CN115964954A - Ultrahigh-performance concrete multi-item performance prediction method and mix proportion optimization design method thereof - Google Patents
Ultrahigh-performance concrete multi-item performance prediction method and mix proportion optimization design method thereof Download PDFInfo
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- 238000005457 optimization Methods 0.000 title claims description 9
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Abstract
The invention discloses a method for predicting multiple performances of ultra-high performance concrete and a method for optimally designing the mix proportion thereof, which comprises the following steps: 1) Establishing a UHPC performance database; 2) Establishing a UHPC performance prediction model based on a UHPC performance database and a machine learning algorithm; 3) Generating a plurality of random mix proportions, inputting the random mix proportions into the UHPC performance prediction model, predicting the performance of the random mix proportions, and calculating the unit volume cost and the unit volume carbon emission of the UHPC by using a polynomial equation to complete the multi-item performance prediction of the ultra-high performance concrete; 4) Establishing a UHPC comprehensive performance evaluation model based on an analytic hierarchy process; 5) The optimal mix proportion of the UHPC is determined, so that the UHPC mix proportion optimal design is completed, and the machine learning algorithm is applied to the performance prediction of the UHPC, so that the waste of manpower, material resources and time is reduced; the analytic hierarchy process is applied to the evaluation of the comprehensive performance of the UHPC mixing proportion, and the optimized design of the UHPC mixing proportion is realized.
Description
Technical Field
The invention relates to the technical field of building design, in particular to a multi-item performance prediction method of ultra-high performance concrete and a mix proportion optimization design method thereof.
Background
Ultra-High Performance Concrete (UHPC) is a novel cement-based composite material, and compared with the traditional Concrete, the Ultra-High Performance Concrete has better mechanical property, working Performance and durability. Although UHPC has not been available for a long time, it has been used in the field of engineering construction due to its excellent properties, and is considered to be one of the most promising building materials for the construction of sustainable infrastructures in the future. However, the large quantities of cement and other desirable high-priced raw materials used in typical UHPC formulations greatly increase the cost of producing UHPC. The mix proportion of UHPC has a significant impact on the performance and cost of UHPC, and multiple target performance levels are usually required to be met when designing the mix proportion of UHPC, and in order to achieve the desired performance and cost of production, the mix proportion of UHPC must be optimally designed.
Traditionally, UHPC mix design requires extensive trial and error testing, which is both cumbersome and expensive, and another problem is that the mix obtained based on the test method is only a viable solution, but not an optimal solution. In recent years, many scholars have conducted extensive research on the design of the mix proportion of UHPC in order to reduce the waste of manpower and material resources caused by the trial-and-error in the early stage and the later stage of UHPC.
At present, researchers try to apply a compact packing model, a multi-scale analysis method and a response surface statistical model to the preparation of ultra-high-strength concrete, and although the methods play an important role in optimizing the UHPC mixing ratio, the methods have the following defects: the closest packing method cannot consider the chemical action of the structure forming process; the multi-scale analysis method has high requirements on microscopic parameters; the RSM method has limited accuracy and cannot be improved with the increase of test samples. There are also scholars applying machine learning algorithms to the prediction of concrete performance, and most focus on predictions in compressive strength, while research on UHPC performance prediction is very limited because it is difficult to collect data to create performance prediction models with various variables.
Disclosure of Invention
The invention aims to solve the problems and provides a method for predicting multiple performances of ultra-high performance concrete, which comprises the following steps:
s1: collecting UHPC performance data and establishing a UHPC performance database;
s2: establishing a UHPC performance prediction model based on a UHPC performance database and a machine learning algorithm;
s3: generating a plurality of random mix proportions, inputting the random mix proportions into a UHPC performance prediction model, predicting the performance of the random mix proportions, and calculating the unit volume cost and the unit volume carbon emission of the UHPC by using a polynomial equation to complete the multi-item performance prediction of the ultra-high performance concrete.
Further, in S1, the mixing amount of silica fume, the mixing amount of fly ash, the mixing amount of mineral powder, the using amount of fine bone, the using amount of coarse bone, the water-cement ratio, the using amount of steel fiber and the using amount of water reducing agent are selected as the input of a UHPC performance database.
Further, in S1, the collected performance data of each type of UHPC are calculated according to 8:2, dividing a training data set and a test data set in proportion, wherein the training data set is used for training a UHPC performance model; the test data set was used to validate the UHPC performance model.
