The present invention relates to relates to methods of making concrete from cement and, in particular, methods of making usable standard concrete from aged or otherwise substandard cement.

Systems and methods for making gypsum board using a liquid gypsum set accelerator. Stucco and water are mixed to form a gypsum slurry, such as in a pin mixer. A gypsum set accelerator is mixed with a liquid medium to form a liquid gypsum set accelerator, such as in an eductor. The liquid gypsum set accelerator is added to the gypsum slurry, which is shaped to form the gypsum board.

The invention relates to a method for layer-by-layer deposition of concrete by providing extrudable concrete. A first flow comprising a binder material and water and a second flow comprising a carrier material, an additional component and water are mixed in a static mixer to form a third flow of extrudable concrete. The material of the second flow has a shorter initial setting time than the material of the first flow. The first flow has a first viscosity V1 and the second flow has a second viscosity V2 so that the ratio V1/V2 ranges between 1/40 and 40. The third flow has a viscosity larger than the viscosity of the first flow and the second flow and a yield stress larger than the yield stress of the first flow and the second flow. The material of the third flow has an initial setting time shorter than initial setting time of the first flow.

The invention further relates to a system to extrude concrete, in particular for layer-by-layer deposition of concrete.

Techniques or processes for efficiently producing concrete using dynamic feedback are disclosed. A concrete plant can use a control system to manage concrete production based on the dynamic feedback. The dynamic feedback can control mixing of concrete ingredients so as to yield uniform particle distribution for the concrete ingredients. The dynamic feedback can also avoid overflow situations as well as yield improved loading of the resulting concrete into a concrete transport vehicle (e.g., concrete truck).

A ready-mixed composition and a pre-mix composition for the production of a concrete material containing sequestered carbon dioxide, a CO.sub.2-containing water used in such compositions, dry-batch and wet-batch processes for sequestering carbon dioxide in concrete material, general method and process for sequestering carbon dioxide in hardening concrete, system and ready-mixed truck to perform such processes and methods for the production of a ready-to-cure carbonated concrete. Compositions comprise a concrete mixture and a CO.sub.2-containing water. The CO.sub.2-containing water comprising water and at least one of blended CO.sub.2 gas bubbles, dissolved H.sub.2CO.sub.3, carbonate ions (CO.sub.3.sup.2), bicarbonate ions (HCO.sup.3−), nanosized alkaline earth metal carbonate and nanosized alkali metal carbonate particles. The concrete mixture comprises a cementitious material, aggregates and at least one CO.sub.2-sequestering chemical for accelerating a CO.sub.2 sequestration speed and maximizing the captured amount of the carbon dioxide.

Example embodiments provide systems and methods for formulating and evaluating a construction composition (such as a mixture for concrete, asphalt, mortar, etc.). According to exemplary embodiments, a predictive model, artificial intelligence, machine learning algorithm, etc., may be trained using historical performance data and current deployment information. Based on a job specification that identifies various requirements for the construction composition and a set of available inputs (e.g., raw materials, mixing techniques, etc.), the model, AI, or algorithm, may output one or more formulations that meet or best approximate the requirements. The formulations may be provided to a simulation to estimate or predict their performance. The performance characteristics of the output formulation(s) may be displayed. Optionally, the system may control mixing machinery to produce the formulation. Some embodiments may use these capabilities to evaluate an existing or proposed construction composition, rather than proposing a new construction composition.

Example embodiments provide systems and methods for performing quality control of a construction composition. According to exemplary embodiments, a predictive model, artificial intelligence, machine learning algorithm, etc., may be trained using historical performance data and current deployment information. Based on a job specification that identifies various requirements for the construction composition and a set of available inputs, the AI/ML/model may output one or more formulations that meet or best approximate the requirements, and an initial batch of the construction composition may be produced. During or after deployment of the construction composition, information about the composition's performance may be received and applied to the AI/ML/model. The system may make real-time updates to the construction composition to improve the consistency or performance of the construction composition, within predefined acceptable change parameters. Optionally, the system may control mixing machinery to produce the updated construction composition.

A mobile volumetric concrete mixing system includes a suction system that vacuums up trench spoils while a trench is being cut. These trench spoils are then screened on-site for particle size to be reused and mixed with water, cement, and/or other admixtures at an auger mixer to form a backfill mixture. This backfill mixture may then be loaded into a hopper that continuously agitates the mixture so that the mixture does not harden before pouring. The agitating hopper is coupled to a discharge chute of the auger mixer and includes one or more augers disposed at various orientations that the backfill mixture is channeled through. From the agitating hopper, the backfill mixture is channeled to an applicator that moves along the trench and that enables the mixture to be quickly poured into the trench with little clean-up required.

In one example, a solver engine may execute a reverse calculation to determine a recipe ingredient set based on a goal descriptor describing a ceramic glaze. A descriptor interface of the solver engine may receive a goal descriptor describing a ceramic glaze. A model applicator of the solver engine may apply a glaze process model to the goal descriptor. The model applicator may automatically reverse calculate a glaze recipe describing a recipe ingredient set to produce the ceramic glaze described by the goal descriptor. A glaze mixing machine interface may direct a glaze mixing machine to mix the recipe ingredient set to produce the ceramic glaze.

Aspects involve estimating transitions in properties of a concrete mixture prior to batching and during transport of the mixture from a first location, such as a concrete batch plant, to a second location, such as a job site and controlling a mixture of ingredients for concrete in response. A tool may execute a method for estimating an initial temperature at batching and a change in temperature of the concrete mixture during transport due to the exothermic reaction of the mixture, percentages and types of ingredients of the mixture, and/or environmental factors along a transit route. A control system at the concrete batching plant may control one or more components of the concrete batch plant in response to the estimated temperature change, such as a dispenser of an ingredient of the mixture and/or a heating or cooling mechanism to adjust the temperature of an ingredient of the concrete mixture.