JPH09277246A - Quality control apparatus at time of production of highly flowable concrete - Google Patents

Abstract

PROBLEM TO BE SOLVED: To impart required flowability to highly flowable concrete. SOLUTION: The shaft torque of a mixer 1 is calculated by a shaft torque operator 35 and, on the basis of the shaft torque signal 36 from the shaft torque operator 35 and the setting signal 38 from a setting device 37, the amt. of correcting water 19 to be added to mortar and the amt. of a correcting chemical admixture to be added to highly flowable concrete are calculated by a kneading simulator 39. A predetermined amt. of correcting water 19 is added to mortar by a water supply controller 45 and a predetermined amt. of the correcting chemical admixture is added to highly flowable concrete by a chemical admixture supply controller 48 to set the slump flow value SF and rotocure time value RT of high flowable concrete to preset values.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a quality control device for producing high-fluidity concrete.

[0002]

2. Description of the Related Art Fresh concrete is produced by kneading cement, water, sand (fine aggregate), gravel (coarse aggregate), and an admixture having a preset mixing ratio with a mixer.

The workability (easiness of flow, ease of driving) of fresh concrete can be represented by a slump value, a slump flow value, a funnel time value, etc., but the above-mentioned constituent materials such as cement, water, sand, gravel, etc. Even when kneading is carried out with a predetermined composition, the workability may exhibit a value different from the design value due to the surface water content and particle size distribution of the aggregate. It also occurs in well-crafted concrete manufacturing equipment.

For this reason, conventionally, an operator operating a concrete manufacturing facility appropriately adjusts the amount of water, which is a constituent material of fresh concrete, based on experience, so that the fresh concrete has a predetermined workability. Was there.

[0005]

However, in recent years,
The fluidity of high-fluidity concrete, which is being researched and developed and does not require compaction, is determined not only by the amount of water, but also by the aggregate particle size, material temperature, and admixture amount. Therefore, as in the case of conventional concrete, simply adjusting the amount of water based on experience may not be able to provide the high-fluidity concrete with the required fluidity.

[0006]

[Knowledge based on survey results] As a result of earnest research to solve the above problems, the inventor has obtained the following findings.

(1) As shown in FIG. 9, when a predetermined amount of cement, water, and aggregate is charged into a mixer to produce high-fluidity concrete, the mixer is mixed in a mortar kneading stage in which cement, water and sand are kneaded. Of the mixer shaft torque (current value / rotation speed or power value / rotation speed when the mixer is electrically driven, hydraulic pressure when the mixer is hydraulically driven) over time. As a result, the axial torque value once increases after the start of kneading, decreases as the mortar approaches the kneading state, and tends to become substantially stable as the mortar kneads.

(2) Further, as shown in FIG. 10, the relationship between the water content of the kneaded mortar and the axial torque value of the mixer when the mortar is kneaded has a tendency of a quadratic function.

(3) Also, the amount of correction water to be replenished varies depending on the characteristics of the cement, sand, etc. constituting the mortar.

(4) Therefore, by learning the relationship between the material characteristics and the corrected water amount by using the learning function of the neural network, the corrected water amount that gives the desired mortar water content can be obtained even if the material characteristics change. It was confirmed that it was required.

(5) As shown in FIG. 11, cement,
The mortar generated by kneading water and sand with a mixer is further mixed with gravel, water, and an admixture, and at the time of high fluid concrete kneading, the rotation speed of the mixer is set to a medium speed, and the shaft torque value of the mixer is set. (When the mixer is electrically driven, the current value / rotation speed, or the electric power value / rotation speed, and when the mixer is hydraulically driven, the hydraulic pressure is measured.) Once it increases, it decreases as the high-fluidity concrete approaches the kneading state, and when the high-fluidity concrete kneads, it tends to be in a substantially stable state.The lower the axial torque value at this time, the higher the water content of the high-fluidity concrete. The rate is high or the amount of admixture is high.

