59 results about "Ready mixed concrete" patented technology

The invention relates to the technical field of dust removal, in particular to an intelligent dust removal device and method for premixed concrete production.The intelligent dust removal device comprises a dust removal tank and a conical filter cylinder arranged in the dust removal tank and used for removing dust, a plurality of filter bags are fixedly connected to the interior of the conical filter cylinder, and a waste gas inlet pipe allowing waste gas to be guided in is connected to one side of the dust removal tank; the dust removal device further comprises a lifting guide plate which is fixedly connected to the interior of the dust removal tank, located below the waste gas inlet pipe and used for receiving waste gas, a plurality of lifting assemblies used for spraying gas are arranged at the top of the lifting guide plate, and the lifting assemblies are used for exciting dust to move upwards to be filtered through the filter bags. A dust collection assembly for sharing the filter load of the filter bag is arranged in the dust supporting backflow plate; and the branch pipe is fixedly connected to the interior of the dust removal tank, one end of the branch pipe is connected with a pulse assembly capable of cleaning the filter bag, and a dust collection box matched with the pulse assembly and used for collecting dust is arranged below the conical filter cylinder.

To provide a pressure feeding technique capable of maintaining quality without interfering with a reinforcing bar and a form by lifting a pressure feeding pipe by a crane, since there is a regulation for placing ready-mixed concrete within a limited time in concrete placing work.SOLUTION: In the concrete placing method, rubber hoses 2A and 2B are connected to both ends of a suspension pipe 10 to which a plurality of pressure feed pipes 1 are connected, one rubber hose 2A of the suspension pipe 10 is connected to a pressure feed pipe 12 from a pump vehicle on the ground, and the other rubber hose 2B is discharged toward a place to be placed, and the suspension pipe 10 is lifted by using a crane 30 for lifting materials installed on the site, and is freely moved and pressure-fed in a suspended state to prevent interference with a reinforcement and a form.SELECTED DRAWING: Figure 1

To improve prediction accuracy of a prediction model constructed by machine learning.SOLUTION: Acquiring color captured image data obtained by capturing an image of an aggregate, generating input image data by sequentially performing first processing for grayscaling, second processing for darkening, and third processing for binarization on the captured image data, acquiring input information including the input image data, and using a prediction model constructed in advance by machine learning so as to output a physical property value of the aggregate according to an input of the input information, A prediction step of acquiring the physical property value corresponding to the input information acquired in the second acquisition step.SELECTED DRAWING: Figure 5

This invention proposes a carbonized calcined coal gangue solid waste-based cementitious material and its preparation method, belonging to the technical field of coal gangue solid waste utilization. The raw materials for the carbonized coal gangue-based cementitious material of this invention include 30-70 parts of calcined coal gangue powder, 20-50 parts of premixed concrete waste, 10-20 parts of cement, 0-5 parts of gypsum, 0.5-1.0 parts of surfactant, and 0.1-0.5 parts of organic amine. First, the coal gangue solid waste is crushed, ground, and calcined. Then, it is mixed with premixed concrete waste, ordinary silicate cement, gypsum, surfactant, and organic amine. Finally, industrial carbon dioxide is introduced and the mixture is dried to obtain the final product. The performance indicators of the carbonized calcined coal gangue solid waste-based cementitious material of this invention are comparable to those of PO 42.5 grade cement, realizing the large-scale utilization of coal gangue solid waste and the preparation of low-carbon cementitious materials. The process is simple and has practical application value.

The invention relates to a mix proportion design method and application of machine-made sand concrete doped with a dry cleaning and slurry wrapping medium, and aims to solve the technical problem of efficient resource utilization of the dry cleaning and slurry wrapping medium of a concrete mixing truck. By means of the machine-made sand concrete mix proportion design method, the slurry wrapping medium generated by dry cleaning of a concrete mixing truck can be completely utilized in a resource mode, the concrete mix proportion design efficiency is improved, the test period is shortened, the cement consumption is reduced, and the economic cost is reduced; the environment pollution caused by waste water and waste slurry generated by washing the concrete mixing transport vehicle is avoided, the development of the dry cleaning technology of the concrete mixing transport vehicle can be promoted, no waste is generated in cleaning of the concrete mixing transport vehicle, and the clean and sustainable green development of the premixed concrete industry is promoted.

