Estimation Of Compressive Strength Research Articles

Harnessing machine learning for accurate estimation of compressive strength of high-performance self-compacting concrete from non-destructive tests: A comparative study

This study investigates the compressive strength (CS-28d) of high-performance self-compacting concrete. To accurately estimate the CS-28d, the study employed regression techniques (linear regression (LR) and stepwise polynomial regression (SPR)) as well as machine learning techniques (M5Prime, Random Forest, Random Tree and REP Tree). The study was based on 600 experimental results were collected from literature, with the rebound number (R), and ultrasonic pulse velocity (Vp) used to predict the CS-28d. The results indicate that the M5Prime model performed the best, with the highest coefficient of determination (Pearson R of 95.06 %) and the lowest root mean square error (RMSE of 4.84516 %). Among the tested regression models, the SPR model demonstrated the best performance and an empirical equation was introduced to estimate the CS-28d of a high-performance self-compacting concrete with high accuracy. An in-depth exploration of the model's sensitivity to its input parameters was conducted to quantify their effect on the CS-28d outcome. The results showed that the rebound number (R) had the most significant impact, with a sensitivity analysis parameter of 95 %. This was followed by the ultrasonic pulse velocity (Vp) had a relatively insignificant effect of only 5 % on the CS-28d values.

Estimation of compressive strength of concrete with manufactured sand and natural sand using interpretable artificial intelligence

To satisfy the design strength of manufactured sand concrete (MSC) in practical engineering applications, a plethora of geotechnical tests are frequently conducted. An effective approach is imperative to reduce the consumption of labor and resources during these tests. The objective of this paper is to introduce an interpretable machine learning (ML) method to evaluate the compressive strength (CS) of MSC. Firstly, a dataset was established by compiling experimental results from 208 literatures. 3382 data points were selected from the dataset for algorithm training. Recursive Feature Elimination with Cross-Validation (RFECV) was employed to select input parameters. Four algorithms with 12 selected input variables and 1 output variable were constructed to predict CS of MSC using Random Forest (RF), Gradient Boosting Decision Trees (GBDT), eXtreme Gradient Boosting algorithm (XGBoost), and Categorical Boosting (CatBoost). The results show that XGBoost has the highest accuracy and generalization ability (R2=0.934, MAE=3.44, RMSE=5.16, MAPE=0.07). To enhance model transparency, SHapley Additive exPlanations (SHAP) was adopted to explain the underlying predictive mechanisms of ML models. Analyses show that, 1) the cement content, curing time, and water content were the main feature parameters influencing the CS of MSC, 2) the cement content, curing time, and water content have a linear increase, logarithmic increase, and exponential decrease with the CS of MSC, respectively. Partial Dependence Plots (PDP) and Individual Conditional Expectation (ICE) plots were used to further analyze the impacts of these significant influencing factors on the CS of MSC. Additionally, the Local Interpretable Model-Agnostic Explanations (LIME) method was employed to investigate thresholds for various material dosages in MSC containing 5–10 % stone powder. Two typical scenarios were selected for analysis, yielding recommended dosage ranges for concrete of two distinct strengths. Finally, a graphical user interface (GUI) for the CS of MSC has been designed, which might be of great use to material engineers. This provides reference and guidance for concrete engineering practice.

Estimation of Compressive Strength of Rubberised Slag Based Geopolymer Concrete Using Various Machine Learning Techniques Based Models

Estimation of Compressive Strength of Rubberised Slag Based Geopolymer Concrete Using Various Machine Learning Techniques Based Models

Predicting compressive strength of hollow concrete prisms using machine learning techniques and explainable artificial intelligence (XAI)

The design of masonry structures requires accurate estimation of compressive strength (CS) of hollow concrete masonry prisms. Generally, the CS of masonry prisms is determined by destructive laboratory testing which results in time and resource wastage. Thus, this study aims to provide machine learning-based predictive models for CS of hollow concrete masonry blocks using different algorithms including Multi Expression Programming (MEP), Random Forest Regression (RFR), and Extreme Gradient Boosting (XGB) etc. A dataset of 159 experimental results was collected from published literature for this purpose. The collected dataset consisted of four input parameters including strength of masonry units (fb), height-to-thickness ratio (h/t), strength of mortar (fm), and ratio of fm/fb and only one output parameter i.e., CS. Out of all the algorithms employed in current study, only MEP and GEP expressed their output in the form of an empirical equation. The accuracy of developed models was assessed using root mean squared error (RMSE), objective function (OF), and R2 etc. Among all algorithms assessed, XGB turned out to be the most accurate having R2 = 0.99 and least OF value of 0.0063 followed by AdaBoost, RFR, and other algorithms. The developed XGB model was also used to conduct different explainable artificial intelligence (XAI) analysis including sensitivity and shapley analysis and the results showed that strength of masonry unit (fb) is the most significant variable in predicting CS. Thus, the ML-based predictive models presented in this study can be utilized practically for determining CS of hollow concrete masonry prisms without requiring expensive and time-consuming laboratory testing.

