CN116108739A - Concrete mortar ready-mix performance prediction method, system and storage medium - Google Patents
Concrete mortar ready-mix performance prediction method, system and storage medium Download PDFInfo
- Publication number
- CN116108739A CN116108739A CN202211565213.XA CN202211565213A CN116108739A CN 116108739 A CN116108739 A CN 116108739A CN 202211565213 A CN202211565213 A CN 202211565213A CN 116108739 A CN116108739 A CN 116108739A
- Authority
- CN
- China
- Prior art keywords
- concrete mortar
- ready
- information
- mixed concrete
- detection
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 239000004570 mortar (masonry) Substances 0.000 title claims abstract description 271
- 239000000203 mixture Substances 0.000 title claims abstract description 37
- 238000000034 method Methods 0.000 title claims abstract description 31
- 238000003860 storage Methods 0.000 title claims abstract description 18
- 238000010276 construction Methods 0.000 claims abstract description 47
- 238000013461 design Methods 0.000 claims abstract description 16
- 238000002360 preparation method Methods 0.000 claims abstract description 10
- 238000001514 detection method Methods 0.000 claims description 168
- 239000000470 constituent Substances 0.000 claims description 78
- 238000012360 testing method Methods 0.000 claims description 16
- 238000004458 analytical method Methods 0.000 claims description 11
- 238000012545 processing Methods 0.000 claims description 6
- 238000010606 normalization Methods 0.000 claims description 5
- 238000003756 stirring Methods 0.000 description 13
- 238000012549 training Methods 0.000 description 6
- 238000003062 neural network model Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 238000005070 sampling Methods 0.000 description 4
- 238000013528 artificial neural network Methods 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 239000003638 chemical reducing agent Substances 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000005204 segregation Methods 0.000 description 2
- 238000012546 transfer Methods 0.000 description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 2
- 230000007613 environmental effect Effects 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/084—Backpropagation, e.g. using gradient descent
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/04—Manufacturing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2113/00—Details relating to the application field
- G06F2113/08—Fluids
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/08—Thermal analysis or thermal optimisation
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Development Economics (AREA)
- Marketing (AREA)
- Evolutionary Computation (AREA)
- Entrepreneurship & Innovation (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Game Theory and Decision Science (AREA)
- General Health & Medical Sciences (AREA)
- Software Systems (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Educational Administration (AREA)
- Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- General Engineering & Computer Science (AREA)
- Molecular Biology (AREA)
- Manufacturing & Machinery (AREA)
- Data Mining & Analysis (AREA)
- Computing Systems (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Mathematical Physics (AREA)
- Primary Health Care (AREA)
- Computational Linguistics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Medical Informatics (AREA)
- Computer Hardware Design (AREA)
- Geometry (AREA)
- Preparation Of Clay, And Manufacture Of Mixtures Containing Clay Or Cement (AREA)
Abstract
The invention discloses a concrete mortar ready-mix performance prediction method, a system and a storage medium, wherein the method comprises the following steps: acquiring first information of ready-mixed concrete mortar when leaving a factory and second information of ready-mixed concrete mortar on a construction site; the first information and the second information are respectively sent to a preset model to obtain a first predicted concrete mortar strength and a second predicted concrete mortar strength; judging whether the strength of the first predicted concrete mortar is greater than or equal to a preset design strength, if so, setting the strength of the second predicted concrete mortar as the final preparation strength of the ready-mixed concrete mortar; if not, the corresponding ready-mixed concrete mortar is not qualified. According to the invention, the strength of the ready-mixed concrete mortar is predicted by detecting the ready-mixed concrete mortar at a construction site and combining site environment factors, so that the engineering quality safety is improved.
Description
Technical Field
The invention relates to the technical field of concrete mortar for constructional engineering, in particular to a concrete mortar ready-mix performance prediction method, a system and a storage medium.
Background
There are many factors affecting the strength of concrete mortars, especially ready mixed concrete mortars are prone to errors during transportation, such as: the strength of the concrete mortar is reduced due to overlong standing time of the ready-mixed concrete mortar caused by traffic jam. At present, the strength of the ready-mixed concrete mortar is mainly determined by the mixing ratio, the construction site is detected by the test block, but the corresponding test block can only be detected after the maintenance is completed, at the moment, the corresponding ready-mixed concrete mortar is constructed, and if the strength of the corresponding ready-mixed concrete mortar is insufficient, the post-reinforcement or the pushing to the rework can only be carried out.
Accordingly, there is a need for improvement in the art.
Disclosure of Invention
In view of the above problems, an object of the present invention is to provide a method, a system and a storage medium for predicting ready-mixed performance of concrete mortar, which can more effectively and more conveniently predict the strength of ready-mixed concrete mortar.
The first aspect of the invention provides a concrete mortar ready-mix performance prediction method, which comprises the following steps:
acquiring first information of ready-mixed concrete mortar when leaving a factory and second information of ready-mixed concrete mortar on a construction site;
the first information and the second information are respectively sent to a preset model to obtain a first predicted concrete mortar strength and a second predicted concrete mortar strength;
judging whether the strength of the first predicted concrete mortar is greater than or equal to a preset design strength, if so, setting the strength of the second predicted concrete mortar as the final preparation strength of the ready-mixed concrete mortar; if not, the corresponding ready-mixed concrete mortar is not qualified.
