Estimation Of Concrete Strength Research Articles

Predicting concrete strength early age using a combination of machine learning and electromechanical impedance with nano-enhanced sensors

To ensure the structural integrity of concrete and prevent unanticipated fracturing, real-time monitoring of early-age concrete's strength development is essential, mainly through advanced techniques such as nano-enhanced sensors. The piezoelectric-based electro-mechanical impedance (EMI) method with nano-enhanced sensors is emerging as a practical solution for such monitoring requirements. This study presents a strength estimation method based on Non-Destructive Testing (NDT) Techniques and Long Short-Term Memory (LSTM) and artificial neural networks (ANNs) as hybrid (NDT-LSTMs-ANN), including several types of concrete strength-related agents. Input data includes water-to-cement rate, temperature, curing time, and maturity based on interior temperature, allowing experimentally monitoring the development of concrete strength from the early steps of hydration and casting to the last stages of hardening 28 days after the casting. The study investigated the impact of various factors on concrete strength development, utilizing a cutting-edge approach that combines traditional models with nano-enhanced piezoelectric sensors and NDT—LSTMs-ANN enhanced with nanotechnology. The results demonstrate that the hybrid provides highly accurate concrete strength estimation for construction safety and efficiency. Adopting the piezoelectric-based EMI technique with these advanced sensors offers a viable and effective monitoring solution, presenting a significant leap forward for the construction industry's structural health monitoring practices.

Compressive and tensile strength estimation of sustainable geopolymer concrete using contemporary boosting ensemble techniques

Abstract Geopolymer concrete (GPC) serves as an environmentally conscious alternative to traditional concrete, offering a sustainable solution for construction needs. The ability to make on-site changes is dependent on the concrete’s strength after casting, which must be higher than the target value. To anticipate the concrete’s strength before it is poured is, thus, quite helpful. Three ensemble machine learning (ML) approaches, including gradient boosting, AdaBoost regressor, and extreme gradient boosting, are presented in this work as potential methods for forecasting GPC’s mechanical strength that incorporates corncob ash. To determine which modeling parameters are crucial, sensitivity analysis was employed. When the compressive strength and split-tensile strength of GPC were tested with ensemble ML models, R 2 values of more than 90% were discovered between the predicted and actual results. Statistics and a k-fold analysis based on the error and coefficient of determination were used to verify the developed models. Slag amount, curing age, and fine aggregate quantity were the three mix proportions that had the most impact on GPC’s mechanical strength, as shown in the sensitivity analysis. The results of this study demonstrated that ensemble boosting approaches could reliably estimate GPC mechanical strength. Incorporating such procedures into GPC quality control can yield significant improvements.

Compressive strength estimation of rice husk ash-blended high-strength concrete using diffGrad-optimized deep learning approach

Compressive strength estimation of rice husk ash-blended high-strength concrete using diffGrad-optimized deep learning approach

Determination of coefficient “s” and apparent activation energy for fib model code’s maturity-based strength function when applied to fly ash concrete

The maturity-based strength function recommended by fib model code 2010 can be easily utilized to estimate the compressive strength of normal-strength or high-strength cement concrete. However, the lack of calculation approaches and guidelines for determining the coefficient “s” and the apparent activation energy Ea in the fib model code’s maturity method have limited its further application to fly ash (FA) concrete. In this study, with reference to ASTM C1074, this limitation is addressed by adopting the equivalent mortar method to determine the coefficient “s” for the concrete under investigation. The feasibility and superiority of the equivalent mortar method were validated through two cases. Moreover, the original strength function in fib model code is modified to incorporate the reaction rate constant, allowing the use of Arrhenius equation to determine the mixture-specific Ea value. The iterative searching method is also supplemented to find the best-fit Ea value. The results demonstrated that the Ea value, obtained either through the linear Arrhenius plot or the iterative searching method, does not match the default Ea value (33.33 kJ/mol) provided by the fib model code, and can achieve relatively lower standard error for strength estimation of FA concrete.

