Concrete performance index risk compensation and dynamic correction method and system

Abstract

This invention discloses a method and system for risk compensation and dynamic correction of concrete performance indicators, relating to the fields of concrete material engineering and construction risk management. The method includes acquiring construction condition parameters and constructing a condition risk vector; establishing a deterministic mapping relationship between the condition risk vector and performance indicator correction amounts, thereby correcting the basic performance indicators to obtain the corrected target performance indicators; inputting the corrected target performance indicators into preset constraints for feasibility assessment; if satisfied, outputting directly; otherwise, optimizing and adjusting to obtain feasible indicators; designing and verifying the mix proportion based on the feasible indicators, and updating the mapping relationship parameters based on the verification results. This invention compensates for condition risks upfront at the mix proportion design stage, avoiding uncontrolled water-cement ratio and performance fluctuations caused by temporary on-site adjustments, and improving the consistency and stability of concrete construction and structural performance.

Description

Technical Field

[0001] This invention belongs to the field of concrete material engineering and construction risk management technology. Specifically, it relates to a method for dealing with complex working conditions involving multiple coupled risks, such as high-temperature environment, long-distance transportation, high-rise pumping, dense reinforcement constraints, continuous large-volume pouring, and strong constraints on the construction period. By systematically identifying and quantifying the risks of the construction conditions, a dynamic mapping relationship is established between the risk vector and the concrete performance index requirements. Furthermore, under the feasible domain and safety constraints of the project, the target performance index is pre-compensated and corrected. Background Technology

[0002] Concrete mix design is typically based on standard test conditions, achieved through indoor trial mixing combined with limited field verification, to meet design requirements such as slump, setting time, strength grade, and durability. However, in actual engineering construction, working conditions are often far more complex than standard test conditions, mainly in the following aspects:

[0003] Temperature effects: High temperatures in summer lead to increased temperature of the mixture, accelerated cement hydration, and shortened effective action time of admixtures, resulting in a significant acceleration of slump loss over time; low temperatures in winter lead to a slowdown in hydration rate and insufficient early strength development.

[0004] Impact of transportation and pumping: Long-distance transportation and on-site waiting exacerbate the degradation of working performance; during high-rise pumping, the pipe wall lubrication layer is easily damaged, the risk of pressure bleeding and segregation increases, and pipe blockage accidents occur frequently.

[0005] Structural constraints: Dense reinforcement and narrow pouring space place higher demands on the concrete's filling capacity, anti-segregation performance, and cohesiveness.

[0006] Impact of construction organization: Continuous large-scale pouring places strict requirements on the available construction time window, and factors such as night construction and strict construction period constraints further increase the difficulty of quality control.

[0007] The aforementioned complex working conditions often exhibit the characteristics of "multiple risk coupling" (such as the combined effects of high temperature, long distance, and high pumping speed), making it difficult for a single performance index (such as initial slump) to fully characterize construction risks. Engineering decisions require a more systematic risk quantification framework.

[0008] To address the aforementioned issues, current engineering solutions largely rely on the experience and on-site adjustments made by technical personnel. Common practices include adding water, admixtures, or adjusting the sand ratio. However, these adjustments lack systematic quantitative support, easily leading to uncontrolled water-cement ratios, increased strength dispersion, the formation of bleeding channels, and decreased durability. Furthermore, experience-based adjustments struggle to achieve synergistic optimization across multiple performance objectives, and the process is not traceable. Once quality problems arise, it becomes difficult to analyze the root causes and continuously improve the system.

[0009] Therefore, there is an urgent need for an engineering approach that incorporates construction risk into the mix design stage. This approach uses performance indicators as the decision-making center and achieves a technical path of "correcting indicators first and then designing mix proportions" through clear risk characterization, deterministic dynamic mapping mechanisms, and feasible domain constraints. This reduces on-site adjustments and improves the consistency, stability, and controllability of concrete construction performance and final structural performance under complex conditions. Summary of the Invention

[0010] In view of the above-mentioned defects in the existing technology, the purpose of this invention is to provide a method and system for risk compensation and dynamic correction of concrete performance indicators, so as to solve the technical problems that existing concrete mix design is difficult to adapt to the multi-risk coupling of complex construction conditions, lack of quantitative basis for on-site temporary adjustments leading to uncontrolled water-cement ratio, large performance fluctuations and difficulty in quality traceability.

[0011] This invention solves the above-mentioned technical problems through the following technical solution: a method for risk compensation and dynamic correction of concrete performance indicators, comprising:

[0012] Obtain the construction condition parameters of the target project, and perform risk identification and quantification on the construction condition parameters to construct a condition risk vector;

[0013] Obtain a preset basic performance index vector, establish a deterministic mapping relationship between the working condition risk vector and the concrete performance index correction amount, determine the performance index correction amount corresponding to the working condition risk vector according to the deterministic mapping relationship, and then superimpose the performance index correction amount onto the basic performance index to obtain the corrected target performance index.

[0014] The modified target performance index is input into preset constraints for feasibility assessment. When the modified target performance index meets the constraints, it is directly output as an implementable index. When the modified target performance index does not meet the constraints, the modified target performance index is mapped to the constraints through optimization and adjustment to obtain an implementable index that meets the constraints.

[0015] Concrete mix design is carried out based on the feasible indicators, and the design results are verified and applied in engineering. The parameters of the deterministic mapping relationship are updated based on the verification results and engineering application data.

[0016] This invention systematically identifies and quantifies construction condition parameters, transforming multi-dimensional condition information such as ambient temperature, transportation distance, pumping conditions, and rebar constraints into a unified condition risk vector, thus achieving a quantitative characterization of complex condition risks. Unlike existing technologies that rely on empirical qualitative judgments, this invention transforms the fuzzy concept of "multi-risk coupling" into a calculable and comparable mathematical vector, providing a quantitative basis for subsequent indicator correction and avoiding decision-making errors caused by incomplete risk identification or subjective judgment bias.