(6) As shown in FIG. 12, the relationship between the slump flow value representing the workability of high-fluidity concrete and the shaft torque value of the mixer when the high-fluidity concrete is kneaded is high in the shaft torque value. However, the slump flow value tends to decrease, and as shown in FIG. 13, the relationship between the funnel time value indicating the workability of high-fluidity concrete and the shaft torque value of the mixer when the high-fluidity concrete is kneaded is as follows. The higher the shaft torque value, the higher the funnel time value.

(7) Also, the amount of the correction admixture to be replenished varies depending on the characteristics of cement, sand, gravel, etc. that compose the high-fluidity concrete.

(8) Then, by learning the relationship between the material characteristics and the amount of the corrective admixture by using the learning function of the neural network, the corrective admixture with which the desired workability can be obtained even when the material characteristics change. It was confirmed that the dosage was required.

[0015]

An object of the present invention is to provide a high fluidity concrete with required fluidity.

[0016]

In order to achieve the above object, in a quality control apparatus for producing high-fluidity concrete of the present invention, cement 11, fine aggregate 8, primary water 16, and correction water 19 are kneaded. And a shaft torque calculator 35 for obtaining a shaft torque of the mixer 1 for generating high-fluidity concrete by kneading the mortar, the coarse aggregate 5, the admixture 24, and the correction admixture 25 with the mortar. Data such as selection, blending ratio, material physical property value, temperature, kneading amount, target slump flow value SFr and target funnel time value RTr, measured slump flow SF value and funnel time value RT, and data obtained by kneading are used as learning data. A setter 37 that outputs whether to handle or not to handle as a setting signal 38, a shaft torque signal 36 from the shaft torque calculator 35, and a setter Based on the setting signal 38 from 7, the amount of correction water 19 to be added to the mortar, the amount of the correction admixture 25 to be added to the high-fluidity concrete, the slump flow value SF of the high-fluidity concrete, the funnel time value RT, and the mortar are corrected. The target shaft torque value T2.1r that the mixer 1 should exhibit after adding and kneading the water 19 and the target shaft torque value T2.2r that the mixer 1 should exhibit after adding and mixing the correction admixture 25 to the high-fluidity concrete are calculated by the neural network. A kneading simulator 39 having a correction water neuro function 40 to be sought, a correction admixture neuro function 41, a slump flow / roto time estimation neuro function 42, and a memory function 43 for recording neuro-learned data.
And a water replenishment controller 45 for adjusting the amount of the correction water 19 to be added to the mortar based on the data signal 44 from the kneading simulator 39, and addition to the high-fluidity concrete based on the data signal 44 from the kneading simulator 39. Admixture replenishment controller 48 for adjusting the amount of corrective admixture 25 to be added
And

In the quality control device for producing high-fluidity concrete of the present invention, the correction to be added to the mortar by the kneading simulator 39 based on the shaft torque signal 36 from the shaft torque calculator 35 and the setting signal 38 from the setter 37. The amount of the water 19 and the amount of the correction admixture 25 to be added to the high-fluidity concrete are obtained, and a predetermined amount of the correction water 19 is added to the mortar by the water replenishment controller 45 and the admixture replenishment controller 48.
A predetermined amount of the correction admixture 25 is added to the high fluidity concrete by.

[0018]

Embodiments of the present invention will be described below with reference to the drawings.

FIG. 1 shows a high-fluidity concrete production facility to which an example of an embodiment of a quality control device for producing high-fluidity concrete according to the present invention is applied.

Reference numeral 1 is a mixer, and the mixer 1 is driven by a motor 2.

3 is a gravel storage tank, 4 is a gravel weighing hopper, and the gravel weighing hopper 4 weighs the gravel (coarse aggregate) 5 cut out from the gravel storage tank 3, and the gravel 5 that has been weighed is measured. It is configured to supply to the mixer 1.

6 is a sand storage tank, 7 is a sand weighing hopper,
The sand weighing hopper 7 is configured to weigh the sand (fine aggregate) 8 cut out from the sand storage tank 6 and supply the sand 8 that has been weighed to the mixer 1.

Reference numeral 9 is a cement storage tank, 10 is a cement weighing hopper, and the cement weighing hopper 10 weighs the cement 11 cut out from the cement storage tank 9,
The measured cement 11 is supplied to the mixer 1.