Asset Assessment of Concrete Structure

Infrastructures which were constructed in the last century are approaching or have exceeded their design life. It is essential to maintain, repair and rehabilitate existing structures to ensure safety and develop a sensible rehabilitation plan. This paper suggests an inspection program for the condition evaluation of existing structures, particularly for structures that are approaching or exceeding their design/campaign lives. Cracking on concrete bridges is often induced by static and cyclic temperature loading. Throughout the service life of the structure, cracks will initiate, propagate, coalesce and form a greater damaged zone. Depending on the location and severity of the crack network; corrosion of reinforcement, debonding between concrete and reinforcement, and spalling of concrete may occur. The implementation of regular inspection or monitoring of structural condition is an essential component of asset management. Early detection of damaged zones can ease scheduling and reduce the cost of repair, which in turn also allows extension of structural lifespan without compromising safety. Similarly, for industrial properties, asset management and rehabilitation programs are paramount as they can significantly reduce capital expenditures in asset maintenance, if implemented prior to or at the early onset of structural degradation. This paper illustrates the use of NDT techniques for the condition assessment of a prestressed concrete bridge, as well as concrete storage silos. Both of which were either approaching or have exceeded their design campaign life and were needing a proper rehabilitation plan. The assessment included crack movement monitoring, crack depth determination, concrete strength estimation, and the detection of debonding between rebar and concrete.

Predicting concrete strength through packing density using machine learning models

This study presents an innovative approach to predict concrete compressive strength using particle packing theories through machine learning techniques. The existing challenge in concrete engineering lies in the accurate estimation of concrete strength, a critical factor in construction. The adoption of particle packing theories, which hold great promise for enhancing concrete performance, has been limited due to the complexity and time-consuming nature of the required calculations. An approach encompassing particle packing models (JD Dewar Model, Compressible Packing Model, and Modified Toufar Model) with machine learning is the novelty of the work. These models optimize the packing density of aggregate proportions while minimizing the void ratio, essential for achieving desired compressive strength criteria. To train the model, a comprehensive dataset comprising 479 concrete mixtures, each associated with known compressive strength values relative to packing density, is utilized. A significant advancement in predicting concrete compressive strength is demonstrated by the results. The approach outperforms traditional empirical models, offering precise and reliable predictions based on packing density. Importantly, this innovation eliminates the need for time-consuming and costly trial-and-error procedures in concrete mix design. The strong performance of various models in predicting concrete strength using particle packing theories is underscored by the study, with R^2 values ranging from 0.664 to 0.999. By combining concepts of particle packing theories and machine learning, a more efficient and reliable method for predicting concrete compressive strength is achieved. This innovation has the potential to revolutionize concrete mix design, leading to more durable and cost-effective construction practices.

Mechanical Strength Estimation of Self-Compacting Geopolymer Concrete using ReLU based Deep Neural Network

ABSTRACT A deep learning artificial neural network model developed in Tensorflow using the ReLU function is presented in this paper to determine the effect of nine different input parameters on the mechanical strength of self-compacting geopolymer concrete (SGC). With the application of the above model, it has been observed that the convergence outcome of our model is predicted with minimum error. The novelty of our model is the ReLU function which improves the accuracy of the results. Our study presents a systematic investigation of the compressive strength, flexural strength, and tensile strengths of SGC depending upon nine vital parameters, such as GFS, Fly ash, NaOH, Na2SiO3 content, Age of specimens, Slump Value, T50, L Box and J Ring value as input. A large number of experiments were conducted to prepare a comprehensive database of the mechanical strength of Flyash-GFS-based SGC. The database has a total of 396 samples which were collected from 132 distinct mixtures, where the minimum and maximum compressive strength value is 7.7 Mpa and 66.7 Mpa, respectively. For the prediction based on deep learning, authors have performed an adequate number of iterations for both training and testing using error estimation parameters such as mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). The error parameter values for the compressive strength of SGC are 2.42, 2.94 and 1.07, respectively, for MAE, RMSE and MSE. The other mechanical properties and their error parameter have also been thoroughly evaluated in our work.

Artificial Neural Network Levenberg–Marquardt Based Algorithm for Compressive Strength Estimation of Concrete Mixed with Magnetic Salty Water

This study develops an ANN-Levenberg-Marquardt hybrid model to estimate compressive strength of concrete mixed with magnetized salty water, demonstrating its accuracy and applicability for sustainable construction materials.