In this solution, the step of obtaining the first information specifically includes:
acquiring n pieces of first detection sample information of ready-mixed concrete mortar when leaving a factory;
extracting the composition elements of the ready-mixed concrete mortar in the n first detection samples and the weight information of the composition elements;
obtaining first ratio information of the constituent elements of the n first detection samples according to the weight information of the constituent elements in the n first detection samples;
obtaining first temperature and first generation time information of the ready-mixed concrete mortar according to the ready-mixed concrete mortar information when leaving a factory;
the first temperature of the ready-mixed concrete mortar, the first generation time information and the first ratio information of n first detection sample constituent elements are set as first information.
In this scheme, still include:
setting the first ratio of the constituent elements in the first detection sample as
The formula is as follows: />
Wherein n is greater than or equal to 3, m is n, A m Representing the ratio of the constituent elements in a first detection sample with the number of m;
will A m Maximum or minimum sum of (a)
Performing contrast analysis to obtain a first detection value;
judging whether the first detection value is larger than a first detection threshold value, if so, invalidating the first detection samples of the corresponding batches, and re-extracting the first detection samples; if not, the first detection sample is valid.
In this solution, the step of obtaining the second information specifically includes:
obtaining n of ready-mixed concrete mortar at construction site ′ Second detection sample information;
extracting said n ′ Pre-mixed concrete mortar in second detection sampleThe constituent elements and weight information of the constituent elements;
according to said n ′ Weight information of the constituent elements in the second detection sample is obtained to obtain n ′ Second ratio information of constituent elements of a second detection sample;
obtaining second temperature of the ready-mixed concrete mortar, second temperature of the construction site and second generation time information according to the ready-mixed concrete mortar information of the construction site;
a second temperature of the ready-mixed concrete mortar, a second temperature of a construction site, second generation time information and n ′ The second ratio information of the constituent elements of the second detection sample is set to the second information.
In this scheme, still include:
setting the second ratio of the constituent elements in the second detection sample as
The formula is as follows:
wherein n is ′ ≥3，m ′ ∈n ′ ，B m′ The representation number is m ′ The ratio of constituent elements in the second test sample;
will B m′ Maximum or minimum sum of (a)
Performing contrast analysis to obtain a second detection value;
judging whether the second detection value is larger than a second detection threshold value, if so, invalidating the second detection samples of the corresponding batches, and re-extracting the second detection samples; if not, the second detection sample is valid.
In this scheme, still include:
extracting the influence factors of the strength of the ready-mixed concrete mortar in the first information or the second information and the parameter values of the corresponding factors;
and carrying out normalization processing on the parameter values of the corresponding factors to obtain the preset model input values of the corresponding factors.
The second aspect of the invention provides a concrete mortar ready-mix performance prediction system, comprising a memory and a processor, wherein the memory stores a concrete mortar ready-mix performance prediction method program, and the concrete mortar ready-mix performance prediction method program realizes the following steps when being executed by the processor:
acquiring first information of ready-mixed concrete mortar when leaving a factory and second information of ready-mixed concrete mortar on a construction site;
the first information and the second information are respectively sent to a preset model to obtain a first predicted concrete mortar strength and a second predicted concrete mortar strength;
judging whether the strength of the first predicted concrete mortar is greater than or equal to a preset design strength, if so, setting the strength of the second predicted concrete mortar as the final preparation strength of the ready-mixed concrete mortar; if not, the corresponding ready-mixed concrete mortar is not qualified.
In this solution, the step of obtaining the first information specifically includes:
acquiring n pieces of first detection sample information of ready-mixed concrete mortar when leaving a factory;
extracting the composition elements of the ready-mixed concrete mortar in the n first detection samples and the weight information of the composition elements;
obtaining first ratio information of the constituent elements of the n first detection samples according to the weight information of the constituent elements in the n first detection samples;
obtaining first temperature and first generation time information of the ready-mixed concrete mortar according to the ready-mixed concrete mortar information when leaving a factory;
the first temperature of the ready-mixed concrete mortar, the first generation time information and the first ratio information of n first detection sample constituent elements are set as first information.
In this scheme, still include:
setting the first ratio of the constituent elements in the first detection sample as
The formula is as follows:
wherein n is greater than or equal to 3, m is n, A m Representing the ratio of the constituent elements in a first detection sample with the number of m;
will A m Maximum or minimum sum of (a)
Performing contrast analysis to obtain a first detection value;
judging whether the first detection value is larger than a first detection threshold value, if so, invalidating the first detection samples of the corresponding batches, and re-extracting the first detection samples; if not, the first detection sample is valid.
In this solution, the step of obtaining the second information specifically includes:
obtaining n of ready-mixed concrete mortar at construction site ′ Second detection sample information;
extracting said n ′ The second detection sample is prepared from the constituent elements of the ready-mixed concrete mortar and the weight information of the constituent elements;
according to said n ′ Weight information of the constituent elements in the second detection sample is obtained to obtain n ′ Second ratio information of constituent elements of a second detection sample;
obtaining second temperature of the ready-mixed concrete mortar, second temperature of the construction site and second generation time information according to the ready-mixed concrete mortar information of the construction site;
a second temperature of the ready-mixed concrete mortar, a second temperature of a construction site, second generation time information and n ′ The second ratio information of the constituent elements of the second detection sample is set to the second information.
In this scheme, still include:
second test sampleThe second ratio of the constituent elements is set as
The formula is as follows:
wherein n is ′ ≥3，m ′ ∈n ′ ，B m′ The representation number is m ′ The ratio of constituent elements in the second test sample;
will B m′ Maximum or minimum sum of (a)
Performing contrast analysis to obtain a second detection value;
judging whether the second detection value is larger than a second detection threshold value, if so, invalidating the second detection samples of the corresponding batches, and re-extracting the second detection samples; if not, the second detection sample is valid.
