Prediction Of Concrete Compressive Strength Research Articles

Environmentally Friendly Concrete Compressive Strength Prediction Using Hybrid Machine Learning

In order to reduce the adverse effects of concrete on the environment, options for eco-friendly and green concretes are required. For example, geopolymers can be an economically and environmentally sustainable alternative to portland cement. This is accomplished through the utilization of alumina-silicate waste materials as a cementitious binder. These geopolymers are synthesized by activating alumina-silicate minerals with alkali. This paper employs a three-step machine learning (ML) approach in order to estimate the compressive strength of geopolymer concrete. The ML methods include CatBoost regressors, extra trees regressors, and gradient boosting regressors. In addition to the 84 experiments in the literature, 63 geopolymer concretes were constructed and tested. Using Python language programming, machine learning models were built from 147 green concrete samples and four variables. Three of these models were combined using a blending technique. Model performance was evaluated using several metric indices. Both the individual and the hybrid models can predict the compressive strength of geopolymer concrete with high accuracy. However, the hybrid model is claimed to be able to improve the prediction accuracy by 13%.

Automatic Modeling for Concrete Compressive Strength Prediction Using Auto-Sklearn

Machine learning is widely used for predicting the compressive strength of concrete. However, the machine learning modeling process relies on expert experience. Automated machine learning (AutoML) aims to automatically select optimal data preprocessing methods, feature preprocessing methods, machine learning algorithms, and hyperparameters according to the datasets used, to obtain high-precision prediction models. However, the effectiveness of modeling concrete compressive strength using AutoML has not been verified. This study attempts to fill the above research gap. We construct a database comprising four different types of concrete datasets and compare one AutoML algorithm (Auto-Sklearn) against five ML algorithms. The results show that Auto-Sklearn can automatically build an accurate concrete compressive strength prediction model without relying on expert experience. In addition, Auto-Sklearn achieves the highest accuracy for all four datasets, with an average R2 of 0.953; the average R2 values of the ML models with tuned hyperparameters range from 0.909 to 0.943. This study verifies for the first time the feasibility of AutoML for concrete compressive strength prediction, to allow concrete engineers to easily build accurate concrete compressive strength prediction models without relying on a large amount of ML modeling experience.

Study of the prediction of the mechanical strength of concrete based on the fractal model of granular distribution

• Paradigm based on the fractal model to study concrete, • Granular fractal-identification model, • Articulation with the excess paste theory, • Porosity of granular mixes concrete, • Specific surface of fines, including cement. Studies revealed that the grain size of concretes have a quasi-perfect fractal distribution of the total grain size. This enabled to identify the basic granular mixture and determine the porosity, based on the granular fractal-identification modulus “GFIM”, comprising the fractal dimension FD of the aggregate involved and the granular range D/d. The aim of this research work to shed new light on the prediction of concrete mechanical compressive strength and introduce a new approach the FD.log.(D/d) modulus. The approach adopted a two-phase concrete model, the first phase being the aggregate and the second phase referring the paste in powder (cement, water, mineral addition and possibly fines). Applying a reading array to experimental compiled results of more than 300 concretes selected from literature provided a clear correlation between the development of concrete compressive strength RC28. The approach thus a new and more instructive and controlling concretes performance on fractal basis.

Prediction of concrete compressive strength with GGBFS and fly ash using multilayer perceptron algorithm, random forest regression and k-nearest neighbor regression

Prediction of concrete compressive strength with GGBFS and fly ash using multilayer perceptron algorithm, random forest regression and k-nearest neighbor regression

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

In modern engineering construction, the compressive strength of concrete determines the safety of engineering structure. BP neural network (BPNN) tends to converge to different local minimum points, and the prediction accuracy is not high in the prediction of the compressive strength of concrete. Therefore, a prediction model based on the BPNN optimized by improved sparrow search algorithm (ISSA) and random forest (RF) is proposed to enhance the generalization ability and prediction accuracy of BPNN for compressive strength of concrete. In terms of algorithm improvement, three improvements are proposed for SSA: Latin hypercube sampling is introduced to initialize the location of sparrows and increase the diversity of sparrows; the somersault foraging strategy is used to enrich the optimal position of producers; and combining with the cyclone foraging mechanism, the position updating process of the scroungers is optimized to obtain a better foraging position. In terms of performance evaluation of the algorithm, the ablation experiment verifies that the three improved strategies have improved effects in SSA, and the performance of ISSA on the CEC2017 benchmark function is better than other peers. In terms of predictive index screening, the important features are selected as the input variables of the model by random forest. The prediction results show that compared with the RF-BPNN model and models optimized by other algorithms, RF-ISSA-BPNN model has the lowest prediction error, and the expected value fits the real value better.