Compressive Strength Estimation of Rice Husk Ash-Blended Concrete Using Deep Neural Network Regression with an Asymmetric Loss Function

Compressive Strength Estimation of Rice Husk Ash-Blended Concrete Using Deep Neural Network Regression with an Asymmetric Loss Function

Drilling resistance testing combined with SonReb methods for nondestructive estimation of concrete strength

This study explores a drilling-based test methodology for nondestructive estimation of in-situ concrete strength. An experimental campaign has been carried out to develop a relationship between the drilling-resistance parameter, DR, and compressive strength of concrete. Rebound hammer (RH) and ultrasonic pulse velocity (UPV) tests were also performed and multivariate regression models complementing DR with RH and UPV data have been developed. A machine learning approach utilizing support vector machines (SVM) was implemented. The experimental data support that combined usage of DR with UPV and/or RH provides a robust tool for compressive strength prediction. Even with the limited data available, the support vector regression model shows promising performance.

Estimation of concrete strength using thermography integrated with deep-learning-based image segmentation: Case studies and economic analysis

This study proposes a novel method that combines infrared thermography and deep-learning-based image segmentation for automated estimation of concrete strength at construction sites. Infrared and truecolor images were captured at various construction sites, and the concreted areas in the truecolor images were segmented using a pre-trained convolutional neural network (CNN)-based model. The temperature data were used in conjunction with a maturity-based equation to predict the strength of the concrete. The proposed method was applied in the laboratory and actual construction sites, and the early age strength of concrete from 1 to 7 d was predicted to be within 3 MPa. A prediction accuracy of more than 80 percent was achieved when compared to experimental results. Moreover, the economic footprint was evaluated by applying the activity-based costing (ABC) technique. The results demonstrated that there is a 30 percent cost reduction compared to the existing method. This showed that the proposed method can estimate the strength of concrete in real time in an economically feasible manner and aid in optimizing the formwork removal time of structures.

Tri-level Framework for Realistic Estimation of Concrete Strength Using Bayesian Data Fusion of UPV and Guided Coring

Identification of in situ concrete characteristic strength (CCS) of deteriorating RC structures directly affects their rehabilitation. Contemporary practices lack the ability to yield CCS or at most produce an unrealistic estimate, because: (i) core tests are few due to technical and economic constraints and their positioning is random, inducing bias in the strength estimate, (ii) non-destructive tests (NDT) are themselves not good estimators of project specific strength, (iii) tests are only local indicators and most practising engineers are unaware of how to process the spatial variability. This paper overcomes these limitations with a novel and implementable framework, for an asset manager to make scientific estimates of CCS. This is a tri-level framework: (1) NDT are performed to capture the spatial variability of the investigated concrete; (2) core extractions are guided by the prior NDT information, to avoid bias in test location selection; (3) NDT and coring results are fused via Bayesian NDT test calibration and updating of the probability distribution of the concrete strength. The proposed framework is successfully implemented for the assessment and rehabilitation of a critical hospital complex in India.

Monitoring and estimate of concrete compressive strength in full curing period using smart piezoelectric modules

Concrete compressive strength is a most important material performance index and can reflect the quality of a concrete material. An innovative test method is proposed for monitoring the development of concrete compressive strength during the full curing period using smart piezoelectric modules (SPMs). The proposed method overcomes shortcomings of traditional methods and can be used for non-destructive, real-time, continuous, in situ monitoring of concrete strength during the whole curing period. A series of experiments demonstrates the validity and effectiveness of the method using SPMs. The energy values of monitoring signals from SPMs is applied as a characteristic identification parameter for concrete compressive strength. The signal waveform and frequency suitable for monitoring concrete compressive strength are determined, and a standard test procedure using SPMs to monitor concrete compressive strength is defined. Finally, equations for correlating the signal energy ratio and compressive strength ratio of different grades of concrete (i.e., C30, C40 and C50) based on the SPM monitoring data are established. The compressive strength of concrete for the given curing age can be effectively predicted by the proposed method, and the established correlation equations can be applied to assess the concrete condition and strength in curing.

A comprehensive evaluation of existing and new model-identification approaches for non-destructive concrete strength assessment

