Abstract: 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.

Abstract: 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.

Abstract: Example embodiments provide systems and methods for formulating and evaluating a construction admixture (such as an admixture 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 construction admixtures that meet or best approximate the requirements. The construction admixtures may be provided to a simulation to estimate or predict their performance. The performance characteristics of the output construction admixture(s) may be displayed. Optionally, the system may control mixing machinery to produce the construction admixture.

Abstract: A method for an admixture producer to provide admixture formulations to a concrete producer, including inputting technical information, wherein the technical information is inputted into the computer control system by the concrete producer, determining types and levels of admixture raw materials that are needed to attain the desired performance characteristics, dispensing the types and quantities of admixture raw materials needed to produce an admixture formulation having properties needed to attain the desired performance characteristics, wherein the admixture raw materials are dispensed from an inventory of admixture raw materials that is maintained in proximity to a concrete mixing facility of the concrete producer, and invoicing the concrete producer for the admixture formulation.

Abstract: Reinforced high early-strength cementitious members are made from water and strength generating ingredients, which include a hydraulic cement, a polycarboxylate dispersant and structural synthetic fiber. The strength generating ingredients provide corrosion resistance by avoiding the use of steel reinforcing members, increased ductility and load bearing capacity while developing a high early-strength of at least about 1,400 pounds per square inch (psi) flexural strength and at least about 7,500 pounds per square inch (psi) compressive strength within about 24 hours after placing the cementitious composition in a mold.

Abstract: Reinforced high early-strength cementitious members are made from water and strength generating ingredients, which include a hydraulic cement, a polycarboxylate dispersant and structural synthetic fiber. The strength generating ingredients provide corrosion resistance by avoiding the use of steel reinforcing members, increased ductility and load bearing capacity while developing a high early-strength of at least about 1,400 pounds per square inch (psi) flexural strength and at least about 7,500 pounds per square inch (psi) compressive strength within about 24 hours after placing the cementitious composition in a mold.