CN-122021461-A - Multi-dimensional optimization decision support method and system for concrete material proportion

CN122021461ACN 122021461 ACN122021461 ACN 122021461ACN-122021461-A

Abstract

The application provides a multidimensional optimization decision support method and system for concrete material proportion, which are applied to the technical field of concrete engineering. Firstly, concrete raw material parameters and design constraint conditions are obtained. Next, candidate mix ratios are generated based on these parameters and conditions, and aggregate porosity and hydration parameters are calculated. Next, hydration reaction simulation and compactness analysis were performed on the candidate mix ratios. And finally, comprehensively utilizing the aggregate porosity, hydration parameters and compactness analysis results to determine the optimal concrete mixing ratio. The application can scientifically and effectively determine the optimal concrete mixing ratio and improve the concrete performance.

Inventors

LIU YANAN
DING GUOJIE
JIANG JINGHUI
JING YANZHONG
XU HAIYAN
SONG ZUODONG
DAI QIMING
LIU YINHUA
YAN JUN
XIE JIAFU
FAN JIE

Assignees

中铁五局集团第二工程有限责任公司
四川农业大学
中铁五局集团有限公司
天津如米基业新材料有限公司

Dates

Publication Date: 20260512
Application Date: 20260408

Claims (10)

1. The multidimensional optimization decision support method for concrete material proportion is characterized by comprising the following steps: Acquiring raw material parameters of concrete, wherein the raw material parameters comprise cement marks, aggregate grading and additive components; obtaining design constraint conditions of concrete; generating at least one set of candidate mix ratios based on the raw material parameters and the design constraint conditions, and calculating aggregate porosity and hydration parameters in the generation process; performing hydration reaction simulation on the candidate mix proportion, and performing compactness analysis based on the aggregate grading to obtain a compactness analysis result; And determining a target mix from the candidate mix based on the aggregate porosity, the hydration parameters, and the compactness analysis result.
2. The method of claim 1, wherein the obtaining raw material parameters of the concrete comprises: Applying continuous pressure to a cement test block under a preset pressure, obtaining deformation data of the cement test block, and taking a pressure value corresponding to a yield point in the deformation data as a cement mark; Acquiring point cloud data of a multi-particle-size aggregate sample, constructing a contact network model of the multi-particle-size aggregate sample based on the point cloud data, and generating the aggregate grading; Introducing the admixture into a separation channel, collecting signal data of the admixture at different migration rates, and determining the admixture component according to the intensity distribution of the signal data; and taking the cement label, the aggregate grading and the additive component as the raw material parameters.
3. The method of claim 1, wherein the obtaining design constraints for the concrete comprises: Acquiring a structural load bearing target value, an emission limit value and a fluidity boundary value of an engineering; Determining a bearing capacity lower limit value in the concrete forming process based on the structural bearing target value; Determining an upper limit value of carbon emission in the concrete production process based on the emission limit value; Determining a deformation tolerance zone in the concrete pouring process based on the fluidity boundary value; And taking the bearing capacity lower limit value, the carbon emission upper limit value and the deformation tolerance interval as the design constraint conditions.
4. The method of claim 1, wherein the generating at least one set of candidate compounding ratios based on the feedstock parameters and the design constraints comprises: determining the dosage of the cementing material based on the cement label, determining the aggregate ratio based on the aggregate grading, determining the additive concentration based on the additive component, and generating an initial mix ratio; calculating the aggregate porosity and the hydration parameters corresponding to the initial mix proportion; Based on the aggregate porosity and the hydration parameter, adjusting the aggregate duty ratio and the additive concentration in the initial mix ratio to obtain an adjusted mix ratio; Judging whether the adjusted mixing proportion meets the bearing capacity lower limit value, the carbon emission upper limit value and the deformation tolerance interval contained in the design constraint condition or not; and if so, taking the adjusted mix ratio as the candidate mix ratio.
5. The method of claim 1, wherein the calculating aggregate porosity and hydration parameters comprises: constructing a skeleton model based on the aggregate grading, and acquiring a pore channel network in the skeleton model; Determining a geometric boundary of a filling area in the pore channel network, and extracting penetration depth and a flow cross section of the geometric boundary; determining the energy conduction rate and the conduction direction of the cement hydration reaction under the geometric boundary constraint based on the cement label; And matching the penetration depth and the flow section with the energy conduction rate and the conduction direction to obtain the aggregate porosity and the hydration parameter.
6. The method of claim 1, wherein the performing hydration reaction simulation on the candidate mix ratio and performing compactness analysis based on the aggregate grading to obtain compactness analysis results comprises: Performing hydration reaction simulation on the candidate mixing ratio, and recording chemical bonding data of cement particles; Acquiring an ion migration track based on the chemical bonding data, and determining a stacking area of hydration products based on the ion migration track; calculating space pose data of the aggregate particles based on the aggregate grading, and determining a gap closure state of the aggregate particles based on the space pose data; mapping the position coordinates of the stacking area to a space grid corresponding to the gap closing state; generating a dense signal if the stacking area covers the gap grid in the space grid, and generating a pore signal if the stacking area does not cover the gap grid; And calculating a comprehensive effect index based on the compaction signal and the pore signal, and taking the comprehensive effect index as the compactness analysis result.
7. The method of claim 1, wherein the determining a target mix from the candidate mix based on the aggregate porosity, the hydration parameters, and the compaction analysis results comprises: Constructing a verification matrix, taking the aggregate porosity and the hydration parameter as a first vector, and taking the compactness analysis result as a second vector; Matching the first vector of each candidate matching ratio with the second vector of the same candidate matching ratio to generate a cooperative identification, wherein the cooperative identification comprises a strong cooperative identification and a weak cooperative identification; counting the strong cooperative identification proportion of each candidate coordination proportion, and taking the candidate coordination proportion with the strong cooperative identification proportion larger than a preset threshold value as a high cooperative scheme set; Calculating a coincidence degree value of each candidate mixing ratio in the high-coordination scheme set based on the bearing capacity lower limit value, the carbon emission upper limit value and the deformation tolerance interval in the design constraint condition; And sequencing the high collaborative scheme set according to the coincidence degree value, and taking the candidate matching ratio of the first sequencing as the target matching ratio.
8. A concrete material proportioning multidimensional optimization decision support system, comprising: the system comprises an acquisition module, a concrete design constraint condition acquisition module and a concrete design constraint condition acquisition module, wherein the acquisition module is used for acquiring raw material parameters of concrete, and the raw material parameters comprise cement marks, aggregate gradation and additive components; the calculation module is used for generating at least one group of candidate mixing ratios based on the raw material parameters and the design constraint conditions, and calculating aggregate porosity and hydration parameters in the generation process; the analysis module is used for carrying out hydration reaction simulation on the candidate mixing ratio, and carrying out compactness analysis based on the aggregate grading to obtain a compactness analysis result; and the determining module is used for determining a target blending ratio from the candidate blending ratios based on the aggregate porosity, the hydration parameters and the compactness analysis result.
9. The computing device is characterized by comprising a processing component and a storage component, wherein the storage component stores one or more computer instructions, and the one or more computer instructions are used for being invoked and executed by the processing component to realize the concrete material proportioning multidimensional optimization decision support method according to any one of claims 1-7.
10. A computer storage medium, wherein a computer program is stored, and when the computer program is executed by a computer, the method for supporting multidimensional optimization decision support of concrete material proportioning according to any one of claims 1 to 7 is realized.