To provide a concrete pump vehicle that can be towed in a stable condition. [Solution] The concrete pump vehicle comprises a frame, a towed section provided at one end of the frame in the front-rear direction, a pump unit mounted on the frame for pumping ready-mix concrete, a drive section mounted on the frame for driving the pump unit, and a running section rotatably attached to the frame, wherein the drive section is positioned between the towed section and the running section in the front-rear direction.

The invention discloses a carbon emission reduction metering method for green low-carbon concrete. The method comprises the following steps: replacing one or more of part of cement, sand, stone and other raw materials with solid waste in a production stage of premixed concrete, dynamically adjusting the replacement rate of the solid waste, monitoring the carbon emission of the material in real time, and realizing carbon emission reduction. According to the method, four key variables are introduced, the solid waste substitution rate is dynamically adjusted by calculating the influence of each variable on the overall carbon emission, the optimization mechanism ensures that the carbon emission of the concrete is minimized on the premise of meeting the engineering performance requirements, and the traditional method mostly depends on a fixed substitution ratio or an empirical formula, so that the method is very practical. And a dynamic variable mechanism is combined with data feedback, so that proportion optimization is more accurate, different engineering requirements and environmental conditions can be adapted, and intelligent development of green building materials is promoted.

Provided is a shape acquisition method for acquiring shape information about a plurality of objects arranged in a grid shape so as to surround a predetermined space, the method including: inserting, into the predetermined space, a rod-shaped sensor holding part for holding a sensor; detecting light from at least one object among the plurality of objects with the sensor held by the sensor holding part; and determining the shape information about the at least one object by using the detection result of the light from the object. For example, the shape of a rebar or the like in a framework can be easily measured without using a large-sized measuring device before pouring ready-mixed concrete.

This invention relates to the field of construction management technology and discloses a method for managing the pouring of ready-mixed concrete on construction sites, including the following steps: Step 1: Data Acquisition Stage: S1.1 By scanning the QR code on the concrete raw material packaging, the type, batch number, and production date information of cement, aggregate, fly ash, and admixtures are automatically entered. This invention constructs a full-dimensional data acquisition network by deploying temperature and humidity sensors, slump sensors, pressure sensors, and vibration sensors. Combined with LoRa wireless transmission technology, data integrity is achieved: temperature data is collected every minute (initial setting stage), and slump data is collected every 5 minutes (final setting stage), forming a continuous temperature curve; rapid response to anomalies: abnormal data is reconstructed through cubic spline interpolation to ensure monitoring continuity; decision support: a 128×128 pixel infrared thermal image and a T×N sensor matrix are provided for the CNN model, improving the accuracy of quality prediction.

The invention relates to the technical field of constructional engineering construction management, and discloses a construction site premixed concrete pouring management method, which comprises the following steps: step 1, a data acquisition stage: S1.1, automatically inputting varieties, batch numbers and production date information of cement, aggregate, fly ash and additives by scanning a two-dimensional code on a concrete raw material package; a temperature and humidity sensor, a slump sensor, a pressure sensor and a vibration sensor are arranged to construct a full-dimensional data acquisition network. By combining with a LoRa wireless transmission technology, data integrity is realized: temperature data is collected every minute (in an initial setting stage), slump data is collected every five minutes (in a final setting stage), and a continuous temperature curve is formed; abnormal quick response: reconstructing abnormal data through cubic spline interpolation to ensure monitoring continuity; and decision support: providing a 128 * 128 pixel infrared thermal image and a T * N sensor matrix for the CNN model, so that the quality prediction accuracy is improved.