In this scheme, still include:
extracting the influence factors of the strength of the ready-mixed concrete mortar in the first information or the second information and the parameter values of the corresponding factors;
and carrying out normalization processing on the parameter values of the corresponding factors to obtain the preset model input values of the corresponding factors.
A third aspect of the present invention provides a computer storage medium having stored therein a concrete mortar ready-mix performance prediction method program which, when executed by a processor, implements the steps of a concrete mortar ready-mix performance prediction method as described in any one of the above.
The invention discloses a concrete mortar ready-mix performance prediction method, a system and a storage medium. The invention judges whether the strength of the ready-mixed concrete mortar in delivery meets the design requirement or not through the detection of the ready-mixed concrete mortar in delivery; and the transportation time of the ready-mixed concrete mortar and the environment of the construction site are combined, the ready-mixed concrete mortar is detected for the second time, whether the strength of the ready-mixed concrete mortar at the construction site meets the design requirement is judged, and the strength of the ready-mixed concrete mortar is predicted by analyzing the environmental factors of the construction site, so that the engineering quality safety is improved. The workability of the ready-mixed concrete mortar on the construction site is judged through the slump value of the ready-mixed concrete mortar.
Drawings
FIG. 1 shows a flow chart of a concrete mortar ready mix performance prediction method of the invention;
FIG. 2 shows a BP neural network structure training and detection process diagram;
FIG. 3 shows a block diagram of a concrete mortar ready mix performance prediction system of the invention.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, in the case of no conflict, the embodiments of the present application and the features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those described herein, and therefore the scope of the present invention is not limited to the specific embodiments disclosed below.
FIG. 1 shows a flow chart of a concrete mortar ready-mix performance prediction method of the invention.
As shown in fig. 1, the first aspect of the present invention provides a method for predicting ready-mixed performance of concrete mortar, including:
s102, acquiring first information of ready-mixed concrete mortar when leaving a factory and second information of ready-mixed concrete mortar on a construction site;
s104, the first information and the second information are respectively sent to a preset model to obtain a first predicted concrete mortar strength and a second predicted concrete mortar strength;
s106, judging whether the strength of the first predicted concrete mortar is larger than or equal to a preset design strength, if so, setting the strength of the second predicted concrete mortar as the final preparation strength of the ready-mixed concrete mortar; if not, the corresponding ready-mixed concrete mortar is not qualified.
When the ready-mixed concrete mortar is transported out of the stirring mill, the first prediction is required to obtain a first predicted concrete mortar strength. If the preset design strength is C30, the strength grade of the corresponding ready-mixed concrete mortar when leaving the factory cannot be lower than C30, and if the strength grade of the corresponding ready-mixed concrete mortar is lower than C30, the corresponding batch of ready-mixed concrete mortar is unqualified and cannot be transported out of the stirring factory, so that the second prediction is not needed; if the first predicted concrete mortar strength is greater than or equal to the preset design strength, the corresponding ready-mixed concrete mortar is allowed to be transported out of the stirring mill, and the second predicted concrete mortar strength of the construction site is set as the final preparation strength of the ready-mixed concrete mortar.
According to an embodiment of the present invention, the step of obtaining the first information specifically includes:
acquiring n pieces of first detection sample information of ready-mixed concrete mortar when leaving a factory;
extracting the composition elements of the ready-mixed concrete mortar in the n first detection samples and the weight information of the composition elements;
obtaining first ratio information of the constituent elements of the n first detection samples according to the weight information of the constituent elements in the n first detection samples;
obtaining first temperature and first generation time information of the ready-mixed concrete mortar according to the ready-mixed concrete mortar information when leaving a factory;
the first temperature of the ready-mixed concrete mortar, the first generation time information and the first ratio information of n first detection sample constituent elements are set as first information.
When the ready mixed concrete mortar leaves the factory, extracting n first detection samples, and detecting the n first detection samples to obtain the constituent elements of the ready mixed concrete mortar and the weight information of the constituent elements, wherein the first ratio of the constituent elements of the n first detection samples is the average value of the ratios of the constituent elements of the n first detection samples. The first generation time information is time information from the completion of stirring of the ready-mixed concrete mortar to the delivery of a stirring plant; the first temperature of the ready-mixed concrete mortar is a temperature value during sampling.
According to an embodiment of the present invention, further comprising:
setting the first ratio of the constituent elements in the first detection sample as
The formula is as follows:
wherein n is greater than or equal to 3, m is n, A m Representing the ratio of the constituent elements in a first detection sample with the number of m;
will A m Maximum or minimum sum of (a)
Performing contrast analysis to obtain a first detection value;
judging whether the first detection value is larger than a first detection threshold value, if so, invalidating the first detection samples of the corresponding batches, and re-extracting the first detection samples; if not, the first detection sample is valid.
The first detection value is set to θ 1 The formula is as follows:
or->
Wherein (A) m ) max Representation A m Maximum value of (A) m ) min Representation A m Is the minimum value of (a). If the preset first detection threshold is 20%, corresponding to theta 1 And if less than or equal to 20 percent is qualified, the first detection sample is invalid. When the re-extracted first detection sample is still invalid, the corresponding is describedThe batch of ready-mixed concrete mortar is unevenly stirred, and is a disqualified product.
