A method and system for risk early warning of dual verification of concrete mix proportions

Abstract

This invention relates to the field of concrete mix design technology, providing a method and system for dual-verification risk early warning of concrete mix proportions. One method includes the following steps: S1: Constructing a rule engine library to convert key control indicators in concrete mix design specifications into programmable logic rules; S2: Constructing an engineering practice data benchmark library to collect successful mix proportion data for each strength grade of completed projects, and establishing the mean, standard deviation, and reasonable range of key parameters for each strength grade through statistical analysis; S3: Obtaining the concrete mix proportion parameters to be verified, and performing rule verification and data verification in parallel. This invention combines regulatory constraints with engineering practice experience, enabling proactive risk identification and intelligent optimization during the mix design stage. This effectively avoids rework and quality hazards caused by delayed risk assessment in traditional methods, improving project quality and safety.

Description

Technical Field

[0001] This invention belongs to the field of concrete mix design technology, and particularly relates to a method and system for risk warning of double verification of concrete mix proportions. Background Technology

[0002] Concrete is the most widely used and influential structural material in construction engineering, and the scientific nature of its mix design directly determines the safety, durability, and economy of the structure. However, for a long time, concrete mix design has mainly relied on engineers' experience and repeated trial mixing, lacking a systematic and digital risk warning mechanism. Existing technologies mainly suffer from the following problems:

[0003] Mix design relies heavily on the individual experience of engineers, and the mix proportions designed by different technicians can vary significantly. When experienced technicians leave or retire, their valuable practical experience is also lost, making it difficult to pass on and reuse within the organization. For example, in the process of optimizing the C50 concrete mix proportion for a certain super high-rise project, it was necessary to conduct 3 to 4 trial mixes to determine a reasonable mix proportion, with a trial mix period of more than 5 days, heavily relying on the trial-and-error experience of technicians.

[0004] The lack of a risk identification mechanism during the mix design phase often leads to problems being discovered only during trial mixing or even construction, resulting in rework and potential quality issues. For example, in one project, the concrete strength was found to be substandard after pouring. Investigation revealed that the excessive fly ash content caused slow early strength development, resulting in significant rework losses by then.

[0005] Therefore, there is an urgent need for a method and system for dual verification of concrete mix proportion risk early warning that can combine regulatory constraints with engineering practice experience to achieve risk identification and intelligent optimization in the mix proportion design stage. Summary of the Invention

[0006] The purpose of this invention is to provide a method and system for risk warning of dual verification of concrete mix proportions, in order to solve the above-mentioned problems.

[0007] This invention is implemented as follows: a method for risk early warning through dual verification of concrete mix proportions, comprising the following steps:

[0008] S1: Build a rule engine library to transform key control indicators in concrete mix design specifications into programmable logic rules;

[0009] S2: Construct a benchmark database of engineering practice data, collect successful mix proportion data of each strength grade of completed projects, and establish the mean, standard deviation and reasonable range of key parameters for each strength grade through statistical analysis;

[0010] S3: Obtain the concrete mix proportion parameters to be verified, and perform rule verification and data verification in parallel;

[0011] S4: Comprehensive assessment and optimization suggestion generation. Combining the rule verification and data verification results of S3, the risk level is determined according to the preset risk level judgment rules, and the optimization rule library is called to generate targeted optimization suggestions.

[0012] In a further embodiment, the key control indicators in S1 include: the upper limit of the water-cement ratio for each strength grade, the lower limit of the minimum amount of cementitious material, the minimum to maximum allowable amount of mineral admixtures, and the minimum to maximum allowable sand ratio within the reasonable range of sand ratio.

[0013] A further proposed solution involves constructing the engineering practice data benchmark library in S2, which includes the following sub-steps:

[0014] S21: Collect successful mix ratio data for each strength level;

[0015] S22: Data cleaning, removing outliers and invalid data;

[0016] S23: Classify by intensity level, calculate the mean and standard deviation of each key parameter, where the mean is the sum of all sample parameter values ​​divided by the sample size, and the standard deviation is the square root of the sum of the squares of the differences between each sample value and the mean divided by the sample size minus one.

[0017] S24: Determine a reasonable range from the mean minus the standard deviation to the mean plus the standard deviation to form a benchmark database for engineering practice data.

[0018] A further proposed solution includes an engineering practice data benchmark library with a dynamic update mechanism. When the number of newly added successful mix proportion data sets reaches a preset threshold for the amount of new data, the mean, standard deviation, and reasonable range of key parameters for each strength level are automatically recalculated, and a benchmark library change report is generated, recording the parameter change trend and fluctuation range. Among these, newly added successful mix proportion data refers to mix proportion data that has completed engineering verification and has been determined to be qualified. The solution also supports both manual triggering and automatic timed updating modes.

[0019] In a further embodiment, in S3:

[0020] Rule verification involves comparing the mix ratio parameters with the S1 rule engine library item by item, identifying violations and alerting to risks.

[0021] Data verification involves matching the mix proportion parameters with the engineering practice data benchmark library of S2, calculating the percentage deviation of each key parameter from the benchmark mean, which is calculated by subtracting the benchmark mean from the input parameter value, dividing by the benchmark mean, and then multiplying by 100%. It also involves determining whether the input parameter value is within a reasonable range and determining the degree of deviation of each parameter based on the magnitude of the deviation and the range judgment results.

[0022] A further proposed solution is as follows: The risk level determination rules in S4 are as follows:

[0023] Low risk: Fully complies with all requirements of the rule verification, and the absolute value of the percentage deviation of all key parameters from the benchmark mean is less than or equal to the first deviation threshold;

[0024] Medium risk: Meets the rule verification requirements, but one or more key parameters satisfy the condition that the absolute value of the deviation percentage is greater than the first deviation threshold and less than or equal to the second deviation threshold;

[0025] High risk: Violation of any requirement of the rule verification, or the absolute value of the percentage deviation of any key parameter is greater than the second deviation threshold.