Prediction of Concrete Compressive Strength in Saline Soil Environments.

Saline soil in Western China contains high concentrations of chloride ions, sulfate ions, and other corrosive ions, and the performance of concrete will substantially deteriorate from exposure to this environment. Therefore, it is of great significance to study and predict the concrete compressive strength in saline soil environments. In this paper, the effects of corrosion on concrete were analyzed from the aspects of surface damage, damage depth, and X-ray diffraction (XRD) of the corrosion products. The effects of corrosion were quantified by damage depth and corrosion depth. Then, considering the corrosion effects combined with Fick’s diffusion law, a time-dependent model of concrete compressive strength and a time-dependent model of damage depth were established. The results show that the deterioration of concrete gradually developed from the surface to the interior, and that the interface of the concrete specimen was equivalent to three parts: a failure zone, a filling zone, and an undisturbed zone. The results also showed that the time-varying model of concrete compressive strength proposed by the author was fully applicable, with an error of less than five percent. The service life of concrete predicted by the damage depth was found to be about 253 months (21.1 years), and the service life predicted by the time-varying compressive strength model was about 187 months (15.6 years). Both prediction results were far less than the normal concrete service life of 50 years. In addition, the long-term compressive strength of the corroded concrete was about 90% of that of the noncorroded concrete, which did not deteriorate with the corrosion time.

Predicting Compressive Strength of Blast Furnace Slag and Fly Ash Based Sustainable Concrete Using Machine Learning Techniques: An Application of Advanced Decision-Making Approaches

The utilization of waste industrial materials such as Blast Furnace Slag (BFS) and Fly Ash (F. Ash) will provide an effective alternative strategy for producing eco-friendly and sustainable concrete production. However, testing is a time-consuming process, and the use of soft machine learning (ML) techniques to predict concrete strength can help speed up the procedure. In this study, artificial neural networks (ANNs) and decision trees (DTs) were used for predicting the compressive strength of the concrete. A total of 1030 datasets with eight factors (OPC, F. Ash, BFS, water, days, SP, FA, and CA) were used as input variables for the prediction of concrete compressive strength (response) with the help of training and testing individual models. The reliability and accuracy of the developed models are evaluated in terms of statistical analysis such as R2, RMSE, MAD and SSE. Both models showed a strong correlation and high accuracy between predicted and actual Compressive Strength (CS) along with the eight factors. The DT model gave a significant relation to the CS with R2 values of 0.943 and 0.836, respectively. Hence, the ANNs and DT models can be utilized to predict and train the compressive strength of high-performance concrete and to achieve long-term sustainability. This study will help in the development of prediction models for composite materials for buildings.

Prediction of compressive strength in plain and blended cement concretes using a hybrid artificial intelligence model

Concrete is a complex engineering material with different ingredients and design variables. Therefore, an accurate prediction of concrete compressive strength (CCS) should consider its nonlinearity. Data-driven machine learning approaches can solve highly non-linear problems. The objective of this paper is to utilize computational intelligence techniques, especially the support vector machine (SVM) and the genetic algorithm (GA) as the hybridized model (SVM-GA) to reliably predict concrete compressive strength based on a set of independent variables such as Ordinary Portland Cement (OPC), Silica Fume (SF), Fly As (FA), Cement Content, Water Content. Such an approach provides a fast and simple way to optimize parameter settings to obtain an optimum laboratory condition of the CCS process. The study shows that the concrete compression strength increases with increasing silica fume (SF), cement content and curing time, and a higher correlation coefficient in SF was observed compared to cement content. In addition, the correlation coefficients for Fly Ash (FA), water and aggregates were found to be −0.38, −0.28 and −0.28, respectively, which has indicated a significant decrease in concrete compressive strength with these parameters and that the addition of FA is more significant in decreasing the compressive strength of concrete than water content and both coarse and fine aggregates and this can be explained by the low pozzolanic reactivity of FA in the early stages of concrete hydration. The experimental results demonstrated that the proposed model has promising potential with correlations of R2 = 0.99 achieved during the testing stages of the prediction of concrete compression strength. Furthermore, the experimental results demonstrate that the predicted values obtained from the model (SVM-GA) correspond closely to the experimental results with the root mean square error (RMSE)= 0.002.