According to an embodiment of the present invention, the step of obtaining the second information specifically includes:
obtaining n of ready-mixed concrete mortar at construction site ′ Second detection sample information;
extracting said n ′ The second detection sample is prepared from the constituent elements of the ready-mixed concrete mortar and the weight information of the constituent elements;
according to said n ′ Weight information of the constituent elements in the second detection sample is obtained to obtain n ′ Second ratio information of constituent elements of a second detection sample;
obtaining second temperature of the ready-mixed concrete mortar, second temperature of the construction site and second generation time information according to the ready-mixed concrete mortar information of the construction site;
a second temperature of the ready-mixed concrete mortar, a second temperature of a construction site, second generation time information and n ′ The second ratio information of the constituent elements of the second detection sample is set to the second information.
When needed, the ready-mixed concrete mortar n is extracted at the construction site ′ A second test sample and for said n ′ Detecting the second detection sample to obtain the constituent elements of the ready-mixed concrete mortar and the weight information of the constituent elements of the construction site, wherein n is as follows ′ A second ratio of the constituent elements of the second detection sample is n ′ Average value of ratios of constituent elements of the second test sample. The second generation time information is time information from the completion of stirring of the ready-mixed concrete mortar to the delivery to the construction site; and the second temperature of the ready-mixed concrete mortar is a temperature value during sampling at a construction site.
According to an embodiment of the present invention, further comprising:
setting the second ratio of the constituent elements in the second detection sample as
The formula is as follows:
wherein n is ′ ≥3，m ′ ∈n ′ ，B m′ The representation number is m ′ Is a second test sample of (a);
will B m′ Maximum or minimum sum of (a)
Performing contrast analysis to obtain a second detection value;
judging whether the second detection value is larger than a second detection threshold value, if so, invalidating the second detection samples of the corresponding batches, and re-extracting the second detection samples; if not, the second detection sample is valid.
The second detection value is set to θ 2 The formula is as follows:
or (b)
Wherein (B) m′ ) max Representation B m′ Maximum value of (B) m′ ) min Representation B m′ Is the minimum value of (a). If the preset second detection threshold is 18%, corresponding to theta 2 And if 18% or less is qualified, the second detection sample is invalid. When the re-extracted second detection sample is invalid, the segregation phenomenon of the ready-mixed concrete mortar in the corresponding batch is indicated, and the ready-mixed concrete mortar needs to be re-stirred uniformly and then sampled and detected.
According to an embodiment of the present invention, further comprising:
extracting the influence factors of the strength of the ready-mixed concrete mortar in the first information or the second information and the parameter values of the corresponding factors;
and carrying out normalization processing on the parameter values of the corresponding factors to obtain the preset model input values of the corresponding factors.
It should be noted thatHowever, since the factors affecting the ready-mixed concrete mortar are different, the parameter values of the corresponding factors are different in units, and therefore, the parameter values of the corresponding factors need to be normalized. The formula is as follows:
wherein a represents the parameter value of the influencing factor, a max The maximum parameter value, k, representing the influencing factor a And (5) representing the influence weight value of the factor a on the ready-mixed concrete mortar.
According to an embodiment of the present invention, further comprising:
calculating the normalized parameter values based on a preset model to obtain predicted concrete mortar strength of corresponding information;
the calculation formula for predicting the strength of the concrete mortar is as follows:
wherein F represents the predicted concrete mortar strength, W xy Representing implicit layer weights in a preset model; a, a y Representing the input value of factor y, D x Representing hidden layer threshold in preset model, f i Presetting an output layer threshold value in a model, W ix The method is characterized in that the method is used for representing the weight value of an output layer in a preset model, X represents the number of hidden layer nodes, X epsilon X, Y represents the number of input layer nodes, Y epsilon Y, and i represents the ith node of the output layer.
It should be noted that, the preset model is a Back Production (BP) neural network model, which includes a plurality of input layers, a plurality of hidden layers and an output layer, where the factors affecting the ready mixed concrete mortar correspond to the input layers, the hidden layers are transfer layers, and the output layer corresponds to the predicted result, that is, the strength of the ready mixed concrete mortar.
According to an embodiment of the present invention, further comprising:
judging whether the second generation time of the ready mixed concrete mortar is greater than or equal to a preset time threshold, if so, stopping the second prediction of the concrete mortar strength, and if the corresponding ready mixed concrete mortar is unqualified; if not, the operation is not stopped.
It should be noted that, the preset time threshold is smaller than the initial setting time of the corresponding ready-mixed concrete mortar, for example, the preset time threshold is 160 minutes, when the second generation time is greater than or equal to 160 minutes, the corresponding ready-mixed concrete mortar is not longer than the initial setting time of the ready-mixed concrete mortar for construction, so that the corresponding ready-mixed concrete mortar is unqualified, and the second prediction of the concrete mortar strength is not needed.
According to an embodiment of the present invention, further comprising:
obtaining slump value information of ready-mixed concrete mortar;
judging whether the slump value of the ready-mixed concrete mortar is in a preset slump range, if so, the workability of the ready-mixed concrete mortar reaches the standard; if not, the workability of the corresponding ready-mixed concrete mortar does not reach the standard.
If the preset slump range is set to be not less than 40 mm, when the slump value of the ready-mixed concrete mortar is less than 40 mm, the workability of the corresponding ready-mixed concrete mortar is not up to the standard, and when the strength of the ready-mixed concrete mortar meets the preset design requirement, the workability of the ready-mixed concrete mortar can be enhanced by adding a water reducing agent and the like.
Fig. 2 shows a BP neural network structure training and detection process diagram.