Reference numeral 12 is a water storage tank, 13 is a water metering hopper, and the water metering hopper 13 extends from the water tank 12 to the open / close valve 1.
Water 15 flowing in via 4 is weighed, and the weighed water 1
5 is supplied to the mixer 1 as primary water 16.

Reference numeral 17 is a flow rate adjusting valve, and the flow rate adjusting valve 1
7 is adapted to allow the water 15 stored in the water storage tank 12 to flow out.

Reference numeral 18 denotes a flow meter, which measures the flow rate of water 15 flowing out of the water storage tank 12 through the flow rate adjusting valve 17 and uses the water 15 as correction water 19 for the mixer. It is designed to supply one.

Reference numeral 20 denotes an admixture storage tank, and a liquid admixture 21 is stored in the admixture storage tank 20.

Reference numeral 22 is a flow rate adjusting valve, and the flow rate adjusting valve 2
No. 2 is designed to allow the admixture 21 stored in the admixture storage tank 20 to flow out.

Reference numeral 23 is a flow meter, which measures the flow rate of the admixture 21 flowing out of the admixture storage tank 20 via the flow rate adjusting valve 22 and uses the admixture 21 or the admixture 24 or The correction admixture 25 is supplied to the mixer 1.

Reference numeral 26 is an admixture storage tank, 27 is an admixture weighing hopper, and the admixture weighing hopper 27 is a powder made of fly ash, blast furnace slag fine powder, limestone fine powder, etc. cut out from the admixture storage tank 26. The admixture 28 of the body is weighed, and the admixture 28 that has been weighed is supplied to the mixer 1.

Reference numeral 29 is an inverter device, and the inverter device 29 is configured to supply a current 30 to the motor 2 for driving the mixer 1.

Reference numeral 31 is a rotation speed detector, and the rotation speed detector 31 is configured to detect the rotation speed of the motor 2 for driving the mixer 1 and output a rotation speed detection signal 32.

Reference numeral 33 is a power meter, and the power meter 33 is configured to measure the power supplied from the inverter device 29 to the motor 2 and output a power measurement signal 34.

Reference numeral 35 is a shaft torque calculator, and the shaft torque calculator 35 has a value of a rotation speed detection signal 32 output from the rotation speed detector 31 and a power measurement signal 34 output from a power meter 33. The shaft torque of the mixer 1 is calculated based on the value of and the shaft torque signal 36 is output.

Reference numeral 37 denotes a setting device, and the setting device 37 selects the operation mode, the compounding ratio, and the material physical property values (fine aggregate coarse particle ratio,
Fine aggregate fine particle fraction, coarse aggregate coarse grain ratio, ratio of admixture to binder composed of powder and granules such as cement and aggregate), temperature, kneading amount, etc., and target slump flow value SFr and target The funnel time value RTr, the actually measured slump flow value SF and the funnel time value RT, and whether or not to treat the data obtained by kneading as learning data are output as a setting signal 38.

Reference numeral 39 is a kneading simulator, and the kneading simulator 39 has a correction water neuro function 40, a correction admixture neuro function 41, a slump flow / rotation time estimation neuro function 42, and a memory function 43. are doing.

The kneading simulator 39 obtains the following values based on the shaft torque signal 36 from the shaft torque calculator 35 and the setting signal 38 from the setter 37, and records the neurolearned data in the memory function. And outputs the data signal 44 as needed.

(1) In order to make the high-fluidity concrete have the target slump flow value SFr and the target funnel time value RTr, the mortar produced by kneading the primary water 16, the sand 8, the cement 11 and the admixture 28 is mixed. The amount of correction water 19 to be added (correction water amount W1 ')

(2) In order to provide the high-fluidity concrete with the target slump flow value SFr and the target funnel time value RTr, with respect to the high-fluidity concrete produced by kneading the admixture 24, the gravel 5, and the above mortar. Amount of corrective admixture 25 to be added (corrective admixture amount A
d ')

(3) Slump flow value SF and funnel time value RT of high-fluidity concrete in which the above-mentioned correction admixture 25 is added and kneaded

(4) The target shaft torque value T2.1r that should be exhibited by the mixer 1 when the shaft torque becomes stable after the correction water 19 is added and kneaded to the mortar.