The utility model provides a kind of image acquisition equipment, belong to concrete transportation monitoring technical field.The equipment includes shell, transmission assembly, driving part and acquisition component.Shell includes the first installation surface and the second installation surface oppositely arranged along first direction.Transmission assembly includes first transmission part and second transmission part, and first transmission part is slidably connected to the first installation surface, and second transmission part is slidably connected to the second installation surface.Driving part is respectively abutted with first, second transmission part along the opposite sides of first direction, to make when driving part rotates, first, second transmission part can move in opposite direction along second direction.Acquisition component is connected with second transmission part.When acquisition component is in standby state, first transmission part can be covered and set in the opening of first installation surface, when acquisition component is in working state, acquisition component can pass through opening and gather image.The image acquisition equipment of the utility model can monitor the state of ready-mixed concrete in real time during transportation.

To improve prediction accuracy when predicting quality by using a prediction model constructed by machine learning.SOLUTION: A quality prediction method of an aggregate includes a first acquisition step of acquiring color captured image data obtained by imaging an aggregate to be evaluated, a pre-processing step of generating input image data by gray-scaling the captured image data, a second acquisition step of acquiring input information including the input image data, a prediction step of acquiring a physical property value corresponding to input information acquired in the second acquisition step using a prediction model constructed in advance so as to output the physical property value of the aggregate according to an input of the input information, and a construction step of constructing the prediction model by machine learning based on training data. IB [lx] is 0.2 times or more of IA [lx] and 5 times or less of IA [lx], where IA [lx] is the illuminance among the environmental conditions when imaging the aggregate for training, and IB [lx] is the illuminance among the environmental conditions when imaging the aggregate to be evaluated.SELECTED DRAWING: Figure 12

To provide a method of immobilizing carbon dioxide present in gas containing carbon dioxide, using ready-mixed concrete sludge, which is of high processing efficiency and can immobilize a large amount of carbon dioxide.SOLUTION: The method of immobilizing carbon dioxide comprises the steps of preparing ready-mixed concrete using a material at least including cement, fine aggregate, coarse aggregate and water, recovering ready mixed concrete sludge which is the residue of the ready mixed concrete and has a higher water / cement ratio relative to the ready mixed concrete, and immobilizing carbon dioxide present in gas containing carbon dioxide by supplying the recovered ready mixed concrete sludge or the water-content adjusted product thereof with gas containing carbon dioxide, when a time in a range of 1.0 to 12 times the time from the preparation of the ready mixed concrete to the beginning (JIS R5201) of the setting of the cement has passed.SELECTED DRAWING: None

To improve the prediction accuracy when predicting the quality of ready-mixed concrete using a predictive model. [Solution] A method for predicting the quality of ready-mixed concrete, comprising: a construction step of constructing a predictive model by machine learning based on training data including multiple datasets; an acquisition step of acquiring multiple feature quantities obtained when manufacturing ready-mixed concrete using a mixer; and a prediction step of using the predictive model to acquire a predicted quality value corresponding to the multiple feature quantities acquired in the acquisition step, wherein each of the multiple datasets is associated with multiple feature quantities and the correct quality value, and the multiple feature quantities include one or more load feature quantities related to the power load value of the mixer when mixing concrete materials or stirring ready-mixed concrete, and in the construction step, a predictive model is constructed such that one or more feature quantities selected from the multiple feature quantities are associated with the predicted quality value by a decision tree.