[0017] This invention establishes a deterministic mapping relationship between the working condition risk vector and the performance index correction amount, directly converting the risk quantification result into the index adjustment range. Compared with the existing "on-site temporary adjustment" mode, this invention achieves pre-emptive risk compensation—systematically correcting performance indicators based on the predicted working condition risks during the mix design stage, rather than passively responding to problems that arise on the construction site. This mechanism effectively avoids high-risk operations such as adding water on-site, ensuring the controllability of the water-cement ratio and the stability of strength from the source. At the same time, the correction method of "overlaying to basic performance indicators" retains the original design benchmark, ensuring the interpretability and traceability of the correction process.

[0018] This invention introduces "pre-set constraints" to assess the feasibility of the modified target performance indicators, ensuring that the final feasible indicators are within the engineering feasible domain and safety boundaries. When the modified indicators exceed the constraints, they are mapped back into the constraints through optimization adjustments, guaranteeing the engineering feasibility of the modified results. This mechanism avoids problems such as unachievable mix proportions or loss of control during construction due to indicators exceeding limits, ensuring that the results of risk compensation can be truly implemented.

[0019] This invention feeds back verification results and engineering application data to update the mapping parameters, forming a complete closed-loop optimization mechanism. Engineering data is accumulated through multiple verification methods to optimize the weights of risk components and correct the regression coefficients of the mapping model, ensuring the accuracy of the mapping relationship continuously improves with the accumulation of engineering data. Simultaneously, the entire process—from risk inputs and mapping outputs to adjudication and verification results—is traceable, achieving full-process traceability and providing data support for quality problem analysis and continuous improvement.

[0020] Furthermore, the construction condition parameters include at least two or more of the following: ambient temperature, mixture temperature, transportation distance, transportation time, waiting time, pumping height, pumping pipe diameter, number of pumping pipe bends, volumetric reinforcement ratio, pouring cross-sectional dimensions, and continuous pouring duration.

[0021] By explicitly listing the aforementioned key working condition parameters, this invention achieves comprehensive coverage of risk factors throughout the entire concrete construction process—from environmental conditions (temperature), transportation (distance, time, waiting), pumping (height, pipe diameter, bends), structural constraints (volume reinforcement ratio, cross-sectional dimensions) to construction organization (continuous pouring duration). This comprehensive coverage avoids the omission of key risk factors, enabling the working condition risk vector to truly reflect the coupling effects of complex working conditions, providing a complete and accurate input basis for subsequent risk quantification and indicator correction. Simultaneously, the explicit listing method also ensures the feasibility of the technical solution.

[0022] Furthermore, the construction condition parameters are subjected to risk identification and quantification to construct a condition risk vector, specifically including:

[0023] For each construction condition parameter, the corresponding normalization function is used to perform dimensionless processing, and the original condition parameter is mapped to the risk component in the interval [0,1] to obtain the condition risk vector composed of multiple risk components.

[0024] The normalization function is one or more of a piecewise linear function, a logic function, or an empirical calibration function, and different operating parameters can select the same or different types of normalization functions.

[0025] By introducing normalization, this invention maps original operating parameters with varying dimensions and vastly different numerical ranges (such as temperature 30-40℃, transportation distance 0-50km, number of bends 0-20, etc.) uniformly to the [0,1] interval, achieving comparability and additivity of different risk factors. The selection of various normalization methods, such as piecewise linear functions, logistic functions, and empirical calibration functions, allows this invention to flexibly choose the most suitable quantification model based on the physical characteristics and engineering experience of different risk factors—for example, a piecewise linear function can be used to reflect the critical threshold effect for temperature, and a logistic function can be used to reflect the nonlinear growth risk for the number of pumping bends. The flexibility of using different functions for different parameters further improves the accuracy and adaptability of risk quantification.

[0026] Furthermore, a deterministic mapping relationship is established between the working condition risk vector and the correction amount of concrete performance indicators, and the performance indicator correction amount corresponding to the working condition risk vector is determined according to the deterministic mapping relationship, specifically including:

[0027] Risk level classification thresholds are set based on historical data or engineering experience, and multiple risk level intervals are divided according to the risk level classification thresholds, with each risk level interval corresponding to a preset mapping model.

[0028] A comprehensive risk score is calculated based on the aforementioned working condition risk vector, and the risk level range into which the risk falls is determined based on the comprehensive risk score.

[0029] Using a mapping model corresponding to the determined risk level range, a deterministic mapping relationship is established between the working condition risk vector and the correction amount of concrete performance index, and the corresponding performance index correction amount is output.

[0030] The mapping model is established through one or more of the following methods: statistical regression analysis, piecewise linear fitting, kernel regression, or engineering lookup table, including linear models, nonlinear models, or piecewise models.

[0031] By introducing a "risk level classification" and "segmented mapping" mechanism, this invention achieves differentiated compensation for risks of different levels—a moderately modified model is used for low risks, while a reinforced modified model is used for high risks, avoiding insufficient or excessive compensation caused by a "one-size-fits-all" approach. The calculation of the comprehensive risk score condenses the multi-dimensional risk vector into a single score value, facilitating level judgment and model switching. The parallel limitation of multiple mapping model establishment methods (statistical regression, piecewise fitting, kernel regression, table lookup) and multiple model types (linear, nonlinear, piecewise) allows those skilled in the art to flexibly select the most suitable modeling method according to the type of engineering data and accuracy requirements, ensuring the accuracy of the mapping relationship and its engineering applicability.

[0032] Furthermore, the basic performance indicators include at least one or more of the construction stage indicators and the hardening stage indicators; wherein:

[0033] The construction stage indicators include at least one of the following: fluidity slump, slump loss over time, pumpability index, water retention index, pressure bleeding threshold, and anti-segregation score threshold.

[0034] The hardening stage indicators include at least one of the following: 28-day compressive strength, early strength threshold, elastic modulus, chloride ion diffusion coefficient, electrical flux index, freeze-thaw resistance grade, and drying shrinkage control index.

