Similar studies in recent years have developed alternative machine learning models for predicting compressive strength of concrete using available mixture optimization data [22,23]. The value of using alternative NDIs and/or blended methods for assessing concrete properties was highlighted by various researchers [19,20], thus paving the way and emphasizing the need for this study. In terms of alternative NDIs, [PERSON] et al. [17] developed a relationship between static elastic modulus from destructive testing and dynamic elastic modulus from non-destructive tests, such as UPV, electrical resistivity, and resonant frequency testing. [PERSON] et al. [24] performed a series of experimental and numerical analyses to find the most robust relationship between static modulus of elasticity and dynamic modulus of concrete obtained from UPV and resonant frequency. The use of non-destructive testing is advantageous since the same samples and/or concrete members can be tested again and again for assessing concrete quality in terms of uniformity and strength gain with regards to the expected values. As concluded earlier, past studies indicated that ultrasonic pulse velocity (UPV) and the corresponding dynamic modulus of elasticity relate well to the compressive strength and modulus of elasticity from static destructive compressive strength testing [25,26]. Thus, such parameters from NDT evaluation can be used as quality control measures during concrete production [25]. In order to estimate concrete strength--and thus modulus--with hardening age, the concept of maturity is used by the concrete community and industry in developing predictive models. Temperature and time are two important parameters considered in this modeling approach [14,26]. The Nurse-Saul maturity function (\(M\)I(\(t\))) equation presented later in the manuscript representsthe relationship between concrete temperature, time, and strength gain. The resulting maturity index (MI) (i.e., sum of the product of time and temperature history during the hydration process) can be calculated in real time in the lab and/or the field. While curing conditions during hydration affect strength gain [5; 27] according to the maturity function, concrete of the same mix that has the same maturity index has approximately the same strength whatever combination of temperature and time is experienced. Maturity models are mixture-specific since the hydration rate in concrete is dependent on mixture ingredients and proportioning [26; 28; 29; 30]. Thus, based on past studies' shortcomings and recommendations [26; 20; 28], it was the objective of this study to explore alternative non-destructive-based testing, such as ultrasonic pulse velocity and resonant frequency, to model maturity of concrete at young ages after casting. Maturity represents the effect of time and temperature during hydration on concrete strength gain. The relationship between concrete quality measures (e.g., compressive strength, UPV, or modulus of elasticity) and maturity index (MI) is mixture-specific [26; 28; 29; 31]. Thus, to overcome the extended maturity testing needed each time a new mixture is developed, it was the second objective of the study to explore for the first time ever a novel approach in maturity modeling by developing \"master curves\" for a group of concrete mixtures. This novelty in maturity modeling includes (i) the development of a representative maturity function (identified herein as \"master curve\") for a group of concrete mixtures with similar ingredients and proportioning and (ii) define the transfer function between individual mixture MI relationships and the \"master curve\" in function of mixture properties.