The common practice of concrete strength assessment is to combine non-destructive techniques (NDT) with core test measurements to develop a conversion model that is used to estimate the strengths at NDT test locations. In this work, different model identification approaches to develop this conversion model are investigated, namely regularized regression, quantile regression, Deming regression, bi-objective approach, geometric mean regression, orthogonal regression, and support vector machine (SVM) regression intending to improve the conversion model prediction capability so that accurate estimates of concrete strength can be obtained with a small number of cores. These approaches: in addition to other existing approaches, are first assessed using synthetic datasets and then tested on a real data case study by developing a set of cumulative distribution functions that provides information regarding the risk of a wrong prediction of concrete strength, outside a tolerable level. The results showed that concrete variability was best estimated using Deming regression, orthogonal regression, and bi-objective approach while local strengths were best estimated using quantile and regularized regression. Further, mean concrete strength estimation was observed to be not significantly affected by the choice of a model identification approach. Based on these results, a hybrid conversion model identification system that uses the bi-objective approach for mean and variability estimation and quantile regression for local strengths estimation is proposed. This study also proposes new tabular displays for the selection of the minimum number of cores required for reliable estimation of strength using information related to the NDT measurements and a choice of an accepted risk in the estimation. These tabular displays were produced using extensive analysis of synthetic data to determine the minimum number of cores required to limit the probability of the wrong estimation of strength within an admissible margin of error. Lastly, a web app was developed to easily provide the conversion models and the minimum number of cores for reliable concrete assessment.

Latest concrete materials dataset and ensemble prediction model for concrete compressive strength containing RCA and GGBFS materials

Testing the compressive strength of concrete using machine learning approaches has high importance for civil engineering. Machine learning approaches provides high accuracy with reduced cost and time. However, such approaches require concrete composition data detailing the type and quantitative ratio of different materials such as water, cement, and aggregate, etc. This study incorporates a dataset where the composition of 125 different kinds of materials has been recorded. Extensive literature review has been carried out for data collection and annotation. The dataset includes traditional, as well as, advanced materials containing both RCA (recycled concrete aggregate) and GGBFS (ground granulated blast furnace slag) along with other main ingredients of concrete mix. Adding RCA and GGBFS to the concrete mix helps to produce environment friendly concrete because these materials are waste and by-products. However, increasing the number of ingredients in the concrete complicates the prediction of concrete compressive strength. Increasing RCA for concrete reduces its mechanical strength which is managed by adding GGBFS, however, the strength depends on the ratios of RCA and GGBFS. So, the compressive strength prediction of concrete containing RCA and GGBFS is crucial task for ensuring the safety of construction projects. This study presents two ensemble models for the accurate prediction of compressive strength new concrete that contains RCA and GGBFS. First ensemble, LRF, combines the LR (linear regression) and RF (random forest) through soft voting. For the second ensemble, CNN (convolutional neural networks) and LSTM (long short term memory) are leveraged. Models’ performance is evaluated through several well-known metrics including R2 (R-square), root mean square, mean absolute error, and mean square error. Results indicate that LRF and CNN-LSTM achieve the highest R2 scores of 0.93 and 0.96, respectively than the state-of-the-art models. LRF is more efficient as compared to CNN-LSTM in terms of computational time. Compared to the traditional concrete strength estimation, machine learning-based compressive strength prediction is accurate, robust and precise.

Compressive Strength Estimation and CO2 Reduction Design of Fly Ash Composite Concrete

Fly ash is broadly utilized to produce concrete materials. This study presents a strength estimation model and a CO2 reduction design method for concrete with fly ash. First, a hydration-based strength (HBS) model is proposed for the evaluation of strength development at different ages of fly ash composite concrete with different mix proportions. Second, CO2 emissions for 1 MPa strength were evaluated. The analysis results show that, as the fly ash-to-binder ratio (FA/B) increased, the CO2 emissions for 1 MPa strength decreased. For concrete with a low water-to-binder ratio (W/B), the addition of high content of fly ash had an obvious dilution effect, which increased the reaction degree of cement and reduced CO2 emissions for 1 MPa strength. Moreover, the extension of the design age could reduce CO2 emissions for 1 MPa strength. Third, a genetic-algorithm-based optimal design model is proposed to find the individual mass of cement and fly ash of low-CO2 concrete. The analysis results show that, as the water contents increased from 160 to 170 kg/m3, to obtain the same strength, cement mass and fly ash mass increased, while the water/binder ratio and fly ash/binder ratio did not change. This means that the reduction in mixed water is one feasible way to lower CO2 emissions. In summary, the proposed strength–emission integrated analysis method is useful for designing sustainable fly ash composite concrete with the desired strength and low levels of CO2 emissions.