[0035] By clearly categorizing performance indicators into two main types—construction stage indicators and hardening stage indicators—and listing the specific indicators under each type, this invention achieves full life-cycle coverage of concrete performance. Construction stage indicators ensure the workability of the mixture during transportation, pumping, and pouring, while hardening stage indicators ensure the mechanical and durability performance of the structure during its service life. This comprehensive coverage avoids the one-sidedness of traditional methods that prioritize strength over construction or construction over durability, enabling risk compensation to balance short-term construction needs with long-term structural safety. The clearly listed specific indicators also ensure that the revised implementable indicators have clear engineering implications and verifiability.

[0036] Furthermore, the preset constraints include the engineering feasible region and the set of safety constraints;

[0037] The feasible domain of the project is jointly defined by the specification limits of performance indicators, project contract indicators, and enterprise internal control standards;

[0038] The set of safety constraints includes at least one or more of the following: upper limit of water-cement ratio, lower limit of cementitious material, upper limit of admixture dosage, adjustable range of sand ratio, or lower limit of durability index.

[0039] By dividing constraints into two levels—the "engineering feasible domain" and the "safety constraint set"—this invention constructs a two-layer constraint guarantee mechanism. The engineering feasible domain ensures that the modified indicators meet external requirements such as specifications, contracts, and enterprise standards, while the safety constraint set ensures that the mix proportion parameters corresponding to the indicators do not exceed the material safety boundaries (e.g., insufficient strength due to an excessively high water-cement ratio, segregation due to excessive admixtures, etc.). This two-layer constraint mechanism guarantees both the compliance of the modified results and the material safety of the mix proportion design. Explicitly listing the specific contents of the safety constraint set (upper limit of water-cement ratio, lower limit of cementitious materials, etc.) provides clear safety boundaries for mix proportion design, avoiding the introduction of new material risks due to indicator modifications.

[0040] Furthermore, when the modified target performance index violates the set of safety constraints, constrained optimization is used to transform the modified target performance index into an implementable index that satisfies the set of safety constraints.

[0041] The constrained optimization includes projection decision, which is implemented by the following formula:

[0042] ;

[0043] in, This represents a vector of enforceable indicators following the ruling. Represents the feasible region of the project; This represents the corrected target indicator vector; This represents a vector of candidate indices within the feasible region of the project.

[0044] By introducing constrained optimization (including projection adjudication) as a means of handling violations of safety constraints, this invention provides a mathematical transformation tool from "infeasible indicators" to "feasible indicators." Projection adjudication achieves the "minimum adjustment" principle by finding the point within the feasible region closest to the corrected indicator—preserving the intention of risk compensation as much as possible while satisfying safety constraints. The explicit mathematical formulas provide a precise algorithm for computer system implementation, ensuring the objectivity, repeatability, and verifiability of the adjudication process, and avoiding the subjective arbitrariness of human experience-based adjudication.

[0045] Furthermore, the optimization adjustment includes a multi-indicator reallocation step, specifically including:

[0046] When improving the fluidity index causes the anti-segregation or water retention index to fail to meet the constraints, the performance index correction amount is allocated among fluidity, cohesiveness and water retention to ensure that the feasible index simultaneously meets the pumpability threshold and the anti-segregation threshold.

[0047] The multi-indicator redistribution is achieved by solving a weighted objective function, and the components of each indicator in the implementable indicator vector are determined through optimization.

[0048] ;

[0049] In the formula, This represents the j-th component in the modified target index vector; This represents the j-th component in the vector of feasible indicators to be determined. This represents the priority weight of the j-th indicator.

[0050] By introducing a multi-index redistribution step, this invention solves the problem of coordinating conflicts among multiple performance indicators. In scenarios where increasing fluidity may compromise anti-segregation performance, a weighted objective function is used to balance fluidity, cohesiveness, and water retention, ensuring that the final feasible indicator simultaneously meets the dual requirements of pumpability and anti-segregation. The priority weights in the weighted objective function can be flexibly set according to engineering needs. For example, pumpability can be given a higher weight in projects with high pumping difficulty, while anti-segregation can be given a higher weight in projects with dense reinforcement, achieving project-demand-oriented collaborative optimization.

[0051] Furthermore, the verification of the design results includes at least one or more of the following: slump test over time, pressure bleeding test, segregation resistance evaluation, strength sampling inspection, or durability sampling inspection; updating the parameters of the deterministic mapping relationship includes updating the weights of the risk components or the regression coefficients of the mapping model.

[0052] By explicitly listing multiple verification methods (slump test over time, pressure bleeding test, segregation resistance evaluation, strength sampling inspection, and durability sampling inspection), this invention provides multi-dimensional effectiveness verification methods to ensure that the modified mix proportions achieve the expected goals in both construction performance and hardening performance. The verification results and engineering application data are fed back to update the mapping relationship parameters (risk component weights, mapping model regression coefficients), forming a data-driven continuous optimization mechanism—as engineering data accumulates, the accuracy of the mapping relationship gradually improves, and the effect of risk compensation is increasingly optimized. This mechanism enables this invention to possess self-learning and adaptive capabilities, allowing it to continuously evolve according to the characteristics of different regions, seasons, and project types, truly achieving an intelligent decision-making effect that becomes more accurate with use.

[0053] Based on the same concept, the present invention also provides a concrete performance index risk compensation and dynamic correction system, including a memory, a processor, and a computer program or instructions stored in the memory, wherein the processor executes the computer program or instructions to implement the concrete performance index risk compensation and dynamic correction method as described above.

[0054] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0055] This invention systematically identifies and quantifies construction condition parameters to construct a condition risk vector, transforming multiple risk couplings into calculable quantitative data. By establishing a deterministic mapping relationship between the condition risk vector and performance index correction amounts, the risk quantification results are pre-emptively applied to the mix design stage for index correction, avoiding uncontrolled water-cement ratios and performance fluctuations caused by on-site adjustments. Pre-set constraints are used to assess the feasibility and optimize the corrected target performance indicators, ensuring that the final implementable indicators simultaneously meet engineering specifications and material safety boundaries. The mapping relationship parameters are updated through verification results and engineering application data feedback, forming a closed-loop optimization mechanism to achieve quality traceability and continuous improvement.