Predicting the Compressive Strength of the Cement-Fly Ash-Slag Ternary Concrete Using the Firefly Algorithm (FA) and Random Forest (RF) Hybrid Machine-Learning Method.

Concrete is the most widely used material in construction. It has the characteristics of strong plasticity, good economy, high safety, and good durability. As a kind of structural material, concrete must have sufficient strength to resist various loads. At the same time, due to the brittleness of concrete, compressive strength is the most important mechanical property of concrete. To solve the disadvantages of the low efficiency of the traditional concrete compressive strength prediction methods, this study proposes a firefly algorithm (FA) and random forest (RF) hybrid machine-learning method to predict the compressive strength of concrete. First, a database is built based on the data of published articles. The dataset in the database contains eight input variables (cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate, and age) and one output variable (concrete compressive strength). Then, the correlation of the eight input variables was analyzed, and the results showed that there was no high correlation between the input variables; thus, they could be used as input variables to predict the compressive strength of concrete. Next, this study used the FA algorithm to optimize the hyperparameters of RF to obtain better hyperparameters. Finally, we verified that the FA and RF hybrid machine-learning model proposed in this study can predict the compressive strength of concrete with high accuracy by analyzing the R values and RSME values of the training set and test set and comparing the predicted value and actual value of the training set and test machine.

High-Performance Concrete Strength Prediction Based on Machine Learning.

High-performance concrete is a new high-tech concrete, produced using conventional materials and processes, with all the mechanical properties required for concrete structures, with high durability, high workability, and high volume stability of the concrete. The compressive strength of high-performance concrete has exceeded 200 MPa. 28-d average strength between 100 to 120 MPa of high-performance concrete has been widely used in engineering. Compressive strength is one of the important parameters of concrete, and carrying out concrete compressive strength prediction is of high reference value for concrete design. Eight variables related to concrete strength are used as the input of the machine learning algorithm, and the compressive strength of HPC is used as the object of study. 60 samples are constructed as the dataset by concrete preparation, and the prediction of compressive strength of HPC is carried out by combining the XGBoost algorithm. In addition, SVR algorithm and RF algorithm are also performed on the same dataset. The results show that the XGBoost model has the highest prediction accuracy among the three machine learning models, and the XGBoost algorithm scores 0.9993 for R2 and 1.372 for RMSE on the test set. The XGBoost algorithm has high prediction accuracy in predicting the compressive strength of HPC, and the choice of model is important for improving the prediction accuracy.

Predicting compressive strength of green concrete using hybrid artificial neural network with genetic algorithm

AbstractWith the growing usage of supplementary cementitious materials (silica fume, fly ash, and ground blast furnace slag, etc.) in concrete, accurate prediction of green concrete compressive strength (CS) has become an issue worth investigating. In this paper, a green concrete CS prediction model was developed using hybrid artificial neural network with genetic algorithm (GA‐ANN) based on 2479 green concrete CS experiment results. Also two prediction models based on support vector regression (SVR) and ANN algorithm were developed for comparison. Five new parameters were constructed based on the original nine influencing parameters of CS, taking all 14 parameters as input variables, the influence of the constructed parameters on the model performance was studied. Feature selection was then performed based on the maximum information correlation (MIC) and sensitivity analysis results, which could improve the model by deleting several parameters. The results showed that the prediction performance of the SVR, ANN, and GA‐ANN models were improved after adding new parameters, the GA‐ANN model has the best prediction performance. The accuracy and robustness of the GA‐ANN model were effectively improved by deleting the input variables with lower MIC and sensitivity values.

