Prediction Of Compressive Strength Research Articles

Prediction of masonry prism strength using machine learning technique: Effect of dimension and strength parameters

The compressive strength of masonry must be determined to design masonry constructions. Although the compressive strength of masonry mostly depends on the compressive strength of the block/brick and mortar, there are several factors, such as joint mortar thickness, prism height, and masonry unit aspect ratio, that also impact the strength of masonry. To predict the compressive strength of masonry prisms, the present study proposes different models of machine learning techniques, including linear regression, decision tree, ridge regression, random forest regression, artificial neural network, and XG Boost. Based on 540 experimental datasets collected from published literature, the performance of the models was compared and the best model was chosen for the forecast of compressive strength of masonry prism. Among the several machine learning models assessed in this work, the ANN model performed best for the prediction of the compressive strength of masonry prisms (R2 = 0.95, RMSE = 1.83 MPa). The sensitivity analysis of the ANN model shows that the compressive strength of the masonry prism is dominantly influenced by the strength of the masonry unit, followed by the thickness of the mortar, and least by the height-to-width ratio of the masonry prism. Therefore, the present study offers a methodical assessment of the compressive strength prediction of masonry prism, which may add to the knowledge base and practical application of this field.

Compressive strength prediction of high-strength concrete using hybrid machine learning approaches by incorporating SHAP analysis

Compressive strength prediction of high-strength concrete using hybrid machine learning approaches by incorporating SHAP analysis

Compressive strength prediction of hydrothermally solidified clay with different machine learning techniques

Hydrothermal solidification technology can transform waste clay into high-strength building materials with lower energy consumption. Few machine learning methods have been applied to strength prediction of hydrothermally solidified materials, whose mechanical properties differ from those of conventional cement-based materials by virtue of the production method. In this study, six machine learning methods, random forest (RF), gated recurrent unit (GRU), k-nearest neighbors (KNN), back-propagation neuron network (BPNN), extreme gradient boosting (XGBoost) and gaussian process regression (GPR), were used to develop the compressive strength prediction models of hydrothermally solidified clay. These methods can efficiently predict the compressive strength without time-consuming and costly tests and guide the optimization of the mix proportions, thus facilitating the application of hydrothermally solidified clay. For this purpose, 140 experimental data obtained from hydrothermally solidified clay with different Ca(OH)2 content, dry density, moisture content, curing time and curing temperature were used to proposed the prediction models. In the modeling process, these five effective variable parameters were considered as the modeling input parameters. The performance of these models was compared by correlation coefficient (R2), mean absolute error (MAE) and root mean square error (RMSE). The validity of these models was confirmed by the K-fold cross-validation method, and the influence weight of each input parameter on the prediction results was characterized by sensitivity analysis. The results demonstrated that the GPR model provided the best prediction with an R2 value of 0.989. In the sensitivity analysis, curing time had the greatest influence on the predicted results, followed by dry density, Ca(OH)2 content, curing temperature and water content. Comprehensively considering the cost, energy consumption and compressive strength, the applicable mix proportion of hydrothermally solidified clay includes Ca(OH)2 content (not less than 16.64%), moisture content (10%), curing temperature (not less than 87.35 °C), curing time (18 h) and dry density (1.9 g/cm3).

An artificial electric field algorithm-based neuro-fuzzy predictor for estimation of compressive strength of concrete structures: A machine learning approach

Machine learning based forecasting are found better to manual and statistical methods in estimating compressive strength of concrete structures. However, there is need of exploring an effective, automated and accurate predictor for this domain. This article proposes an artificial electric field algorithm-based neuro-fuzzy network (AEFA+NFN) for prediction of compressive strength of concrete structures. A single hidden layer neural network (SHNN) is used as the base model and its inputs are fuzzified using Gaussian triangular membership function with a degree of membership to different classes. The optimal number of input data, hidden neurons, bias and weights for hidden layer are decided by AEFA. The model is evaluated on samples from a publicly available dataset with curing ages at 3, 7, 14, and 28 days. Considering four sample series, the AEFA+NFN produced an average MAPE of 0.092073 and ARV of 0.139731 which are better compared to others. The experimental outcomes and analysis are in favor of the AEFA+NFN based forecasting.