[0056] This invention transforms the risk of complex working conditions from "experience-based correction" to "scientific pre-control," significantly improving the consistency, stability, and controllability of concrete construction performance and structural performance. Attached Figure Description

[0057] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only one embodiment of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0058] Figure 1 This is a flowchart of the concrete performance index risk compensation and dynamic correction method in an embodiment of the present invention;

[0059] Figure 2 This is a schematic diagram of risk identification and data acquisition under complex working conditions in an embodiment of the present invention;

[0060] Figure 3 This is a schematic diagram illustrating the construction and normalization of the working condition risk vector R in an embodiment of the present invention;

[0061] Figure 4 This is a schematic diagram illustrating the calculation process of deterministic mapping relationships and performance index corrections in an embodiment of the present invention;

[0062] Figure 5 This is a schematic diagram of the modified performance index-driven mix design and verification closed loop in an embodiment of the present invention;

[0063] Figure 6 This is a schematic diagram of the projection decision of the engineering feasible region and the safety constraint set in an embodiment of the present invention;

[0064] Figure 7 This is a schematic diagram comparing key indicators and engineering results before and after implementation in an embodiment of the present invention. Detailed Implementation

[0065] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. 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.

[0066] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0067] Example 1: High temperature + long distance transportation + high-rise pumping conditions.

[0068] This embodiment uses the concrete construction of the core tube of a super high-rise building in the core area of ​​a city as an example to illustrate the specific implementation process of the method of the present invention. Figure 1 As shown, the method for risk compensation and dynamic correction of concrete performance indicators includes the following steps:

[0069] Step S1: Obtain the construction condition parameters of the target project, identify and quantify the risks of the construction condition parameters, and construct a condition risk vector.

[0070] The construction condition parameters of this invention include, but are not limited to, two or more of the following: ambient temperature, mixture temperature, transportation distance, transportation time, waiting time, pumping height, pumping pipe diameter, number of pumping pipe bends, volumetric reinforcement ratio (or steel reinforcement volume ratio), pouring cross-sectional dimensions, and continuous pouring duration.

[0071] Specifically, the construction conditions for the target project in this embodiment are as follows: summer high-temperature construction, ambient temperature 36℃; factory temperature of the mixture 32℃; transportation distance 25km, transportation time 50min; on-site waiting time 25min; pumping height 90m; pumping pipe diameter 125mm; number of pumping pipe bends 12; the pouring location is a shear wall with a volumetric reinforcement ratio of 2.8%; the pouring cross-sectional dimensions are 600mm×1200mm; the continuous pouring time is expected to be 6 hours. These 11 construction condition parameters constitute the original set of conditions.

[0072] Figure 2The document showcases various construction condition parameters that need to be collected, including environmental factors (ambient temperature, mixture temperature), transportation factors (transportation distance, transportation time, waiting time), pumping factors (pumping height, pumping pipe diameter, number of bends), structural constraints (reinforcement volume ratio, casting cross-sectional dimensions), and time and construction organization factors (continuous casting duration). These parameters are input through a data acquisition and processing platform for subsequent construction of the condition risk vector.

[0073] Risk identification and quantification are performed on each construction condition parameter in the original set of working condition parameters, such as... Figure 3 As shown. Specifically, for each construction condition parameter, the corresponding normalization function is used to perform dimensionless processing, mapping the original condition parameter to the risk component in the interval [0,1], resulting in a condition risk vector composed of multiple risk components.

[0074] Different types of normalization functions are selected for different operating parameters: piecewise linear functions are used for continuous parameters such as temperature and transportation distance, while logical functions are used for discrete parameters such as the number of bends, to more accurately reflect the changing patterns of risk. The piecewise linear function expression in this embodiment is:

[0075] (1)

[0076] in, and These represent the corresponding operating parameters. The lower limit and the upper limit.

[0077] Taking ambient temperature as an example, assuming a lower limit of 20℃ and an upper limit of 40℃, 36℃ is mapped as a risk component. (Calculation: (36−20) / (40−20)=0.8). Similarly, by applying the corresponding normalization function to the other parameters, the working condition risk vector R is obtained:

[0078] (2)

[0079] The specific figures are as follows: Transportation distance risk =0.75 (with a threshold of 0~40km); Transportation time risk =0.70 (set threshold 0~80min); waiting time risk =0.65 (threshold set at 0~40min); Pumping height risk =0.80 (set threshold 0~120m); Risk of elbow quantity =0.70 (set threshold 0~20); Volumetric reinforcement ratio risk =0.60 (set threshold 0~4%); Cross-sectional dimension risk =0.50 (normalized to minimum size); Risk of continuous pouring time =0.55 (set the threshold to 0~10h).

[0080] Step S2: Establish a deterministic mapping relationship between the working condition risk vector and the correction amount of concrete performance index, and determine the performance index correction amount corresponding to the working condition risk vector based on the deterministic mapping relationship.

[0081] First, risk level thresholds are set based on historical engineering data: low risk range [0, 0.3), medium risk range [0.3, 0.7], and high risk range [0.7, 1.0]. Each risk level range corresponds to a preset mapping model—low risk uses linear model M1, medium risk uses nonlinear model M2, and high risk uses reinforced linear model M3. The mapping model is established through one or more of the following methods: statistical regression analysis, piecewise linear fitting, kernel regression, or engineering lookup table. In this embodiment, M1 uses piecewise linear fitting, M2 uses kernel regression, and M3 uses ridge regression (constrained regression).

[0082] Based on historical engineering quality statistics and accident retrospective analysis, the weight vector W for each risk component (or risk factor) is determined (obtained through multi-project sample optimization): W=[0.15,0.10,0.12,0.08,0.08,0.12,0.05,0.10,0.08,0.06,0.06]; the comprehensive risk score is calculated according to the following formula:

[0083] (3)

[0084] Risk represents the overall risk score; This represents the k-th risk component in the working condition risk vector R; This represents the corresponding weighting coefficient. Risk=0.78 falls into the high-risk range [0.7,1.0], therefore the mapping model M3 corresponding to high risk is adopted.

[0085] The high-risk mapping model M3 is constructed using ridge regression, and its matrix form is as follows:

[0086] (4)

[0087] Where △Q represents the vector of corrections to concrete performance indicators.