Leveraging a Hybrid Machine Learning Approach for Compressive Strength Estimation of Roller-Compacted Concrete with Recycled Aggregates

In recent years, the use of recycled aggregate (RA) in roller-compacted concrete (RCC) for pavement construction has been increasingly attractive due to various environmental and economic benefits. Early determination of the compressive strength (CS) is crucial for the construction and maintenance of pavement. This paper presents the idea of combining metaheuristics and an advanced gradient boosting regressor for estimating the compressive strength of roller-compacted concrete containing RA. A dataset, including 270 samples, has been collected from previous experimental works. Recycled aggregates of construction demolition waste, reclaimed asphalt pavement, and industrial slag waste are considered in this dataset. The extreme gradient boosting machine (XGBoost) is employed to generalize a functional mapping between the CS and its influencing factors. A recently proposed gradient-based optimizer (GBO) is used to fine-tune the training phase of XGBoost in a data-driven manner. Experimental results show that the hybrid GBO-XGBoost model achieves outstanding prediction accuracy with a root mean square error of 2.64 and a mean absolute percentage error less than 8%. The proposed method is capable of explaining up to 94% of the variation in the CS. Additionally, an asymmetric loss function is implemented with GBO-XGBoost to mitigate the overestimation of CS values. It was found that the proposed model trained with the asymmetric loss function helped reduce overestimated cases by 17%. Hence, the newly developed GBO-XGBoost can be a robust and reliable approach for predicting the CS of RCC using RA.

Evaluating enhanced predictive modeling of foam concrete compressive strength using artificial intelligence algorithms

Artificial intelligence algorithms have recently demonstrated their efficacy in accurately predicting concrete properties by optimizing mixing proportions and overcoming design limitations. In this regard, foam concrete (FC) production presents a unique challenge, necessitating extensive experimental trials to attain specific properties such as compressive strength (CS). In this context, linear regression (LR), support vector regression (SVR), a multilayer-perceptron artificial neural network (MLP-ANN), and Gaussian process regression (GPR) algorithms, were used to predict the CS of FC. 261 experimental results were utilized, incorporating input variables such as density, water-to-cement ratio, and fine aggregate-to-cement ratio. During the training phase, 75 % of the experimental dataset was utilized. The experimental data is then validated using metrics such as coefficient of determination (R2), root mean square error, and root mean error. In comparison, the GPR algorithm reveals high-accuracy towards the estimation of CS, as proved by its high R2-value, which equals 0.98, while the R2 for ANN, SVR, and LR are 0.97, 0.90, and 0.89, respectively. Additionally, parametric and sensitivity analyses were used to assess the performance of the GPR and LR algorithms. Results revealed that density exerted the most significant influence on CS, with the GPR model showing a pronounced negative impact of fine aggregate-to-cement ratio on CS, particularly in low-density FC, contrasting with the LR model. This study confirmed that the GPR algorithm provided reliable accuracy in predicting the CS of FC. Therefore, it is recommended to utilize the prediction algorithms within the range of input variables employed in this investigation for optimal results.

Multiscale Models to Evaluate the Impact of Chemical Compositions and Test Conditions on the Mechanical Properties of Cement Mortar for Tile Adhesive Applications.

This study aims to develop systematic multiscale models to accurately predict the compressive strength of cement mortar for tile adhesive applications, specifically tailored for applications in the construction industry. Drawing on data from 200 cement mortar tests conducted in previous studies, various factors such as cement/water ratios, curing times, cement/sand ratios, and chemical compositions were analyzed through static modeling techniques. The model selection involved utilizing various approaches, including linear regression, pure quadratic, interaction, M5P tree, and artificial neural network models to identify the most influential parameters affecting mortar strength. The analysis considered the water/cement ratio, testing ages, cement/sand ratio, and chemical compositions, such as silicon dioxide, calcium dioxide, iron (III) oxide, aluminum oxide, and the pH value. Evaluation metrics, such as the determination coefficient, mean absolute error, root-mean-square error, objective function, scatter index, and a-20 index, were employed to ensure the accuracy of the compressive strength estimates. Additionally, empirical equations were utilized to predict flexural and tensile strengths based on the compressive strength of the cement mortar for tile adhesive applications.

