Application of Advanced Machine Learning Approaches to Predict the Compressive Strength of Concrete Containing Supplementary Cementitious Materials (original) (raw)

Comparison of Machine Learning Approaches with Traditional Methods for Predicting the Compressive Strength of Rice Husk Ash Concrete

Crystals, 2021

Efforts are being devoted to reducing the harmful effect of the construction industry around the globe, including the use of rice husk ash as a partial replacement of cement. However, no method is available to date to predict the compressive strength (CS) of rice husk ash blended concrete (RHAC). In this study, advanced machine learning techniques (artificial neural network, artificial neuro-fuzzy inference system) were used to predict the CS of RHAC. Based on the published literature, six inputs, i.e., age of specimen, percentage of rice husk ash, percentage of superplasticizer, aggregates, water, and amount of cement, were selected. Results obtained from machine learning methods were compared with traditional methods such as linear and non-linear regressions. It was observed that the performance of machine learning methods was superior to traditional methods for determining the CS of RHAC. This study will prove beneficial in minimizing the cost and time of executing laboratory exp...

Prediction of Compressive Strength of Fly-Ash-Based Concrete Using Ensemble and Non-Ensemble Supervised Machine-Learning Approaches

Applied Sciences, 2021

The utilization of waste material, such as fly ash, in the concrete industry will provide a valuable alternative solution for creating an eco-friendly environment. However, experimental work is time-consuming; employing soft machine learning techniques can accelerate the process of forecasting the strength properties of concrete. Ensemble machine learning modeling using Python Jupyter Notebook was employed in the forecasting of compressive strength (CS) of high-performance concrete. Multilayer perceptron neuron network (MLPNN) and decision tree (DT) were used as individual learning which then ensembled with bagging and boosting to provide strong correlations. Random forest (RF) and gradient boosting regression (GBR) were also used for prediction. A total of 471 data points with input parameters (e.g., cement, fine aggregate, coarse aggregate, superplasticizer, water, days, and fly ash), and an output parameter of compressive strength (CS), were retrieved to train and test the indivi...

Machine learning-based model for predicting concrete compressive strength

International Journal of GEOMATE, 2021

This study aims at applying a machine learning-based model to establish the relationship between different input variables to the 28-day compressive strength of normal and High-Performance Concrete (HPC). An Artificial Neural Network (ANN) model was trained, validated, and tested using a comprehensive database consisted of 361 records gathered from the previously circulated source. Various models with different learning algorithms and neuron numbers in the hidden layer were examined to attain the best performance model. The examination results revealed that the ANN model using the "trainlm" learning algorithm delivered the best prediction outcomes with the overall coefficient of determination (R 2) of 0.9277. The influence of input parameters on the output was also examined by performing the sensitivity analysis. It was observed that the compressive strength of concrete at 28 days was more responsive to the changes in the cement parameter (CM) and the amount of water (WT). In contrast, the 28-day concrete compressive strength was found less sensitive to the variation of the fly ash (FL) parameter.

Intelligent Design of Construction Materials: A Comparative Study of AI Approaches for Predicting the Strength of Concrete with Blast Furnace Slag

Materials

Concrete production by replacing cement with green materials has been conducted in recent years considering the strategy of sustainable development. This study researched the topic of compressive strength regarding one type of green concrete containing blast furnace slag. Although some researchers have proposed using machine learning models to predict the compressive strength of concrete, few researchers have compared the prediction accuracy of different machine learning models on the compressive strength of concrete. Firstly, the hyperparameters of BP neural network (BPNN), support vector machine (SVM), decision tree (DT), random forest (RF), K-nearest neighbor algorithm (KNN), logistic regression (LR), and multiple linear regression (MLR) are tuned by the beetle antennae search algorithm (BAS). Then, the prediction effects of the above seven machine learning models on the compressive strength of concrete are evaluated and compared. The comparison results show that KNN has higher R...

Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm

Materials

Machine learning techniques are widely used algorithms for predicting the mechanical properties of concrete. This study is based on the comparison of algorithms between individuals and ensemble approaches, such as bagging. Optimization for bagging is done by making 20 sub-models to depict the accurate one. Variables like cement content, fine and coarse aggregate, water, binder-to-water ratio, fly-ash, and superplasticizer are used for modeling. Model performance is evaluated by various statistical indicators like mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). Individual algorithms show a moderate bias result. However, the ensemble model gives a better result with R2 = 0.911 compared to the decision tree (DT) and gene expression programming (GEP). K-fold cross-validation confirms the model’s accuracy and is done by R2, MAE, MSE, and RMSE. Statistical checks reveal that the decision tree with ensemble provides 25%, 121%, and 49% enhancement for ...

Predicting Compressive Strength of Concrete Containing Industrial Waste Materials: Novel and Hybrid Machine Learning Model

Advances in Civil Engineering

In the construction and cement manufacturing sectors, the development of artificial intelligence models has received remarkable progress and attention. This paper investigates the capacity of hybrid models conducted for predicting the compressive strength (CS) of concrete where the cement was partially replaced with ground granulated blast-furnace slag ( FS ) and fly ash ( FA ) materials. Accurate estimation of CS can reduce the cost and laboratory tests. Since the traditional method of calculation CS is complicated and requires lots of effort, this article presents new predictive models called SVR − PSO and SVR − GA , that are a hybridization of support vector regression ( SVR ) with improved particle swarm algorithm ( PSO ) and genetic algorithm ( GA ). Furthermore, the hybrid models (i.e., SVR − PSO and SVR − GA ) were used for the first time to predict CS of concrete where the cement component is partially replaced. The improved PSO and GA are given essential roles in tuning the...

Developing a novel machine learning method to predict the compressive strength of fly ash concrete in different ages

2019

Estimating the compressive strength of concrete before fabricating, it has been one of the most important challenges because designing a mixture proportion by experimental methods needs expert workers, consumes energy, and wastes materials. Therefore, in this study, the influences of materials and the age of samples on the compressive strength of fly ash concrete are investigated, and a novel method for predicting the compressive strength is presented. To this end, water cycle algorithm and genetic algorithm are utilized, and their outcomes are compared with the classical regression models. Various performance indicators are used to gauge the accuracy of the models. By analyzing the results, it is comprehended that the water cycle algorithm is the most accurate model according to all performance indicators. Besides, the outcomes of water cycle algorithm and genetic algorithm are by far better than those of classical methods. The mean absolute error of water cycle algorithm, genetic ...

The Use of Machine Learning Models in Estimating the Compressive Strength of Recycled Brick Aggregate Concrete

2021

The focus of this study is to investigate the applicability of Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN), and Multiple Linear Regression (MLR) in modeling the compressive strength of Recycled Brick Aggregate Concrete (RBAC). A comparative study on the application of the aforementioned models is developed based on statistical tools such as coefficient of determination, mean absolute error, root mean squared error, and some others, and the application potential of each of these models is investigated. To study the effects of RBAC factors on the performance of representative data-driven models, the Sensitivity Analysis (SA) method is used. The findings revealed that ANN with R2 value of 0.9102 has a great application potential in predicting the compressive strength of concrete. In the absence of ANN, ANFIS with R2 value of 0.8538 is also an excellent substitute for predictions. MLR was shown to be less effective than the preceding models and is only...

Use of Artificial Intelligence Methods for Predicting the Strength of Recycled Aggregate Concrete and the Influence of Raw Ingredients

Materials

Cracking is one of the main problems in concrete structures and is affected by various parameters. The step-by-step laboratory method, which includes casting specimens, curing for a certain period, and testing, remains a source of worry in terms of cost and time. Novel machine learning methods for anticipating the behavior of raw materials on the ultimate output of concrete are being introduced to address the difficulties outlined above such as the excessive consumption of time and money. This work estimates the splitting-tensile strength of concrete containing recycled coarse aggregate (RCA) using artificial intelligence methods considering nine input parameters and 154 mixes. One individual machine learning algorithm (support vector machine) and three ensembled machine learning algorithms (AdaBoost, Bagging, and random forest) are considered. Additionally, a post hoc model-agnostic method named SHapley Additive exPlanations (SHAP) was performed to study the influence of raw ingred...