Further, in S2, the machine learning algorithm includes linear regression, BP network neural, decision tree, random forest, gradient boosting tree, and extreme gradient boosting tree.
Further, in S2, by establishing RMSE. MAE, MAPE and R 2 Evaluating the performance of the UHPC performance model, wherein RMSE is used to reflect the deviation between predicted values and test values; MAE is used to reflect the actual situation of prediction error; MAPE is used to reflect the ratio of prediction error to actual value; r 2 For displaying the degree of linear correlation between predicted and tested values, the smaller the RMSE, MAE and MAPE, and the R 2 The closer to 1, the better the predictive model performance.
Further, the formula for RMSE is expressed as:
the calculation formula for MAE is expressed as:
the calculation formula for MAPE is expressed as:
R 2 is expressed as:
RMSE, MAE, MAPE and R 2 In the formula (c), y i Representing an actual value;
representing a predicted value; />
Represents an average of actual values; n represents the number of test samples.
Further, in S3, the calculation formula of the manufacturing cost per unit volume of the UHPC is:
Cost＝P c m c +P sf m sf +P fa m fa +P sl m sl +P FA m FA +P CA m CA +P W m W +P SF m SF +P SP m SP ；
wherein, P c Is the unit price of cement; p sf Is the unit price of silica fume; p fa Is the unit price of the fly ash; p sl Is the unit price of the mineral powder; p FA Monovalent for fine aggregate; p CA Is a coarse aggregate unit price; p W Is the unit price of water; p SF Is the unit price of the steel fiber; p SP Is the unit price of the high-efficiency water reducing agent;
the calculation formula of the carbon emission per unit volume of UHPC is as follows:
CO 2 ＝C c m c +C sf m sf +C fa m fa +C sl m sl +C FA m FA +C CA m CA +C W m W +C SF m SF +C SP m SP ；
wherein, C c Carbon emission per unit weight of cement; c sf Carbon emission per unit weight of silica fume; c fa The carbon emission per unit weight of the fly ash; c sl The carbon emission is the carbon emission per unit weight of the mineral powder; c FA Carbon emission per unit weight of fine aggregate; c CA Carbon emission per unit weight of coarse aggregate; c W Carbon emission per unit weight of water; c SF Carbon emission per unit weight of steel fiber; c SP The carbon emission per unit weight of the high-efficiency water reducing agent.
A mix proportion optimization design method of ultra-high performance concrete comprises the following steps:
SA1: establishing a UHPC comprehensive performance evaluation model, and determining weights of different performances in the UHPC comprehensive performance evaluation model through an analytic hierarchy process;
and SA2: and taking the group with the lowest compressive strength in the prediction result as a reference group, calculating the relative increase rate of each performance of the UHPC with other ratios relative to the corresponding performance of the reference group, combining the performance weights obtained by an analytic hierarchy process, quantitatively comparing the comprehensive performance of each ratio of the UHPC, and determining the optimal ratio of the UHPC according to the indexes of the comprehensive performance, thereby completing the optimal design of the UHPC ratio.
Further, in the SA1, based on an analytic hierarchy process, the UHPC comprehensive performance evaluation model comprises a target layer, a criterion layer and a scheme layer, wherein the target layer takes UHPC comprehensive performance evaluation as a target; the standard layer takes compressive strength, bending strength, fluidity, shrinkage, unit volume manufacturing cost and unit volume carbon emission as evaluation criteria; the scheme layer takes a plurality of random mix ratios as an evaluation scheme.
Further, in SA2, the increase rate of compressive strength is expressed as:
the flexural strength increase rate is expressed as: />
The fluidity growth rate is expressed as: />
The shrinkage growth rate is expressed as: />
The rate of increase in cost per unit volume is expressed as:
the increase rate of the carbon deposit emission per unit is expressed as: />
Compared with the prior art, the invention has the beneficial effects that:
1. the analytic hierarchy process is applied to the evaluation of the comprehensive performance of the UHPC mixing proportion, and the optimized design of the UHPC mixing proportion is realized.
2. The machine learning algorithm is applied to the performance prediction of UHPC, so that the waste of manpower, material resources and time is reduced.