Investigation of mechanical properties of high-performance concrete via multi-method of regression tree approach

Concrete's workability and durability are influenced by its mechanical properties, including Tensile and Compressive strength. High-performance concrete exhibits non-linear relationships between its compressive and tensile strength characteristics and the proportions of its constituent materials, such as water, aggregate, cement, and admixtures. The investigation of the relations between the constituents of concrete and its mechanical properties has posed a challenging issue. This paper seeks to develop a range of single, hybrid, and ensemble models to resolve the problem of accurately estimating the mechanical properties of concrete according to its constituent components as input variables. In this regard, various frameworks incorporating a machine learning technique called decision tree and four metaheuristic optimization algorithms were utilized in model development to emulate the values of tensile and compressive strength. The findings indicated that the decision tree-dynamic arithmetic optimization algorithm hybrid model produced results with an correlation of determination of 0.9919 in compressive strength and 0.9933 in tensile strength, which were on average 1–3 % higher than that of the decision tree-dandelion optimizer algorithm, decision tree-arithmetic optimization algorithm, and decision tree-coot optimization algorithm, indicating superior optimization performance of dynamic arithmetic optimization algorithm. Additionally, the powerful prediction capability of the decision tree + dynamic arithmetic optimization algorithm + dandelion optimizer algorithm + coot optimization algorithm + arithmetic optimization algorithm ensemble model was noticeable with R2 of 0.9882 and 0.9936 in compressive and tensile strength estimation reliable to be used for various data produced in the future. In general, the utilization of coupling techniques to generate hybrid and ensemble models can enhance the accuracy of predictions while reducing the time and costs associated with experimental procedures.

Estimation of concrete compressive strength from non-destructive tests using a customized neural network and genetic algorithm

This paper introduces a novel approach for deriving an equation aimed at predicting concrete compressive strength, leveraging two non-destructive test results: ultrasonic pulse velocity and rebound hammer value. Traditional regression methods often fail to capture the complex non-linear relationships crucial for accurate concrete CS estimation, leading to limited effectiveness. In contrast, while machine learning models excel at mapping these non-linear relationships from non-destructive test results, their intricate internal mechanisms can be opaque, posing challenges for practical application and comprehension by engineers. This study aims to bridge these gaps by developing an estimation equation derived from the decoded weights of a neural network specifically designed to capture the intricate nonlinear mapping relationship. Initially, the equation is intricate and lengthy, comprising multiple terms. To streamline and refine it, a genetic algorithm is employed, focusing solely on the most pivotal terms. Consequently, the refined prediction equation demonstrates superior estimation performance with an RMSE of 3.40 MPa, an MAE of 2.70 MPa, an R2 of 0.92, and an R of 0.97 compared to several existing formulations. Furthermore, a comparative analysis is undertaken to assess the impact of the degree of equation simplification on its predictive accuracy. The findings offer insights into the pivotal roles of exponential function terms in enhancing prediction performance, as well as elucidating trigonometric function terms contribute the model’s complex non-linear mapping capability.

Evaluation of Bone Microstructure Parameters by Using Tomographic Methods and Compressive Strength Estimation

Objective: The aim of this study was to evaluate the microstructure of the mandible by micro computed tomography (µCT), cone beam computed tomography (CBCT) and computed tomography (CT) and to estimate the compressive strength of the bone based on the values obtained by these methods. Methods: Thirty specimens obtained from ex-vivo sheep mandible were scanned by µCT cone beam computed tomography and computed tomography. These specimens were also subjected to compression testing and compression strength values were calculated. Morphometric parameters were evaluated using ImageJ software Bland-Altman lower upper bound agreement and ICC coefficient were used to evaluate the agreement between the tomography methods used and the gold standard. Linear and multivariate stepwise regression analysis was performed to calculate the compression strength value based on the radiomorphometric parameters. Statistical significance level was accepted as .05. Results: Bone Surface/Total Volume, Bone Volume/Total Volume and Degree of Anistoropy parameters evaluated by CBCT and Fractal Dimension parameter evaluated by CT showed a statistically significant agreement with the gold standard method µCT. Bone Volume/Total Volume and Degree of Anistoropy parameters obtained with µCT (R2:0.75), Bone Volume/Total Volume, Degree of Anistoropy , Connectivity Density parameters (R2:0.62), and the Structure Model Index parameter (R2:0.13) obtained by CT can be used to predict the compression strength value. Conclusion: Bone compression strength can be estimated by CBCT and µCT methods in a desired level. Bone Volume/Total Volume and Degree of Anistoropy parameters are significant determinants of bone mechanical property in not only µCT but also CBCT method.