Prediction and multi-objective optimization of workability and compressive strength of recycled self-consolidating mortar using Taguchi design method

Concrete is the most consumed material in the construction industry. Using recycled aggregates (RA) and silica fume (SF) in concrete and mortar could preserve natural aggregates (NA) and reduce CO2 emissions and construction and demolition waste (C&DW) generation. Optimizing the mixture design based on both fresh and hardened properties of recycled self-consolidating mortar (RSCM) has not been performed. In this study, multi-objective optimization of mechanical properties and workability of RSCM containing SF was performed via Taguchi Design Method (TDM) with four main variables including cement content, W/C ratio, SF content and superplasticizer content at three different levels. SF was used to decrease the environmental pollution caused by cement production as well as compensating the negative effect of RA on the mechanical properties of RSCM. The results revealed that TDM could appropriately predict the workability and compressive strength of RSCM. Also, mixture design containing W/C = 0.39, SF = 6%, cement = 750 kg/m3 and SP = 0.33% was found as the optimum mixture having the highest compressive strength and acceptable workability as well as low cost and environmental concerns.

Effect of inclusion of natural pozzolan and silica fume in cement - based mortars on the compressive strength utilizing artificial neural networks and support vector machine

This study illustrates the effect of incorporating natural pozzolan (NP) and silica fume (SF) in cement-based mortars on the compressive strength. Up to 40% of the weight of cement in cement mortars can be replaced by NP powder. NP in its natural state can be used to replace up to 40% of the volume of sand in cement mortars. The replacement levels of cement by weight with SF are 5% and 10% to improve the mechanical properties of the NP-based cement mortars. Experiments were conducted to assess the compressive strength of cement mortars containing NP at 28 and 180 days of curing. The water/cement ratio was 0.49 for all the specimens. Models for the compressive strength of NP-based cement mortars and NP-SF-based cement mortars were formulated based on nonlinear regression analysis, artificial neural network (ANN) , and support vector machine (SVM) approach to explore the impact and exchanges of cement, NP, and SF mixtures. The RMSE of the nonlinear, ANN, and SVM models revealed that the RMSE of the ANN is significantly less than that of the nonlinear and SVM models. A sensitivity analysis for the predicted compressive strength of NP-based cement mortars and NP-SF-based cement mortars was conducted. The application of the SVM technique was evaluated through four different kernels, including sigmoid, linear, polynomial, and radial basis function (RBF). The RBF kernel shows better performance and offers more accurate results than the sigmoid, linear, and polynomial kernels. The results showed that the NP replacing cement and the SF replacing cement are the most dominant parameters for the prediction of compressive strength. Finally, the developed models from this study can be used to predict the compressive strength of cement mortars containing NP and SF.

An Application of BP Neural Network to the Prediction of Compressive Strength in Circular Concrete Columns Confined with CFRP

The neural network comprises many neurons with extensive interconnections operating parallel and performing specific functions. This paper establishes a BP neural network prediction model for the compressive strength of CFRP-confined concrete based on a large number of experimental data to study the predictive ability of the BP neural network on the compressive strength of CFRP-confined concrete and the output performance of the neural network model. The model is based on a BP neural network that has been trained using many experimental data. An investigation is being conducted on the effect of different data combinations on the accuracy of the predictions made by the neural network model. The high-precision BP network model is created into generic and simplified formulae for application convenience. These formulas are developed based on the theory of neural networks. The neural network models' findings and the empirical formulae for making predictions are compared and discussed. The BP neural network accurately predicts the compressive strength of CFRP-confined concrete, with over 90% of its data points having less than 15% error. In comparison, the regression model shows less accuracy, with less than 70% of its data points having an error within 15%. Compared to traditional regression models, the simple linear equation derived using Purelin instead of Sigmoid as the transfer function only adds a constant term. The average value of prediction/test results is 1.011. The analysis results show that BP neural network can extract the input and output parameters' data information well and obtain a high-accuracy prediction model. The coefficient of variation is 0.112, which indicates that the prediction accuracy and stability are greater than average.