[0026] In a further proposed approach, the first deviation threshold and the second deviation threshold are determined based on the statistical analysis results of multiple engineering projects and multiple sets of mix proportion data, thereby achieving precise quantification of the degree of risk.

[0027] A further proposed solution involves an optimization rule base constructed based on experimental data relating fly ash content to early strength and water-cement ratio to strength margin, which includes the following quantitative rules:

[0028] When the fly ash content is greater than or equal to the first content threshold, the early strength growth rate of concrete decreases. It is recommended to adjust the fly ash content to be less than this threshold.

[0029] When the water-cement ratio exceeds the upper limit of the water-cement ratio by a value greater than or equal to the first deviation value, the average strength allowance in the later stage decreases. It is recommended to reduce the water-cement ratio or increase the amount of cementitious material.

[0030] When no mineral powder is added, it is recommended to add mineral powder within the range of the lower limit to the upper limit of the mineral powder content to replace an equal amount of cement.

[0031] The input module is used to obtain the concrete mix proportion parameters to be verified.

[0032] The rules engine module stores the logical rules for key control indicators in concrete mix design specifications.

[0033] The engineering practice data benchmark module stores the mean, standard deviation, and reasonable range of key parameters for each strength level established through statistical analysis.

[0034] The dual verification module performs rule verification and data verification in parallel. The data verification adopts the method of calculating the percentage deviation of each key parameter from the benchmark mean.

[0035] The assessment output module outputs the risk level and optimization suggestions based on the preset risk level determination rules.

[0036] The display module is used to show the risk level, violations, deviations, and optimization suggestions.

[0037] A further solution also includes a data update module, which triggers a dynamic update mechanism. When the number of newly added mix proportion data sets reaches a preset threshold for the amount of new data, the engineering practice data benchmark module is automatically or manually updated, and a benchmark library change report is generated.

[0038] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0039] This invention combines regulatory constraints with engineering practice experience, enabling proactive risk identification and intelligent optimization during the mix design stage. This effectively avoids rework and quality hazards caused by delayed risk assessment (such as discovering insufficient strength after pouring) in traditional methods, thereby improving project quality and safety.

[0040] In this invention, the parallel processing mechanism of rule verification and data verification significantly shortens the verification time; at the same time, the design specifications are transformed into programmable logic rules, replacing manual item-by-item comparison, avoiding human omissions or subjective biases, and improving the standardization and automation level of verification.

[0041] In this invention, an engineering practice data benchmark library is constructed and supports dynamic updates. Successful mix proportion data of completed projects are transformed into quantifiable statistical benchmarks (mean, standard deviation, and reasonable range), enabling the digital reuse of historical experience, reducing reliance on personal experience, and significantly reducing the cost and cycle of repeated trial mixes.

[0042] In this invention, the risk level (low / medium / high) is accurately quantified based on the absolute value of the deviation percentage and dual thresholds (first deviation threshold and second deviation threshold). Combined with the optimization rule base (such as quantitative rules for fly ash content, water-cement ratio deviation, mineral powder content, etc.), targeted optimization suggestions are generated to guide designers to quickly adjust the mix proportions, forming a "verification-evaluation-optimization" closed loop, which improves the scientificity and efficiency of mix proportion design. Attached Figure Description

[0043] Figure 1 A schematic diagram illustrating the steps of the double-verification risk warning method for concrete mix proportions;

[0044] Figure 2 A schematic diagram illustrating the steps involved in building a benchmark database for engineering practice data;

[0045] Figure 3 This is a schematic diagram of a dual-verification risk early warning system for concrete mix proportions. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0047] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0048] like Figure 1 As shown, a method for risk warning of double verification of concrete mix proportions provided in an embodiment of the present invention includes the following steps:

[0049] In step S1, a rule engine library is constructed to transform key control indicators in concrete mix design specifications into programmable logic rules. The purpose of this step is to centrally manage and digitize the hard constraints scattered across various standards and specifications. For example, provisions regarding the upper limit of water-cement ratio and the minimum amount of cementitious materials in the "Specification for Mix Design of Ordinary Concrete" (JGJ 55-2011) can be manually entered into a text file, expressed in simple "if-then" statements, such as "if the water-cement ratio is greater than 0.45, then it is unqualified." These rules can be read and executed by a basic script program, thereby achieving a preliminary compliance check of the input parameters. This approach avoids potential omissions or subjective judgment biases that may occur during manual verification, improving the standardization of verification.

[0050] In step S2, an engineering practice data benchmark library is constructed, collecting successful mix proportion data for each strength grade of completed projects. Statistical analysis is used to establish the mean, standard deviation, and reasonable range for key parameters at each strength grade. This step aims to quantify and systematize the company's long-term accumulated engineering practice experience. For example, verified qualified concrete mix proportion data for different strength grades such as C30 and C40 can be manually selected from completed project archives, and this data (such as water-cement ratio, sand ratio, and cementitious material dosage) can be entered into a spreadsheet. Subsequently, the statistical functions of the spreadsheet software are used to calculate the mean and standard deviation of each key parameter at each strength grade, and a simple empirical range is set as a reasonable range. The establishment of this benchmark library allows new mix proportion designs to be compared with historical successful experiences, thereby identifying potential empirical deviations.

[0051] In step S3, the concrete mix proportion parameters to be verified are obtained, and rule verification and data verification are performed in parallel. This step is the core of this method, achieving a comprehensive evaluation of the mix proportion by simultaneously performing verifications from two different dimensions. For example, when a designer inputs a new set of C30 concrete mix proportion parameters, the system can simultaneously launch two independent processes: one process is responsible for comparing these parameters with the rule engine library established in step S1 to check for any violations of normative requirements; the other process is responsible for comparing these parameters with the engineering practice data benchmark library of C30 strength grade established in step S2 to assess the degree of deviation from historical successful experiences. This parallel processing method can significantly improve verification efficiency and ensure that comprehensive verification results are obtained in a short time.