[0088] When establishing this mapping model, it is necessary to calibrate the parameter mapping matrix A and the bias vector b using historical engineering data. The calibration process employs regularized least squares optimization, with the following mathematical form:

[0089] (5)

[0090] in, Let be the vector of observed index corrections for the i-th historical project sample. This correction can be obtained in two ways: first, by calculating the difference between the project's "target index" (original design index) and the "optimal index required to meet construction and quality requirements under actual working conditions"; second, by analyzing the statistical results of multiple comparative project samples. In this embodiment, It includes multiple components such as slump correction, time loss rate correction, and pumpability index correction.

[0091] R i Let be the working condition risk vector for the i-th historical engineering sample.

[0092] A is a parameter mapping matrix, representing the contribution weight of each risk component to the correction amount of each performance index. The rows of the matrix correspond to different performance index correction amounts, and the columns correspond to different risk components.

[0093] b is the bias vector, representing the base correction (i.e., the baseline correction when the risk is zero).

[0094] λ is the regularization coefficient, used to control the complexity of the model and prevent overfitting. The larger λ is, the closer the elements of matrix A are to zero, and the simpler the model; the smaller λ is, the more the model tends to accurately fit historical data. In this embodiment, λ is determined to be 0.1 through cross-validation.

[0095] Let Frobenius norm be the norm of matrix A, defined as the square root of the sum of the squares of all elements of the matrix. This term is added as a regularization term to the optimization objective to constrain the size of the elements of matrix A and prevent individual risk components from having excessively large weights, which could lead to model instability.

[0096] Let be the sum of squared fitting errors for all historical engineering samples, representing the deviation between the model's predicted values ​​and the actual observed values.

[0097] By solving the above optimization problem, the optimal parameter mapping matrix A and bias vector b are obtained. In this embodiment, ridge regression calculation is performed based on a database containing 120 historical project samples to obtain the specific parameters of the high-risk mapping model M3. Substituting the current project's working condition risk vector R=[0.80,0.75,0.70,0.65,0.80,0.70,0.60,0.50,0.55] into the model, the performance index correction amount is calculated:

[0098] (6)

[0099] Step S3: Obtain the preset basic performance index, and add the performance index correction amount to the basic performance index to obtain the corrected target performance index.

[0100] The original design strength grade of this project is C50. The basic performance index vector in this embodiment includes construction stage indexes and hardening stage indexes:

[0101] Construction stage indicators: initial slump S0 = 200 mm; slump loss rate over time λs ≤ 20 mm / h (i.e., 2h loss ≤ 40 mm); pumpability index Πp (characterized by pumping pressure gradient) ≤ 0.15 MPa / m; water retention index (pressure bleeding rate) ≤ 10%; anti-segregation score threshold ≥ 80.

[0102] Hardening stage index: 28-day compressive strength f 28 ≥60MPa; chloride ion diffusion coefficient ≤1.2×10 −12 m 2 / s; Freeze-thaw resistance grade ≥ F200; Elastic modulus ≥ 3.45 × 10⁻⁶ 4 MPa; Drying shrinkage control index ≤4×10 −4 .

[0103] The performance index correction amount ΔQ obtained in step S2 is superimposed on the basic performance index to obtain the corrected target performance index:

[0104] Initial slump S0′=200+30=230 mm; slump loss over time λs′≤20−5=15mm / h; pumpability index Πp′≤0.15+0.03=0.18MPa / m; water retention index (pressure bleeding rate)≤10%−1%=9%; anti-segregation score threshold≥80+5=85; other hardening stage indices remain unchanged.

[0105] Figure 4 The diagram illustrates the calculation process for deterministic mapping relationships and performance index corrections. First, the basic performance index Q is obtained. Then, based on the operating condition risk vector R, the corresponding mapping model M(R) is selected (a linear mapping can be used). or nonlinear regression The performance index correction amount ΔQ is calculated, and finally the correction amount ΔQ is superimposed on the basic performance index Q to obtain the corrected target performance index Q′=Q+ΔQ.

[0106] Step S4: Input the modified target performance index into the preset constraints for feasibility assessment. When the modified target performance index meets the constraints, it is directly output as an implementable index. When the modified target performance index does not meet the constraints, the modified target performance index is mapped into the constraints through optimization and adjustment to obtain an implementable index that meets the constraints.

[0107] The preset constraints include the feasible region Ω and the set of safety constraints C. The feasible region Ω is jointly defined by the specification limits of performance indicators, engineering contract indicators, and internal control standards of the enterprise.

[0108] Slump upper limit 250mm, lower limit 180mm; slump loss rate over time upper limit 25mm / h; pumpability index upper limit 0.20MPa / m; water retention index (pressure bleeding rate) upper limit 12%, lower limit 5%; segregation resistance score threshold lower limit 75 points; 28-day strength lower limit 55MPa (contract requirement), upper limit none; chloride ion diffusion coefficient upper limit 1.5×10 −12 m 2 / s; Freeze-thaw resistance grade lower limit F150; Elastic modulus lower limit 3.4×10 4 MPa; upper limit of drying shrinkage control index 5×10 −4 .

[0109] Safety constraint set C includes at least: a water-cement ratio upper limit of 0.38; and a cementitious material lower limit of 400 kg / m³. 3 The maximum admixture dosage is 2.5%; the adjustable sand ratio is 38%~44%; the lower limit of durability index (chloride ion diffusion coefficient ≤1.2×10⁻⁶) is... −12 m 2 / s, freeze-thaw resistance grade ≥F200, etc.).

[0110] By inputting the modified target performance index vector Q′ into the constraints for feasibility assessment, it can be seen that Q′ satisfies the engineering feasible region Ω and does not violate the safety constraint set C. The direct output is the feasible index vector Q′′=Q′.

[0111] It should be noted that although the comprehensive risk score of 0.78 falls within the high-risk range, the performance indicators have been adjusted to a reasonable level during the mix design stage through the risk compensation mechanism of this invention. This ensures that the corrected indicators can not only meet the challenges of high-risk working conditions but also fully satisfy the specification limits and material safety boundaries. This is precisely the advantage of the "pre-emptive risk compensation" of this invention—it eliminates the need for reactive adjustments after problems occur on-site, but rather anticipates risks and makes scientific compensations during the design stage, ultimately ensuring that the achieved indicators remain within a controllable range.