Accuracy Prediction of Compressive Strength of Concrete Incorporating Recycled Aggregate Using Ensemble Learning Algorithms: Multinational Dataset

The use of alternative materials and recycling in construction has gained popularity in recent years as part of the industry’s commitment to sustainability. One such material, recycled aggregates, has been extensively studied over the past two decades for its potential to replace natural aggregates in cement-based composites. However, the unique properties of recycled aggregates make traditional concrete mix design methods ineffective in determining their target compressive strength. To address this challenge, four machine learning models based on ensemble learning algorithms, including CatBoost regressor (CatBoost), light gradient-boosting machine regressor (LGBM), random forest regressor (RFR), and extreme gradient-boosting regressor (XGBoost), were employed to predict the compressive strength of recycled aggregate concrete. Results demonstrate that the proposed models are highly accurate and generalizable, with high coefficients of determination and low error predictions. The CatBoost model performed the best, exhibiting an R2 of 0.938 and low mean absolute error and root mean squared error values of 2.639 and 3.885, respectively, in the blind evaluation process. Although the random forest regression algorithm performed the least well among the four models, it still outperformed conventional machine learning algorithms such as support vector machines and artificial neural networks. The findings in this study suggested that the CatBoost model is the optimal choice for predicting concrete’s compressive strength due to its high accuracy and low prediction error.

Prediction of compressive strength of BFRC using soft computing techniques

Prediction of compressive strength of BFRC using soft computing techniques

A comparative study of machine learning algorithms for the prediction of compressive strength of rice husk ash-based concrete

A comparative study of machine learning algorithms for the prediction of compressive strength of rice husk ash-based concrete

Compressive strength prediction of carbon fiber-reinforced pultruded profiles including realistic volumetric fiber orientations

A compressive strength prediction method considering 3D fiber orientation distributions is developed. The method quantifies volumetric fiber orientations using the structure tensor image analysis method on X-ray computed micro-tomography data. The estimated fiber orientations are subsequently mapped to a finite element mesh. The finite element model is solved using a three-dimensional non-linear constitutive model implemented as a user-subroutine in AbaqusTM. A model capable of describing the elastic–plastic behavior of fibrous composites and simulating strain localization in the form of kink bands. The compressive strength of a carbon-epoxy pultruded profile is predicted based on three volumetric subsets of the profile and compared with experimental obtained compression test measurements. The strength predictions of the three volumetric subsets are in the range of 16%–42% higher than the experimental results. It is argued that the experimental results do not represent the full potential of the material compressive strength due to stress concentrations at the load introduction and that the model predictions can describe a potential for the actual compressive strength of the carbon-epoxy pultruded profile.

Compressive strength prediction of admixed HPC concrete by hybrid deep learning approaches

The estimation of compressive strength includes time-consuming, finance-wasting, and laboring approaches to undertaking High-performance concrete (HPC) production. On the other side, a vast volume of concrete consumption in industrial construction requires an optimal mix design with different percentages to reach the highest compressive strength. The present study considered two deep learning approaches to handle compressive strength prediction. The robustness of the deep model was put high through two novel optimization algorithms as a novelty in the research world that played their precise roles in charge of model structure optimization. Also, a dataset containing cement, silica fume, fly ash, the total aggregate amount, the coarse aggregate amount, superplasticizer, water, curing time, and high-performance concrete compressive strength was used to develop models. The results indicate that the AMLP-I and GMLP-I models served the highest prediction accuracy. R2 and RMSE of AMLP-I stood at 0.9895 and 1.7341, respectively, which declared that the AMLP-I model could be presented as the robust model for estimating compressive strength. Generally, using optimization algorithms to boost the capabilities of prediction models by tuning the internal characteristics has increased the reliability of artificial intelligent approaches to substitute the more experimental practices.

Prediction of compressive strength of geopolymer concrete by using ANN and GPR

Prediction of compressive strength of geopolymer concrete by using ANN and GPR

Compressive strength prediction of concrete blended with carbon nanotubes using gene expression programming and random forest: hyper-tuning and optimization

The strength of carbon nanotubes (CNTs) and cement composites is dependent on multiple variables. In addition, CNTs added to a cement-based matrix can boost its strength. However, the information related to CNTs characteristics is limited and scarce. Their incorporation may substantially enhance the mechanical and durability properties of cementitious mixtures. Despite challenges such as high cost and workability problems. Therefore, proper consumption of these materials must be used to attain desired qualities. The principal plan of this investigation is to create predictive framework by utilizing machine-learning algorithms. Gene expression programming (GEP), and the random forest algorithm (RFA) is employed to estimate the compressive strength of concrete mixed with CNTs. GEP is used as an individual approach, and RFA is used as an ensemble method to depict the most influential model. The outcomes of the two models are assessed by employing external K-fold cross-validation, and a comparison is done. A comprehensive database is established comprising 282 data points for the CS with blended CNTs. The model is then calibrated using six inputs, including curing time (CT), water-to-cement ratio (W/C), fine aggregate (FA), carbon nanotube content (CNTs), cement content (CC), and coarse aggregate (CA). In addition, the predicted results are validated using k-fold cross-validation, and performance metrics, such as mean absolute error (MAE), root squared error (RSE), correlation coefficient (R2), root mean square error (RMSE), and relative root mean square error (RRMSE). The result shows that RF regression with the nth estimator shows robust accuracy by showing minimal errors as analyzed to individual RF and GEP models. Likewise, the nth model depicts higher R2 = 0.96, and validation results demonstrate low errors. Moreover, the GEP model excels in terms of prediction through the empirical equation. In addition, Shapley analysis (SHAP) is performed to check the distribution of parameters to output. The result reveals that curing time, cement, and water to binder have substantial influence of CNT based concrete composite.

Prediction of compressive strengths of pumice-and diatomite-containing cement mortars with artificial intelligence-based applications

In this study, two different Artificial neural networks (ANN) and two different adaptive network-based fuzzy inference systems (ANFIS) models were constructed to predict the compressive strength of 7 different cement mortar samples with or without pumice and/or diatomite on different days. Five parameters including day, PC, pumice, diatomite and water were employed as the inputs, and the compressive strength was used as the output variable. The compressive strengths used in the model construction were obtained from laboratory experiments accounting for a total of 168 data. Statistical methods such as R2, RMS and MAPE preferred in the literature were used to compare the four different models. According to the test results obtained from R2, RMS and MAPE, ANN and ANFIS models were able to make very good predictions performance. For this reason, it can be said that these cement mortars' compressive strength can be estimated with a very small error and in a short time with both ANN and ANFIS models.

Optimizing compressive strength prediction models for rice husk ash concrete with evolutionary machine intelligence techniques

This research intended to increase the understanding of using modern machine intelligence techniques, including multi-expression programming (MEP) and gene expression programming (GEP), for the compressive strength (CS) prediction of rice husk ash (RHA) concrete. In addition, SHapley Additive exExplanations (SHAP) analysis was made to study the impact and interaction of raw materials on the CS of RHA concrete. A comprehensive database of 192 points with six inputs (cement, specimen age, RHA, superplasticizer, water, and fine aggregate) was used for developing prediction models. This research determined that both GEP and MEP models for the CS prediction of RHA concrete yielded reliable results, which were in close agreement with the real CS. Comparing the performance of both GEP and MEP models, it was noted that MEP, with an R2 of 0.89, outperformed the GEP model having an R2 of 0.83. Additionally, SHAP analysis indicated that specimen age was the most vital measure, followed by cement, which positively correlated with CS of RHA. The overall effect of RHA was found to be more positive, suggesting RHA utilization in the optimal range of 75–100 kg/m3 in the RHA concrete mix. The use of prediction models and SHAP analysis will help the building industry assess material properties and raw material effects faster and more economical.

Prediction of compressive strength of geopolymer concrete using random forest machine and deep learning

Prediction of compressive strength of geopolymer concrete using random forest machine and deep learning

Axial compressive behaviour of cold-formed steel single-stud wall panels with one-sided sheathing and two-sided dissimilar sheathing board configurations: Experimental and analytical study

For practical reasons, it is typical for different types of sheathings (referred to as dissimilar) to be connected at both flanges of the stud (lipped channel-section) in cold-formed steel (CFS) panelized construction (e.g. one side has interior sheathing and the other exterior sheathing). However, limited studies are available on the axial behaviour of CFS studs with different combinations of sheathings present at both-flanges of a stud. This novel experimental study investigates the structural behaviour of nineteen (19) CFS panels with sheathing on one side only and different sheathing on both flanges of the wall panel using combinations of eight (8) different sheathing board types [fibre-cement boards (FCB) (8 mm/10 mm/12 mm thick); calcium silicate boards (12 mm thick); orient strand board (12.5 mm thick); and heavy duty FCB (6 mm/9 mm/12 mm thick)]. Also, first time an attempt is made to study the effect of dissimilar configuration using eight (8) different sheathing board types on axial strength of CFS panels in India. Experimentally tested single stud wall panel specimens consist of; one bare single-stud; eight (8) specimens sheathed with different types of sheathing boards on one flange only; and ten (10) specimens with combinations of different sheathing boards on both-flanges. Average test results are obtained by performing two (02) tests for each specimen. The strength enhancement of a bare single-stud from the sheathing boards has been investigated by testing specimens with a single layer of sheathing at one-flange and combinations of different sheathing boards on both-flanges. The ultimate axial compressive strength, load–deformation behaviour and failure patterns are the parameters compared. A new analytical and semi-analytical design methodologies namely; Rayleigh–Ritz (R-R) method and finite-strip method based on direct strength method (DSM) are adopted for evaluating the axial compressive strength of the CFS wall panels with dissimilar sheathing. Limited studies are available to verify the efficacy of R-R method and DSM to estimate the axial compressive strength of CFS wall panels with dissimilar sheathing on both flanges. Results establish that DSM may be used efficiently rather than the R-R method for axial compressive strength prediction of dissimilar sheathed CFS wall panels. The predicted values show good agreement with the experimental test results, and therefore, demonstrate that the semi-analytical method may be applied efficiently in design.

Compressive Strength Prediction of Rice Husk Ash Concrete Using a Hybrid Artificial Neural Network Model.

The combination of rice husk ash and common concrete both reduces carbon dioxide emission and solves the problem of agricultural waste disposal. However, the measurement of the compressive strength of rice husk ash concrete has become a new challenge. This paper proposes a novel hybrid artificial neural network model, optimized using a reptile search algorithm with circle mapping, to predict the compressive strength of RHA concrete. A total of 192 concrete data with 6 input parameters (age, cement, rice husk ash, super plasticizer, aggregate, and water) were utilized to train proposed model and compare its predictive performance with that of five other models. Four statistical indices were adopted to evaluate the predictive performance of all the developed models. The performance evaluation indicates that the proposed hybrid artificial neural network model achieved the most satisfactory prediction accuracy regarding R2 (0.9709), VAF (97.0911%), RMSE (3.4489), and MAE (2.6451). The proposed model also had better predictive accuracy than that of previously developed models on the same data. The sensitivity results show that age is the most important parameter for predicting the compressive strength of RHA concrete.

Assessment of compressive strength of jet grouting by machine learning

Jet grouting is one of the most popular soil improvement techniques, but its design usually involves great uncertainties that can lead to economic cost overruns in construction projects. The high dispersion in the properties of the improved material leads to designers assuming a conservative, arbitrary and unjustified strength, which is even sometimes subjected to the results of the test fields. The present paper presents an approach for prediction of the uniaxial compressive strength (UCS) of jet grouting columns based on the analysis of several machine learning algorithms on a database of 854 results mainly collected from different research papers. The selected machine learning model (extremely randomized trees) relates the soil type and various parameters of the technique to the value of the compressive strength. Despite the complex mechanism that surrounds the jet grouting process, evidenced by the high dispersion and low correlation of the variables studied, the trained model allows to optimally predict the values of compressive strength with a significant improvement with respect to the existing works. Consequently, this work proposes for the first time a reliable and easily applicable approach for estimation of the compressive strength of jet grouting columns.