Prediction Of Concrete Compressive Strength Research Articles

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.

Sustainable of rice husk ash concrete compressive strength prediction utilizing artificial intelligence techniques

Sustainable of rice husk ash concrete compressive strength prediction utilizing artificial intelligence techniques

T2RFIS: type-2 regression-based fuzzy inference system

This article discusses a novel type-2 fuzzy inference system with multiple variables in which no fuzzy rules are explicitly defined. By using a rule-free system, we avoid the serious disadvantage of rule-based systems, which are burdened with the curse of dimensionality. In the proposed system, Gaussian membership functions are used for its inputs, and linearly parameterized system functions are used to obtain its output. To obtain the system parameters, a genetic algorithm with multi-objective function is applied. In the presented method, the genetic algorithm is combined with a feature selection method and a regularized ridge regression. The objective functions consist of a pair in which one function is defined as the number of active features and the other as the validation error for regression models or the accuracy for classification models. In this way, the models are selected from the Pareto front considering some compromise between their quality and simplification. Compared to the author’s previous work on the regression-based fuzzy inference system, a new inference scheme with type-2 fuzzy sets has been proposed, and the quality has been improved compared to the system based on type-1 fuzzy sets. Four experiments involving the approximation of a function, the prediction of fuel consumption, the classification of breast tissue, and the prediction of concrete compressive strength confirmed the efficacy of the presented method.

Correction: Prediction of high-performance concrete compressive strength using deep learning techniques

Correction: Prediction of high-performance concrete compressive strength using deep learning techniques

Deep learning based concrete compressive strength prediction model with hybrid meta-heuristic approach

In concrete design, the compressive strength of the concrete is the critical parameter that defines the quality of concrete. Determination of the compressive strength of concrete by laboratory testing is the conventional way, however, with the help of advancements in technology, the development of an exact predicting technique for the compressive strength of concrete will reduce time as well as cost to a greater extent. There are various types of concrete, as concrete is the most used material and has wide applications in the construction sector and hence huge article is being done to increase the performance of concrete. In this paper, the compressive strength of High-Performance Concrete and Fiber Reinforced High Strength Self-Compacting Concrete is predicted by next three main stages, namely (a) pre-processing, (b) feature extraction, and (c) prediction of concrete compressive strength. Initially, in the data pre-processing phase, duplicate data samples are removed. Subsequently, various features like statistical features, symmetrical uncertainty-based features, higher order statistical features, ReliefF feature, and are derived from the pre-processed data. In the prediction phase of concrete compressive strength, a hybrid algorithm is designed by mingling the concepts of Deep Belief Network and Long Short-Term Memory. Moreover, The Rock Hyraxes Updated Sparrow Algorithm is used to fine-tune the weights of DBN to improve the prediction accuracy of the suggested work. The suggested hybrid optimization method is the hybridization of the standard Sparrow Search Algorithm and Rock Hyraxes Swarm Optimization, respectively. Eventually, the output from the optimized hybrid method will reveal the compression energy of concrete. Ultimately, the efficiency of the suggested method is verified by the comparative estimation. The MAPE of the suggested method is 235.32, which is 30.6%, 21.64%, 20.4%, and 11.4% superior than the CLPSO, ABC, SSA + Hybrid Classifier and RHSO + Hybrid Classifier, respectively.

Prediction of high-performance concrete compressive strength through novel structured neural network

The difficulties in determining the compressive strength of concrete are inherited due to the various nonlinearities rooted in the mix designs. These difficulties raise dramatically considering the modern mix designs of high-performance concrete. Presents study tries to define a simple approach to link the input ingredients of concrete with the resulted compressive with a high accuracy rate and overcome the existing nonlinearity. For this purpose, the radial base function is defined to carry out the modeling process. The optimal results were obtained by determining the optimal structure of radial base function neural networks. This task was handled well with two precise optimization algorithms, namely Henry’s gas solubility algorithm and particle swarm optimization algorithm. The results defined both models’ best performance earned in the training section. Considering the root mean square error values, the best value stood at 2.5629 for the radial base neural network optimized by Henry’s gas solubility algorithm, whereas the same value for the the radial base neural network optimized by particle swarm optimization was 2.6583 although both hybrid models provided acceptable output results, the radial base neural network optimized by Henry’s gas solubility algorithm showed higher accuracy in predicting high performance concrete compressive strength.

Research on a concrete compressive strength prediction method based on the random forest and LCSSA-improved BP neural network

The compressive strength of high-performance concrete (HPC) determines the safety of the structural engineering in modern construction projects. The compressive strength of high-performance concrete (HPC) is a highly nonlinear function of its components. To better predict the compressive strength of HPC, the initial population of the Sparrow Search Algorithm was improved based on Logistic Chaos Mapping and Nonlinear Decreasing Inertia Weight Method, and the initial weights and thresholds of the BP neural network are optimized using this algorithm to establish the RF-LCSSA-BP model for predicting the mechanical properties of HPC. Finally, the RF-LCSSA-BP model, classic algorithms, and improved SSA were used to predict the compressive strength of HPC under the influence of six factors, and the prediction results are compared and analyzed. The results show that the RF-LCSSA-BP model can predict the compressive strength of HPC better and has more advantages than the traditional methods in terms of goodness of fit and prediction accuracy. Its training error and prediction error are less than 5%, and its R2 value is close to 1. It can significantly reduce the test requirements and time costs and has important engineering significance for predicting the strength of concrete and concrete mix design.

Prediction of high-performance concrete compressive strength using deep learning techniques

Prediction of high-performance concrete compressive strength using deep learning techniques

High strength concrete compressive strength prediction using an evolutionary computational intelligence algorithm

High strength concrete compressive strength prediction using an evolutionary computational intelligence algorithm

Estimating Compressive Strength of Concrete Using Neural Electromagnetic Field Optimization.

Concrete compressive strength (CCS) is among the most important mechanical characteristics of this widely used material. This study develops a novel integrative method for efficient prediction of CCS. The suggested method is an artificial neural network (ANN) favorably tuned by electromagnetic field optimization (EFO). The EFO simulates a physics-based strategy, which in this work is employed to find the best contribution of the concrete parameters (i.e., cement (C), blast furnace slag (SBF), fly ash (FA1), water (W), superplasticizer (SP), coarse aggregate (AC), fine aggregate (FA2), and the age of testing (AT)) to the CCS. The same effort is carried out by three benchmark optimizers, namely the water cycle algorithm (WCA), sine cosine algorithm (SCA), and cuttlefish optimization algorithm (CFOA) to be compared with the EFO. The results show that hybridizing the ANN using the mentioned algorithms led to reliable approaches for predicting the CCS. However, comparative analysis indicates that there are appreciable distinctions between the prediction capacity of the ANNs created by the EFO and WCA vs. the SCA and CFOA. For example, the mean absolute error calculated for the testing phase of the ANN-WCA, ANN-SCA, ANN-CFOA, and ANN-EFO was 5.8363, 7.8248, 7.6538, and 5.6236, respectively. Moreover, the EFO was considerably faster than the other strategies. In short, the ANN-EFO is a highly efficient hybrid model, and can be recommended for the early prediction of the CCS. A user-friendly explainable and explicit predictive formula is also derived for the convenient estimation of the CCS.