[0112] Step S5: Design the concrete mix proportion based on feasible indicators, verify the design results and apply them in engineering, and update the parameters of the deterministic mapping relationship based on the verification results and engineering application data.

[0113] The mix design was carried out based on the feasible index vector Q′′. By appropriately increasing the fineness of the mineral admixtures, optimizing the admixture system and sand ratio, the mix proportion parameters were determined as follows: water-cement ratio 0.36, cementitious material dosage 420 kg / m³. 3(60% cement + 25% fly ash + 15% mineral powder), admixture dosage 2.2%, sand ratio 42%.

[0114] The design results shall be verified, and the verification methods shall include at least one or more of the following: slump test over time, pressure bleeding test, segregation resistance evaluation, strength sampling inspection, and durability sampling inspection:

[0115] Slump test over time: Slump at the machine outlet was 235mm, slump after 2 hours was 210mm, loss was 25mm, meeting the ≤30mm requirement; Pressure bleeding test: Pressure bleeding rate was 8%, meeting the ≤9% requirement; Segregation resistance evaluation: No segregation was observed visually and during the slump test, segregation resistance score was 88 points; Strength sampling inspection: Average compressive strength after 28 days was 63.5MPa, standard deviation was 2.8MPa, meeting the C50 evaluation requirements; Durability sampling inspection: Chloride ion diffusion coefficient was 1.1×10⁻⁶. −12 m 2 / s, passed 250 freeze-thaw cycles, elastic modulus 3.5×10 4 MPa, drying shrinkage value 3.8×10 −4 .

[0116] On-site verification results: Pumping continuity was good, and no pipe blockage occurred; slump remained within the construction window; 28-day strength met design requirements, and batch dispersion was reduced.

[0117] The verification results of this project and the engineering application data (actual pumping pressure, on-site slump retention, solid strength, durability test results, etc.) are entered into the database to update the weights of risk components or the regression coefficients of the mapping model, so as to achieve continuous optimization of the method.

[0118] Figure 5 This is a schematic diagram of the closed-loop design and verification of mix proportions driven by the modified performance index of this invention. The mix proportion is designed based on the feasible index Q′′ to obtain a new mix proportion. The design results are verified through laboratory or field testing (including slump test over time, pressure bleeding test, segregation resistance evaluation, strength sampling inspection, durability sampling inspection, etc.). The verification results and engineering data are recorded and fed back to update the risk component weights or mapping model M(R) parameters, forming a complete closed-loop optimization mechanism.

[0119] Example 2: Risk compensation for dense reinforcement constraints and narrow cross-section casting.

[0120] This embodiment takes the concrete construction of a node area of ​​a subway station as an example to illustrate the specific implementation of the multi-index redistribution steps.

[0121] The construction conditions for the target project in this embodiment are as follows: dense reinforcement in the shear wall and joint area, with a volumetric reinforcement ratio of 3.5% (relatively high); a casting cross-sectional size of 400mm × 800mm (narrow), limiting vibration and requiring higher cohesion and anti-segregation properties; a pumping height of 15m (medium-low), a transportation distance of 15km (medium), and an ambient temperature of 25℃. After obtaining the construction condition parameters, normalization was performed according to step S1 in Embodiment 1 to construct the condition risk vector R, in which the volumetric reinforcement ratio risk and cross-sectional size risk are dominant, at 0.85 and 0.80 respectively.

[0122] The comprehensive risk score calculated based on the working condition risk vector is 0.62, falling into the medium-risk range [0.3, 0.7). A medium-risk mapping model M2 is adopted. M2 is established through piecewise linear fitting. The performance index corrections output by the mapping model M2 are: initial slump +25mm, segregation resistance score threshold +15% (i.e., an increase of 15 points), and water retention index (pressure bleeding rate) −2% (i.e., stricter requirements). The corrected target index vector Q′ is:

[0123] Initial slump S0′=200+25=225 mm; segregation resistance score threshold ≥80+15=95; pressure bleeding rate ≤10%−2%=8%.

[0124] The preset constraints include the feasible region Ω and the safety constraint set C. Inputting the modified target index vector Q′ into the constraints for feasibility assessment reveals that while the initial slump S0′ meets the feasible region requirements (180mm~230mm), historical experience suggests that under the high reinforcement density and narrow cross-section conditions of this project, a slump of 225mm may lead to coarse aggregate settlement and a decrease in segregation resistance. Although the segregation resistance score threshold of 95 points is high, achieving this value in actual construction is difficult, and the water retention index of 8% is already close to the lower limit. At this point, increasing fluidity poses a potential threat to cohesiveness and water retention, meeting the condition of "when increasing the fluidity index leads to the segregation resistance or water retention index failing to meet the constraints." Therefore, the multi-index redistribution step is initiated.

[0125] The performance index corrections are allocated among flowability, cohesiveness, and water retention in a trade-off, and feasible indices are determined by solving a weighted objective function.

[0126] (7)

[0127] In the formula, This represents the j-th component in the modified target index vector Q′; This represents the j-th component in the vector of feasible indicators Q′′ to be determined; This indicates the priority weight of the j-th indicator. Priority weights are set according to engineering requirements: anti-segregation has a priority weight of 0.5 (most important), water retention has a priority weight of 0.3, and fluidity has a priority weight of 0.2.

[0128] Solving the optimization problem of the above formula (7), we obtain the feasible indicators after redistribution: the initial slump is adjusted to 210mm (reduced by 15mm to release some of the fluidity requirements); the anti-segregation score threshold is adjusted to 90 points (reduced by 5 points, but still higher than the base value); the pressure bleeding rate is maintained at ≤8% (unchanged).

[0129] The mix design was carried out based on the feasible index vector Q′′: a water-binder ratio of 0.34 was adopted, the fineness of the mineral admixtures was increased, and the composition of the admixtures was optimized (thickening component + 0.1%).

