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Comparison of strength-maturity relationships; the logarithmic function, Eq. (13), does not fit the data as well as the linear hyperbolic function, Eq. (7).
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The maturity method is a technique to account for the combined effects of time and temperature on the strength development of concrete. The method provides a relatively simple approach for making reliable estimates of in-place strength during construction. The origin of the method can be traced to work on steam curing of concrete carried out in Eng...
Citations
... One approach to determine the readiness of repaired pavements to be opened for traffic is to monitor the maturity of the concrete. The maturity method relies on the measured temperature history of the concrete to estimate the strength development during the curing period (Carino and Lew 2001). The temperature-time history of the concrete is used to determine its maturity index (ASTM 2019), and the relationship between maturity and strength is established beforehand. ...
The expedient repair of damaged airfield pavements is important to eliminate long closure periods. Rapid-setting cement (RSC) is promising for achieving target strength in a much shorter duration (4-6 h) compared to conventional repair methods. However, when the objective is to reopen the pavement within 1-2 h, there is a lack of methods to quantify the development of mechanical properties during the first few hours. In this study, a simplified maturity method is proposed to monitor the very early-strength development of two commercially available RSC mixes with different strength development mechanisms. The results were validated using hydration chemistry data from scanning electron microscope (SEM) experiments and ultrasonic pulse velocity (UPV) tests. The experimental results show that such a maturity-based approach is effective and robust in characterizing the early-strength development of the two RSC mixes despite variations in ambient temperature conditions.
... Numerous standards and guidelines have recognized and documented these functions' varying approaches and parameters. This diversity in proposed functions [19][20][21][22][23] reflects the evolving understanding and application of the maturity method in concrete technology [24][25][26][27][28]. Devices like wired temperature loggers, maturity loggers, and thermocouples are extensively used to collect temperature and predicted strength data from the sensors embedded within the concrete [29][30][31]. ...
Concrete is one of the most widely used building materials due to its durability and cost-effectiveness. Accurate prediction of its compressive strength during early stages is crucial for construction project management, particularly for decisions related to formwork removal and scheduling subsequent activities. Traditional methods for strength assessment, such as concrete cube testing, are labor-intensive and may not represent in-situ conditions accurately. This study introduces an innovative, cost-effective IoT-based system using the maturity method to predict concrete compressive strength in real-time. The proposed system leverages easily accessible hardware and open-source software to provide real-time data on concrete strength development, enhancing monitoring accuracy and operational efficiency. We employed the Nurse-Saul maturity equation and developed a comprehensive calibration process to establish a reliable maturity-strength relationship. The system’s implementation was tested on an actual construction site, focusing on optimizing sensor layout and ensuring continuous data transmission. Results demonstrated high accuracy of strength predictions, with percentage errors within acceptable limits as per ASTM standards. Challenges such as Wi-Fi connectivity, power management, and basic data security were addressed during the deployment phase. The study highlights significant cost savings and improved monitoring capabilities compared to traditional methods, providing a practical solution for construction project managers. Future research will focus on scaling the system for larger projects, integrating advanced analytics, enhancing data security, and developing wireless sensor setups to reduce labor further and improve efficiency. This research underscores the transformative potential of low-cost IoT systems in the construction industry, offering practical solutions for real-time concrete strength monitoring.
... The maturity method is a non-destructive approach for estimating the strength of concrete and has been proven to be a useful tool, especially in the fabrication of prestressed and prefabricated concrete products [1,2]. The Nurse-Saul function is the most commonly used maturity method in the concrete industry, as it takes into consideration the linear dependence of the maturity value on temperature [3,4,5]. This approach is based on the idea that each concrete mixture has a unique strength-time/temperature relationship, and for concretes of similar mix designs, the same strength will develop at a given maturity value [6]. ...
... The Nurse-Saul function assumes that the rate of strength development is a linear function of temperature. So, the maturity index, , is calculated based on the temperature history as [5,9]: ...
