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 strength enhancing admixture for cementitious and/or pozzolanic compositions including, based on the total dry weight of the admixture, calcium silicate hydrate in an amount of from about 0.5 to about 94 weight percent, and: i) at least one alkanolamine in an amount of from about 0.5 to about 55 weight percent; ii) at least one inorganic accelerator in an amount of from about 0.5 to about 85 weight percent; and iii) at least one carbohydrate in an amount of from about 0.5 to about 50 weight percent; wherein the calcium silicate hydrate includes a product of a reaction of a water-soluble calcium compound with a water-soluble silicate compound in presence of a water-soluble dispersant; and wherein the at least one inorganic accelerator includes any inorganic accelerator(s) other than calcium silicate hydrate.

Abstract: A strength enhancing admixture for cementitious and/or pozzolanic compositions including, based on the total dry weight of the admixture, calcium silicate hydrate in an amount of from about 0.5 to about 94 weight percent, and: i) at least one alkanolamine in an amount of from about 0.5 to about 55 weight percent; ii) at least one inorganic accelerator in an amount of from about 0.5 to about 85 weight percent; and iii) at least one carbohydrate in an amount of from about 0.5 to about 50 weight percent; wherein the calcium silicate hydrate includes a product of a reaction of a water-soluble calcium compound with a water-soluble silicate compound in presence of a water-soluble dispersant; and wherein the at least one inorganic accelerator includes any inorganic accelerator(s) other than calcium silicate hydrate.

Abstract: In a vehicle door with a cross-arm type window lift and a lock assembly, which can be produced more easily and cost-effectively by employing modular construction and by using individual components for several different functions. The invention is distinguished by the guide rail of the cross-arm window lift simultaneously functioning as the support for the lock assembly, by the guide for guiding the window being integrated in the base body of the lock assembly and by the outer edge of a base plate supporting a window lift having a circumferential profiled sealing section in the interior sheet metal door panel for sealing a mounting opening, so that the protection of electrical/electronic components is provided in the dry space without additional steps.