Prediction Of Concrete Compressive Strength Research Articles

Prediction of concrete compressive strength using a Deepforest-based model

Concrete compressive strength testing is crucial for construction quality control. The traditional methods are both time-consuming and labor-intensive, while machine learning has been proven effective in predicting the compressive strength of concrete. However, current machine learning-based algorithms lack a thorough comparison among various models, and researchers have yet to identify the optimal predictor for concrete compressive strength. In this study, we developed 12 distinct machine learning-based regressors to conduct a thorough comparison and to identify the optimal model. To study the correlation between compressive strength and various factors, we conducted a comprehensive analysis and selected blast furnace slag, superplasticizer, age, cement, and water as the optimized factor subset. Based on this foundation, grid search and fivefold cross-validation were employed to establish the hyperparameters for each model. The results indicate that the Deepforest-based model demonstrates superior performance compared to the 12 models. For a more comprehensive evaluation of the model’s performance, we compared its performance with state-of-the-art models using the same independent testing dataset. The results demonstrate that our model achieving the highest performance (R2 of 0.91), indicating its accurate prediction capability for concrete compressive strength.

Concrete Compressive Strength Prediction Using Combined Non-Destructive Methods: A Calibration Procedure Using Preexisting Conversion Models Based on Gaussian Process Regression

Non-destructive testing (NDT) techniques are crucial in making informed decisions about reconstructing or repairing building structures. The SonReb method, a combination of the rebound hammer (RH) and the ultrasonic pulse velocity (UPV) tests, is widely used for this purpose. To evaluate the compressive strength, CS, of the concrete under investigation, the ultrasonic pulse velocity Vp and the rebound index R must be mapped to the compressive strength CS using a suitable conversion model, the identification of which requires supplementing the NDT measurements with destructive-type measurements (DT) on a relatively large number of concrete cores. An approach notably indicated in all cases where the minimization of the number of cores is essential is to employ a pre-existing conversion model, i.e., a model derived from previous studies conducted in the literature, which must be appropriately calibrated. In this paper, we investigate the performance of Gaussian process regression (GPR) in calibrating the pre-existing SonReb conversion models, exploiting their ability to handle nonlinearity and uncertainties. The numerical results obtained using experimental data collected from the literature show that GPR calibration is very effective, outperforming, in most cases, the standard multiplicative and additive techniques used to calibrate the SonReb models.

Concrete compressive strength prediction by artificial neural network approach

Concrete compressive strength prediction by artificial neural network approach

Machine learning and interactive GUI for concrete compressive strength prediction

Concrete compressive strength (CS) is a crucial performance parameter in concrete structure design. Reliable strength prediction reduces costs and time in design and prevents material waste from extensive mixture trials. Machine learning techniques solve structural engineering challenges such as CS prediction. This study used Machine Learning (ML) models to enhance the prediction of CS, analyzing 1030 experimental CS data ranging from 2.33 to 82.60 MPa from previous research databases. The ML models included both non-ensemble and ensemble types. The non-ensemble models were regression-based, evolutionary, neural network, and fuzzy-inference-system. Meanwhile, the ensemble models consisted of adaptive boosting, random forest, and gradient boosting. There were eight input parameters: cement, blast-furnace-slag, aggregates (coarse and fine), fly ash, water, superplasticizer, and curing days, with the CS as the output. Comprehensive performance evaluations include visual and quantitative methods and k-fold cross-validation to assess the study’s reliability and accuracy. A sensitivity analysis using Shapley-Additive-exPlanations (SHAP) was conducted to understand better how each input variable affects CS. The findings showed that the Categorical-Gradient-Boosting (CatBoost) model was the most accurate prediction during the testing stage. It had the highest determination-coefficient (R2) of 0.966 and the lowest Root-Mean-Square-Error (RMSE) of 3.06 MPa. The SHAP analysis showed that the age of the concrete was the most critical factor in the predictive accuracy. Finally, a Graphical User Interface (GUI) was offered for designers to predict concrete CS quickly and economically instead of costly computational or experimental tests.

Concrete Compressive Strength Prediction by Ensemble Machine Learning Approach

The prediction of concrete compressive strength is a crucial aspect of ensuring the structural integrity and durability of construction projects. In recent years, machine learning approaches have improved upon the limitations of empirical formulas and laboratory testing methods for predicting concrete compressive strength. This study utilizes ensemble machine-learning techniques, such as Bagging, XGBoost, and Stacking models, to enhance the accuracy of concrete compressive strength prediction models. A five-fold cross-validation technique was applied to mitigate the problems of underfitting or overfitting in the regression model. Furthermore, various statistical indices were employed to compare the forecasting performance of these ensemble techniques. The prediction performance of this research revealed that XGBoost achieved the highest R-squared value of 93%, followed by Stacking and Bagging regression models at 92%. Consequently, this research underscores the potential of ensemble techniques as valuable tools in the domain of civil engineering, paving the way for more reliable and efficient construction practices.