The maturity test is a method for estimating the in-situ compressive strength of concrete. Before using the method to determine the properties of the desired concrete, it is necessary to perform a standard calibration process as one of the requirements of the maturity test. The standard calibration process is both time-consuming and costly. According to the maturity test standards, this process should be repeated for each concrete mix design. The requirement has become a significant impediment to the widespread use of the maturity method. In this paper, the early age calibration method has been tested for 5 fiber self-compacting concrete mix designs. According to the research, the strength-maturity curve based on the 2-day and 3-day strength values has an error of less than 6%. The average error is less than 3%. These results show that the early ages calibration method has been able to accurately produce the strength-maturity calibration curve in fiber reinforced self-compacting concrete.
... The heat generation depends on the binder compositions and, for example, the heat evolution in a mix with a high proportion of ground granulated blast furnace slag (GGBS) will differ from that of a mix solely with CEM I Portland cement [24,25]. Early age temperature development is of particular interest because it is linked to maturity, or degree of hydration, and the strength of the concrete section [8,13]. The maturity method characterises the concrete maturity based on the cumulative heat generated after casting, thus allowing the completeness of the hydration reactions to be estimated. ...
... (2) Only a small amount of experimental data are needed for curve fitting, which is very simple in calculation. For concrete strength evaluation, the common mathematical models are hyperbolic models [19,20], exponential models [21,22], polyno- mial models, and mixed models [23,24]. For carbonation depth prediction, the basic idea of mathematical prediction model is to directly establish the relationship between carbonation depth and age of concrete by using some function curve [25][26][27][28][29][30]. ...
Carbonation is one of the critical issues affecting the durability of reinforced concrete. Evaluating the depth of concrete carbonation is of great significance for ensuring the quality and safety of construction projects. In recent years, various prediction algorithms have been developed for evaluating concrete carbonation depth. This article provides a detailed overview of the existing prediction models for concrete carbonation depth. According to the data processing methods used in the model, the existing prediction models can be divided into mathematical curve models and machine learning models. The machine learning models can be further divided into the following categories: artificial neural network model, decision tree model, support vector machine model, and combined models. The basic idea of the mathematical curve model is to directly establish the relationship between the carbonation depth and age of concrete by using certain function curves. The advantage of the mathematical curve model is that only a small amount of experimental data is needed for curve fitting, which is very convenient for engineering applications. The limitation of the curve model is that it can only consider the influence of some factors on the carbonation depth of concrete, and the prediction accuracy cannot be guaranteed. The advantage of using the machine learning model to predict the carbonation depth of concrete is that many factors can be considered at the same time. When there are sufficient experimental data, the trained machine learning model can give more accurate prediction results than the mathematical curve model. The main defect of the machine learning model is that it needs a lot of experimental data as training samples, so it is not as convenient as the mathematical curve model in engineering applications. A future research direction may be to combine a machine learning model with a mathematical curve model to evaluate the carbonation depth of concrete more accurately.
... Fig. 5a illustrates the temperature profiles of concretes exposed to the laboratory environment (22 ± 1 • C or 19 ± 1 • C). For the assessment of onsite performance of concretes, an index called maturity [34] was defined for assessing the hydration and hardening of concrete. The correlation between maturity and strength (or shrinkage and setting) has been established in many previous investigations [35][36][37][38]. ...
... Fig. 7 shows that although C245 has a higher value in both UPV and formation factor, the development of penetration resistance is slower than that of C255 in the first 5 h. This is mainly due to the impact of temperature on the hydration and setting [47,48], which is also reflected by the lower maturity index in Fig. 4. To evaluate the effect of temperature on hydration of low carbon concrete, the equivalent curing age (t e ) of concrete was calculated by Eq. (5) [34]. ...
... In previous investigations, the compressive strength was widely correlated to UPV [7,9,13,44,[73][74][75] or maturity of concrete mixtures [13,34,36,38]. For highlighting the advantage of the formation factor, the correlations of compressive strength in relation to maturity and UPV have been regressed in Fig. 14. ...