Guide to non-destructive concrete strength assessment: Homogeneity tests and sampling plans

Non-Destructive testing (NDT) of concrete can be used in conjunction with destructive techniques (e.g., coring) for an efficient assessment of concrete strength in existing structures. A major drawback that hinders the efficient use of test measurements is the limited description specified by the relevant standards regarding how the overall investigation program should be carried out. This study discusses two elements of the investigation program: homogeneity of different concrete zones and the locations for core extraction. Different homogeneity measures have been investigated to identify heterogeneous concrete zones using extensive statistical analysis of synthetic datasets. The considered homogeneity measures were: t-test, Kolmogorov-Smirnov (KS) test, earth mover's distance, and Cramér-von Mises distance. The results of the analysis indicated that the earth mover's distance and Cramér-von Mises distance outperform the traditional t-test in the identification of heterogenous concrete zones, and hence, recommended to be used for concrete assessment. Furthermore, different sampling plans of core locations, namely, similarity sampling, cluster sampling, proportional stratified sampling, and post-sampling are investigated through the generation of risk curves as indicators of the estimation quality of each sampling plan. It was found that a reduction in the probability of inaccurate concrete strength estimation is feasible. As such, the utilization of sampling plans in concrete assessment can enhance the reliability of the assessment.

Performance of maturity method for estimation of concrete strength based on cubic specimens

To assess the condition of the structures of buildings and structures, a comprehensive analysis of the factors affecting their performance characteristics - concrete strength, protective layer, and rebar diameter, thermal conductivity and moisture content of concrete, adhesion of protective and facing coatings, frost resistance and water resistance of concrete - is required. However, with all the variety of monitored parameters the control of concrete strength has a special place because when assessing the condition of the structure the determining factor is the compliance of the actual strength of concrete to the design requirements. This paper presents studies on the determination of concrete strength based on modern technology, wireless sensor as well as instrumentation IPS. These methods are particularly effective in the case of non-linear relationships between different system parameters, as in the behavior of concrete parameters. They can also provide rapid measurements by continuously monitoring the internal condition of the reinforced concrete structure. The found graduation dependences and determination coefficients equal to 0.9234 and 0.9702 justify the convergence of the results of the two methods of strength gain control.

Applying Artificial Intelligence to Improve On-Site Non-Destructive Concrete Compressive Strength Tests

In the construction industry, non–destructive testing (NDT) methods are often used in the field to inspect the compressive strength of concrete. NDT methods do not cause damage to the existing structure and are relatively economical. Two popular NDT methods are the rebound hammer (RH) test and the ultrasonic pulse velocity (UPV) test. One major drawback of the RH test and UPV test is that the concrete compressive strength estimations are not very accurate when comparing them to the results obtained from the destructive tests. To improve concrete strength estimation, the researchers applied artificial intelligence prediction models to explore the relationships between the input values (results from the two NDT tests) and the output values (concrete strength). In-situ NDT data from a total of 98 samples were collected in collaboration with a material testing laboratory and the Professional Civil Engineer Association. In-situ NDT data were used to develop and validate the prediction models (both traditional statistical models and AI models). The analysis results showed that AI prediction models provide more accurate estimations when compared to statistical regression models. The research results show significant improvement when AI techniques (ANNs, SVM and ANFIS) are applied to estimate concrete compressive strength in RH and UPV tests.

Core-to-Cubic Strength Ratio for Historical-Like Concrete

Coring is considered to provide the best estimate of concrete compressive strength in existing structures and is commonly used to calibrate Non-Destructive and Moderately Destructive Techniques. Historical concrete, produced in the pre-code period until the ‘20s, significantly differs from modern concrete due to lack of standardization, improper rules of thumbs and to aggregate shape (round, smooth and often excessively large aggregates) and proportioning. Therefore, the applicability of the procedures calibrated on modern concrete to a historical one, also coring, is an issue that needs to be discussed. In this paper, an experimental campaign on historical-like concrete, i.e. with the same defects as historical concrete, aims at identifying the reliability of drilled cores due to the effect of round aggregates. The results show that standard procedures commonly used on modern concrete cannot be directly applied to historical concrete: drilled cores suffer from scale effects (core diameter) and from cutting damage of the material much more than modern concrete. In detail, the core-to-cubic ratio, that modern codes assume in the range 0.70-0.85, due to the dimension and shape of the aggregates is found inside a larger range, 0.70-1.00, and, as opposed to modern concrete, is found to be decreasing as concrete strength increases. Besides, the diameter of the core is found to have a relevant effect on the estimate of the material compressive strength and on the core-to-cubic strength ratio, pointing out that the dimension of the core affects the results much more than for modern concrete. This latter result, which needs further research, points out that historical concretes may be rather different from modern ones and probably need larger cores to be drilled than modern concrete due to the larger dimension of aggregates that are often found in pre-code concrete.