A clustering machine learning approach for improving concrete compressive strength prediction

AbstractThis study investigates the application of clustering techniques to enhance the accuracy of hierarchical classification and regression (HCR) models for predicting concrete compressive strength (CCS). Following the hypothesis that integrating clustering at the initial levels of model hierarchy reduces classification errors and prevents their propagation to subsequent levels, HCR models were developed utilizing both the unweighted pair group method with arithmetic mean (UPGMA) and hard clustering (HC) methods. Findings demonstrate that models using UPGMA significantly outperform those based on HC. Furthermore, it was demonstrated that further hierarchical clustering allows for multilayered HCR models that improve predictive performance by further leveraging parent–child relationships within data clusters. Overall, this study demonstrates that the proposed methodology can be introduced in the model development pipeline to enhance the prediction accuracy of CCS models.

Application of Fully Connected Neural Network-Based PyTorch in Concrete Compressive Strength Prediction

Application of Fully Connected Neural Network-Based PyTorch in Concrete Compressive Strength Prediction

Concrete Breaking Strength Prediction Using Machine Learning

Concrete Breaking Strength Prediction Using Machine Learning

Comparison of Predictive Modeling Concrete Compressive Strength with Machine Learning Approaches

Accurately predicting concrete compressive strength is fundamental for optimizing mix designs, ensuring structural integrity, and advancing sustainable construction practices. Increased demands for safer, more durable infrastructure necessitate effective predictive concrete compressive strength models. This research aims to compare the effectiveness of six machine learning models such as Linear Regression, Random Forest, Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Gradient Boosting, and XGBoost to predict concrete compressive strength. Used a dataset of 1030 instances with varying mixture compositions, conducted extensive exploratory data analysis, and applied feature engineering and data scaling to enhance model performance. Assessments were performed with a 5-fold cross-validation approach with the R-squared (R²) metric. In addition, the SHAP value is used to understand the influence of each feature on the compressive strength results. The results revealed that XGBoost significantly outperformed other models, achieving an average R² value of 0.9178 with a standard deviation of 0.0296. Notably, Random Forest and Gradient Boosting also demonstrated robust capabilities. Based on our experiment, these models effectively predicted compressive strengths close to actual measured values, confirming their practical applicability in civil engineering. SHAP values provided insights into the significant impact of age and cement quantity on model outputs. These results highlight the advanced ensemble methods' effectiveness in concrete compressive strength prediction and underscore the importance of feature engineering in enhancing model accuracy.

Performance Comparison of Machine Learning Models for Concrete Compressive Strength Prediction.

This study explores the prediction of concrete compressive strength using machine learning models, aiming to overcome the time-consuming and complex nature of conventional methods. Four models-an artificial neural network (ANN), a multiple linear regression, a support vector machine, and a regression tree-are employed and compared for performance, using evaluation metrics such as mean absolute deviation, root mean square error, coefficient of correlation, and mean absolute percentage error. After preprocessing 1030 samples, the dataset is split into two subsets: 70% for training and 30% for testing. The ANN model, further divided into training, validation (15%), and testing (15%), outperforms others in accuracy and efficiency. This outcome streamlines compressive strength determination in the construction industry, saving time and simplifying the process.

Optimizing sustainable concrete compressive strength prediction: A new particle swarm optimization‐based metaheuristic approach to neural network modeling for circular economy and disaster resilience

AbstractThis study aims to predict the final‐age compressive strengths of sustainable concrete series produced using different aggregate types with high accuracy using an artificial intelligence model and to reduce environmental degradation and conserve rapidly decreasing natural resources within the scope of sustainable development and circular economy goals. For this purpose, 45 different sustainable concrete series containing all‐natural, recycled, natural, and recycled concrete aggregates were produced and subjected to compressive strength tests at 7 and 28 days. In addition, this study also considers whether deep neural network models, which have gained popularity in recent years but have high time and resource costs, or shallow models are more suitable for determining the compressive strength values with high accuracy. For this purpose, a method is proposed that can automatically generate the optimal neural network model using a metaheuristic approach that eliminates the human factor. For this purpose, the proposed method was extensively compared with different classical machine learning algorithms. The proposed method predicted the 7 and 28‐day compressive strength with a coefficient of determination of 0.999 and presented better compressive strength predictions than all other algorithms. Considering the number of buildings to be constructed after earthquakes, the widespread use of concrete, and the importance of compressive strength, the proposed method will likely provide significant gains within sustainable development, circular economy, and disaster risk reduction.