Application of supplementary cementitious materials for production of low CO2 concrete affects the reaction kinetics, which alters the setting time and strength development. The different early-age behavior is of concern for quality control of concrete. Non-destructive test is very useful for monitoring the quality of low CO2 binder systems. This paper presents a new technique to monitor the electrical conductivity and temperature at different depths of hydrating concrete. Indices from monitoring system (conductivity, maturity and formation factor) are compared with data from widely-used methods (ultrasonic pulse velocity, penetration resistance and isothermal calorimetry). Results show that indices from the system can replicate the hydration evolution, setting time and compressive strength of low CO2 concrete. Electrical conductivity of concrete is very sensitive to mineral reactions and it reflects the hydration kinetic consistent with evolution of heat release. Linear correlations are found for penetration resistance in relation to ultrasonic pulse velocity, formation factor and maturity, respectively. The effects of binder type and water-to-binder ratio on hardening are strongly dependent on temperature. The proposed approach enables to include all these factors in characterizing the hardening process of concrete onsite. It is shown that formation factor performs better than ultrasonic pulse velocity on indicating the setting process. Formation factor is also a good parameter for quantitative description of compressive strength development, which is independent of the binder types, mixture proportions and curing ages.
... The main advantage of this method is that it can be carried out with only a small amount of experimental data. At present, the commonly used concrete compressive strength-age curve models are the exponential model [27][28][29], the logarithmic model [30,31], the hyperbolic model [32,33], and the polynomial model [33,34]. Yang et al. [27] tested the compressive strength and elastic modulus of high-strength concrete within 28 days via experiments and proposed an exponential model for data fitting. ...
... Zhao [31] measured the compressive strength of concrete at 3, 7, 28, and 60 days via experiments and fitted the corresponding logarithmic function formula via data regression. Carino [32] and Viviani [33] fitted the changes in compressive strength of concrete over time using hyperbolic models, thereby establishing a prediction of compressive strength over age of concrete. Jin [34] used a polynomial model to fit the test data of early-age compressive strength and elastic modulus of high-strength concrete. ...
During the process of pouring and solidification of concrete, the compressive strength and elastic modulus of concrete exhibit dynamic growth patterns. The mechanical properties of concrete usually remain stable in the later stage (28 days after pouring). Studying appropriate curve models to accurately evaluate the changes in early mechanical properties of concrete has always been an important topic in the field of concrete materials. This work proposes a new dual parameter curve model for accurately evaluating the growth pattern of early compressive strength and elastic modulus of concrete. A comparative study was conducted between the proposed new curve model and existing curve models using 18 sets of experimental data from 10 literature sources. The research results indicate that the fitting average error and standard deviation of this new curve model are significantly smaller than the existing curve models. For some examples, the fitting error and standard deviation of the new model are only about 20%–30% of those of the existing models. The main advantages of this new curve model lie in two aspects. The first advantage is that this new curve model only contains two unknown parameters, so only a small amount of experimental data is required for data fitting and does not require complex mathematical operations. The second advantage is that this new curve model has a wide range of applications, which include compressive strength evaluation and elastic modulus evaluation and can also be extended to the evaluation of the mechanical properties of other materials similar to concrete.
... Also, in the Arrhenius equation, the strength acquisition rate follows the Arrhenius exponential equation (Wilde 2013). This function allows for a non-linear relationship between the strength development rate and curing temperature by introducing activation energy and gas constant (Carino and Lew 2001). ...
... Accordingly, the maturity should be calculated according to the "datum temperature", which is the lowest temperature at which the acquisition of strength is observed. Therefore, the maturity index, M(t), is calculated based on the temperature history as (Kaburu 2016;Carino and Lew 2001): ...
... This section aims to develop a practical framework to determine the maturity curve without performing a time-consuming standard calibration process. Let's consider the functional form of the maturity curve as (Malhotra and Carino 2003;Kaburu 2016;Carino and Lew 2001): ...