The invention belongs to the field of cement admixtures, and provides a green and environment-friendly water reducing agent material and application thereof.The green and environment-friendly water reducing agent material is prepared by compounding core-shell structure nanoparticles (the particle size D50 is 80-200 nm, and the shell thickness is 5-25 nm) with polycarboxylic acid macromolecules as cores and silicon dioxide as shells, the polycarboxylic acid macromolecules and sodium gluconate; high-solid low-viscosity preparation with the solid content being 40-50% and the viscosity being 50-200 mPa.s is achieved, the initial fluidity of neat paste is larger than or equal to 260 mm and the retention rate in 30 min is larger than or equal to 90% when the water-binder ratio is 0.28-0.32 and the mixing amount is 0.2-1.5%, and the water reducing rate is larger than or equal to 25% and the slump retention rate in 60 min is larger than or equal to 85% when concrete is prepared with the water-binder ratio being 0.28-0.40. The technical contradictions of high water-reducing rate and high fluidity keeping coupling, high solid content-low viscosity and nanoparticle dispersion stability synergy and high early strength and long construction slump retaining time balance are synchronously solved, and the water-reducing agent is suitable for the fields of premixed concrete and cast-in-place concrete.

To enable discharge of ready mixed concrete with a single vehicle during an emergency without adding a driving source.SOLUTION: A mixer vehicle 100 comprises: a mixer drum 10 rotatably mounted on a vehicle; a water tank 31 in which water used for cleaning is stored; and a water pump 32 having a pump 32a connected to the water tank 31 and a motor 32b for driving the pump 32a. The motor 32b is detachably or disconnectably connected to the mixer drum 10 and is capable of rotating the mixer drum 10.SELECTED DRAWING: Figure 3

To improve prediction accuracy of a prediction model constructed by machine learning.SOLUTION: Acquiring color captured image data obtained by capturing an image of an aggregate, generating input image data by sequentially performing first processing for grayscaling and second processing for performing edge detection on the captured image data, acquiring input information including the input image data, and using a prediction model constructed in advance by machine learning so as to output a physical property value of the aggregate according to an input of the input information, A prediction step of acquiring the physical property value corresponding to the input information acquired in the second acquisition step.SELECTED DRAWING: Figure 5

The present application relates to the technical field of remote control, in particular to a ready-mixed concrete multi-station remote centralized control system and a control method, the control system comprising a control node arranged at each production line of a mixing station; a remote centralized control module comprising an engineer station computer and n control units, each control unit being used to control the control nodes of at least two production lines; a communication module used to communicatively connect the control nodes arranged in the mixing station in a region to the same network to realize collaborative control of multiple control nodes; the remote centralized control module and the control nodes arranged at the production lines in the mixing station perform data interaction through the communication module, and the engineer station computer uniformly manages and schedules the control units and the control nodes to dynamically manage and schedule the collaborative production of any two production lines in the region. The present application realizes collaborative production of multiple mixing stations, optimizes resource allocation and production scheduling, improves the working environment, saves costs and improves production efficiency.

The application discloses a kind of pre-mixed concrete intelligent sampling and working performance monitoring device, including power system, sampling system, test system, determination system and cleaning system;Wherein sampling system is set to the test system directly above, wherein sampling system includes rounded trapezoidal sampling bucket with gate, test system is located below the sampling bucket for receiving and sample recovery, and the determination system is used to detect the slump and spread of concrete sample in the sampling system.The application realizes the automatic sampling and working performance real-time monitoring of pre-mixed concrete in the production process, is convenient to operate, effectively reduces the work pressure of staff, and guarantees product quality, promotes the development of concrete quality control application field, lays a good foundation for intelligent monitoring of building materials, and has significant practical engineering value.

To improve prediction accuracy of a prediction model constructed by machine learning.SOLUTION: A first acquisition step of acquiring color captured image data obtained by capturing an image of a bone material; and a second acquisition step of sequentially performing a first process of converting the captured image data into grayscale data and a second process of binarizing the captured image data by Otsu's binarization on the captured image data. A quality prediction method of an aggregate, comprising a pre-processing step of generating input image data, a second acquisition step of acquiring input information including the input image data, and a prediction step of acquiring a physical property value according to the input information acquired in the second acquisition step by using a prediction model constructed in advance by machine learning so as to output the physical property value of the aggregate according to an input of the input information.SELECTED DRAWING: Figure 5