[0052] In step S4, a comprehensive evaluation and optimization suggestion generation process is performed. Combining the rule verification and data verification results from S3, the risk level is determined according to preset risk level judgment rules, and targeted optimization suggestions are generated by calling the optimization rule library. This step aims to transform the verification results into an understandable risk assessment and actionable improvement plan. For example, some simple risk judgment logic can be preset: if any violation is found during rule verification, it is directly judged as high risk; if the rule verification passes, but the data verification shows that a key parameter (such as the water-cement ratio) deviates significantly from the historical average, it is judged as medium risk; if all verifications meet the requirements, it is judged as low risk. Based on the determined risk level, the system can select and display corresponding general suggestions from a preset list of optimization suggestions, such as "the water-cement ratio is too high, it is recommended to reduce it appropriately" or "the sand ratio deviates from the historical range, it is recommended to review it." In this way, designers can quickly understand the risk status of the mix proportion and obtain preliminary improvement directions.

[0053] In a preferred embodiment of the present invention, the key control indicators in S1 include: the upper limit of the water-cement ratio for each strength grade. Minimum amount of cementitious materials Minimum allowable dosage range of mineral admixtures Up to maximum dosage Minimum sand ratio within the reasonable range of sand ratio Up to maximum sand ratio Wherein, the minimum limit value of the amount of cementitious material used is... The unit is kilograms per cubic meter.

[0054] In this embodiment, the water-cement ratio is one of the core parameters in concrete mix design, which directly affects the strength, durability and workability of concrete.

[0055] Upper limit of water-to-glue ratio This refers to the maximum permissible ratio of water to cementitious materials by mass at a specific strength grade to ensure that concrete performance meets design requirements. This upper limit is typically determined based on national standards, industry specifications, or engineering experience. Its purpose is to limit the amount of water used in concrete, preventing excessive water from reducing concrete strength and increasing porosity, thereby affecting its density and durability. Cementitious materials are the key components in concrete that provide strength and durability.

[0056] Minimum amount of cementitious materials This refers to the minimum required amount of cementitious materials per cubic meter of concrete at a specific strength grade to ensure sufficient strength and durability. This lower limit is also set according to relevant specifications and standards. Its purpose is to ensure that the concrete has sufficient cementitious activity to form a dense structure, resist external erosion, and avoid insufficient strength, shrinkage cracking, or decreased durability due to insufficient cementitious materials. Mineral admixtures in concrete improve workability, reduce heat of hydration, and enhance later-stage strength and durability.

[0057] Minimum dosage within the allowable dosage range Up to maximum dosage This refers to the permissible range of the proportion of mineral admixtures in the total amount of cementitious materials at a specific strength grade. The determination of this range is usually based on the type and properties of the admixture, the concrete strength grade, and the specific requirements of the project for early strength, later strength, and durability. Its purpose is to guide the rational use of mineral admixtures, maximizing their advantages in improving performance and reducing costs while avoiding adverse effects on concrete performance due to excessively low or high dosages.

[0058] Sand ratio refers to the percentage of sand volume or mass to the total volume or mass of aggregates. The minimum sand ratio within the reasonable range is... Up to maximum sand ratio This refers to the range within which the sand ratio should be controlled to ensure good workability and density of concrete at a specific strength grade. Determining this range typically considers aggregate gradation, particle shape, concrete slump requirements, and construction techniques. Its purpose is to optimize aggregate gradation, ensure good fluidity and anti-segregation properties in the concrete mix, facilitate construction, and ultimately form dense, uniform, hardened concrete. It avoids excessive sand ratio leading to sticky, water-bleeding concrete, or excessive sand ratio leading to segregation and a rough texture.

[0059] In addition, the minimum amount of cementitious materials will be set as follows: The unit is clearly defined as kilograms per cubic meter, which can eliminate verification errors that may be caused by inconsistent units and ensure that the rule engine library can make accurate quantitative judgments when processing and comparing data.

[0060] This application's solution specifies and quantifies the key control indicators in the concrete mix design specification, clarifying the core parameters and their value ranges that the rule engine library S1 needs to include. Specifically, the upper limit of the water-cement ratio... Minimum amount of cementitious materials The allowable dosage range of mineral admixtures ( to and the reasonable range of sand ratio () to The settings enable the rule engine library to make precise logical judgments on key performance indicators across multiple dimensions, such as concrete strength, durability, and workability. After these quantitative indicators are converted into programmable logic rules, the concrete mix proportion parameters to be verified can be compared item by item with these preset specification requirements in the subsequent rule verification S3.

[0061] As a specific implementation method, the key control indicators in S1 can be set according to national standards such as the "Specification for Mix Proportion Design of Ordinary Concrete" (JGJ 55-2011) and the "Standard for Durability Design of Concrete Structures" (GB / T 50476-2019). For example, for C30 strength grade concrete, its key control indicators can be specifically set as follows: upper limit of water-cement ratio. It can be set to 0.47; minimum cementitious material dosage. The concentration can be set to 330 kg / m³; for pumped C30 concrete, the sand ratio can be set between 35% and 45%, i.e. =35%, =45%. These specific values ​​and ranges will be encoded into logical judgment statements in the rule engine library S1, such as "IF (water-to-glue ratio > 0.47) THEN Risk Warning: Water-to-glue ratio exceeds limit". In this way, the rule engine library S1 can automatically and systematically perform specification verification, ensuring that every mix proportion to be verified meets the most basic engineering requirements.

[0062] like Figure 2 As shown, in a preferred embodiment of the present invention, the construction of the engineering practice data benchmark library in S2 includes the following sub-steps:

[0063] S21: Collect successful mix ratio data for each strength level;

[0064] S22: Data cleaning, removing outliers and invalid data;

[0065] S23: Classify by strength level and calculate key parameters. mean and standard deviation :

[0066] ,

[0067] in For the sample size, For the first Key parameter values ​​for each sample;

[0068] S24: Determine a reasonable range This will form a benchmark database for engineering practice data.