Description

Multi-dimensional optimization decision support method and system for concrete material proportion Technical Field The invention relates to the technical field of concrete engineering, in particular to a multidimensional optimization decision support method and system for concrete material proportion. Background The concrete is used as the structural material with the largest dosage in modern constructional engineering, and the design of the mixing proportion directly relates to the structural safety, the construction performance and the service life. The core task of the mix proportion design is to determine the optimal mass proportion relation among the components such as cement, aggregate, water, additive and the like on the premise of meeting the requirements of bearing capacity of an engineering structure, fluidity of a construction process and durability. At present, the concrete mix proportion design mainly adopts a method based on an empirical formula, namely, according to the engineering design strength grade, parameters such as cement strength, a cement proportion empirical curve, an aggregate grading table and the like are combined, an initial mix proportion is obtained through table lookup or simple calculation, and then a final scheme is obtained through trial mix and adjustment. The method is simple and convenient to operate, but has the defects that firstly, the traditional method is difficult to comprehensively consider the coupling relation between the aggregate porosity and hydration reaction, so that the compactness of the obtained mixture ratio is often not optimal, the strength development and durability performance of concrete are affected, secondly, the carbon emission constraint becomes increasingly hard index of engineering construction, the traditional method lacks a mechanism for taking the upper limit of carbon emission as the design constraint into an optimization flow, the mechanical property is difficult to be ensured, meanwhile, the green low-carbon requirement is difficult to be met, thirdly, when the complex aggregate grading and various additive combinations are faced, the traditional method relies on repeated trial matching to approach an optimal solution, the trial matching period is long, the material waste is large, and the global optimal scheme is difficult to systematically search. Therefore, it is needed to provide a concrete mix determining method capable of comprehensively considering multidimensional factors such as aggregate porosity, hydration parameters and compactness, and uniformly incorporating multiple constraints such as bearing capacity, carbon emission and fluidity into an optimization decision so as to improve scientificity and efficiency of mix design. Disclosure of Invention In view of the above, the present invention aims to provide a multidimensional optimization decision support method and system for concrete material proportioning, which can automatically generate candidate mix ratios based on raw material parameters and multiple design constraint conditions, and screen out optimal target mix ratios from candidate schemes through hydration reaction simulation and compactness analysis, so that the accuracy and efficiency of concrete mix ratio design are significantly improved on the premise of meeting a bearing capacity lower limit value, a carbon emission upper limit value and a deformation tolerance interval. The specific scheme is as follows: In a first aspect, the application discloses a concrete material proportioning multidimensional optimization decision support method, which comprises the following steps: Acquiring raw material parameters of concrete, wherein the raw material parameters comprise cement marks, aggregate grading and additive components; obtaining design constraint conditions of concrete; generating at least one set of candidate mix ratios based on the raw material parameters and the design constraint conditions, and calculating aggregate porosity and hydration parameters in the generation process; performing hydration reaction simulation on the candidate mix proportion, and performing compactness analysis based on the aggregate grading to obtain a compactness analysis result; And determining a target mix from the candidate mix based on the aggregate porosity, the hydration parameters, and the compactness analysis result. Optionally, the method comprises the steps of applying continuous pressure to a cement test block under preset pressure, obtaining deformation data of the cement test block, taking a pressure value corresponding to a yield point in the deformation data as a cement label, obtaining point cloud data of a multi-particle-size aggregate sample, constructing a contact network model of the multi-particle-size aggregate sample based on the point cloud data, generating aggregate grading, introducing an additive into a separation channel, collecting signal data of the additive at different migration rates, and determi