(5) Correcting admixture 2 in high-fluidity concrete
After adding 5 and kneading, when the shaft torque became stable, the mixer 1
Target shaft torque value T2.2r

Of these values, the corrected water amount W1 'is obtained by the corrected water neuro-function 40, and the corrected admixture amount Ad' and the target shaft torque value T2.1r are the corrected admixture neuro-function 41. The slump flow value SF, the funnel time value RT and the target shaft torque value T
2.1r is determined by the slump flow and funnel time estimation neuro function 42.

Reference numeral 45 is a water replenishment controller, which is based on the setting signal 38 from the setter 37, the data signal 44 from the kneading simulator 39, and the flow rate detection signal 46 from the flow meter 18. , A valve actuation signal 47 for actuating the flow rate adjusting valve 17 is output.

Reference numeral 48 denotes an admixture replenishment controller. The admixture replenishment controller 48 has a setting signal 38 from a setting device 37, a data signal 44 from a kneading simulator 39, and a flow meter 23.
The valve operation signal 50 for operating the flow rate adjusting valve 22 is output based on the flow rate detection signal 49 from the.

The operation of the high-fluidity concrete production facility shown in FIG. 1 will be described below with reference to the flow charts shown in FIGS. 2 to 7 and the graph shown in FIG.

In the facility for producing high-fluidity concrete shown in FIG. 1, before the production of high-fluidity concrete, when the database is created, the operation mode of the setter 37 is set to the data collection mode.

Further, the data such as the mixing ratio of the high-fluidity concrete to be produced, the physical properties of the material, the temperature, the kneading amount, the target slump flow value SFr and the target funnel time value RTr
And the assumed corrected water amount W1 ′ and the corrected admixture amount A
By inputting d'and the setter 37, the above data,
A setting signal 38 corresponding to the target value and the assumed amount is output from the setting device 37 to the kneading simulator 39, the water replenishment controller 45, and the admixture replenishment controller 48.

Next, the sand 8 stored in the sand storage tank 6, the cement 11 stored in the cement storage tank 9,
The admixture 28 stored in the admixture storage tank 26 is weighed by the sand weighing hopper 7, the cement weighing hopper 10, and the admixture weighing hopper 27 in accordance with the mixing ratio and the kneading amount, and
The water 15 stored in the water storage tank 12 is measured as the primary water 16 by the water measuring hopper 13 in accordance with the mixing ratio and the kneading amount, and the sand 1, the cement 11, the primary water 1 is added to the mixer 1.
6. After adding the admixture 28, the inverter 2 drives the motor 2 to rotate the mixer 1 at a high speed.

When the motor 2 is operated, the rotation speed of the mixer 1 is detected by the rotation speed detector 31, the rotation speed detector 31 outputs a rotation speed detection signal 32, and the inverter device 29 outputs the rotation speed to the motor 2. The supplied power is measured by the power meter 33, and the power meter 33 outputs a power measurement signal 34.

The shaft torque calculator 35 determines the shaft torque of the mixer 1 based on the rotation speed detection signal 32 and the power measurement signal 34, and outputs a shaft torque signal 36.

At this time, the change in the axial torque of the mixer 1 with time increases once the kneading is started, and then decreases as the mortar approaches a kneading state, and the mortar kneads further, as shown in FIG. And a substantially stable shaft torque value T1 is exhibited.

When the time required for the mortar to knead elapses from the start of kneading, a valve actuation signal 47 based on the setting signal 38 from the setting device 37 is output from the water replenishment controller 45 to the flow rate adjusting valve 17, and this By the water storage tank 1
The amount of water 15 corresponding to the corrected water amount W1 ′ from 2 is the corrected water 1
9 is added to the mortar kneaded by the mixer 1.

When the mortar is kneaded, the gravel 5 stored in the gravel storage tank 3 is weighed by the gravel weighing hopper 4 in accordance with the mixing ratio and the kneading amount, and the gravel 5 is put into the mixer 1. The admixture 21 stored in the admixture storage tank 20 is measured by the flow meter 23, and the admixture 21 is used as the admixture 24 to the mixer 1 so that the integrated flow rate will be in accordance with the mixing ratio and the kneading amount. After the supply, the inverter 2 drives the motor 2 to rotate the mixer 1 at a medium speed.