[0069] In this embodiment, step S21 involves collecting successful mix proportion data for each intensity level, aiming to provide a comprehensive and representative raw dataset for the benchmark library. This data can be obtained from various sources, such as importing validated successful mix proportion records in batches from an enterprise's internal project management system, laboratory reports, or historical project archives; or, through collaboration with external databases, industry alliances, or research institutions, obtaining widely validated successful mix proportion datasets.

[0070] In step S22, data cleaning is performed to remove outliers and invalid data to ensure data quality and reliability. Data cleaning can employ various techniques, such as using preset data verification rules (e.g., setting reasonable physical ranges for parameters like water-cement ratio and sand ratio, such as ensuring the water-cement ratio cannot be negative) and performing data integrity checks (e.g., marking missing key parameters as invalid). This allows for the automatic or semi-automatic identification and removal of data that does not meet the requirements.

[0071] In step S23, key parameters are calculated according to strength level. mean and standard deviation This step aims to accurately quantify the central tendency and dispersion of parameters across different strength grades. Specifically, database queries and statistical analysis tools can be used to group the cleaned data according to concrete strength grades (e.g., C30, C40, C50, etc.), and then calculate the arithmetic mean and sample standard deviation for key parameters such as water-cement ratio, cementitious material content, and sand ratio within each group. Alternatively, a dedicated data processing module can be developed. This module receives the categorized data, incorporates built-in functions for calculating the mean and standard deviation, automatically performs statistical analysis, and stores the results.

[0072] In step S24, the reasonable range is determined. This process ultimately forms an engineering practice data benchmark library. This step aims to provide clear criteria for subsequent data verification. The mean calculated based on S23... and standard deviation The system can directly apply formulas to determine the reasonable range for each key parameter. These range values, along with the corresponding strength level and parameter name, are stored in a structured database, forming a benchmark database of engineering practice data that can be queried and compared.

[0073] The proposed solution constructs an engineering practice data benchmark library through the aforementioned systematic steps, addressing the issues of low data utilization and insufficient benchmark reliability in traditional methods. First, step S21, by extensively collecting successful mix proportion data for various intensity levels, provides a comprehensive and representative raw dataset for the benchmark library, ensuring broad applicability for subsequent verification. Second, step S22, data cleaning, effectively eliminates outliers and invalid data, significantly improving the overall quality and reliability of the data and avoiding the negative impact of inaccurate information on statistical results. Based on this, step S23 calculates the mean and standard deviation of key parameters according to intensity level, accurately quantifying the central tendency and dispersion of parameters at different intensity levels, providing a solid mathematical foundation for establishing scientific benchmarks. Finally, step S24 determines reasonable intervals based on the mean and standard deviation, forming an operable benchmark library. By defining specific parameter ranges, the benchmark library possesses practical application value, facilitating rapid assessment of parameter deviations during data verification. These steps work together to achieve standardized processing and knowledge transformation of engineering practice data, providing a reliable data foundation for the subsequent double-verification risk warning method for concrete mix proportions. This enables data verification to accurately assess the degree of deviation of the mix proportion parameters to be verified, thereby improving the accuracy and efficiency of the entire risk warning system.

[0074] The following is a concrete example to illustrate this. Suppose an engineering project needs to build a benchmark database of engineering practice data for C30 concrete. First, in step S21, the system can collect hundreds of sets of detailed mix proportion data from all successfully applied C30 concrete mix proportion projects of the company over the past ten years, including key parameters such as water-cement ratio, cementitious material dosage, and sand ratio. Next, in step S22, the system cleans this raw data. For example, if a set of data is found to have a negative water-cement ratio or a cementitious material dosage significantly lower than the industry standard lower limit, it is marked as invalid data and removed. At the same time, for certain parameters, such as sand ratio, if its value exceeds three times the standard deviation calculated based on historical data, it is identified as an outlier and processed. Subsequently, in step S23, the system performs statistical analysis on the cleaned C30 concrete mix proportion data, calculating the mean and standard deviation of the C30 concrete water-cement ratio, as well as the mean and standard deviation of other key parameters such as cementitious material dosage and sand ratio. Finally, in step S24, based on these statistical results, the system determines a reasonable range for the water-cement ratio of C30 concrete and generates corresponding reasonable ranges for other key parameters. These range data, along with the mean and standard deviation, constitute a benchmark database of engineering practice data for C30 concrete, which can be used for subsequent mix proportion verification.

[0075] In a preferred embodiment of the present invention, the engineering practice data benchmark library has a dynamic update mechanism, which updates when the number of newly added mix proportion data groups increases. Reaching the preset threshold for new data volume ,Right now At that time, the average value of key parameters for each strength level will be automatically recalculated. Standard deviation and reasonable range It generates a baseline library change report, recording the trend and fluctuation range of parameter changes; among them, newly added successful mix proportion data refers to mix proportion data that has completed engineering verification and has been judged to be qualified; it also supports two modes: manual trigger update and automatic timed update.

[0076] In this embodiment, the engineering practice data benchmark library has a dynamic update mechanism, meaning that the benchmark library can automatically adjust the statistical parameters stored internally based on the input of new data to maintain the timeliness and accuracy of the data. This could be a background service program that continuously monitors new data in a specific data storage location and initiates an update process when specific conditions are met; or it could be a functional module integrated into a data management system that allows users or system administrators to configure update strategies and trigger conditions.

[0077] When the number of groups with newly added successful combination ratio data Reaching the preset threshold for new data volume In this case, the threshold condition is used to control the update frequency, ensuring that an update is triggered only when enough new data has been accumulated, thus avoiding resource waste. The system can maintain a counter to record the number of qualified mix proportion data sets added since the last update. When the counter reaches a preset threshold, an update is triggered; alternatively, the system can periodically check the amount of data in the newly added data storage area and compare it with a preset threshold to determine whether to initiate an update.

[0078] Automatically recalculate the mean values ​​of key parameters for each strength level. Standard deviation and reasonable range This ensures that the statistical benchmark is always based on the latest and most comprehensive dataset. This can be achieved by using data processing scripts or statistical analysis software libraries to perform these calculations, taking all existing data (including new data) as input; or by using incremental calculation methods to quickly update the existing mean and standard deviation based on the new data, thereby improving computational efficiency.