[0130] Verify the design results:

[0131] Slump test over time: slump at the machine was 215mm, and slump after 2 hours was 195mm, with a loss of 20mm, which meets the requirements; Pressure bleeding test: pressure bleeding rate was 7.5%, which meets the ≤8% requirement; Segregation resistance evaluation: no segregation was observed through the gaps between the reinforcing bars, and the segregation resistance score was 92 points; Strength sampling test: 28-day compressive strength was 52.3MPa, which meets the C45 evaluation requirements.

[0132] On-site verification results: The pump successfully passed through the area with dense reinforcement, and the forming quality was good.

[0133] The verification results of this project and the engineering application data (actual pumping pressure, on-site slump retention, solid strength, durability test results, etc.) are entered into the database to update the weights of risk components or the regression coefficients of the mapping model, thereby achieving closed-loop optimization of the method.

[0134] Example 3: Risk compensation for low-temperature construction and early strength window.

[0135] This embodiment takes winter construction in northern China as an example to illustrate the process of using constrained optimization (including projection adjudication) to handle situations where the corrected target performance index violates the safety constraint set.

[0136] The construction conditions for the target project in this embodiment are as follows: winter construction, ambient temperature -2℃, requiring a demolding strength threshold of 8MPa within 24 hours and a C40 strength after 28 days, while ensuring that durability is not reduced. Construction condition parameters are obtained and normalized according to step S1 in Embodiment 1: the ambient temperature risk (low-temperature risk) is obtained by normalizing the ambient temperature (-2℃) using an empirical calibration function. Based on historical project experience, when the winter construction temperature is in the range of -5℃ to 0℃, the low-temperature risk component has a value of 0.7 to 0.9. This project uses the midpoint value of 0.85, indicating a relatively high low-temperature risk, which needs to be fully compensated in the index correction. Other parameters have moderate risks, thus constructing the condition risk vector R.

[0137] The comprehensive risk score calculated based on the working condition risk vector is 0.7, falling into the high-risk range [0.7, 1.0]. The high-risk mapping model M3 (a low-temperature-specific model established through constrained regression) is adopted. The performance index corrections output by the mapping model M3 are: early strength target +3MPa, setting time +2h, water-cement ratio −0.02. The corrected target index vector Q′ is:

[0138] 24h compressive strength ≥ 8 + 3 = 11 MPa; initial setting time ≥ 6 + 2 = 8h (assuming initial setting time of foundation is 6h); water-cement ratio ≤ 0.44 − 0.02 = 0.42 (assuming water-cement ratio of foundation is 0.44).

[0139] Safety constraint set C includes an upper limit of water-cement ratio of 0.40 (due to stricter low-temperature antifreeze requirements and to ensure that durability is not reduced, the water-cement ratio must not exceed 0.40) and a lower limit of cementitious material of 400 kg / m³. 3 The maximum admixture dosage is 2.5%, etc. The modified target index vector Q′ is input for inspection: water-cement ratio 0.42 > 0.40, violating the safety constraint set C. Therefore, constrained optimization is needed to transform it into an implementable index that satisfies the safety constraint set; the constrained optimization includes projection decision, which is achieved by the following formula:

[0140] (8)

[0141] in, This represents a vector of enforceable indicators following the ruling. This represents the feasible region of the project (which must actually satisfy the set of safety constraints simultaneously). This represents a vector of candidate indices within the feasible region of the project.

[0142] Figure 6 This is a schematic diagram of the decision projection of the feasible domain and safety constraint set of the present invention. Figure 4In this context, Ω represents the feasible region, defined by the specification limits of performance indicators, engineering contract indicators, and internal control standards of the enterprise; C represents the set of safety constraints, including material safety boundaries such as the upper limit of the water-cement ratio and the lower limit of cementitious materials. When the modified target performance indicator Q′ does not meet the constraints, it is mapped to the feasible region through projection adjudication to obtain the feasible indicator. In actual adjudication, the safety constraint set C must be satisfied simultaneously, i.e. .

[0143] In this embodiment, the safety constraint set has an upper limit of 0.40 for the water-to-glue ratio, while the water-to-glue ratio in Q′ is 0.42. Therefore, Q′ needs to be projected to the feasible region that satisfies the water-to-glue ratio ≤ 0.40. Solving the projection decision problem, we obtain Q′′:

[0144] The water-cement ratio was adjusted to 0.40 (boundary value); the early (1d) strength was adjusted to 10.5MPa (because the reduced water-cement ratio may affect the strength, it was recalculated through the mapping relationship); the setting time was adjusted to 8.5h (fine adjustment).

[0145] Mix design based on Q′′: Use early-strength admixtures, increase cement ratio, control water-cement ratio at 0.40, and cementitious material at 420 kg / m³. 3 Verify the design results:

[0146] Strength sampling inspection: early strength 10.8MPa, 28-day strength 46.2MPa, meeting the requirements; setting time measurement: initial setting time 8.5h, within the construction window; durability sampling inspection: freeze-thaw resistance, chloride ion diffusion and other indicators are qualified.

[0147] The validation results are used to update the mapping model parameters (such as regression coefficients) to improve the prediction accuracy of low-temperature conditions.

[0148] Figure 7 This diagram illustrates the comparison of key indicators and engineering results before and after the application of this invention. Compared with traditional empirical methods, the method of this invention reduces concrete strength dispersion from 75% to 55%, pump blockage rate from 60% to 40%, frequency of on-site temporary water addition from 80% to 45%, and durability qualification rate from 75% to 88%, significantly improving the consistency, stability, and controllability of concrete construction performance and structural performance.

[0149] Example 4

[0150] This invention also provides a concrete performance index risk compensation and dynamic correction system. The system includes a memory, a processor, and a computer program or instructions stored in the memory. The processor executes the computer program or instructions to implement the concrete performance index risk compensation and dynamic correction method in this invention.

[0151] Although not shown, the system includes a processor that can perform various appropriate operations and processes based on programs and / or data stored in read-only memory (ROM) or loaded from a storage portion into random access memory (RAM). The processor can be a multi-core processor or may contain multiple processors. In some embodiments, the processor may include a general-purpose main processor and one or more specialized coprocessors, such as a central processing unit, graphics processing unit (GPU), neural network processor (NPU), digital signal processor (DSP), etc. Various programs and data required for device operation are also stored in the RAM. The processor, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0152] The processor and memory described above are used together to execute programs / instructions stored in the memory. When the program / instructions are executed by the computer, they can implement the methods, steps, or functions described in the above embodiments.