At this time, the change in the axial torque of the mixer 1 with time increases once the gravel 5 is put in, as shown in FIG. 8, and then decreases as the high-fluidity concrete approaches the kneading state, and further increases. When the fluidized concrete mixes up, it exhibits a substantially stable shaft torque value T2.1.

When the time required for kneading the high-fluidity concrete elapses after the gravel 5 is charged, the valve actuation signal 50 based on the setting signal 38 from the setting device 37 is adjusted by the admixture replenishment controller 48 to adjust the flow rate. Output to valve 22,
As a result, the corrected admixture amount Ad ′ from the admixture storage tank 20 is obtained.
The amount of the admixture 21 according to is added to the high-fluidity concrete to be kneaded by the mixer 1 as the correction admixture 25.

When the corrective admixture 25 is added to the high-fluidity concrete, the axial torque of the mixer 1 is slightly reduced as shown in FIG. 8 to exhibit a substantially stable axial torque value T2.2, which is equivalent to that of the high-fluidity concrete. Kneading is completed.

When the kneading of the high-fluidity concrete is completed, the slump flow value SF and the funnel time value RT of the high-fluidity concrete are measured by a conventionally known measuring instrument,
The slump flow value SF and the funnel time value RT obtained by the actual measurement are set and input to the setter 37.

Furthermore, when the setting device 37 is set so that the data obtained by the kneading is treated as learning data, the correction water neuro function 40, the correction admixture neuro function 41, and the slump flow / rotation time estimation of the kneading simulator 39 are performed. The mixing ratio of the high-fluidity concrete input to the setting device 37 before the kneading is started by the neuro function 42,
Material physical value, temperature, kneading amount, target slump flow value SF
r, target funnel time value RTr, estimated correction water amount W
1'and the corrected admixture amount Ad ', the axial torque value T1 when the mortar is kneaded, the axial torque value T2.1 after adding the correction water 19 to the high-fluidity concrete, and the correction admixture 25 to the high-fluidity concrete Torque value T after
2.2, Slump flow value SF and funnel time value RT after completion of mixing of high-fluidity concrete obtained by actual measurement
Is performed and the result of this neuro learning is recorded in the memory function 43 as a database.

After the kneading of the high-fluidity concrete is completed, if the setting device 37 is set so as not to treat the data obtained by the kneading as learning data, the above-mentioned neuro learning is not performed.

In the high-fluidity concrete production facility shown in FIG. 1, when the high-fluidity concrete is produced, the operation mode of the setting device 37 is set to the automatic operation mode.

Further, the data such as the mixing ratio of the high-fluidity concrete to be produced, the physical properties of the material, the temperature, the kneading amount, the target slump flow value SFr and the target funnel time value RTr
When and are input to the setting device 37, the setting signal 38 corresponding to the above-mentioned data and target value is sent from the setting device 37 to the kneading simulator 39, the water replenishment controller 45, and the admixture replenishment controller 48.
Is output to.

At this time, in the slump flow / rotation time estimation neuro function 42 of the kneading simulator 39, a neural network as shown in FIG.
The target shaft torque value T2.2r which the mixer 1 should exhibit when the correction admixture 25 is added to the high-fluidity concrete and the shaft torque is stabilized is the target slump flow value SFr and the target funnel time input to the setter 37. The error Q is obtained by evaluating the error Q based on data such as the value RTr, the compounding ratio, the material property value, the air temperature, and the kneading amount.

Further, in the correction admixture neuro-function 41 of the kneading simulator 39, when the correction water 19 is added to the mortar and kneaded by the neural network as shown in FIG. 7, the axial torque becomes stable. Mixer 1
The target shaft torque value T2.1r to be exhibited by the target shaft torque value T2.2r obtained by the slump flow / rotation time estimation neuro function 42, the mixing ratio input to the setter 37, the material physical property value, the temperature, and the kneading The error Q is obtained while being evaluated based on data such as quantity.

