Concrete compressive strength prediction model based on multiple factors

Abstract

The invention belongs to the field of civil engineering concrete compressive strength prediction, and relates to a concrete compressive strength prediction model based on multiple factors. A complex prediction formula is established through the glue ratio, the use amount of the cementing material, the characteristics of the aggregate, the mineral admixture, the admixture and other influence factors, and the method has important significance for accurately predicting the compressive strength of the concrete.

Description

Technical Field

[0001] The invention relates to a concrete strength prediction method, and in particular to a concrete compressive strength prediction model based on multiple factors. Background Art

[0002] The 28-day compressive strength of concrete, as a core indicator to measure its quality and performance, occupies a pivotal position in various engineering practices. This strength value not only directly reflects the basic mechanical properties of concrete materials, but also profoundly affects the safety, stability and durability of engineering structures. Therefore, accurately predicting the 28-day compressive strength of concrete is of great importance and far-reaching significance for ensuring the rationality of engineering design, guiding the scientific nature of the construction process, and controlling engineering quality.

[0003] In order to achieve an accurate prediction of the 28-day compressive strength of concrete, we often need to comprehensively consider the mix ratio of concrete and various material properties. The mix ratio of concrete, that is, the ratio of components such as water, cement, aggregate (including sand and stone) and possible admixtures, is one of the key factors that determine the performance of concrete. The material properties cover many aspects such as the type and strength grade of cement, the physical and mechanical properties of aggregates, the quality of water, and the type and dosage of admixtures. Based on these complex factors, researchers and engineers can gradually establish a comprehensive and complex formula or mathematical model through in-depth research and practical experience. This model can comprehensively consider various changes in concrete mix ratios and material properties, and through mathematical operations and logical reasoning, make a relatively accurate prediction of the compressive strength of 28-day-old concrete. Such a prediction not only helps to reasonably determine the strength grade and mix ratio of concrete in the engineering design stage, but also provides a strong basis for quality control during the construction process, thereby ensuring the quality and safety of the entire project. Summary of the invention

[0004] In view of the limitations of current technology, the core problem that the present invention aims to solve is to develop a new method for predicting the compressive strength of concrete with multiple factors, which can simplify the operation process, shorten the time consumption, and ensure that the error between the predicted results and the actual measured values ​​is small and the accuracy is high.

[0005] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0006] Step 1: Establish a prediction model for concrete compressive strength based on multiple factors, as shown in formula (2):

[0007]

[0008] f C28 : compressive strength of concrete at 28 days of age (MPa);

[0009] k is the empirical coefficient, which ranges from 19 to 23; α is the empirical index related to the effective glue ratio, which ranges from 0.4 to 0.8; β is the empirical index related to the mixing water content and the aggregate ratio, which ranges from 0.1 to 0.6; A is the empirical coefficient, which ranges from 13 to 26; B 1 The relevant empirical coefficient ranges from 7 to 14; C is an empirical index related to age, ranging from 0.01 to 0.05; D is an empirical index related to the ratio of aggregate specific surface area to cement paste volume, ranging from 0.02 to 0.07;

[0010] C eff :Total amount of cementitious materials (kg / m 3 );

[0011] W: Mixing water consumption (kg / m 3 );

[0012] B: Total amount of cementitious materials (kg / m 3 );

[0013] t: age (days), here it is 28 days.

[0014] S a / V c : Ratio of aggregate specific surface area to cement paste volume;

[0015] k i : The influence coefficient of the i-th admixture;

[0016] φ i : mass ratio of the i-th admixture;

[0017] n: number of admixture types.

[0018] Step 2: Set the total amount of cementitious material C eff and mixing water volume W; ratio of aggregate specific surface area to cement paste volume

[0019] Step 3: Substitute the relevant parameters in step 2 into formula (2) to realize the prediction of concrete compressive strength based on multiple factors.