As shown in fig. 2, historical data of the ready-mixed concrete mortar is obtained, and the historical data is processed to obtain a training sample and a test sample, wherein the training sample is input into the BP neural network model for training, and the predicted ready-mixed concrete mortar strength output by the BP neural network model is enabled to be continuously close to the real strength by continuously adjusting the weight W. And confirming through a test sample, for example, the preset accuracy threshold is 95%, which shows that when the test sample is 100 cases and the result output by the BP neural network model is not less than 95 cases with the same actual strength of the premixed concrete mortar in the test sample, the training of the corresponding BP neural network structure is finished.
FIG. 3 shows a block diagram of a concrete mortar ready mix performance prediction system of the invention.
As shown in fig. 3, a second aspect of the present invention provides a concrete mortar ready-mix performance prediction system 3, including a memory 31 and a processor 32, where the memory stores a concrete mortar ready-mix performance prediction method program, and when the concrete mortar ready-mix performance prediction method program is executed by the processor, the following steps are implemented:
acquiring first information of ready-mixed concrete mortar when leaving a factory and second information of ready-mixed concrete mortar on a construction site;
the first information and the second information are respectively sent to a preset model to obtain a first predicted concrete mortar strength and a second predicted concrete mortar strength;
judging whether the strength of the first predicted concrete mortar is greater than or equal to a preset design strength, if so, setting the strength of the second predicted concrete mortar as the final preparation strength of the ready-mixed concrete mortar; if not, the corresponding ready-mixed concrete mortar is not qualified.
When the ready-mixed concrete mortar is transported out of the stirring mill, the first prediction is required to obtain a first predicted concrete mortar strength. If the preset design strength is C30, the strength grade of the corresponding ready-mixed concrete mortar when leaving the factory cannot be lower than C30, and if the strength grade of the corresponding ready-mixed concrete mortar is lower than C30, the corresponding batch of ready-mixed concrete mortar is unqualified and cannot be transported out of the stirring factory, so that the second prediction is not needed; if the first predicted concrete mortar strength is greater than or equal to the preset design strength, the corresponding ready-mixed concrete mortar is allowed to be transported out of the stirring mill, and the second predicted concrete mortar strength of the construction site is set as the final preparation strength of the ready-mixed concrete mortar.
According to an embodiment of the present invention, the step of obtaining the first information specifically includes:
acquiring n pieces of first detection sample information of ready-mixed concrete mortar when leaving a factory;
extracting the composition elements of the ready-mixed concrete mortar in the n first detection samples and the weight information of the composition elements;
obtaining first ratio information of the constituent elements of the n first detection samples according to the weight information of the constituent elements in the n first detection samples;
obtaining first temperature and first generation time information of the ready-mixed concrete mortar according to the ready-mixed concrete mortar information when leaving a factory;
the first temperature of the ready-mixed concrete mortar, the first generation time information and the first ratio information of n first detection sample constituent elements are set as first information.
When the ready mixed concrete mortar leaves the factory, extracting n first detection samples, and detecting the n first detection samples to obtain the constituent elements of the ready mixed concrete mortar and the weight information of the constituent elements, wherein the first ratio of the constituent elements of the n first detection samples is the average value of the ratios of the constituent elements of the n first detection samples. The first generation time information is time information from the completion of stirring of the ready-mixed concrete mortar to the delivery of a stirring plant; the first temperature of the ready-mixed concrete mortar is a temperature value during sampling.
According to an embodiment of the present invention, further comprising:
setting the first ratio of the constituent elements in the first detection sample as
The formula is as follows:
wherein n is greater than or equal to 3, m is n, A m Representing the ratio of the constituent elements in a first detection sample with the number of m;
will A m Maximum or minimum sum of (a)
Performing contrast analysis to obtain a first detection value;
judging whether the first detection value is larger than a first detection threshold value, if so, invalidating the first detection samples of the corresponding batches, and re-extracting the first detection samples; if not, the first detection sample is valid.
The first detection value is set to θ 1 The formula is as follows:
or->
Wherein (A) m ) max Representation A m Maximum value of (A) m ) min Representation A m Is the minimum value of (a). If the preset first detection threshold is 20%, corresponding to theta 1 And if less than or equal to 20 percent is qualified, the first detection sample is invalid. And when the re-extracted first detection sample is invalid, indicating that the stirring of the ready-mixed concrete mortar of the corresponding batch is uneven, and the ready-mixed concrete mortar is a non-qualified product.
According to an embodiment of the present invention, the step of obtaining the second information specifically includes:
obtaining n of ready-mixed concrete mortar at construction site ′ Second detection sample information;
extracting said n ′ The second detection sample is prepared from the constituent elements of the ready-mixed concrete mortar and the weight information of the constituent elements;
according to said n ′ Weight information of the constituent elements in the second detection sample is obtained to obtain n ′ Second ratio information of constituent elements of a second detection sample;
obtaining second temperature of the ready-mixed concrete mortar, second temperature of the construction site and second generation time information according to the ready-mixed concrete mortar information of the construction site;
a second temperature of the ready-mixed concrete mortar, a second temperature of a construction site, second generation time information and n ′ The second ratio information of the constituent elements of the second detection sample is set to the second information.
When needed, the ready-mixed concrete mortar n is extracted at the construction site ′ A second test sample and for said n ′ Detecting the second detection sample to obtain ready-mixed concrete mortar of the construction siteConstituent element and weight information of the constituent element, the n ′ A second ratio of the constituent elements of the second detection sample is n ′ Average value of ratios of constituent elements of the second test sample. The second generation time information is time information from the completion of stirring of the ready-mixed concrete mortar to the delivery to the construction site; and the second temperature of the ready-mixed concrete mortar is a temperature value during sampling at a construction site.