An advanced concrete drum cleaning system that includes a dynamically controlled oscillating nozzle, a boom zeroing function, a breakaway mounting plate, and a hydraulic assembly for enhanced safety. The system features a motor-driven nozzle having a stroke length that can be dynamically adjusted in real-time using feedback from an encoder. A controller with a user interface allows operators to precisely control and monitor the nozzle's position. Additionally, the system includes a boom zeroing function, enabling the operator to set and maintain a reference position for the boom within the concrete drum. The hydraulic assembly, consisting of lifting and lowering components, allows the nozzle housing to quickly disengage from obstructions to prevent damage. Moreover, the breakaway mounting plate provides a means for limiting damage when the nozzle housing is unable to disengage from obstructions.

To improve the prediction accuracy when using predictive models to forecast the quality of ready-mixed concrete. [Solution] A method for predicting the quality of ready-mixed concrete, comprising: a construction step of constructing a predictive model by machine learning based on training data including multiple datasets; an acquisition step of acquiring multiple feature quantities obtained when manufacturing ready-mixed concrete using a mixer; and a prediction step of using the predictive model to acquire a predicted quality value corresponding to the multiple feature quantities acquired in the acquisition step, wherein each of the multiple datasets is associated with multiple feature quantities and the correct quality value, and the multiple feature quantities include one or more load feature quantities related to the power load value of the mixer when mixing concrete materials or stirring ready-mixed concrete, and in the construction step, a predictive model is constructed such that one or more feature quantities selected from the multiple feature quantities are associated with the predicted quality value by a decision tree.

An advanced concrete drum cleaning system that includes a dynamically controlled oscillating nozzle, a boom zeroing function, a breakaway mounting plate, and a hydraulic assembly for enhanced safety. The system features a motor-driven nozzle having a stroke length that can be dynamically adjusted in real-time using feedback from an encoder. A controller with a user interface allows operators to precisely control and monitor the nozzle's position. Additionally, the system includes a boom zeroing function, enabling the operator to set and maintain a reference position for the boom within the concrete drum. The hydraulic assembly, consisting of lifting and lowering components, allows the nozzle housing to quickly disengage from obstructions to prevent damage. Moreover, the breakaway mounting plate provides a means for limiting damage when the nozzle housing is unable to disengage from obstructions.

To reduce weight of a tip hose.SOLUTION: A tip hose 10 according to the present invention is provided via a taper pipe in a docking hose 3 connected to a pump 2 for pumping ready mixed concrete. The tip hose 10 comprises a first cylindrical part 11 which is provided at a base end and which is diameter-reduced toward a tip side, and a second cylindrical part 12 which is provided in the tip side of the first part 11 and which has a constant diameter. At least a radial outside of the first part 11 is made of cylindrical rubber.SELECTED DRAWING: Figure 1

To provide a quality evaluation device and a quality evaluation method that can enhance the accuracy of determining the quality of ready mixed concrete.SOLUTION: According to an embodiment, a quality evaluation device is a quality evaluation device 1 for ready mixed concrete that flows down along an inclined surface of a chute. The quality evaluation device 1 includes a camera 2 for capturing images of the chute and the ready mixed concrete, and a chute shape determination unit 11 for determining a three-dimensional shape of the chute from an image captured by the camera 2, and a concrete property identification unit 12 for identifying the ready mixed concrete flowing down from the image captured by the camera 2. The concrete property identification unit 12 includes a height calculation unit 13 for calculating the height of the ready mixed concrete relative to an intersection point between the ready mixed concrete and the chute in a plane orthogonal to the direction in which the ready mixed concrete flows down. The quality evaluation device 1 includes a quality determination unit 16 for determining the quality of the ready mixed concrete on the basis of the property of the ready mixed concrete calculated by the concrete property identification unit 12.SELECTED DRAWING: Figure 3