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.

Concrete Breaking Strength Prediction Using Machine Learning

Concrete Breaking Strength Prediction Using Machine Learning

The unconfined compressive strength estimation of rocks using a novel hybridization technique based on the regulated Gaussian processor

The unconfined compressive strength (UCS) of rocks is a crucial factor in geotechnical engineering, assuming a central role in various civil engineering undertakings, including tunnel construction, mining operations, and the design of foundations. The precision in forecasting UCS holds paramount importance in upholding the security and steadfastness of these endeavors. This article introduces a fresh methodology for UCS prognostication by amalgamating Gaussian process regression (GPR) with two pioneering optimization techniques: sand cat swarm optimization (SCSO) and the equilibrium slime mould algorithm (ESMA). Conventional techniques for UCS prediction frequently encounter obstacles like gradual convergence and the potential for becoming ensnared in local minima. In this investigation, GPR is the foundational predictive model due to its adeptness in managing nonlinear associations within the dataset. The fusion of GPR with cutting-edge optimizers is envisioned to elevate the precision and expeditiousness of UCS prognostications.An extensive collection of rock samples, each accompanied by UCS measurements, is harnessed to assess the suggested methodology. The efficacy of the GPSC and GPES models is juxtaposed with the conventional GPR technique. The findings reveal that incorporating SCSO and ESMA optimizers into GPR brings about a noteworthy enhancement in UCS prediction accuracy and expedites convergence. Notably, the GPSC models exhibit exceptional performance, evidenced by an exceptional R2 value of 0.995 and an impressively minimal RMSE value of 1.913. These findings emphasize the GPSC model’s potential as an exceedingly auspicious tool for experts in the realms of engineering and geology. It presents a sturdy and dependable method for UCS prediction, a resource of immense value in augmenting the security and efficiency of civil engineering endeavors.

Machine learning-based compressive strength estimation in nanomaterial-modified lightweight concrete

Abstract The development of nanotechnology has led to the creation of materials with unique properties, and in recent years, numerous attempts have been made to include nanoparticles in concrete in an effort to increase its performance and create concrete with improved qualities. Nanomaterials are typically added to lightweight concrete (LWC) with the goal of improving the composite’s mechanical, microstructure, freshness, and durability qualities. Compressive strength is the most crucial mechanical characteristic for all varieties of concrete composites. For this reason, it is essential to create accurate models for estimating the compressive strength (CS) of LWC to save time, energy, and money. In addition, it provides useful information for planning the construction schedule and indicates when the formwork should be removed. To predict the CS of LWC mixtures made with or without nanomaterials, nine different models were proposed in this study: the gradient-boosted trees (GBT), random forest, tree ensemble, XGBoosted (XGB), Keras, simple regression, probabilistic neural networks, multilayer perceptron, and linear relationship model. A total of 2,568 samples were gathered and examined. The most significant factors influencing CS during the modeling process were taken into account as input variables, including the amount of nanomaterials, cement, water-to-binder ratio, density, the content of lightweight aggregates, type of nano, fine and coarse aggregate content, and water. The performance of the suggested models was assessed using a variety of statistical measures, including the coefficient of determination (R 2), scatter index, mean absolute error, and root-mean-squared error (RMSE). The findings showed that, in comparison to other models, the GBT model outperformed the others in predicting the compression strength of LWC mixtures enhanced with nanomaterials. The GBT model produced the best results, with the greatest value of R 2 (0.9) and the lowest value of RMSE (5.286). Furthermore, the sensitivity analysis showed that the most important factor influencing the prediction of the CS of LWC enhanced with nanoparticles is the water content.

Optimizing building material selection: A machine learning approach for efficient concrete compressive strength forecasting