Extreme fine-tuning and explainable AI model for non-destructive prediction of concrete compressive strength, the case of ConcreteXAI dataset

This groundbreaking study introduces a novel approach employing Extreme Fine-Tuning (XFT) combined with Explainable Artificial Intelligence (XAI) for the accurate, non destructive prediction of concrete compressive strength. By analyzing a state-of-the-art dataset containing 18,480 data points, this study developed a deep neural network that, through extensive hyperparameter optimization, achieves unprecedented prediction accuracy of approximately 98.7%. The novelty of this research lies in the sophisticated integration of XFT and XAI techniques, which not only significantly enhances prediction accuracy but also provides insightful explanations of the model’s decision-making process, shedding light on the factors influencing concrete strength. This dual focus on accuracy and explainability represents a significant advancement in the application of Artificial Intelligence (AI) in material science and civil engineering, offering a powerful tool for researchers and practitioners.This study culminates in a model that outperforms existing methodologies in predicting concrete compressive strength, with an accuracy superior to 98.5% in both instances, testing and validation. By integrating XAI into this approach, we have also opened new avenues for understanding the complex relationships between concrete composition and its mechanical properties. This study marks a substantial step forward in the non-destructive evaluation of construction materials. It sets a new benchmark for transparency and interpretability in AI models within the engineering domain.

Towards a Reliable Design of Geopolymer Concrete for Green Landscapes: A Comparative Study of Tree-Based and Regression-Based Models

The design of geopolymer concrete must meet more stringent requirements for the landscape, so understanding and designing geopolymer concrete with a higher compressive strength challenging. In the performance prediction of geopolymer concrete compressive strength, machine learning models have the advantage of being more accurate and faster. However, only a single machine learning model is usually used at present, there are few applications of ensemble learning models, and model optimization processes is lacking. Therefore, this paper proposes to use the Firefly Algorithm (AF) as an optimization tool to perform hyperparameter tuning on Logistic Regression (LR), Multiple Logistic Regression (MLR), decision tree (DT), and Random Forest (RF) models. At the same time, the reliability and efficiency of four integrated learning models were analyzed. The model was used to analyze the influencing factors of geopolymer concrete and determine the strength of their influencing ability. According to the experimental data, the RF-AF model had the lowest RMSE value. The RMSE value of the training set and test set were 4.0364 and 8.7202, respectively. The R value of the training set and test set were 0.9774 and 0.8915, respectively. Therefore, compared with the other three models, RF-AF has a stronger generalization ability and higher prediction accuracy. In addition, the molar concentration of NaOH was the most important influencing factors, and its influence was far greater than the other possible factors including NaOH content. Therefore, it is necessary to pay more attention to NaOH molarity when designing geopolymer concrete.

Prediction of concrete compressive strength using deep neural networks based on hyperparameter optimization

Prediction of concrete compressive strength using deep neural networks based on hyperparameter optimization

Deep Learning based Concrete Compressive Strength Prediction with Modified Resilient Backpropagation Training

Deep Learning based Concrete Compressive Strength Prediction with Modified Resilient Backpropagation Training

Retracted: Prediction of Concrete Compressive Strength Based on the BP Neural Network Optimized by Random Forest and ISSA

Retracted: Prediction of Concrete Compressive Strength Based on the BP Neural Network Optimized by Random Forest and ISSA

Efficient compressive strength prediction of concrete incorporating recycled coarse aggregate using Newton’s boosted backpropagation neural network (NB-BPNN)

This study advances the field of concrete compressive strength prediction by introducing an innovative approach incorporating recycled coarse aggregates and the Newton's Boosted Backpropagation Neural Network (NB-BPNN) model. Initial statistical validation of the dataset ensures its integrity for subsequent model training. The dataset analysis reveals variations in the usage of essential ingredients, with cement playing a pivotal role and showing a substantial range of 338.57 kg/m³ . Robust data preprocessing, including outlier handling, proved critical, substantially enhancing machine learning model performance. Post-outlier treatment, XGBoost and MLP models improved, while the decision tree model's performance saw a minor decrease. The NB-BPNN model's effectiveness in predicting concrete compressive strength was a cornerstone of this research. It demonstrated a remarkable average R² score of 0.95, explaining about 95% of the variance in compressive strength. The RMSE and MAE values of 3.205 MPa and 2.349 MPa, respectively, validated its accuracy and practical utility. Moreover, comparative evaluations across different compressive strength ranges emphasized the NB-BPNN model's superiority. While other models yielded varying results, the NB-BPNN consistently performed well, highlighting its efficiency and unique advantages in this domain. Feature importance analysis, through Partial Dependence Analysis (PDA) and SHapley Additive exPlanations (SHAP), identified key variables influencing the NB-BPNN model. Variables such as Cement, GGBS, Binder, Superplasticizer, and Water/Binder were found to significantly impact compressive strength, aligning with domain knowledge. This study underscores the NB-BPNN model's potential to revolutionize concrete compressive strength prediction and encourages further research in optimizing concrete mix design and construction practices.