According to an embodiment of the present invention, further comprising:
setting the second ratio of the constituent elements in the second detection sample as
The formula is as follows:
wherein n is ′ ≥3，m ′ ∈n ′ ，B m′ The representation number is m ′ Is a second test sample of (a);
will B m′ Maximum or minimum sum of (a)
Performing contrast analysis to obtain a second detection value;
judging whether the second detection value is larger than a second detection threshold value, if so, invalidating the second detection samples of the corresponding batches, and re-extracting the second detection samples; if not, the second detection sample is valid.
The second detection value is set to θ 2 The formula is as follows:
or (b)
Wherein (B) m′ ) max Representation B m′ Maximum value of (B) m′ ) min Representation B m′ Is the minimum value of (a). If the preset second detection threshold is 18%, corresponding to theta 2 And if 18% or less is qualified, the second detection sample is invalid. When the re-extracted second detection sample is invalid, the segregation phenomenon of the ready-mixed concrete mortar in the corresponding batch is indicated, and the ready-mixed concrete mortar needs to be re-stirred uniformly and then sampled and detected.
According to an embodiment of the present invention, further comprising:
extracting the influence factors of the strength of the ready-mixed concrete mortar in the first information or the second information and the parameter values of the corresponding factors;
and carrying out normalization processing on the parameter values of the corresponding factors to obtain the preset model input values of the corresponding factors.
The parameter values of the corresponding factors are normalized because the factors affecting the ready-mixed concrete mortar are different and the parameter values of the corresponding factors are different. The formula is as follows:
wherein a represents the parameter value of the influencing factor, a max The maximum parameter value, k, representing the influencing factor a And (5) representing the influence weight value of the factor a on the ready-mixed concrete mortar.
According to an embodiment of the present invention, further comprising:
calculating the normalized parameter values based on a preset model to obtain predicted concrete mortar strength of corresponding information;
the calculation formula for predicting the strength of the concrete mortar is as follows:
wherein F represents the predicted concrete mortar strength, W xy Representing implicit layer weights in a preset model; a, a y Representing the input value of factor y, D x Representing hidden layer threshold in preset model, f i Presetting an output layer threshold value in a model, W ix Represents the weight value of the output layer in the preset model,x represents the number of hidden layer nodes, X epsilon X, Y represents the number of input layer nodes, Y epsilon Y, i represents the ith node of the output layer.
It should be noted that, the preset model is a Back Production (BP) neural network model, which includes a plurality of input layers, a plurality of hidden layers and an output layer, where the factors affecting the ready mixed concrete mortar correspond to the input layers, the hidden layers are transfer layers, and the output layer corresponds to the predicted result, that is, the strength of the ready mixed concrete mortar.
According to an embodiment of the present invention, further comprising:
judging whether the second generation time of the ready mixed concrete mortar is greater than or equal to a preset time threshold, if so, stopping the second prediction of the concrete mortar strength, and if the corresponding ready mixed concrete mortar is unqualified; if not, the operation is not stopped.
It should be noted that, the preset time threshold is smaller than the initial setting time of the corresponding ready-mixed concrete mortar, for example, the preset time threshold is 160 minutes, when the second generation time is greater than or equal to 160 minutes, the corresponding ready-mixed concrete mortar is not longer than the initial setting time of the ready-mixed concrete mortar for construction, so that the corresponding ready-mixed concrete mortar is unqualified, and the second prediction of the concrete mortar strength is not needed.
According to an embodiment of the present invention, further comprising:
obtaining slump value information of ready-mixed concrete mortar;
judging whether the slump value of the ready-mixed concrete mortar is in a preset slump range, if so, the workability of the ready-mixed concrete mortar reaches the standard; if not, the workability of the corresponding ready-mixed concrete mortar does not reach the standard.
If the preset slump range is set to be not less than 40 mm, when the slump value of the ready-mixed concrete mortar is less than 40 mm, the workability of the corresponding ready-mixed concrete mortar is not up to the standard, and when the strength of the ready-mixed concrete mortar meets the preset design requirement, the workability of the ready-mixed concrete mortar can be enhanced by adding a water reducing agent and the like.
A third aspect of the present invention provides a computer storage medium having stored therein a concrete mortar ready-mix performance prediction method program which, when executed by a processor, implements the steps of a concrete mortar ready-mix performance prediction method as described in any one of the above.
The invention discloses a concrete mortar ready-mix performance prediction method, a system and a storage medium, wherein the method comprises the following steps: acquiring first information of ready-mixed concrete mortar when leaving a factory and second information of ready-mixed concrete mortar on a construction site; the first information and the second information are respectively sent to a preset model to obtain a first predicted concrete mortar strength and a second predicted concrete mortar strength; judging whether the strength of the first predicted concrete mortar is greater than or equal to a preset design strength, if so, setting the strength of the second predicted concrete mortar as the final preparation strength of the ready-mixed concrete mortar; if not, the corresponding ready-mixed concrete mortar is not qualified. According to the invention, the strength of the ready-mixed concrete mortar is predicted by detecting the ready-mixed concrete mortar at a construction site and combining site environment factors, so that the engineering quality safety is improved.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present invention may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or an optical disk, or the like, which can store program codes.
Alternatively, the above-described integrated units of the present invention may be stored in a computer-readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solutions of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.