Prediction of Concrete Compressive Strength Using a Back-Propagation Neural Network Optimized by a Genetic Algorithm and Response Surface Analysis Considering the Appearance of Aggregates and Curing Conditions

In the present research, the authors have attempted to examine the compressive strength of conventional concrete, which is made using different aggregate sizes and geometries considering various curing temperatures. To this end, different aggregate geometries (rounded and angular) were utilized in various aggregate sizes (10, 20, and 30 mm) to prepare 108 rectangular cubic specimens. Then, the curing process was carried out in the vicinity of wind at different temperatures (5 °C < T < 30 °C). Next, the static compression experiments were performed on 28-day concrete specimens. Additionally, each test was repeated three times to check the repeatability of the results. Finally, the mean results were reported as the strength of concrete specimens. Response Surface Analysis (RSA) was utilized to determine the interaction effects of different parameters including the appearance of aggregates (shape and size) and curing temperature on the concrete strength. Afterwards, the optimum values of parameters were reported based on the RSA results to achieve maximum compressive strength. Moreover, to estimate concrete strength, a back-propagation neural network (OBPNN) optimized by a genetic algorithm (GA) was used. The findings of this study indicated that the developed neural network approach is greatly consistent with the experimental ones. Additionally, the compressive strength of concrete can be significantly increased (about 30%) by controlling the curing temperature in the range of 5–15 °C.

Accurate prediction of concrete compressive strength based on explainable features using deep learning

Recently, a number of machine-learning models have been proposed for the prediction of 28-day compressive strength of concrete using constituent material information as inputs. These models required a series of unexplainable features to be pre-proportioned and predetermined via experiments. Therefore, the a priori knowledge and experience of concrete engineers in terms of concrete formulation and proportioning are unfortunately neglected and wasted in this prediction logic, which might lead to serious predictive errors in concrete design and construction. In this study, a deep-learning based “factors-to-strength” approach that considers multiple explainable features and therefore takes advantage of existing job-site proportioning information is presented for concrete strength prediction. A deep convolutional neural network is proposed and trained using a data set consisting of 380 groups of concrete mixes. The accuracy and reliability of the model are validated by comparing with three models – SVM, ANN, and AdaBoost – using a data set prepared experimentally. The results show that the proposed model achieves high coefficients of determination (0.973 for the training set and 0.967 for the test set), demonstrating its excellent accuracy and generalization ability. This new model also reveals the interplay between varying explainable features in determining the compressive strength of concrete, hence facilitating an interactive experience for engineers to maneuver familiar and understandable factors for concrete strength design.

Latest concrete materials dataset and ensemble prediction model for concrete compressive strength containing RCA and GGBFS materials

Testing the compressive strength of concrete using machine learning approaches has high importance for civil engineering. Machine learning approaches provides high accuracy with reduced cost and time. However, such approaches require concrete composition data detailing the type and quantitative ratio of different materials such as water, cement, and aggregate, etc. This study incorporates a dataset where the composition of 125 different kinds of materials has been recorded. Extensive literature review has been carried out for data collection and annotation. The dataset includes traditional, as well as, advanced materials containing both RCA (recycled concrete aggregate) and GGBFS (ground granulated blast furnace slag) along with other main ingredients of concrete mix. Adding RCA and GGBFS to the concrete mix helps to produce environment friendly concrete because these materials are waste and by-products. However, increasing the number of ingredients in the concrete complicates the prediction of concrete compressive strength. Increasing RCA for concrete reduces its mechanical strength which is managed by adding GGBFS, however, the strength depends on the ratios of RCA and GGBFS. So, the compressive strength prediction of concrete containing RCA and GGBFS is crucial task for ensuring the safety of construction projects. This study presents two ensemble models for the accurate prediction of compressive strength new concrete that contains RCA and GGBFS. First ensemble, LRF, combines the LR (linear regression) and RF (random forest) through soft voting. For the second ensemble, CNN (convolutional neural networks) and LSTM (long short term memory) are leveraged. Models’ performance is evaluated through several well-known metrics including R2 (R-square), root mean square, mean absolute error, and mean square error. Results indicate that LRF and CNN-LSTM achieve the highest R2 scores of 0.93 and 0.96, respectively than the state-of-the-art models. LRF is more efficient as compared to CNN-LSTM in terms of computational time. Compared to the traditional concrete strength estimation, machine learning-based compressive strength prediction is accurate, robust and precise.

