The three-dimensional load distribution vector is a mathematical vector that describes the distribution of loads at each monitoring point on the curved section of an irregular beam in three-dimensional space, including information on load magnitude, direction, and location.

4. The construction method for load decomposition and transfer of irregular beam curved formwork according to claim 3, characterized in that, The support system's connection stability matrix is ​​a mathematical matrix that describes the interaction and stability performance between the connection nodes of the support structure. It includes the stiffness coefficient, load-bearing capacity, and deformation compatibility parameters of each connection point.

5. The construction method for load decomposition and transfer of irregular beam curved formwork according to claim 4, characterized in that, The load transfer path fluctuation evaluation vector is a mathematical vector that characterizes the fluctuation characteristics and transfer efficiency of the load on different transfer paths. It reflects the energy loss and path stability during the load transfer process, and the fluctuation amplitude value is ∈ [0, 1).

6. The construction method for load decomposition and transfer of irregular beam curved formwork according to claim 5, characterized in that, The load control threshold equation set includes amplitude threshold calculation equation and coefficient threshold calculation equation. The amplitude threshold calculation equation is used to determine the critical control value of load jump, and the coefficient threshold calculation equation is used to determine the critical control value of transmission path fluctuation.

7. The construction method for load decomposition and transfer of irregular beam curved formwork according to claim 6, characterized in that, The load transfer effect evaluation vector is a mathematical vector that comprehensively evaluates the overall performance of the load transfer system, including key performance indicators such as transfer efficiency, stability, and continuity.

8. The construction method for load decomposition and transfer of irregular beam curved formwork according to claim 7, characterized in that, The prestressed steel tensioning stability control matrix is ​​a mathematical model used to control the coordination of various parameters during the prestressed steel tensioning process, ensuring uniform tension force distribution and stability of the tensioning process.

9. The construction method for load decomposition and transfer of irregular beam curved formwork according to claim 8, characterized in that, The load transfer dynamic equilibrium equation set includes the load distribution equilibrium equation and the support stiffness adjustment equation. The load distribution equilibrium equation is used to calculate the optimal load distribution ratio at each support point, and the support stiffness adjustment equation is used to determine the stiffness adjustment parameters of the support system.

10. The construction method for load decomposition and transfer of irregular beam curved formwork according to claim 9, characterized in that, The stress state of critical force transmission nodes refers to the stress distribution at the core force transmission location that has a decisive influence on the overall stability of the load transmission system, including the stress magnitude, direction, and trend of change.

Images