[0153] The above description only discloses specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or modifications 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 method for risk compensation and dynamic correction of concrete performance indicators, characterized in that, The method includes: Obtain the construction condition parameters of the target project, and perform risk identification and quantification on the construction condition parameters to construct a condition risk vector; Obtain a preset basic performance index vector, establish a deterministic mapping relationship between the working condition risk vector and the concrete performance index correction amount, determine the performance index correction amount corresponding to the working condition risk vector according to the deterministic mapping relationship, and then superimpose the performance index correction amount onto the basic performance index to obtain the corrected target performance index. The modified target performance index is input into preset constraints for feasibility assessment. When the modified target performance index meets the constraints, it is directly output as an implementable index. When the modified target performance index does not meet the constraints, the modified target performance index is mapped to the constraints through optimization and adjustment to obtain an implementable index that meets the constraints. Concrete mix design is carried out based on the feasible indicators, and the design results are verified and applied in engineering. The parameters of the deterministic mapping relationship are updated based on the verification results and engineering application data.

2. The method for risk compensation and dynamic correction of concrete performance indicators according to claim 1, characterized in that, The construction condition parameters include at least two or more of the following: ambient temperature, mixture temperature, transportation distance, transportation time, waiting time, pumping height, pumping pipe diameter, number of pumping pipe bends, volumetric reinforcement ratio, pouring cross-sectional dimensions, and continuous pouring duration.

3. The method for risk compensation and dynamic correction of concrete performance indicators according to claim 1, characterized in that, The construction condition parameters are subjected to risk identification and quantification to construct a condition risk vector, specifically including: For each construction condition parameter, the corresponding normalization function is used to perform dimensionless processing, and the original condition parameter is mapped to the risk component in the interval [0,1] to obtain the condition risk vector composed of multiple risk components. The normalization function is one or more of a piecewise linear function, a logic function, or an empirical calibration function, and different operating parameters can select the same or different types of normalization functions.

4. The method for risk compensation and dynamic correction of concrete performance indicators according to claim 1, characterized in that, Establish a deterministic mapping relationship between the working condition risk vector and the correction amount of concrete performance indicators, and determine the performance indicator correction amount corresponding to the working condition risk vector based on the deterministic mapping relationship, specifically including: Risk level classification thresholds are set based on historical data or engineering experience, and multiple risk level intervals are divided according to the risk level classification thresholds, with each risk level interval corresponding to a preset mapping model. A comprehensive risk score is calculated based on the aforementioned working condition risk vector, and the risk level range into which the risk falls is determined based on the comprehensive risk score. Using a mapping model corresponding to the determined risk level range, a deterministic mapping relationship is established between the working condition risk vector and the correction amount of concrete performance index, and the corresponding performance index correction amount is output. The mapping model is established through one or more of the following methods: statistical regression analysis, piecewise linear fitting, kernel regression, or engineering lookup table, including linear models, nonlinear models, or piecewise models.

5. The method for risk compensation and dynamic correction of concrete performance indicators according to claim 1, characterized in that, The basic performance indicators include at least one or more of the construction stage indicators and the hardening stage indicators; wherein: The construction stage indicators include at least one of the following: fluidity slump, slump loss over time, pumpability index, water retention index, pressure bleeding threshold, and anti-segregation score threshold. The hardening stage indicators include at least one of the following: 28-day compressive strength, early strength threshold, elastic modulus, chloride ion diffusion coefficient, electrical flux index, freeze-thaw resistance grade, and drying shrinkage control index.

6. The method for risk compensation and dynamic correction of concrete performance indicators according to claim 1, characterized in that, The preset constraints include the engineering feasible region and the set of safety constraints; The feasible domain of the project is jointly defined by the specification limits of performance indicators, project contract indicators, and enterprise internal control standards; The set of safety constraints includes at least one or more of the following: upper limit of water-cement ratio, lower limit of cementitious material, upper limit of admixture dosage, adjustable range of sand ratio, or lower limit of durability index.

7. The method for risk compensation and dynamic correction of concrete performance indicators according to claim 6, characterized in that, When the modified target performance index violates the set of safety constraints, constrained optimization is used to transform the modified target performance index into an implementable index that satisfies the set of safety constraints. The constrained optimization includes projection decision, which is achieved by the following formula: ； in, This represents a vector of enforceable indicators following the ruling. Represents the feasible region of the project; This represents the corrected target indicator vector; This represents a vector of candidate indices within the feasible region of the project.

8. The method for risk compensation and dynamic correction of concrete performance indicators according to claim 1, characterized in that, The optimization and adjustment includes a multi-indicator reallocation step, specifically including: When improving the fluidity index causes the anti-segregation or water retention index to fail to meet the constraints, the performance index correction amount is allocated among fluidity, cohesiveness and water retention to ensure that the feasible index simultaneously meets the pumpability threshold and the anti-segregation threshold. The multi-indicator redistribution is achieved by solving a weighted objective function, and the components of each indicator in the implementable indicator vector are determined through optimization. ； In the formula, This represents the j-th component in the modified target index vector; This represents the j-th component in the vector of feasible indicators to be determined. This represents the priority weight of the j-th indicator.

9. The method for risk compensation and dynamic correction of concrete performance indicators according to claim 1, characterized in that, The verification of the design results includes at least one or more of the following: slump test over time, pressure bleeding test, segregation resistance evaluation, strength sampling inspection, or durability sampling inspection; the parameters for updating the deterministic mapping relationship include updating the weights of the risk components or the regression coefficients of the mapping model.

10. A concrete performance index risk compensation and dynamic correction system, comprising a memory, a processor, and a computer program or instructions stored in the memory, characterized in that, The processor executes the computer program or instructions to implement the concrete performance index risk compensation and dynamic correction method as described in any one of claims 1 to 9.

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