Claims (10)
1. The method for predicting the ready-mixed performance of the concrete mortar is characterized by comprising the following steps of:
acquiring first information of ready-mixed concrete mortar when leaving a factory and second information of ready-mixed concrete mortar on a construction site;
the first information and the second information are respectively sent to a preset model to obtain a first predicted concrete mortar strength and a second predicted concrete mortar strength;
judging whether the strength of the first predicted concrete mortar is greater than or equal to a preset design strength, if so, setting the strength of the second predicted concrete mortar as the final preparation strength of the ready-mixed concrete mortar; if not, the corresponding ready-mixed concrete mortar is not qualified.
2. The method for predicting ready mixed performance of concrete mortar according to claim 1, wherein the step of obtaining the first information comprises the following steps:
acquiring n pieces of first detection sample information of ready-mixed concrete mortar when leaving a factory;
extracting the composition elements of the ready-mixed concrete mortar in the n first detection samples and the weight information of the composition elements;
obtaining first ratio information of the constituent elements of the n first detection samples according to the weight information of the constituent elements in the n first detection samples;
obtaining first temperature and first generation time information of the ready-mixed concrete mortar according to the ready-mixed concrete mortar information when leaving a factory;
the first temperature of the ready-mixed concrete mortar, the first generation time information and the first ratio information of n first detection sample constituent elements are set as first information.
3. The method for predicting ready-mixed performance of concrete mortar according to claim 2, further comprising:
setting the first ratio of the constituent elements in the first detection sample as
The formula is as follows:
wherein n is greater than or equal to 3, m is n, A m Representing the ratio of the constituent elements in a first detection sample with the number of m;
will A m Maximum or minimum sum of (a)
Performing contrast analysis to obtain a first detection value;
judging whether the first detection value is larger than a first detection threshold value, if so, invalidating the first detection samples of the corresponding batches, and re-extracting the first detection samples; if not, the first detection sample is valid.
4. The method for predicting ready mixed performance of concrete mortar according to claim 1, wherein the step of obtaining the second information comprises the following steps:
obtaining n of ready-mixed concrete mortar at construction site ′ Second detection sample information;
extracting said n ′ The second detection sample is prepared from the constituent elements of the ready-mixed concrete mortar and the weight information of the constituent elements;
according to said n ′ Weight information of the constituent elements in the second detection sample is obtained to obtain n ′ Second ratio information of constituent elements of a second detection sample;
obtaining second temperature of the ready-mixed concrete mortar, second temperature of the construction site and second generation time information according to the ready-mixed concrete mortar information of the construction site;
a second temperature of the ready-mixed concrete mortar, a second temperature of a construction site, second generation time information and n ′ The second ratio information of the constituent elements of the second detection sample is set to the second information.
5. The method for predicting ready mixed performance of concrete mortar according to claim 4, further comprising:
setting the second ratio of the constituent elements in the second detection sample as
The formula is as follows: />
Wherein n is ′ ≥3，m ′ ∈n ′ ，B m′ The representation number is m ′ The ratio of constituent elements in the second test sample;
will B m′ Maximum or minimum sum of (a)
Performing contrast analysis to obtain a second detection value;
judging whether the second detection value is larger than a second detection threshold value, if so, invalidating the second detection samples of the corresponding batches, and re-extracting the second detection samples; if not, the second detection sample is valid.
6. The method for predicting ready-mixed performance of concrete mortar according to claim 1, further comprising:
extracting the influence factors of the strength of the ready-mixed concrete mortar in the first information or the second information and the parameter values of the corresponding factors;
and carrying out normalization processing on the parameter values of the corresponding factors to obtain the preset model input values of the corresponding factors.
7. The system for predicting the ready mixing performance of the concrete mortar is characterized by comprising a memory and a processor, wherein the memory stores a ready mixing performance predicting method program of the concrete mortar, and the method for predicting the ready mixing performance of the concrete mortar is implemented by the processor when being executed by the processor as follows:
acquiring first information of ready-mixed concrete mortar when leaving a factory and second information of ready-mixed concrete mortar on a construction site;
the first information and the second information are respectively sent to a preset model to obtain a first predicted concrete mortar strength and a second predicted concrete mortar strength;
judging whether the strength of the first predicted concrete mortar is greater than or equal to a preset design strength, if so, setting the strength of the second predicted concrete mortar as the final preparation strength of the ready-mixed concrete mortar; if not, the corresponding ready-mixed concrete mortar is not qualified.
8. The concrete mortar ready mix performance prediction system according to claim 7, wherein the first information obtaining step specifically includes:
acquiring n pieces of first detection sample information of ready-mixed concrete mortar when leaving a factory;
extracting the composition elements of the ready-mixed concrete mortar in the n first detection samples and the weight information of the composition elements;
obtaining first ratio information of the constituent elements of the n first detection samples according to the weight information of the constituent elements in the n first detection samples;
obtaining first temperature and first generation time information of the ready-mixed concrete mortar according to the ready-mixed concrete mortar information when leaving a factory;
the first temperature of the ready-mixed concrete mortar, the first generation time information and the first ratio information of n first detection sample constituent elements are set as first information.
9. The concrete mortar ready mix performance prediction system of claim 8, further comprising:
setting the first ratio of the constituent elements in the first detection sample as
The formula is as follows:
wherein n is greater than or equal to 3, m is n, A m Representing the ratio of the constituent elements in a first detection sample with the number of m;
will A m Maximum or minimum sum of (a)
Performing contrast analysis to obtain a first detection value;
judging whether the first detection value is larger than a first detection threshold value, if so, invalidating the first detection samples of the corresponding batches, and re-extracting the first detection samples; if not, the first detection sample is valid.