Next, the sand storage tank 6, the cement storage tank 9,
The sand 8, cement 11, and admixture 28 in an amount corresponding to the mixing ratio and the kneading amount from the admixture storage tank 26 are charged into the mixer 1, and the amount of water 15 corresponding to the mixing ratio and the kneading amount is supplied from the water storage tank 12. In addition to supplying the primary water 16 which is
The inverter 2 drives the motor 2 to rotate the mixer 1 at high speed.

When the motor 2 is operated, the rotation speed of the mixer 1 is detected by the rotation speed detector 31, the rotation speed detector 31 outputs a rotation speed detection signal 32, and the inverter device 29 outputs the rotation speed to the motor 2. The supplied power is measured by the power meter 33, and the power meter 33 outputs a power measurement signal 34.

The shaft torque calculator 35 calculates the shaft torque of the mixer 1 based on the rotation speed detection signal 32 and the power measurement signal 34, and outputs a shaft torque signal 36.

As shown in FIG. 8, the shaft torque of the mixer 1 decreases as the mortar approaches the kneading state, and when the mortar kneads, the shaft torque value T1 is substantially stable.

On the other hand, in the correction water neuro function 40 of the kneading simulator 39, the correction water 1 to be added to the mortar is made by the neural network as shown in FIG.
The amount of 9 (corrected water amount W1 ′) is the shaft torque signal 36 from the shaft torque calculator 35, the mixture ratio input to the setter 37,
The error Q is evaluated while being evaluated on the basis of data such as physical properties of material, temperature, kneading amount and the like.

When the time required for kneading the mortar from the start of kneading elapses, the kneading simulator 39
A valve actuation signal 47 based on the data signal 44 from the water replenishment controller 45 is output to the flow rate adjusting valve 17, whereby the amount of water 1 corresponding to the corrected water amount W1 ′ from the water storage tank 12 is increased.
5 is added as correction water 19 to the mortar kneaded by the mixer 1.

When the mortar has been kneaded, the gravel 5 in an amount corresponding to the mixing ratio and the kneading amount is charged from the gravel storage tank 3 into the mixer 1, and the mixing ratio and the kneading amount are changed from the admixture storage tank 20. The amount of the admixture 21 is supplied to the mixer 1 as the admixture 24, and the motor 2 is operated by the inverter device 29 to rotate the mixer 1 at a medium speed.

At this time, the change with time of the shaft torque of the mixer 1 decreases as the high-fluidity concrete approaches the kneading state, as shown in FIG. It exhibits a torque value T2.1.

On the other hand, in the correction admixture neuro-function 41 of the kneading simulator 39, the amount of the correction admixture 25 to be added to the high-fluidity concrete (correction admixture amount Ad 'by a neural network as shown in FIG. 4). )
Is calculated while the error Q is being evaluated based on the shaft torque signal 36 from the shaft torque calculator 35 and the data such as the mixture ratio, the material property value, the temperature, and the kneading amount input to the setter 37.

When the time required for kneading the high-fluidity concrete elapses after the gravel 5 is put in, the valve actuation signal 50 based on the data signal 44 from the kneading simulator 39 is adjusted by the admixture supplement controller 48 to adjust the flow rate. Valve 22
Is output to the correction admixture 2 from the admixture storage tank 20 in an amount corresponding to the correction admixture amount Ad ′.
5 is added to the high-fluidity concrete that is kneaded by the mixer 1.

When the compensating admixture 25 is added to the high-fluidity concrete, the axial torque of the mixer 1 is slightly decreased as shown in FIG. 8 to exhibit a substantially stable axial torque value T2.2, and Kneading is completed.

On the other hand, in the slump flow / roto time estimation neuro function 42 of the kneading simulator 39, a neural network as shown in FIG.
The slump flow value SF and the funnel time value RT of the high-fluidity concrete whose kneading has been completed are calculated by the shaft torque calculator 35.
It is obtained based on the shaft torque signal 36 from, the mixture ratio, the material property value, the temperature, the kneading amount, etc. input to the setting device 37.

When the kneading of the high-fluidity concrete is completed, the slump flow value SF and the funnel time value RT of the high-fluidity concrete are measured by a conventionally known measuring instrument,
The slump flow value SF and the funnel time value RT obtained by the actual measurement are set and input to the setter 37.