[0079] Generate a baseline library change report, recording parameter change trends and fluctuation ranges, aiming to provide transparency and traceability of the update process. The system can automatically generate a document containing a comparison of key parameter statistical values ​​before and after the update, percentage changes, and update time; or store this change information in a dedicated log database and provide a query interface so that users can view historical change records and trend charts at any time.

[0080] Newly added successful mix proportion data refers to mix proportion data that has completed engineering verification and has been determined to be qualified. This emphasizes the importance of data quality, ensuring that only reliable data can affect the benchmark database. It can be required that new data be reviewed and approved by the project manager or quality engineer before being entered into the system, and marked as "verified and qualified" in the system; alternatively, an automated verification rule can be set up to perform a preliminary check on newly entered data, and only data that passes verification will be included in the category of "successful mix proportion data".

[0081] It supports both manual and automatic scheduled updates, providing flexible update options. Manual updates can be triggered via a button or command on the user interface; automatic scheduled updates can be configured through a task scheduler to execute the update operation at a fixed time.

[0082] In a preferred embodiment of the present invention, in step S3:

[0083] Rule verification involves comparing the mix ratio parameters with the S1 rule engine library item by item, identifying violations and alerting to risks.

[0084] Data verification involves matching the mix proportion parameters with the S2 engineering practice data benchmark database based on similarity, and calculating each key parameter. Compared with the benchmark mean percentage of deviation :

[0085]

[0086] in These are the key parameter values ​​for input;

[0087] judge Is it within a reasonable range? Within the range, the degree of deviation of each parameter is determined based on the magnitude of the deviation and the interval judgment results.

[0088] In this embodiment, rule verification involves comparing the concrete mix proportion parameters to be verified with the rule engine library of S1 item by item, identifying violations that do not meet the specifications, and highlighting the risks. The rule engine library is constructed by converting key control indicators in the concrete mix design specifications into programmable logic rules. For example, a rule-based expert system can be used to convert specification clauses into logical judgment statements in the form of "IF-THEN". When input parameters trigger specific conditions, the system automatically identifies and marks violations. Another implementation method is to use a configurable rule management platform, allowing users to define and maintain rules using a graphical interface or scripting language. The system dynamically loads and executes these rules at runtime, thereby achieving automated specification verification of mix proportion parameters.

[0089] Data verification involves matching the concrete mix proportion parameters to be verified against the S2 engineering practice data benchmark database. The engineering practice data benchmark database stores successful mix proportion data for each strength grade of completed projects and establishes the mean values ​​of key parameters for each strength grade through statistical analysis. Standard deviation and reasonable range During the data verification process, the key parameters are first calculated. Compared with the benchmark mean percentage of deviation Its calculation formula is ,in The input key parameter values ​​are used for calculation. This calculation can be performed through the mathematical operation module of the software system, which receives the input parameter values ​​and the mean value retrieved from the benchmark database, and automatically completes the percentage calculation. Simultaneously, it determines the input key parameter values. Is it within a reasonable range? Internal. The judgment process is a comparison. Is it greater than or equal to the lower limit of the interval and less than or equal to the upper limit of the interval? Finally, based on the percentage deviation... Size and Is it within a reasonable range? The judgment results within the range are used to comprehensively determine the degree of deviation of each parameter. For example, different deviation levels can be set, such as "slight deviation", "moderate deviation" or "severe deviation", and the judgment can be made in combination with the threshold of deviation percentage and the state inside and outside the range.

[0090] This application's solution achieves a comprehensive evaluation of concrete mix proportion parameters by executing rule verification and data verification in parallel. Upon obtaining the concrete mix proportion parameters to be verified, the system simultaneously initiates two independent verification processes. The rule verification process compares the input mix proportion parameters one by one with the preset specification logic rules in S1, quickly identifying any items that do not comply with mandatory specification requirements, ensuring the compliance of the mix proportion design. Simultaneously, the data verification process matches the same mix proportion parameters with an engineering practice data benchmark database built based on historical successful experience in S2. During this process, the degree of deviation of each key parameter from the benchmark mean is quantified by accurately calculating the percentage deviation. Furthermore, it determines whether the parameters fall within a statistically reasonable range, providing a more comprehensive perspective for assessing the degree of deviation. These two verification processes operate independently but their results complement each other. Rule verification provides "hard" compliance constraints, while data verification provides "soft" practical experience references and quantifies the degree of deviation. Ultimately, combining the results of both methods allows for a more objective and accurate assessment of the risks associated with the mix design, laying a solid foundation for subsequent risk level determination and optimization recommendations. This dual-verification mechanism effectively overcomes the shortcomings of traditional methods that rely solely on standards or experience, significantly improving the scientific rigor and reliability of risk warnings.

[0091] In a preferred embodiment of the present invention, the risk level determination rule in S4 is as follows:

[0092] Low risk: Fully complies with all requirements of the rule verification, and all key parameters deviate from the benchmark mean by a percentage. The absolute value is less than or equal to the first deviation threshold. ;

[0093] Medium risk: Meets the rule verification requirements, but one or more key parameters meet the first deviation threshold. Less than absolute value and The absolute value is less than or equal to the second deviation threshold. ;

[0094] High risk: Violation of any requirement of the rule verification, or any key parameter meeting the percentage deviation requirement. The absolute value is greater than the second deviation threshold. .