The maturity method is used as a non-destructive test to estimate the compressive strength of concrete. The main problem of this method is its dependence on the concrete mix design. This research presents a method for predicting the strength of self-compacting concrete in a specific range, without repeating the time-consuming and expensive standard calibration process. To demonstrate the ability of the method, 15 self-compacting concrete mix designs were prepared and their strength and maturity were determined at the ages of 2, 3, 7, 14, and 28 days. Based on the proposed method, the fc = a ln Ma + b relation was fitted to the mean of the data. Then scale factors for 3 mixes were calculated based on the early ages data (for example 3-day data) and the mix-specific strength-maturity curves were determined without a standard calibration process. The proposed method shows a high accuracy of more than 94% in these examples. Based on these results, it is not necessary to redo the entire calibration process within the reasonable range of changes in the mix design on the site, and the calibration curve can be presented in this way.
... An alternative approach to monitoring the early-age strength development of Rapid Set ® cement concrete is by determining its concrete maturity and correlating it with in-situ strength. The concept of using the maturity method to predict concrete strength gained attention in the 1970s, following incidents of building collapses and failures during construction [12]. In 1987, ASTM C 1074 standardized the maturity method after Federal Reliability of strength-maturity correlation to predict in-place Rapid Set ® cement concrete strength by Mahmood It is worth noting that the "crossover" effect, which limits the use of maturity methods to predict the behaviour of concrete with high temperatures during the early stages of installation, has been reported in the literature [13]. ...
This paper details the formulation of a strength-maturity correlation for Rapid Set ® cement concrete and its utilisation in predicting the in-place strength of slabs installed at Melbourne Tullamarine Airport taxiway Sierra as per the project specification. The paper presents the critical stages of construction and presents findings of laboratory tests, field measurements, and numerical calculations. The analysis reported is based on the largest set of temperature data ever recorded on Rapid Set ® cement concrete (and ultra-high early-strength concrete) in Oceania. Besides the strength-maturity correlation, the monitoring of in-place concrete temperature provided information on the temperature profile of the concrete at several key locations, indicative of the phases of the exothermic hydration reaction. Based on the recorded temperatures and proposed numerical procedures, the actual strength of the cast-in-place concrete was predicted that assisted in identifying the optimal time for contraction joint cuts and reopening the pavements to air traffic. This contributed to shortening the work shifts and speeding up the work schedule. A trend in maturity function and the concrete temperature was established and can be reliably utilised to predict in-place Rapid Set ® cement concrete strength in similar future applications. Reliability of strength-maturity correlation to predict in-place Rapid Set ® cement concrete strength by Mahmood and Hampton 7th Concrete Pavements Conference, October 2023-2
... Meanwhile, scholars still have a great controversy over the value of the reference temperature T 0 in the mathematical model. Various factors such as cement variety, water-cement ratio, etc. can affect T 0 [31,32]. These all lead to the unsatisfactory strength prediction effect of the TTF model. ...
... The maturity theory involves multiple influencing factors and has a direct correlation with the mechanical properties of concrete [31]. However, this method still has shortcomings in predicting the strength of concrete under thermal curing conditions. ...
A R T I C L E I N F O Keywords: Concrete Strength prediction Maturity method Thermal curing Crossover effect A B S T R A C T The maturity method is widely used to estimate early-age concrete strength. However, the traditional maturity models exhibit limited predictive capability for late concrete strength under thermal curing conditions due to the influence of the "crossover effect". This study developed a curing scheme for Standard Portland cement concrete in the absence of supplementary cementitious materials at temperatures ranging from 5 • C to 50 • C and analyzed the temperature variations inside thermally cured concrete specimens. The findings reveal that an increase in curing temperature and time between 30 • C and 50 • C and 8 and 72 h respectively led to an increase in the early strength and a decrease in the late strength of concrete, due to the "crossover effect". Additionally, a linear relationship was found between curing temperature and the late strength reduction coefficient. Utilizing this relationship, a modified maturity model that considers the "crossover effect" was proposed, improving the accuracy of predicting concrete strength under thermal curing conditions (with a prediction error of less than 10%). The research outcomes are of significant guiding significance for winter construction by ensuring the quality of concrete, reducing construction accidents, and improving construction efficiency.