Optimization of Feature Set for Artificial Intelligence Concrete Compressive Strength Prediction

Optimization of Feature Set for Artificial Intelligence Concrete Compressive Strength Prediction

Machine learning prediction of concrete compressive strength with data enhancement

The compressive strength of concrete can be predicted by machine learning. One thousand thirty samples of concrete compressive strength data were used as the dataset. Machine learning was applied to prediction of concrete compressive strength with seven machine learning algorithms. To improve data utilization and generalization ability of machine learning model, ten data sets were constructed by feature reorganization for data augmentation. Compared with other machine learning models, the XGBoost model based on Boosting tree algorithm had the highest prediction accuracy and the most robust generalization ability. With different multi-feature combination input conditions, the R2 score of the XGBoost algorithm was 0.9283, the MAE score was 3.4292, the MAPE score was 12.5656, and the RMSE score was 5.2813. The error accumulation curve of the XGBoost algorithm was analyzed. When the compressive strength of concrete is at 5–20MPa, the error contribution rate is higher. When the concrete compressive strength is at 20–40MPa, the prediction result error of the model drops sharply. When the strength reaches 40MPa, the error contribution rate of the model tends to converge and the error contribution rate is stable between 1 and 1.2, which indicates that the model has high prediction accuracy when the compressive strength is higher than 40 MPa.

Application of artificial neural networks for prediction of microbial concrete compressive strength

Application of artificial neural networks for prediction of microbial concrete compressive strength

Soft computing techniques for the prediction of concrete compressive strength using Non-Destructive tests

This study presents a comparative assessment of conventional soft computing techniques in estimating the compressive strength (CS) of concrete utilizing two non-destructive tests, namely ultrasonic pulse velocity and rebound hammer test. In specific, six conventional soft computing models namely back-propagation neural network (BPNN), relevance vector machine, minimax probability machine regression, genetic programming, Gaussian process regression, and multivariate adaptive regression spline, were used. To construct and validate these models, a total of 629 datasets were collected from the literature. Experimental results show that the BPNN attained the most accurate prediction of concrete CS based on both ultrasonic pulse velocity and rebound number values. The results of the employed MARS and BPNN models are significantly better than those obtained in earlier studies. Thus, these two models are very potential to assist engineers in the design phase of civil engineering projects to estimate the concrete CS with a greater accuracy level.

Data-driven model for ternary-blend concrete compressive strength prediction using machine learning approach

Ternary-blend concrete is a complex composite material, and the nonlinearity in its compressive strength behavior is unquestionable. Entirely many models have been developed to accurately predict the ternary-blend concrete compressive strength, such as ANN, SVM, random forest, decision tree, to mention but a few. This study underscores the better predictive performance and successful application of the least square support vector machine (LSSVM), a machine learning model for predicting the compressive strength of ternary-blend concrete. Coupled simulated annealing (CSA) was applied to the LSSVM model as an optimization algorithm. In addition, the genetic programming (GP) model was used as a benchmark model to compare the performance of the LSSVM-CSA model. The predictive performance of the LSSVM-CSA was compared with that of some of the proposed models in well-known studies where the same datasets were used. The model proposed in this study outperformed other studies, yielding an R2 value of 0.954.

Modelling and Prediction of Concrete Compressive Strength Using Machine Learning

The compressive strength of concrete plays an important role in determining the durability and performance of concrete. Due to rapid growth in material engineering finalizing an appropriate proportion for the mix of concrete to obtain the desired compressive strength of concrete has become cumbersome and a laborious task further the problem becomes more complex to obtain a rational relation between the concrete materials used to the strength obtained. The development in computational methods can be used to obtain a rational relation between the materials used and the compressive strength using machine learning techniques which reduces the influence of outliers and all unwanted variables influence in the determination of compressive strength. In this paper basic machine learning technics Multilayer perceptron neural network (MLP), Support Vector Machines (SVM), linear regressions (LR) and Classification and Regression Tree (CART), have been used to develop a model for determining the compressive strength for two different set of data (ingredients). Among all technics used the SVM provides a better results in comparison to other, but comprehensively the SVM cannot be a universal model because many recent literatures have proved that such models need more data and also the dynamicity of the attributes involved play an important role in determining the efficacy of the model.