The invention provides a premixed concrete quality detection method and device, a medium and a product, and relates to the technical field of image processing, and the method enables a target image detection model to capture and enhance time sequence change information related to concrete internal flow from an input image sequence through a space-time dynamic sensing module and a space-time attention module. Characteristics containing dynamic information are purified on different scales through a channel-space double attention module, and key apparent characteristics under each scale are focused. And finally, the adaptive feature pyramid network dynamically adjusts the fusion strategy according to the image content, and performs intelligent weighted fusion on the multi-scale dynamic features. The mode of combining time sequence dynamic analysis and self-adaptive multi-scale space analysis enables the target image detection model to form more comprehensive identification on the state of the concrete, and the technical problem that a concrete state identification scheme is poor in identification effect under complex and dynamic actual working conditions in a transportation tank car is solved.

To decrease variation in prediction accuracy resulting from bias in datasets for machine learning.SOLUTION: A method for predicting quality of ready-mixed concrete includes: an acquisition step for acquiring input information containing information related to an electric power load value of a mixer; a prediction step for predicting quality of ready-mixed concrete, on the basis of a prediction model constructed beforehand so as to output a quality value that indicates the quality of ready-mixed concrete according to an input of input information, and the input information acquired in the acquisition step; and a construction step for constructing a prediction model according to machine learning based on a plurality of datasets for learning. Each of the plurality of datasets for learning includes quality values, and input information associated with the quality values. In the construction step, before constructing the prediction model, the plurality of datasets for learning is corrected to reduce a difference in the number of data per quality value in a data frequency distribution representing a distribution of the number of data per quality value in the plurality of datasets for learning.SELECTED DRAWING: Figure 10

The invention discloses a kind of ready-mixed concrete with hydrolyzed polymaleic anhydride as main chelating agent, according to weight percentage, including the following components: cement 14.9% 38.9%, admixture 0 21.9%, machine-made sand 32.1% 71.7%, coarse aggregate 0 45.7%, chelating agent 0.0073% 0.011%, water reducer 0.3% 0.53% and water;The proportion of the chelating agent is the weight percentage accounting for the sum of cement and admixture, and the chelating agent is hydrolyzed polymaleic anhydride, and sodium polyacrylate or aminotri(methylene phosphonic acid) are further added in the chelating agent. The present invention applies chelating agent to ready-mixed concrete, improves concrete working performance, can make the viscosity of concrete mixture reduce, rheology is enhanced, and coarse aggregate ratio is improved, has positive significance to the development of ready-mixed concrete.

To improve the accuracy of quality prediction while avoiding the complication of work involved in quality prediction when performing quality prediction using a prediction model.SOLUTION: A quality prediction method for ready-mixed concrete includes: an acquisition step of acquiring input information including first information relating to at least one of a power load value of a mixer that produces ready-mixed concrete by mixing concrete materials or that mixes the ready-mixed concrete, vibration caused by falling or flowing down of the ready-mixed concrete, and an image of the ready-mixed concrete, and second information relating to a target quality of the ready-mixed concrete when in use; and a prediction step of predicting the quality of the ready-mixed concrete based on a prediction model previously constructed by machine learning so as to output quality information indicating the quality of the ready-mixed concrete in response to input of the input information and the input information acquired in the acquisition step.SELECTED DRAWING: Figure 5

To improve prediction accuracy of a prediction model constructed by machine learning.SOLUTION: Acquiring color captured image data obtained by capturing an image of an aggregate; generating input image data by sequentially performing a first process of grayscaling and a second process of binarizing by adaptive binarization on the captured image data; acquiring input information including the input image data; and using a prediction model constructed in advance by machine learning so as to output a physical property value of the aggregate in response to an input of the input information, A prediction step of acquiring the physical property value corresponding to the input information acquired in the second acquisition step, wherein in the adaptive binarization in the second processing, a binarization threshold is set for each pixel by weighted averaging.SELECTED DRAWING: Figure 5