Price changes in construction materials have a significant impact on building construction projects. Such price variations occur at random and at varying rates over time. A system that can estimate the magnitude and quantity of the change in material prices with reasonable accuracy is required. The primary goal is to create a machine-learning model that can predict the type of building material chosen based on environmental factors. The compressive strength of concrete is critical in defining its mechanical qualities. Long laboratory testing is needed to determine the compressive strength of concrete. The capacity of powerful machine learning algorithms to forecast concrete compressive strength speeds up these lengthy experimental methods while also lowering expenses. This study provides abilities to precisely anticipate and categorize numerous qualities and traits of distinct materials. The framework includes a broad dataset that details materials, composition, and performance characteristics. Machine learning algorithms such as logistic regression (LR), decision trees (DT), and random forests (RF) train models on the training data. The models are hyper-parameter tweaked and feature developed to achieve the most outstanding performance. The k-fold method is used throughout the training and assessment phase to guarantee robustness and reduce bias. The F1 score and Receiver Operating Characteristic-Area Under Curve (ROC-AUC) curve are two performance measures used to measure how accurate and predictive the trained models are. The study findings provide insights into the qualities of the materials, facilitating improved material selection, quality assurance, and decision-making in the building sector. In the analyses, the best accuracy value was 99.92%, and the precision value was 88.83% using the LR algorithm. As a result, it was determined that the LR algorithm had the least execution 57.826 ms, and is thus the most suitable for use in concrete compressive strength estimation.

A novel compressive strength estimation approach for 3D printed fiber-reinforced concrete: integrating machine learning and gene expression programming

A novel compressive strength estimation approach for 3D printed fiber-reinforced concrete: integrating machine learning and gene expression programming

Automated estimation of early-age concrete compressive strength using EMI signature-driven deep learning technique

Concrete is a vital construction material for civil infrastructure, whose early-age compressive strength estimation plays a critical role in determining the proper time for formwork removal and the readiness of the structures for service. Electromechanical impedance (EMI) technique has been proven to be a non-destructive and effective method for monitoring concrete early strength gain. Nonetheless, such a technique heavily relies on effective frequency band selection and statistical parameter-based analysis, which may prevent the technique from a real-time operation. This study proposes an EMI signature-driven deep learning (DL) technique to enable the automated estimation of early-age concrete compressive strength. A novel DL framework called multi-scale learning residual denoising network (MLRD-Net) was developed to autonomously learn sensitive features from raw EMI signatures and ultimately estimate the compressive strength. The EMI signatures were collected during the strength development process of concrete specimens produced from two different mix designs based on strength. Two independent databases were constructed from recorded EMI signatures to test the effectiveness of the MLRD-Net. The results demonstrated the excellent performance of the proposed model with coefficients of determination exceeding 0.99 for both databases. In addition, the MLRD-Net showed superior performances over other methods in terms of prediction accuracy, noise resistance and fitness capacities, indicating its great potential for real-time and accurate estimation of the strength gain of early-age concrete.

Estimation of compressive strength of recycled aggregate concrete using advanced meta-heuristic algorithms and random forest analysis

Estimation of compressive strength of recycled aggregate concrete using advanced meta-heuristic algorithms and random forest analysis

Compressive Strength Estimation of Manufactured Sand Concrete Using Hybrid ANN Paradigms Constructed with Meta-heuristic Algorithms

Compressive Strength Estimation of Manufactured Sand Concrete Using Hybrid ANN Paradigms Constructed with Meta-heuristic Algorithms

Comparative Analysis of Gradient-Boosting Ensembles for Estimation of Compressive Strength of Quaternary Blend Concrete

Concrete compressive strength is usually determined 28 days after casting via crushing of samples. However, the design strength may not be achieved after this time-consuming and tedious process. While the use of machine learning (ML) and other computational intelligence methods have become increasingly common in recent years, findings from pertinent literatures show that the gradient-boosting ensemble models mostly outperform comparative methods while also allowing interpretable model. Contrary to comparison with other model types that has dominated existing studies, this study centres on a comprehensive comparative analysis of the performance of four widely used gradient-boosting ensemble implementations [namely, gradient-boosting regressor, light gradient-boosting model (LightGBM), extreme gradient boosting (XGBoost), and CatBoost] for estimation of the compressive strength of quaternary blend concrete. Given components of cement, Blast Furnace Slag (GGBS), Fly Ash, water, superplasticizer, coarse aggregate, and fine aggregate in addition to the age of each concrete mixture as input features, the performance of each model based on R2, RMSE, MAPE and MAE across varying training–test ratios generally show a decreasing trend in model performance as test partition increases. Overall, the test results showed that CatBoost outperformed the other models with R2, RMSE, MAE and MAPE values of 0.9838, 2.0709, 1.5966 and 0.0629, respectively, with further statistical analysis showing the significance of these results. Although the age of each concrete mixture was found to be the most important input feature for all four boosting models, sensitivity analysis of each model shows that the compressive strength of the mixtures does increase significantly after 100 days. Finally, a comparison of the performance with results from different ML-based methods in pertinent literature further shows the superiority of CatBoost over reported the methods.