[0095] In this embodiment, the risk level determination rule of this application forms a hierarchical and quantitative risk assessment system by combining the results of rule verification and data verification. Upon receiving the concrete mix proportion parameters to be verified, the system performs rule verification and data verification in parallel. Rule verification first conducts a preliminary screening of the mix proportion parameters to ensure they comply with the mandatory indicators in the concrete mix proportion design specification. Any mix proportion that violates the specification will be directly determined as high-risk, reflecting the priority guarantee of specification compliance. If the mix proportion passes the rule verification, the system further performs data verification, calculating the percentage deviation of each key parameter from the mean in the engineering practice data benchmark database. The system then compares the absolute values ​​of these deviation percentages with a preset first deviation threshold. Second deviation threshold Compare the percentage deviations of all key parameters. The absolute values ​​of all are less than or equal to This indicates that the mix proportion not only conforms to the specifications but also highly aligns with a large amount of successful practical data, and therefore is judged to be low-risk. If there are percentage deviations in some key parameters... The absolute value is between and If the deviation is between 0.5 and 0.6%, it indicates that while the mix design is compliant, there is a certain degree of deviation from practical experience, and it is judged as medium risk. Furthermore, if any key parameter deviates by a certain percentage... The absolute value exceeds Regardless of whether the rule verification passes, the mix proportion will be judged as high-risk, emphasizing that a significant deviation from successful practical experience also constitutes high risk. Through this progressive and quantitative comparison mechanism, this scheme can objectively and consistently assess the risk level of concrete mix proportions, avoiding the subjectivity and inconsistency of traditional manual judgment, thereby improving the accuracy and reliability of risk warning.

[0096] In a preferred embodiment of the present invention, the first deviation threshold Second deviation threshold It is determined based on the statistical analysis results of multiple engineering projects and multiple sets of mix proportion data, so as to achieve precise quantification of the degree of risk.

[0097] In this embodiment, the solution of this application uses a first deviation threshold. The second deviation threshold β is determined through statistical analysis, enabling the dual-verification risk warning method for concrete mix proportions to base its risk level assessment on objective data rather than subjective experience. After obtaining the concrete mix proportion parameters to be verified, the dual-verification module performs rule verification and data verification in parallel. Specifically, the data verification matches the mix proportion parameters with a benchmark database of engineering practice data, calculating the similarity between each key parameter x and the benchmark mean. percentage of deviation Subsequently, the evaluation output module combines the rule verification and data verification results to determine the risk level according to the preset risk level determination rules. It is through statistical analysis of multiple engineering projects and multiple sets of mix proportion data that the first deviation threshold is accurately determined. Second deviation threshold These thresholds serve as key dividing points for risk level determination, providing a solid engineering practice basis for classifying low, medium, and high risks. This data-driven threshold setting method ensures the scientific rigor and reliability of risk level determination, avoiding inaccurate risk quantification caused by improper threshold settings. Therefore, when the system determines a risk level to be low, engineers can be confident that the mix proportion has a high success rate in engineering practice; when it is determined to be medium risk, it indicates a potential problem requiring further attention and adjustment; and when it is determined to be high risk, it indicates a serious defect in the mix proportion, necessitating significant modifications. This risk assessment based on precise quantitative thresholds significantly improves the accuracy and guidance of the entire dual-verification risk warning method, thereby effectively preventing potential quality hazards.

[0098] In a preferred embodiment of the present invention, the optimization rule base is constructed based on experimental data on the relationship between fly ash content and early strength, and the relationship between water-cement ratio and strength margin, and includes the following quantitative rules:

[0099] When fly ash content Greater than or equal to the first doping threshold At that time, the early strength growth rate of concrete decreased, and the decrease was [missing information]. It is recommended to adjust the fly ash content to less than ;

[0100] When the water-cement ratio Exceeding the upper limit of water-to-glue ratio out-of-range Greater than or equal to the first deviation value At that time, the average strength surplus decreased in the later stage, and the decrease was as follows: It is recommended to reduce the water-cement ratio or increase the amount of cementitious material.

[0101] When no mineral powder is added, it is recommended to add the minimum amount of mineral powder. Up to the upper limit of mineral powder content Mineral powder can replace an equivalent amount of cement within the specified range.

[0102] In this embodiment, the optimization rule base is a knowledge set used to generate suggestions for adjusting concrete mix proportions. Its function is to ensure that the generated optimization suggestions have scientific basis and practical reliability, avoiding subjective experience-based judgments. This rule base can be developed through extensive concrete mix proportion experiments in the laboratory, systematically studying the quantitative relationship between key parameters such as fly ash content and water-cement ratio and performance indicators such as early strength and later strength allowance of concrete, and storing these relationships in the form of mathematical models or logical judgments. Furthermore, it can also combine experimental data and performance feedback from historical engineering projects, using data mining and machine learning algorithms to extract the inherent patterns between parameters and performance from massive amounts of data, and transform them into executable optimization rules.

[0103] The quantitative rules governing the relationship between fly ash content and early strength serve to provide clear adjustment suggestions when the fly ash content exceeds a reasonable range, thus ensuring the early strength development of concrete. These rules can be based on a series of early strength (e.g., 7-day compressive strength) test data of concrete specimens with different fly ash contents. The rules can then fit a functional relationship between fly ash content and the rate of decrease in the early strength growth rate, and establish an empirical "first content threshold." "When the actual fly ash content exceeds this threshold, an adjustment suggestion is triggered. Alternatively, an expert system or decision tree model can be used to encode different fly ash content ranges with their corresponding early intensity impact and suggested adjustment directions. For example, when the fly ash content..." When a certain critical value is reached or exceeded, the system determines that early strength growth may be impaired and suggests reducing the fly ash content.

[0104] The quantitative rules governing the relationship between water-cement ratio and strength allowance serve to provide adjustment recommendations when the water-cement ratio exceeds the upper limit specified in the code, thereby ensuring the long-term performance and durability of concrete. These rules can be established based on experimental data of the allowance between the later-stage strength (e.g., 28-day compressive strength) and the design strength of concrete under different water-cement ratio conditions, to determine the water-cement ratio deviation threshold. The average decrease in the later strength margin A quantitative model between them was established, and a "first deviation value" was set. When the water-cement ratio exceeds this value, a recommendation to reduce the water-cement ratio or increase the amount of cementitious materials is triggered. Alternatively, regression analysis can be used to analyze the correlation between the water-cement ratio and the excess strength of concrete. Exceeding the upper limit of water-to-glue ratio And out of tolerance When the preset threshold is reached, the system calculates the possible reduction in the later intensity margin based on the pre-established intensity loss model and provides corresponding adjustment strategies.

