The present invention allows for better control over strength in concrete mixes and mix designs, while minimizing the over-use of cement and promoting sustainability within the industry. Disclosed are novel method and system which employ a diagnostic delta data (DDD) curve, or, in other words, data that displays a curvilinear relationship when plotted on a visual graph, as obtained by considering the differences (e.g., subtractive differences or ratios) as between (i) target slump and target (or maximum) water content, and (ii) slump and water content values as determined using an automated slump monitoring system which measures slump and water content in the concrete mix during delivery. This DDD curve can then be compared to monitored delta slump and delta water content for later or other deliveries, such that adjustments can be made to the concrete mix or mix design, in a manner that encourages avoidance of cement over-dosing or over-prescription.

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.