10. A computer storage medium, characterized in that a concrete mortar ready-mix performance prediction method program is stored in the computer storage medium, which, when executed by a processor, implements the steps of a concrete mortar ready-mix performance prediction method according to any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211565213.XA CN116108739B (en) | 2022-12-07 | 2022-12-07 | Concrete mortar ready-mix performance prediction method, system and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211565213.XA CN116108739B (en) | 2022-12-07 | 2022-12-07 | Concrete mortar ready-mix performance prediction method, system and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116108739A true CN116108739A (en) | 2023-05-12 |
| CN116108739B CN116108739B (en) | 2024-07-12 |
Family
ID=86260594
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211565213.XA Active CN116108739B (en) | 2022-12-07 | 2022-12-07 | Concrete mortar ready-mix performance prediction method, system and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116108739B (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2009184273A (en) * | 2008-02-07 | 2009-08-20 | Pacific Technos Corp | Method and system for controlling concrete material composition |
| CN108828200A (en) * | 2018-07-10 | 2018-11-16 | 珠海春禾新材料研究院有限公司 | A kind of ready-mixed concrete mixture travel position simulation test device and method |
| CN114242181A (en) * | 2021-11-10 | 2022-03-25 | 中铁二十局集团有限公司 | Desert sand concrete strength prediction model training method, device, equipment and medium |
| CN114430710A (en) * | 2019-08-01 | 2022-05-03 | Gcp应用技术有限公司 | Coordinated concrete delivery and placement |
| US20220253734A1 (en) * | 2020-11-16 | 2022-08-11 | Parham Aghdasi | Machine learning methods to optimize concrete applications and formulations |
| CN115293051A (en) * | 2022-08-25 | 2022-11-04 | 广东省科学院江门产业技术研究院有限公司 | Concrete rheological property prediction method based on BP neural network |
-
2022
- 2022-12-07 CN CN202211565213.XA patent/CN116108739B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2009184273A (en) * | 2008-02-07 | 2009-08-20 | Pacific Technos Corp | Method and system for controlling concrete material composition |
| CN108828200A (en) * | 2018-07-10 | 2018-11-16 | 珠海春禾新材料研究院有限公司 | A kind of ready-mixed concrete mixture travel position simulation test device and method |
| CN114430710A (en) * | 2019-08-01 | 2022-05-03 | Gcp应用技术有限公司 | Coordinated concrete delivery and placement |
| US20220253734A1 (en) * | 2020-11-16 | 2022-08-11 | Parham Aghdasi | Machine learning methods to optimize concrete applications and formulations |
| CN114242181A (en) * | 2021-11-10 | 2022-03-25 | 中铁二十局集团有限公司 | Desert sand concrete strength prediction model training method, device, equipment and medium |
| CN115293051A (en) * | 2022-08-25 | 2022-11-04 | 广东省科学院江门产业技术研究院有限公司 | Concrete rheological property prediction method based on BP neural network |
Non-Patent Citations (1)
| Title |
|---|
| 陈斌，等: "基于粗糙集( RS) 和支持向量机( SVM) 的混凝土性能预测实证研究", 《水力发电学报》, 31 December 2011 (2011-12-31), pages 251 - 257 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116108739B (en) | 2024-07-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110263431B (en) | Concrete 28d compressive strength prediction method | |
| EP3924906B1 (en) | Systems and methods for formulating or evaluating a construction composition | |
| CN115144571A (en) | Construction site concrete strength detection model establishing method, device, equipment and medium | |
| Špak et al. | Comparison of conventional and advanced concrete technologies in terms of construction efficiency | |
| CN117195163B (en) | Slump prediction method and device based on concrete formula and readable medium | |
| EP3924908A1 (en) | Systems and methods for formulating or evaluating a construction admixture | |
| CN116108739B (en) | Concrete mortar ready-mix performance prediction method, system and storage medium | |
| CN110309922A (en) | A kind of network model training method and device | |
| CN117195083B (en) | Slump prediction method and device based on current curve and readable medium | |
| CN115510754A (en) | Method and system for predicting and controlling performance of cement concrete | |
| CN113988676B (en) | Safety management method and system for water treatment equipment | |
| CN116362629B (en) | Intelligent PC building quality monitoring and management method and system | |
| CN116775622B (en) | Method, device, equipment and storage medium for generating structural data | |
| CN106682328B (en) | Vibration deformation measurement calculation method for vertical high-rise structure vibration isolation system | |
| CN109002715A (en) | A kind of Malware recognition methods and system based on convolutional neural networks | |
| CN110197045B (en) | Method, device, equipment and storage medium for determining machine-made sand concrete mixing proportion | |
| CN115078698A (en) | Premixed concrete quality detection and control method and system | |
| CN116011053A (en) | Setting method and device of building drawing, electronic equipment and storage medium | |
| CN115879672A (en) | Water treatment equipment management method and system based on Internet of things | |
| Won et al. | Development of PYMUS+ Code for Quantitative Evaluation of Nuclear Material Accounting (NMA) System | |
| CN115114849B (en) | Intelligent design method and device for low-carbon self-compacting concrete | |
| CN107038316B (en) | Flexural strength evaluation method and device | |
| CN107133399B (en) | Compressive strength evaluation method and device | |
| Terro et al. | Utilization of six sigma in quality improvement of recycled aggregates concrete | |
| CN111825386A (en) | Method for predicting strength of premixed concrete |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |