Prediction Of Compressive Strength Research Articles

Compressive strength prediction of Portland cement clinker using machine learning by XRD data

Accurately predicting the 28-day compressive strength of Portland cement clinkers is challenging due to their complex multiphase nature, especially when production processes and environmental conditions vary. To address this issue, the TransXRD model was introduced, which is a novel deep learning approach that combines the Transformer architecture with CNN design. 214 clinker samples were collected from 6 cement plants. The diffraction peak sequence of samples ranging from 10° to 55° was used as required input for the model. A combination of one-dimensional convolution is incorporated to extract local features, and self-attention mechanisms are leveraged to perform global feature integration. XRD data were directly utilized, bypassing additional processing to minimize signal loss. Compared to other DNN (deep neural network) models, the TransXRD model demonstrates promising performance, achieving a root mean square error (RMSE) of 1.15, a mean squared error (MSE) of 1.33, a mean absolute error (MAE) of 0.93, and a mean absolute percentage error (MAPE) of 1.76 on the test set. Notably, in the training set, 100% of the data exhibit prediction errors within 2 MPa, while within the test set, 90% of the prediction errors are within 2 MPa. The application potential of XRD data in predicting the compressive strength of cement clinker also offers novel insights into predicting the performance of other materials.

Utilizing ANFIS for strength characteristics forecasting in variable heat-cured geopolymer composites

Reducing cement usage through geopolymer concrete (GPC) can be crucial for resolving environmental concerns. The byproduct of the coal and steel industry, such as fly ash (FA) and ground granulated blast furnace slag (GGBS), is mainly used as a precursor in the production of GPC due to the availability of aluminosilicate products in it. In this work, nano silica is added to the GPC in different percentages and GPC is cured at different temperatures to study the change in the strength of GPC due to change in curing temperature. The compressive strength, flexural strength, and split tensile strength up to 68.99 MPa, 5.89 MPa, 6.82 MPa, respectively was achieved after 28 days of heat curing. However, testing GPC experimentally to anticipate its strength is time-consuming and expensive. Therefore, the ability to precisely forecast concrete strength can be achieved through machine learning. In this study, the correlation for both nano silica (%) and curing temperature towards the strength of concrete is also investigated using adaptive neuro fuzzy interference system (ANFIS) models. ANFIS results showed better compressive, split tensile, and flexural strength prediction with the highest R2 0.9801, 0.9943, and 0.9877, respectively. The performance efficiency of the developed ANFIS model is more significant minimum error for compressive strength prediction (MAE = 0.21375, RMSE = 0.02574, MAPE = 0.42), split tensile strength prediction (MAE = 0.07375, RMSE = 0.0923, MAPE = 1.46) and flexural strength prediction (MAE = 0.04, RMSE = 0.0728, MAPE = 0.78) of GPC cured under different temperature conditions. The results from experiments show that the prediction efficiency of the model is also good, as indicated by the correlation coefficient (R). This model is helpful for further designing the proportion of material for achieving good strength of GPC.

Prediction of compressive strength of glass powder concrete based on artificial intelligence

The investigation of glass powder concrete (GPC) is motivated by the imperative to alleviate the pronounced environmental impact associated with conventional concrete construction and untreated waste glass. Compressive strength (CS) is a crucial criterion for assessing concrete quality. Traditionally, determining compressive strength relies on resource-intensive compression tests. This study addresses these challenges by evaluating GPC's compressive strength using fundamental and hybrid machine learning models. The study employed four machine learning models: Backpropagation Neural Network (BPNN), Extreme Gradient Boosting (XGB), Support Vector Regression (SVR), and Random Forest Regression (RFR). Additionally, four hybrid models (SSA-BPNN, SSA-XGB, SSA-SVR, SSA-RFR) were devised using the Sparrow Search Algorithm (SSA) to forecast compressive strength. Input variables included cement content, moisture content, sand content, coarse aggregate content, glass powder (GP) content, and curing age. The study reveals that optimized models generally outperform baseline models in predicting accuracy. Specifically, SSA-XGB demonstrated the highest accuracy, with R2 = 0.9645 and RMSE = 3.0640 MPa. Feature importance analysis highlights cement content and curing age as the most influential variables affecting GPC. Two-dimensional partial dependence plots (PDP) elucidate the interrelationship between GP, curing age, and cement content, confirming that, due to the volcanic ash effect and increased alkalinity, GP makes a more substantial contribution to strength. In summary, this study provides systematic recommendations for forecasting GPC, contributing significantly to both literature and practical applications. And the database containing 1045 samples will be disclosed in Appendix Afor researchers to use.

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.

Comprehensive analysis of machine learning models for predicting concrete compressive strength

Predicting the compressive strength of concrete provides several benefits including insights into its durability, potential lifespan, and its suitability for specific construction projects. Recent advances in machine learning algorithms have prompted their application to compressive strength modeling and prediction. In this paper, we carry out a comprehensive comparative analysis of the existing machine learning algorithms. Unlike, other current studies in this domain, we consider the entire spectrum of algorithms ranging from basic linear models to advanced meta-learning methods. The results show that there exists a trade-off between speed and accuracy of the models. We find that while ensemble models such as stacking and gradient boost provide superior accuracy, they are significantly slower than linear models.

Adaptive residual convolutional neural network for compressive strength prediction of energetic materials using SEM images

The microstructure of materials has a great influence on the macroscopic mechanical properties of materials, and the relationship between them is of great significance to the design of materials. With the development of artificial intelligence and deep learning, many researchers have used convolutional neural networks (CNN) to correlate microscopic images with material properties, and achieved good results. However, for different types of material performance prediction tasks and datasets of different sizes, researchers need to design a special CNN network structure, and the adjustment steps of network structure parameters are cumbersome and time consuming. In this paper, an adaptive residual convolutional neural network with variable depth and width is proposed. We take the prediction error of the test dataset of the specific material performance prediction task as the evaluation index, and adaptively adjusts the network structure based on simulated annealing algorithm to find the best network structure. Finally, it is verified on the dataset composed of scanning electron microscopy (SEM) images and compressive strength of polymer explosive materials. Mean absolute percentage error (MAPE) of our model is 1.3% and the goodness of fit (R2 ) is 0.943. Further, we use Grad-CAM to explore the correlation between material microstructure and material properties.

Integrated approach to assessing strength in slag-based geopolymer mortars: experimental study and modeling with advanced techniques

The study consists of two main parts. In the initial phase, a variety of slag-based geopolymer mortars with different activator concentrations were prepared. These mortars underwent curing in both water and air environments for periods of 3, 7, 28, and 90 days, after which their compressive strength was evaluated at the conclusion of each curing interval. The second phase of the study is dedicated to the development of innovative models for estimating the compressive strength based on the data gathered. To achieve this, a range of techniques including multi-gene genetic programming (MGGP), artificial neural networks (ANN), XGBoost, SVM-Gauss, long short-term memory (LSTM), and convolutional neural networks (CNN) were employed to formulate a model capable of estimating compressive strength accurately. The study made use of various performance evaluation metrics such as mean squared error (MSE), root mean squared error (RMSE), R-squared, mean absolute error (MAE), and scatter index (SI) to assess the precision of the MGGP method in evaluating slag-based geopolymer mortars under both water and air curing conditions. The findings indicate that the equations generated by the MGGP method exhibit a high level of precision when juxtaposed with experimental outcomes. This research endeavors to enhance the prediction of compressive strength in geopolymer mortars, a subject that has garnered significant interest in scholarly literature.

Sustainable Construction Analytics: Smart Modeling for Compressive Strength Prediction in Arabic Gum-Modified Mortar

Sustainable Construction Analytics: Smart Modeling for Compressive Strength Prediction in Arabic Gum-Modified Mortar

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.

Forecasting the strength of preplaced aggregate concrete using interpretable machine learning approaches

Preplaced aggregate concrete (PAC) also known as two-stage concrete (TSC) is widely used in construction engineering for various applications. To produce PAC, a mixture of Portland cement, sand, and admixtures is injected into a mold subsequent to the deposition of coarse aggregate. This process complicates the prediction of compressive strength (CS), demanding thorough investigation. Consequently, the emphasis of this study is on enhancing the comprehension of PAC compressive strength using machine learning models. Thirteen models are evaluated with 261 data points and eleven input variables. The result depicts that xgboost demonstrates exceptional accuracy with a correlation coefficient of 0.9791 and a normalized coefficient of determination (R2) of 0.9583. Moreover, Gradient boosting (GB) and Cat boost (CB) also perform well due to its robust performance. In addition, Adaboost, Voting regressor, and Random forest yield precise predictions with low mean absolute error (MAE) and root mean square error (RMSE) values. The sensitivity analysis (SA) reveals the significant impact of key input parameters on overall model sensitivity. Notably, gravel takes the lead with a substantial 44.7% contribution, followed by sand at 19.5%, cement at 15.6%, and Fly ash and GGBS at 5.9% and 5.1%, respectively. The best fit model i.e., XG-Boost model, was employed for SHAP analysis to assess the relative importance of contributing attributes and optimize input variables. The SHAP analysis unveiled the water-to-binder (W/B) ratio, superplasticizer, and gravel as the most significant factors influencing the CS of PAC. Furthermore, graphical user interface (GUI) have been developed for practical applications in predicting concrete strength. This simplifies the process and offers a valuable tool for leveraging the model's potential in the field of civil engineering. This comprehensive evaluation provides valuable insights to researchers and practitioners, empowering them to make informed choices in predicting PAC compressive strength in construction projects. By enhancing the reliability and applicability of predictive models, this study contributes to the field of preplaced aggregate concrete strength prediction.

Prediction of Unconfined Compressive Strength of Stabilized Sand Using Machine Learning Methods

Prediction of Unconfined Compressive Strength of Stabilized Sand Using Machine Learning Methods

A comparative study of ensemble machine learning models for compressive strength prediction in recycled aggregate concrete and parametric analysis

A comparative study of ensemble machine learning models for compressive strength prediction in recycled aggregate concrete and parametric analysis

Application of metaheuristic algorithms for compressive strength prediction of steel fiber reinforced concrete exposed to high temperatures

The demand for steel fiber-reinforced concrete (SFRC) in construction has surged, particularly due to its enhanced fire resistance, leading to extensive research on its residual properties after elevated temperature exposure. However, conducting experimental tests can be a time-consuming process, demanding significant resources in terms of time, cost, and human resources. In contrast, machine learning (ML) models can rapidly simulate outcomes, enabling researchers to explore a wide range of scenarios efficiently. The previous literature studies used either ensemble or individual models; however, this study made a unique approach to utilize hybrid models, which are more precise compared to the other types of ML models. Accordingly, a data-rich framework containing 304 data samples was utilized in this study to develop an efficient and robust predictive model for the compressive strength (CS) of SFRC at higher temperatures. Support vector regression (SVR) in combination with three distinct optimization algorithms, specifically, particle swarm optimization (PSO), the firefly algorithm (FFA), and grey wolf optimization (GWO) were utilized to develop the hybrid models. In addition, typical ML techniques such as random forest (RF) and decision tree (DT) were used for comparative analysis. Among the five models, the SVR-FFA hybrid model showcased superior predictive accuracy, with the SVR-GWO model following closely. The correlation coefficients (R) for both models exceeded 0.99. The SVR-FFA model demonstrated relative root mean square error (RMSE) and mean absolute error (MAE) values of 0.0375 and 0.0248, respectively, while the SVR-GWO model exhibited corresponding values of 6.0560 and 5.1326. The SHapley Additive exPlanation (SHAP) technique revealed that the influence of temperature is more significant compared to the heating rate. Moreover, a considerable enhancement was observed in CS as the steel fiber volume fraction (Vf) reached 1.5%; on the other hand, no further improvement was noticed when Vf exceeded the 1.5% threshold. The proposed hybrid models are robust and accurate methods to estimate the CS of SFRC in field applications.

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.

Impact of thermal loads on silica fume-modified lightweight concrete: Machine learning approach to assess compressive strength evolution

Fire resistance of concrete is complex to comprehend as it is a composite material with ingredients of varied thermal properties. As the behaviour of concrete subjected to thermal loads is complex and required exhaustive experimentation, the use of soft computing techniques can be beneficial for predicting the residual compressive strength of concrete. Leveraging the power of machine learning (ML), specifically Artificial Neural Network (ANN), Random Forest (RF), and AdaBoost (AdB) models, this study aims to overcome the limitations of conventional prediction methods. Models were developed for the prediction of residual compressive strength of light weight concrete with silica fume (SF) exposed to thermal loads. Cement, silica fume, lightweight pumice aggregate, water-to-cement ratio, superplasticizer, and temperature were taken as input attributes while residual compressive strength was taken as output. Firstly, the data were subjected to extensive statistical analysis before the models were created. Additionally, hyperparameter tuning using grid search was done to increase the models' efficiency. The coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE) as model evaluation parameters were used to evaluate the performance of various developed ML models. When the performance of models was compared, it was found that the AdaBoost (AdB) model outperformed all other ML models. Hence, this study highlights the significance of ML techniques in addressing complex engineering challenges and underscore the practical implications for optimizing structural design and performance.