Application of deep learning in civil engineering: boosting algorithms for predicting strength of concrete

Machine learning (ML) has been applied in civil engineering to predict the compressive strength of concrete with high accuracy. In this paper, five boosting ensemble algorithms, i.e., XGBoost, AdaBoost, GBDT, LightGBM, and CatBoost, were used to predict the compressive strength of high-performance concrete (HPC). The models were evaluated using performance indicators such as R2, root mean square error (RMSE), and mean absolute error (MAE). The results showed that the CatBoost model had the highest accuracy with a R2 (0.970) and a RMSE (2.916). The prediction accuracy of the model was increased through hyperparameter optimization, which got a higher with a R2 (0.975) and a RMSE (2.863). Meanwhile, the SHapley Additive exPlanations (SHAP) method was used to explain the output results of the optimal model (CatBoost), which generated explainable insights that further revealed the complex relationship between the prediction model parameters. The results showed that AGE, W/B, and W/C had the most impact on high-performance concrete compressive strength (HPCCS) prediction, which was similar to the results of sensitivity analysis. This study provided a theoretical basis and technical guidance for developing the mix design of a new high-performance concrete (HPC) system. In the future, the interpretable results of the model output should be iteratively checked and validated in the actual laboratory in order to provide guidance for engineering practice.

Prediction of concrete residual compressive strength under elevated temperatures: Response surface methodology (RSM) approach

Exposure of concrete to elevated temperatures causes irreversible damage to the concrete structure and poses a serious threat to its service life. Due to the importance of concrete fire performance, extensive research has been conducted to investigate its behavior under different conditions of elevated temperatures. The properties of concrete are significantly affected by various factors, including heating temperatures, heating durations, and cooling methods. Among these factors, the residual compressive strength of concrete is considered the most crucial characteristic after exposure to elevated temperatures. This paper aims to develop mathematical models for analyzing and predicting the relative residual compressive strength of concrete at high temperatures. Three independent factors were identified in this study: heating temperatures, heating duration, and cooling method. Two groups of datasets on the relative residual compressive strength of concrete under elevated temperatures were reviewed and collected from previous studies, serving as the benchmark dataset and validation dataset, respectively. Response Surface Methodology (RSM) was employed to analyze the datasets. The results of various statistical parameters, such as the coefficient of determination, sum of squares, F-value, and P-value, indicate the significance of the predicted model for estimating concrete's relative residual compressive strength under elevated temperatures. The RSM analysis reveals that heating temperatures have the most significant effect on the relative residual compressive strength of concrete. In summary, the RSM model shows a strong correlation with the validation datasets, with a coefficient of determination (R2) of 0.7869.

Optimal boosting method of HPC concrete compressive and tensile strength prediction

AbstractThe evaluation of fly ash (FA) and micro‐silica (MS) effects on the mechanical properties of concrete at various ages prompts the search for efficient factors in forecasting the compressive strength (CS) and tensile strength (TS), which are extendable for future investigation as well as practical use. The main aim of this article is to overcome the complexity caused by nonlinearity, which is rooted in the relationship between input variables and outputs. Also, the nonlinearity is getting worse through the existence of admixtures. The Adaptive Boosting (ADA) approach was utilized in the related study as a hybrid model to provide an optimal relation between the ingredients and the mechanical strengths for evaluating the parameters most useful in predicting the CS and TS of high‐performance concrete. Furthermore, model boosting is applied using the starling murmuration optimization (SMO), termite queen algorithm (TQO), and gannet optimization algorithm (GOA) algorithms to reach the highest convergence. As a result of assessing the related models, the ADA‐SMO obtained a coefficient correlation of 0.9981 and 0.9825 for the predictions of CS and TS, respectively. Considering the adaptive framework provided to have the best compatibility with the complex physics of the problem, the ADA‐SMO earned the highest level of accuracy in estimating CS and TS among other hybrid models and was introduced as a trustable model in the prediction of strength properties.