3. The compressive strength, the fluidity, the bending strength, the shrinkage and the unit volume cost of the UHPC are simultaneously brought into the UHPC mix proportion optimization design category, and the requirements of practical engineering application are better met.
Drawings
FIG. 1 is a flow chart of a method for predicting and optimally designing multiple performance of ultra-high performance concrete according to the invention;
FIG. 2 is a graph of the results of prediction of compressive strength, bending strength, fluidity and shrinkage performance of UHPC using extreme gradient boosting algorithm according to the present invention.
Detailed Description
The method for predicting various properties of ultra-high performance concrete and optimally designing the mix ratio according to the present invention will be described in more detail with reference to the schematic drawings, in which preferred embodiments of the present invention are shown, it being understood that those skilled in the art can modify the invention described herein while still achieving the advantageous effects of the present invention, and therefore, the following description should be construed as being widely known to those skilled in the art and not as limiting the present invention.
In the description of the present invention, it should be noted that, for the terms of orientation, such as "central", "lateral", "longitudinal", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", etc., indicate orientations and positional relationships based on the orientations or positional relationships shown in the drawings, which are merely for convenience of description and simplification of the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and should not be construed as limiting the specific scope of the present invention.
As shown in fig. 1, a method for multi-performance prediction and mix proportion optimization design of ultra-high performance concrete includes the following steps:
the method comprises the following steps: a UHPC performance database is established.
And establishing a UHPC performance database from UHPC performance data collected from a large number of documents, selecting input and output variables of the model, and dividing a training data set and a test data set of the model.
Wherein, the input of the model is: silica fume mixing amount, fly ash mixing amount, mineral powder mixing amount, fine aggregate using amount, coarse aggregate using amount, water-glue ratio, steel fiber using amount and water reducing agent using amount. Each type of data collected was partitioned into a training data set and a test data set of the model according to the scale of 8:2.
Step two: and establishing a UHPC performance prediction model.
The machine learning algorithm selected by the invention comprises linear regression, BP neural network, decision tree, random forest, gradient lifting tree and extreme gradient lifting tree
Performance evaluation of the model:
the invention considers four model performance evaluation indexes, which are respectively: RMSE, MAE, MAPE, R2, defined as follows:
in the formula y i Represents an actual value,
Representing predicted values>
Represents the average of the actual values and n represents the number of test data samples.
RMSE reflects the deviation between predicted and measured values, MAE reflects the actual condition of the prediction error, MAPE reflects the ratio of the prediction error to the actual value, R 2 The degree of linear correlation between predicted and tested values is shown, the smaller the RMSE, MAE, MAPE, and R 2 The closer to 1, the better the predictive model performance. The prediction results are shown in fig. 2.
Step three: the performance of UHPC was predicted.
And generating a large number of random mix ratios, inputting the mix ratios into an established UHPC performance prediction model, and predicting the performance of the large number of random mix ratios. And (3) calculating the unit volume cost and the carbon emission of the UHPC by using a polynomial equation, wherein the calculation formula is as follows:
Cost＝P c m c +P sf m sf +P fa m fa +P sl m sl +P FA m FA +P CA m CA +P W m W +P SF m SF +P SP m SP (5)
CO 2 ＝C c m c +C sf m sf +C fa m fa +C sl m sl +C FA m FA +C CA m CA +C W m W +C SF m SF +C SP m SP (6)
in the formula, P c 、P sf 、P fa 、P sl 、P FA 、P CA 、P W 、P SF And P SP Respectively is the unit price of cement, silica fume, fly ash, mineral powder, fine aggregate, coarse aggregate, water, steel fiber and high efficiency water reducing agent.
C c 、C sf 、C fa 、C sl 、C FA 、C CA 、C W 、C SF And C SP The carbon emissions per unit weight of cement, silica fume, fly ash, mineral powder, fine aggregate, coarse aggregate, water, steel fiber and high-efficiency water reducing agent are respectively.
Step four: and establishing a UHPC comprehensive performance evaluation model.
And dividing the UHPC comprehensive performance evaluation system into a target layer, a standard layer and a scheme layer according to an analytic hierarchy process. Target layer: the combination property of UHPC; a criterion layer: compressive strength, bending strength, fluidity, shrinkage, unit volume manufacturing cost and unit volume carbon emission; scheme layer: 50000 optional UHPC mixing ratio. As shown in Table 1, a UHPC composite performance evaluation weight matrix was constructed according to an analytic hierarchy process and each performance weight was calculated.