[0020] As a further solution of the present invention, the prediction model of concrete compressive strength based on multiple factors, the total amount of cementitious materials C eff :

[0021]

[0022] C: cement consumption (kg / m 3 );

[0023] M j: The amount of the jth mineral admixture (kg / m 3 ), such as fly ash, silica fume, slag powder, etc.;

[0024] k j ': The activity coefficient of the jth mineral admixture, reflecting its relative contribution to strength (generally 0 <k j '<1);

[0025] m: the number of types of mineral admixtures.

[0026] As a further solution of the present invention, the prediction model of concrete compressive strength based on multiple factors has an effective water-binder ratio range of 0.35 to 0.55, S a / V c The value range is 3 to 5; the mixing water consumption ranges from 105 to 330 kg / m 3 The total amount of cementitious materials ranges from 300 to 600 kg / m 3 , calculate the amount of each cementitious material:

[0027] C=α C ×C eff

[0028] M j =α j ×C eff (j=1,2,.....,m)

[0029] Among them, α C and α j is the mass ratio of cement to the jth admixture;

[0030] Calculate the total amount of cementitious materials C eff ;

[0031] Determine the amount of aggregate: Calculate the amount of coarse and fine aggregates by volume method or mass method;

[0032] Determine the amount of admixture φ i : Determined based on the percentage of the total amount of cementitious materials.

[0033] As a further solution of the present invention, the prediction model of concrete compressive strength based on multiple factors, the aggregate property influence term S a / V c :S a : Specific surface area of ​​aggregate (m 2 / kg), is related to the particle size and shape of aggregate, V c : Volume of cement paste (m 3 ); admixtures include water reducing agent, air entraining agent, expansion agent, etc., and their influence is measured by the influence coefficient k i and mass ratio φi reflect.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] (1) The present invention can quickly predict the compressive strength of three-dimensional porous graphene 3D printed concrete by using water-cement ratio, cementitious material dosage, aggregate properties, mineral admixtures, admixtures and other influencing factors.

[0036] (2) The present invention reduces the time for repeated tests, accelerates the test progress, and significantly improves the accuracy and reliability of the prediction results. DETAILED DESCRIPTION

[0037] The following will be combined with the specific embodiments of the present invention to elaborate the technical solutions therein in detail and comprehensively. It should be noted that the embodiments described here are only some examples of the present invention, rather than exhaustive of all possible implementation methods. Based on these embodiments, all other embodiments that can be derived by those skilled in the art without the need for creative work should be deemed to fall within the scope of protection of the present invention.

[0038] In this specific implementation, the raw materials for producing concrete based on multiple factors include cement, fly ash, slag, silica fume, coarse aggregate and natural fine aggregate. In addition, the concrete based on multiple factors is not limited to the above raw materials when the present invention is implemented. Among the above raw materials, cement is ordinary silicate cement, wherein the fine aggregate is medium sand, and the coarse aggregate is crushed stone, and the grade is matched.

[0039] Five groups of multi-factor concrete tests and verifications were carried out, and the concrete raw material mix ratio is shown in Table 1:

[0040] Table 1

[0041]

[0042] The unit of each raw material in Table 1 is kg, among which the cement grade is ordinary Portland cement with PO 42.5.

[0043] By formula:

[0044]

[0045] The calculated predicted value (in MPa) is then compared with the measured value (in MPa) at 28 days. The comparison is shown in Table 2:

[0046] serial number Measured value Predicted value Absolute error Relative error <![CDATA[Y 预测 / AND 实测 ]]> 1 38.8 37.6 1.2 3.09 0.969 2 37.4 35.9 1.5 4.01 0.960 3 33.3 31.8 1.5 4.50 0.955 4 30.7 29.9 0.8 2.61 0.974 5 27.6 26.6 1 3.62 0.964

[0047] After in-depth analysis of the data in Table 2, we found that the prediction formula in the present invention is within 1.5MPa of the measured value when predicting concrete strength, with the minimum error being only 0.8MPa, the maximum error being 1.5MPa, and the average error reaching 1.2MPa. It is worth mentioning that the average relative error is only 3.57%, and the ratio of the predicted value to the measured value (Y predicted / Y measured) is stable in the range of 0.955 to 0.974, showing an accuracy rate of more than 95%. These data fully prove that the prediction formula in the present invention is not only highly correlated when calculating the 28d compressive strength of concrete, but also has excellent prediction accuracy and precision.