Comparative Analysis of Ensemble Learning Techniques and ANN for Prediction of Compressive Strength of Concrete Mix

Abstract: In this study, non-linear machine learning regressors and an artificial neural network (ANN) were modeled to predict the compressive strength of concrete mixes. A dataset of 1030 mix design data points with a wide range of compressive strength varying from M10 to M80 is taken into account. The data set comprised of 8 input parameters namely- cement, water, fine & coarse aggregate, flyash, slag, superplasticizer, and age. The paper also analyzes effects on the prediction of compressive strength due to waiving of the parameters with lesser importance factors. To improve the performances of models by reducing the variance, biases, and errors, K-fold cross-validation was examined. To examine the performances of the models, metrics such as coefficient of determination (R2) and mean square error (MSE) were employed. Most of the models can predict the compressive strength with good accuracy. Out of the 7-machine learning regressors and ANN models employed, Gradient Boosting Regressor yielded the best prediction accuracy of 0.9561 with the lowest MSE value of 0.0513.

Mechanistic model for the compression strength prediction of masonry columns strengthened with fibre–polymer composites

A mechanistic model is presented for the strength prediction of squared columns made of masonry with a periodic arrangement and strengthened with a fibre–polymer composite jacketing. The formulation is based on an incremental plasticity theory that relies on equilibrium, compatibility, and kinematic equations. The strength domain of brick units and mortar joints is bounded by a multi-surface yield criterion: a Mohr–Coulomb strength domain with a linear cap in compression and a Rankine cut-off in tension. An elasto-plastic response with limited ductility is assumed for both masonry components. Differently, the FRP response is assumed elastic with a brittle failure governed by a limited tensile strain. Phenomenological-based assumptions are undertaken and justified. Details are also provided for the computational implementation of the procedure. The model accuracy is validated against experimental data on masonry squared columns and compared with existing standard-based formulas. Results demonstrate it provides real-time and accurate compressive strength solutions for squared masonry columns with or without a polymer-based wrapping and yet requiring few input parameters for the masonry constituents and reinforcement.

Comprehensive study on the Python-based regression machine learning models for prediction of uniaxial compressive strength using multiple parameters in Charnockite rocks

The strength of rock under uniaxial compression, commonly known as Uniaxial Compressive Strength (UCS), plays a crucial role in various geomechanical applications such as designing foundations, mining projects, slopes in rocks, tunnel construction, and rock characterization. However, sampling and preparation can become challenging in some rocks, making it difficult to determine the UCS of the rocks directly. Therefore, indirect approaches are widely used for estimating UCS. This study presents two Machine Learning Models, Simple Linear Regression and Step-wise Regression, implemented in Python to calculate the UCS of Charnockite rocks. The models consider Ultrasonic Pulse Velocity (UPV), Schmidt Hammer Rebound Number (N), Brazilian Tensile Strength (BTS), and Point Load Index (PLI) as factors for forecasting the UCS of Charnockite samples. Three regression metrics, including Coefficient of Regression (R2), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE), were used to evaluate and compare the performance of the models. The results indicate a high predictive capability of both models. Notably, the Step-wise model achieved a testing R2 of 0.99 and a training R2 of 0.988 for predicting Charnockite strength, making it the most accurate model. The analysis of the influential factors indicates that UPV plays a significant role in predicting the UCS of Charnockite.

Multi-objective optimization design of recycled aggregate concrete mixture proportions based on machine learning and NSGA-II algorithm

This paper employs Support Vector Regression (SVR), Random Forest Regression (RF), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB) algorithms to establish the compressive strength prediction models for Recycled Aggregate Concrete (RAC) and analyze the influence of ten inputs on RAC compressive strength. Combined with the best prediction model, the Non-dominated Sorting Genetic Algorithm Ⅱ (NSGA-II) is applied for multi-objective optimization of mixture proportions in RAC addressing cost, carbon emissions, and compressive strength as key objectives. The results demonstrate that the RAC compressive strength prediction model using the GB algorithm exhibits the highest accuracy, with the R2 value of 0.99 for the training set and 0.85 for the testing set. Feature importance and SHAP analysis reveal that cement and water contents are the primary factors affecting the compressive strength of RAC. Among the three mineral admixtures including Silica Fume (SF), Fly Ash (FA), and Ground Granulated Blast Furnace Slag (GGBFS), SF exhibits superior improvement in the compressive strength of RAC compared to FA and GGBFS. Partial dependency plot analysis indicates that concrete strength remains unaffected by recycled coarse aggregate content within the range of 0 to 300 kg/m3. Pareto fronts of a tri-objective mixture optimization problem for RAC are successfully obtained through the GB model combined with the NSGA-II algorithm, which can guide the optimization of RAC preparation.