TABLE 1 UHPC comprehensive Performance evaluation weight matrix
Step five: and (4) UHPC (ultra high performance polycarbonate) mix proportion optimization design.
And regarding the mixture ratio as a reference group, and calculating the relative increase rate of each property of the UHPC with other ratios relative to the corresponding property of the reference group and combining the property weight obtained by an analytic hierarchy process, thereby calculating the comprehensive property of the UHPC mixture ratio. And quantitatively comparing the comprehensive performance of all the proportions of the UHPC, and determining the optimal proportion of the UHPC according to the indexes of the comprehensive performance, thereby finishing the optimal design of the proportion of the UHPC. The calculation formula is as follows:
in the formula, σ 0 、Fle 0 、Flu 0 、Shr 0 、P 0 、C 0 Respectively the compression strength, bending strength, fluidity, shrinkage value, unit volume manufacturing cost and unit volume carbon emission corresponding to the combination ratio of the reference group; sigma i 、Fle i 、Flu i 、Shr i 、P i 、C i Respectively the compressive strength, the bending strength, the fluidity, the shrinkage value, the unit volume manufacturing cost and the unit volume carbon emission corresponding to the ith mixing proportion in the random mixing proportion;
respectively the compression strength growth rate, the bending strength growth rate, the fluidity growth rate, the shrinkage growth rate, the unit volume manufacturing cost growth rate and the unit volume carbon emission growth rate of the ith mixing ratio in the random mixing ratio compared with the mixing ratio of the reference group; />
The increasing rate of the comprehensive performance of the ith matching ratio in the random matching ratio compared with the matching ratio of the reference group is shown by the plus sign in the formulaThe performance of the UHPC under the mix proportion is better than that of the corresponding performance of the reference group, and the negative sign in the formula indicates that the performance of the UHPC under the mix proportion is not better than that of the corresponding performance of the reference group, and is greater than or equal to that of the reference group>
The larger the ratio, the better the combination property.
The above description is only a preferred embodiment of the present invention, and does not limit the present invention in any way. It will be understood by those skilled in the art that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A multi-item performance prediction method for ultra-high performance concrete is characterized by comprising the following steps:
s1: collecting UHPC performance data and establishing a UHPC performance database;
s2: establishing a UHPC performance prediction model based on a UHPC performance database and a machine learning algorithm;
s3: generating a plurality of random mixing ratios, inputting the random mixing ratios into the UHPC performance prediction model, predicting the performance of the random mixing ratios, and calculating the unit volume cost and the unit volume carbon emission of the UHPC by using a polynomial equation to complete the multi-item performance prediction of the ultra-high performance concrete.
2. The method for predicting the multiple properties of the ultra-high performance concrete according to claim 1, wherein in the step S1, the amount of silica fume, the amount of fly ash, the amount of mineral powder, the amount of fine bones, the amount of coarse bones, the water-to-glue ratio, the amount of steel fibers and the amount of water reducer are selected as input of the UHPC property database.
3. The method for predicting the multinomial properties of ultra-high performance concrete according to claim 2, wherein in the step S1, the collected performance data of each type of UHPC are calculated according to the following formula 8:2, dividing a training data set and a testing data set in proportion, wherein the training data set is used for training the UHPC performance model; the test data set is used to validate the UHPC performance model.
4. The method for predicting the multiple properties of the ultra-high performance concrete according to claim 1, wherein in S2, the machine learning algorithm comprises linear regression, BP network neural, decision tree, random forest, gradient lifting tree and extreme gradient lifting tree.
5. The method of predicting properties of ultra-high performance concrete according to claim 1, wherein in S2, RMSE, MAE, MAPE and R are established 2 Evaluating the performance of the UHPC performance model, wherein RMSE is used to reflect the deviation between predicted values and test values; MAE is used to reflect the actual situation of prediction error; MAPE is used to reflect the ratio of prediction error to actual value; r is 2 For displaying the degree of linear correlation between predicted and tested values, the smaller the RMSE, MAE and MAPE, and the R 2 The closer to 1, the better the predictive model performance.