Further, when the setting device 37 is set so that the data obtained by the kneading is treated as learning data, the correction water neuro function 40, the correction admixture neuro function 41, and the slump flow / rotation time estimation of the kneading simulator 39 are performed. The mixing ratio of the high-fluidity concrete input to the setting device 37 before the kneading is started by the neuro function 42,
Material physical value, temperature, kneading amount, target slump flow value SF
r, target funnel time value RTr, estimated correction water amount W1 ′ and correction admixture amount A obtained in kneading
d ', shaft torque value T when kneading mortar
1. Axial torque value T2.1 after adding compensating water 19 to high fluidity concrete, compensating admixture 2 to high fluidity concrete
Neuro-learning is performed on the axial torque value T2.2 after adding 5, the slump flow value SF and the funnel time value RT after completion of the mixing of the high-fluidity concrete obtained by actual measurement, and the result of this neuro-learning is stored as a database. It is recorded in the memory function 43.

When the setting device 37 is set so that the data obtained by the kneading is not treated as learning data after the kneading of the high-fluidity concrete is completed, the above-mentioned neuro learning is not performed.

As described above, in the high-fluidity concrete production facility shown in FIG. 1, the corrected water amount W1 'is adjusted by the kneading simulator 39 based on the shaft torque signal 36 from the shaft torque calculator 35 and the setting signal 38 from the setter 37. The corrected admixture amount Ad 'is obtained, the correction water 19 corresponding to the corrected water amount W1' is added to the mortar by the water replenishment controller 45, and the correction admixture corresponding to the corrected admixture amount Ad 'is added by the admixture supplement controller 48. Since the agent 25 is added to the high-fluidity concrete, the slump flow value SF and the funnel time value RT of the high-fluidity concrete can be set to preset values. Therefore, the fluidity concrete is required to be an appropriate high-fluidity concrete. The fluidity of can be provided.

The quality control apparatus for producing high-fluidity concrete according to the present invention is not limited to the above-mentioned embodiment, and various modifications can be made without departing from the scope of the present invention. Is.

[0082]

As described above, according to the quality control device for producing high-fluidity concrete of the present invention, based on the shaft torque signal 36 from the shaft torque calculator 35 and the setting signal 38 from the setting device 37, The amount of the correction water 19 to be added to the mortar and the amount of the correction admixture 25 to be added to the high-fluidity concrete are obtained by the kneading simulator 39, and a predetermined amount of the correction water 19 is added to the mortar and mixed by the water replenishment controller 45. Since a predetermined amount of the correction admixture 25 is added to the high-fluidity concrete by the agent replenishment controller 48, it is possible to set the slump flow value SF and the funnel time value RT of the high-fluidity concrete to preset values. It is possible to obtain an excellent effect that concrete can be provided with a desired fluidity in high-fluidity concrete.

[Brief description of drawings]

FIG. 1 is a conceptual diagram showing an example of a high-fluidity concrete production facility to which an example of an embodiment of a quality control device for producing high-fluidity concrete of the present invention is applied.

FIG. 2 is a flow chart showing a data processing procedure of the quality control device at the time of manufacturing high fluidity concrete of the present invention.

FIG. 3 is a flowchart for obtaining a corrected water amount by a correction water neuro function in the quality control device at the time of manufacturing high fluidity concrete of the present invention.

FIG. 4 is a flowchart for obtaining a corrected admixture amount by a correction admixture neuro function in the quality control device for producing high-fluidity concrete according to the present invention.

FIG. 5 is a flowchart for obtaining a slump flow value and a funnel time value by a slump flow / rotation time estimation neuro function in the quality control device at the time of manufacturing high fluidity concrete of the present invention.

FIG. 6 shows a case where a target shaft torque value to be exhibited by a mixer after adding and mixing a corrective admixture to high-fluidity concrete by a slump flow / rotation time estimation neuro function in a quality control device during production of high-fluidity concrete of the present invention is obtained. It is a flowchart.

FIG. 7 is a flowchart for obtaining a target shaft torque value to be exhibited by the mixer after the correction water is added and kneaded in the mortar by the correction admixture neuro function in the quality control device during production of the high-fluidity concrete of the present invention.