[0105] Furthermore, the optimization rules for concrete without mineral powder aim to improve concrete performance and economy by incorporating mineral powder, guiding designers to consider adding mineral powder to improve certain concrete properties (such as durability and workability) or reduce costs when no mineral powder is added. These rules can be based on experimental studies of mineral powder replacing cement to determine the reasonable range of mineral powder dosage (lower limit of mineral powder dosage) while ensuring performance. Up to the upper limit of mineral powder content This suggestion is triggered when the system detects that no mineral powder is added to the mix proportion. Alternatively, a decision matrix can be established by analyzing the impact of different mineral powder dosages on the comprehensive performance of concrete, such as strength, durability, and bleeding. When the mix proportion does not contain mineral powder, the system recommends a suitable range of mineral powder dosages based on preset optimization objectives (such as improving durability or reducing costs).

[0106] This application's solution constructs an optimization rule base based on extensive experimental data, transforming the complex performance variation patterns of concrete materials into quantifiable logical rules. After the concrete mix proportion parameters to be verified pass the rule and data verification in step S3, if potential risks or optimization space are found, the evaluation output module will invoke this optimization rule base. Specifically, this rule base can intelligently determine, based on the input mix proportion parameters, whether there are problems such as excessive fly ash content leading to insufficient early strength, excessive water-cement ratio affecting later strength margin, or insufficient utilization of mineral powder optimization performance. For example, when the system detects excessive fly ash content... Reaching or exceeding the preset first doping threshold At the same time, the decline in the growth rate revealed by the experimental data The system will immediately suggest adjusting the fly ash content to less than [amount missing]. This proactively avoids early strength risks. Similarly, when the water-cement ratio... Exceeding the upper limit of water-to-glue ratio And out of tolerance Reaching or exceeding the first deviation value At that time, the system will calculate the average reduction in the later stage strength margin based on the experimental data. Based on the quantitative relationship, it is recommended to reduce the water-cement ratio or increase the amount of cementitious materials to ensure the long-term performance of concrete. Furthermore, for mix proportions without mineral powder, the system will recommend a lower limit for the amount of mineral powder added, based on experimental data on mineral powder replacing cement. Up to the upper limit of mineral powder content Within a certain range, mineral powder can replace an equivalent amount of cement to improve material utilization efficiency and overall performance. This quantitative rule based on experimental data makes optimization suggestions no longer vague empirical judgments, but specific guidance with clear scientific basis and operability. Combined with the standard rule engine library built by S1 and the engineering practice data benchmark library built by S2, it forms a double verification and intelligent optimization closed loop, which greatly improves the scientificity and accuracy of mix design and achieves a seamless connection from risk identification to risk avoidance.

[0107] like Figure 3 As shown, a dual-verification risk early warning system for concrete mix proportions includes:

[0108] The input module is used to obtain the concrete mix proportion parameters to be verified.

[0109] The rules engine module stores the logical rules for key control indicators in concrete mix design specifications.

[0110] The engineering practice data benchmark module stores the mean values ​​of key parameters for each strength level established through statistical analysis. Standard deviation and reasonable range ;

[0111] The dual-verification module performs rule verification and data verification in parallel. The data verification uses the percentage deviation of each key parameter from the benchmark mean. Calculation method;

[0112] The assessment output module outputs the risk level and optimization suggestions based on the preset risk level determination rules.

[0113] The display module is used to show the risk level, violations, deviations, and optimization suggestions.

[0114] In this embodiment, the solution of this application achieves a deep integration of regulatory constraints and engineering practice experience through a parallel mechanism of rule verification and data verification. Since the rule engine module transforms design specifications into programmable logic rules, it replaces manual verification, significantly reducing the workload and risk of oversight in specification verification. The statistical benchmark established based on the engineering practice data benchmark module transforms historical successful data into quantifiable experience references, effectively solving the problem of low data utilization. Furthermore, the parallel processing mechanism of the dual verification module greatly shortens the verification time, and the optimization suggestions generated by the evaluation output module directly guide design adjustments, avoiding repeated trial mixing. Through the above technical solutions, the efficiency of concrete mix design is improved, trial mixing costs are reduced, and potential risks can be identified at the design stage, fundamentally solving the technical defects such as reliance on manual experience, high trial mixing costs, and delayed risk identification.

[0115] like Figure 3 As shown, in a preferred embodiment of the present invention, a data update module is also included, which is used to trigger a dynamic update mechanism. When the number of groups with newly added mix proportion data reaches a preset threshold for the amount of newly added data, the engineering practice data benchmark module is automatically or manually updated, and a benchmark library change report is generated.

[0116] In this embodiment, the data update module is a functional unit within the system, primarily responsible for managing and executing data update operations of the engineering practice data benchmark module. This module can be an independent software component, responsible for receiving new data, determining update conditions, initiating the update process, and generating update reports. This module can be integrated into the main system or run as a standalone background service, such as a service node in a microservice architecture.

[0117] A dynamic update trigger mechanism refers to the system's ability to automatically or semi-automatically initiate the update process of the data benchmark database based on preset conditions or user commands. The triggering methods for this mechanism can include:

[0118] 1) Monitor the number of groups with newly added successful combination data, and automatically trigger when it reaches the preset threshold for the amount of new data;

[0119] 2) Receive external instructions from system administrators or authorized users, such as clicking the "Update" button through the user interface;

[0120] 3) Automatic, scheduled updates are performed according to a preset timetable, such as daily, weekly, or monthly updates during periods of low system load. These triggering methods ensure that the benchmark library can respond promptly to data accumulation or management needs.