6. The method of predicting the multi-performance of ultra-high performance concrete according to claim 1, wherein the RMSE is calculated by the formula:
the calculation formula of the MAE is expressed as follows:
the calculation formula of the MAPE is expressed as:
said R is 2 Is expressed as:
the RMSE, MAE, MAPE and R 2 In the formula (c), y i Representing an actual value;
representing a predicted value; />
Represents an average of actual values; n represents the number of test samples. />
7. The method for predicting the multinomial performance of the ultra-high performance concrete according to claim 1, wherein in the step S3, the calculation formula of the unit volume cost of the UHPC is as follows:
Cost＝P c m c +P sf m sf +P fa m fa +P sl m sl +P FA m FA +P CA m CA +P W m W +P SF m SF +P SP m SP ；
wherein, P c Is the unit price of cement; p is sf Is the unit price of silica fume; p fa Is the unit price of the fly ash; p sl Is the unit price of the mineral powder; p FA Monovalent for fine aggregate; p is CA Is a coarse aggregate unit price; p W Is the unit price of water; p SF Is the unit price of the steel fiber; p SP Is the unit price of the high-efficiency water reducing agent;
the calculation formula of the carbon emission per unit volume of the UHPC is as follows:
CO 2 ＝C c m c +C sf m sf +C fa m fa +C sl m sl +C FA m FA +C CA m CA +C W m W +C SF m SF +C SP m SP ；
wherein, C c Carbon emission per unit weight of cement; c sf Carbon emission per unit weight of silica fume; c fa The carbon emission per unit weight of the fly ash; c sl The carbon emission per unit weight of the mineral powder; c FA Carbon emission per unit weight of fine aggregate; c CA Carbon emission per unit weight of coarse aggregate; c W Carbon emission per unit weight of water; c SF Carbon emission per unit weight of steel fiber; c SP The carbon emission per unit weight of the high-efficiency water reducing agent.
8. A mix proportion optimization design method of ultra-high performance concrete, based on any one of the multi-performance prediction methods of ultra-high performance concrete of claims 1-7, characterized by comprising the following steps:
SA1: establishing a UHPC comprehensive performance evaluation model, and determining weights of different performances in the UHPC comprehensive performance evaluation model through an analytic hierarchy process;
and SA2: and taking the group with the lowest compressive strength in the prediction result as a reference group, calculating the relative increase rate of each performance of the UHPC with other ratios relative to the corresponding performance of the reference group, combining the performance weights obtained by an analytic hierarchy process, quantitatively comparing the comprehensive performance of each ratio of the UHPC, and determining the optimal ratio of the UHPC according to the indexes of the comprehensive performance, thereby completing the optimal design of the UHPC ratio.
9. The mix proportion optimization design method for the ultra-high performance concrete according to claim 8, wherein in the SA1, based on the analytic hierarchy process, the UHPC comprehensive performance evaluation model comprises a target layer, a criterion layer and a scheme layer, and the target layer takes UHPC comprehensive performance evaluation as a target; the criterion layer takes compressive strength, bending strength, fluidity, shrinkage, unit volume manufacturing cost and unit volume carbon emission as evaluation criteria; the scheme layer takes a plurality of the random mix ratios as an evaluation scheme.
10. The method for optimally designing the mix proportion of the ultra-high performance concrete as claimed in claim 9, wherein in the SA2, the increase rate of the compressive strength is expressed as:
the flexural strength increase rate is expressed as:
the fluidity growth rate is expressed as: />
The shrinkage growth rate is expressed as: />
The rate of increase in cost per unit volume is expressed as:
the increase rate of the carbon deposit emission per unit is expressed as: />
/>
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| CN116205694A (en) * | 2023-05-04 | 2023-06-02 | 品茗科技股份有限公司 | Method, device, equipment and medium for automatic recommending mix proportion by cost quota |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116205694A (en) * | 2023-05-04 | 2023-06-02 | 品茗科技股份有限公司 | Method, device, equipment and medium for automatic recommending mix proportion by cost quota |
| CN116205694B (en) * | 2023-05-04 | 2023-10-24 | 品茗科技股份有限公司 | Method, device, equipment and medium for automatic recommending mix proportion by cost quota |
| CN116756466A (en) * | 2023-06-20 | 2023-09-15 | 贵州省公路工程集团有限公司 | Superfluid concrete evaluation method and superfluid concrete evaluation system for steel truss-concrete combined arch bridge |
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