FIG. 8 is a graph showing an example of changes over time in the axial torque value of the mixer when producing high-fluidity concrete using the high-fluidity concrete production equipment shown in FIG.

FIG. 9 is a graph showing changes over time in the shaft torque value of the mixer during mortar kneading.

FIG. 10 is a graph showing the relationship between the water content in kneaded mortar and the shaft torque value of the mixer.

FIG. 11 is a graph showing the change over time of the axial torque value of the mixer when kneading high-fluidity concrete.

FIG. 12 is a graph showing the relationship between the axial torque value of a mixer and the slump flow value of high-fluidity concrete when high-fluidity concrete is kneaded.

FIG. 13 is a graph showing the relationship between the shaft torque value of the mixer and the funnel time value of the high-fluidity concrete when the high-fluidity concrete is kneaded.

[Explanation of symbols]

 1 Mixer 5 Gravel (coarse aggregate) 8 Sand (fine aggregate) 11 Cement 16 Primary water 19 Corrective water 24 Admixture 25 Corrective admixture 35 Axial torque calculator 36 Axial torque signal 37 Setting signal 38 Setting signal 39 Kneading simulator 40 Neuro Function for Corrected Water 41 Neuro Function for Corrected Admixture 42 Neuro Function for Slump Flow / Rot Time Estimation 43 Memory Function 44 Data Signal 45 Water Replenishment Controller 48 Admixture Replenishment Controller

 ─────────────────────────────────────────────────── ─── Continuation of the front page (72) Inventor Kazuma Ozawa 377-1-308 Kubo Hiraga, Matsudo City, Chiba Prefecture (72) Inventor Toshiaki Saito 3-15-15 Toyosu, Koto-ku, Tokyo Ishikawajima Harima Heavy Industries Ltd. In the second technical center (72) Inventor Shigeki Murayama 3-15-15 Toyosu, Koto-ku, Tokyo Ishikawajima Harima Heavy Industries Co., Ltd. Toni Technical center (72) Inventor Tetsuo Hamaguchi 3174 Showa-cho, Kanazawa-ku, Yokohama-shi, Kanagawa Address Ishikawajima Construction Machinery Co., Ltd. Yokohama Plant (72) Inventor Hiroyuki Arai 2-6-21 Yaesu, Chuo-ku, Tokyo Ishikawajima Construction Materials Co., Ltd.

Claims (1)

[Claims] 1. A mortar is produced by kneading cement (11), fine aggregate (8), primary water (16) and compensating water (19), and the mortar, coarse aggregate (5) and admixture. (24), Axial torque calculator (35) for obtaining the axial torque of the mixer (1) for producing high-fluidity concrete by kneading the corrective admixture (25), selection of operation mode, mixing ratio, and physical properties of material , Data such as temperature and kneading amount, target slump flow value (SFr) and target funnel time value (RTr), measured slump flow (SF) value and funnel time value (RT), and data obtained by kneading as learning data Set signal (3
8) output as a setter (37), a shaft torque signal (36) from the shaft torque calculator (35), a setter (37)
Amount of compensating water (19) to be added to the mortar, amount of compensating admixture (25) to be added to the high-fluidity concrete, slump flow value (SF) and funnel of the high-fluidity concrete based on the setting signal (38) from Time value (RT), target shaft torque value (T2.1r) that the mixer (1) should exhibit after adding and mixing the correction water (19) to the mortar, and after adding and mixing the correction admixture (25) to the high-fluidity concrete Target shaft torque value that mixer 1 should exhibit (T2.2r)
The corrected water neuro function (40) and the corrected admixture neuro function (4
1), a kneading simulator (39) having a slump flow and funnel estimation neuro function (42) and a memory function (43) for recording neuro-learned data
And the data signal from the kneading simulator (39) (4
Water replenishment controller (45) for adjusting the amount of correction water (19) to be added to the mortar based on 4), and the addition to high flow concrete based on the data signal (44) from the kneading simulator (39). An admixture supplement controller (48) for adjusting the amount of a corrective admixture (25), and a quality control device for producing high-fluidity concrete.