[0121] Automatic or manual updates to the engineering practice data benchmark module refer to the process by which the data update module, after being triggered by the dynamic update mechanism, recalculates and replaces the mean, standard deviation, and reasonable ranges of key parameters for each intensity level stored in the engineering practice data benchmark module. Automatic updates are typically executed by the system's background program according to preset logic and algorithms, requiring no manual intervention; this can be done through batch scripts or service programs. Manual updates allow users to initiate the update process through the system's management interface under specific needs, such as when a large amount of important new data is imported at once or when urgent benchmark corrections are required. The update process involves integrating existing and new data and regenerating benchmark parameters using statistical methods.

[0122] A benchmark database change report is a document or record automatically generated by the system after a data update is completed. This report details the update time, changes in key parameters (such as mean, standard deviation, and reasonable range) before and after the update, the source and quantity of new data, and the trend and range of parameter changes. This report can be used to audit, trace, and evaluate the evolution of the benchmark database, providing users with a transparent view of data management.

[0123] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for risk early warning of concrete mix proportion double-core verification, characterized in that, Includes the following steps: S1: Build a rule engine library to transform key control indicators in concrete mix design specifications into programmable logic rules; S2: Construct a benchmark database of engineering practice data, collect successful mix proportion data of each strength grade of completed projects, and establish the mean, standard deviation and reasonable range of key parameters for each strength grade through statistical analysis; S3: Obtain the concrete mix proportion parameters to be verified, and perform rule verification and data verification in parallel; S4: Comprehensive assessment and optimization suggestion generation. Combining the rule verification and data verification results of S3, the risk level is determined according to the preset risk level judgment rules, and the optimization rule library is called to generate targeted optimization suggestions.

2. The method according to claim 1, characterized in that, The key control indicators in S1 include: The upper limit of water-cement ratio for each strength grade, the lower limit of minimum cementitious material dosage, the minimum to maximum allowable dosage range of mineral admixtures, and the minimum to maximum reasonable sand ratio range.

3. The method according to claim 2, characterized in that, The construction of the engineering practice data benchmark library in S2 includes the following sub-steps: S21: Collect successful mix ratio data for each strength level; S22: Data cleaning, removing outliers and invalid data; S23: Classify by intensity level, calculate the mean and standard deviation of each key parameter, where the mean is the sum of all sample parameter values ​​divided by the sample size, and the standard deviation is the square root of the sum of the squares of the differences between each sample value and the mean divided by the sample size minus one. S24: Determine a reasonable range from the mean minus the standard deviation to the mean plus the standard deviation to form a benchmark database for engineering practice data.

4. The method according to claim 3, characterized in that, The engineering practice data benchmark library has a dynamic update mechanism. When the number of newly added successful mix proportion data sets reaches the preset threshold for the amount of new data, the mean, standard deviation, and reasonable range of key parameters for each strength level are automatically recalculated, and a benchmark library change report is generated to record the parameter change trend and fluctuation range. Among them, newly added successful mix proportion data refers to mix proportion data that has been verified in engineering and judged to be qualified. It also supports two modes: manual trigger update and automatic timed update.

5. The method according to claim 3, characterized in that, In S3: Rule verification involves comparing the mix ratio parameters with the S1 rule engine library item by item, identifying violations and alerting to risks. Data verification involves matching the mix proportion parameters with the engineering practice data benchmark library of S2, calculating the percentage deviation of each key parameter from the benchmark mean, which is calculated by subtracting the benchmark mean from the input parameter value, dividing by the benchmark mean, and then multiplying by 100%. It also involves determining whether the input parameter value is within a reasonable range and determining the degree of deviation of each parameter based on the magnitude of the deviation and the range judgment results.

6. The method according to claim 5, characterized in that, The risk level determination rules in S4 are as follows: Low risk: Fully complies with all requirements of the rule verification, and the absolute value of the percentage deviation of all key parameters from the benchmark mean is less than or equal to the first deviation threshold; Medium risk: Meets the rule verification requirements, but one or more key parameters satisfy the condition that the absolute value of the deviation percentage is greater than the first deviation threshold and less than or equal to the second deviation threshold; High risk: Violation of any requirement of the rule verification, or the absolute value of the percentage deviation of any key parameter is greater than the second deviation threshold.

7. The method according to claim 6, characterized in that, The first deviation threshold and the second deviation threshold are determined based on the statistical analysis results of multiple engineering projects and multiple sets of mix proportion data, so as to achieve accurate quantification of the degree of risk.

8. The method according to claim 1, characterized in that, The optimized rule base is constructed based on experimental data on the relationship between fly ash content and early strength, and the relationship between water-cement ratio and strength margin, and includes the following quantitative rules: When the fly ash content is greater than or equal to the first content threshold, the early strength growth rate of concrete decreases. It is recommended to adjust the fly ash content to be less than this threshold. When the water-cement ratio exceeds the upper limit of the water-cement ratio by a value greater than or equal to the first deviation value, the average strength allowance in the later stage decreases. It is recommended to reduce the water-cement ratio or increase the amount of cementitious material. When no mineral powder is added, it is recommended to add mineral powder within the range of the lower limit to the upper limit of the mineral powder content to replace an equal amount of cement.

9. A dual-verification risk early warning system for concrete mix proportions, applied to the method described in any one of claims 1-8, characterized in that, include: The input module is used to obtain the concrete mix proportion parameters to be verified. The rules engine module stores the logical rules for key control indicators in concrete mix design specifications. The engineering practice data benchmark module stores the mean, standard deviation, and reasonable range of key parameters for each strength level established through statistical analysis. The dual verification module performs rule verification and data verification in parallel. The data verification adopts the method of calculating the percentage deviation of each key parameter from the benchmark mean. The assessment output module outputs the risk level and optimization suggestions based on the preset risk level determination rules. The display module is used to show the risk level, violations, deviations, and optimization suggestions.

10. The system according to claim 9, characterized in that, It also includes a data update module, which is used to trigger a dynamic update mechanism. When the number of groups with newly added mix proportion data reaches the preset threshold for the amount of new data, the engineering practice data benchmark module is automatically or manually updated, and a benchmark library change report is generated.

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