[0048] Finally, it should be emphasized that the above implementation cases are only intended to illustrate the technical solutions of the present invention, and do not constitute a limitation on its application. Although we have described the present invention in detail by referring to the preferred implementation cases, professionals in this field should recognize that it is entirely possible to make various adjustments and changes in form and details without departing from the core spirit and protection scope of the present invention defined by the attached claims.

Claims

1. A prediction model for concrete compressive strength based on multiple factors, characterized in that The following steps are involved: Step 1: Establish a prediction model for concrete compressive strength based on multiple factors, as shown in formula (1): f C28 : compressive strength of concrete at 28 days of age (MPa); k is related to the empirical coefficient, and its range is: 19 ~ 23; α is related to the empirical index of the effective glue ratio, and its range is: 0.4~0.8; β is the empirical index related to the mixing water consumption and the aggregate ratio, and its range is: 0.1~0.6; A is the empirical coefficient, and its range is: 13~26; B1 is the empirical coefficient, and its range is: 7~14; C is the empirical index related to the age, and its range is: 0.01~0.05; D is the empirical index related to the ratio of the specific surface area of ​​the aggregate to the volume of the cement paste, and its range is: 0.02~0.07; C eff :Total amount of cementitious materials (kg / m3); W: mixing water consumption (kg / m3); B: Aggregate parameters, which may be related to the characteristics of the aggregate (kg / m 3 ); t: age (days), here it is 28 days. S a / V c : Ratio of aggregate specific surface area to cement paste volume; k i : The influence coefficient of the i-th admixture; φ i : mass ratio of the i-th admixture; n: number of admixture types; Step 2: Set the total amount of cementitious material C eff and mixing water volume W; ratio of aggregate specific surface area to cement paste volume Step 3: Substitute the relevant parameters in step 2 into formula (1) to predict the compressive strength of concrete based on multiple factors.

2. A prediction model for concrete compressive strength based on multiple factors according to claim 1, characterized in that: Total amount of cementitious materials C eff : C: cement consumption (kg / m 3 ); M j : The amount of the jth mineral admixture (kg / m 3 ), such as fly ash, silica fume, slag powder, etc.; k j ': The activity coefficient of the jth mineral admixture, reflecting its relative contribution to strength (generally 0 <k j '<1); m: the number of types of mineral admixtures.

3. A prediction model for concrete compressive strength based on multiple factors according to claim 1, characterized in that: In step 1, the effective water-binder ratio ranges from 0.35 to 0.55; S a / V c The value range is 3 to 5, and the mixing water consumption ranges from 105 to 330 kg / m 3 The total amount of cementitious materials used ranges from 300 to 600 kg / m 3 , calculate the amount of each cementitious material: C = α C ×C eff M j =a j ×C eff (j=1,2,.....,m) Among them, α C and α j is the mass ratio of cement to the jth admixture; Calculate the total amount of cementitious materials C eff ; Determine the amount of aggregate: Calculate the amount of coarse and fine aggregates by volume method or mass method; Determine the amount of admixture φ i : Determined based on the percentage of the total amount of cementitious materials.

4. A prediction model for concrete compressive strength based on multiple factors according to claim 1, characterized in that: Aggregate property influence term S a / V c :S a : Specific surface area of ​​aggregate (m 2 / kg), is related to the particle size and shape of aggregate, V c : Volume of cement paste (m 3 ); admixtures include water reducing agent, air entraining agent, expansion agent, etc., and their influence is measured by the influence coefficient k i and mass ratio φ i reflect.

Images