Compressive Strength Of High Performance Concrete Research Articles

Prediction of compressive strength of high-performance concrete using optimization machine learning approaches with SHAP analysis

Prediction of compressive strength of high-performance concrete using optimization machine learning approaches with SHAP analysis

Predicting Compressive Strength of High-Performance Concrete Using Hybridization of Nature-Inspired Metaheuristic and Gradient Boosting Machine

This study proposes a novel integration of the Extreme Gradient Boosting Machine (XGBoost) and Differential Flower Pollination (DFP) for constructing an intelligent method to predict the compressive strength (CS) of high-performance concrete (HPC) mixes. The former is employed to generalize a mapping function between the mechanical property of concrete and its influencing factors. DFP, as a metaheuristic algorithm, is employed to optimize the learning phase of XGBoost and reach a fine balance between the two goals of model building: reducing the prediction error and maximizing the generalization capability. To construct the proposed method, a historical dataset consisting of 400 samples was collected from previous studies. The model’s performance is reliably assessed via multiple experiments and Wilcoxon signed-rank tests. The hybrid DFP-XGBoost is able to achieve good predictive outcomes with a root mean square error of 5.27, a mean absolute percentage error of 6.74%, and a coefficient of determination of 0.94. Additionally, quantile regression based on XGBoost is performed to construct interval predictions of the CS of HPC. Notably, an asymmetric error loss is used to diminish overestimations committed by the model. It was found that this loss function successfully reduced the percentage of overestimated CS values from 47.1% to 27.5%. Hence, DFP-XGBoost can be a promising approach for accurately and reliably estimating the CS of untested HPC mixes.

Using radial basis function for estimating the high-performance concrete compressive strength with optimizing AVOA and SSA

Compressive strength (CS) is concrete’s most important mechanical property, as it plays an important role in setting design criteria. Thus, an accurate and early assessment of the CS of concrete can minimize time, labor, and cost. This paper investigated the ability of the Radial Basis Function (RBF) to handle the prediction of CS. The nonlinearities raised from the novel utilized admixtures between the input variables and output CS is tried to be conducted with the RBF model. In order to make a flexible framework combination of the RBF model with the African Vulture Optimization (AVOA) and Salp Swarm Algorithm (SSA) techniques are considered. The results achieved from the RBF-AVOA model indicated good agreement between the actual and predicted values. The proposed model provides a very accurate HPC compressive strength prediction. In addition, the correlation coefficient R2 is equal to (0.997), and the values of mean absolute error (MAE) (0.1917 MPa), root mean square error (RMSE) (0.937 MPa), and variance account coefficient (VAF) (99.73%) are low. The performance of the RBF-AVOA model, compared to other models, provided the desired advantage and more stable predictions. AVOA plays a key role in modeling results, improving generalization capabilities, avoiding redundant data, and decreasing uncertainty.

Using the meta-heuristic algorithms to optimize the radial basis function for predicting high-performance concrete compressive strength and slump flow

Using the meta-heuristic algorithms to optimize the radial basis function for predicting high-performance concrete compressive strength and slump flow

Effect of nanosized titanium dioxide and zinc oxide as antimicrobial agents on early compressive strength of high-performance concrete

Incorporation of antimicrobial agents in concrete can enhance the resistance of the concrete to biodeterioration. However, there is lack of adequate knowledge on the effect of antimicrobial agents on performance measures of high-performance concrete (HPC), particularly during early strength development, when the hydration reaction is ongoing, is not well understood. Therefore, the effect of antimicrobial agents on early compressive strength (CS) of HPC was investigated. The cement in HPC samples was partially replaced with nanosized titanium dioxide (TiO2) and zinc oxide (ZnO) by up to 2% by weight of the cement. Using response surface methodology, 21 combinations of TiO2 and ZnO contents were generated. The combinations were adopted for preparation of the samples. Individual and combined effects of TiO2 and ZnO on 7-day CS of the samples were evaluated. The standard form of the second-order response surface model is adopted to develop a model to fit the strength data. Statistics of the data verify that the model can be employed to predict the 7-day CS of HPC that employs nanosized TiO2 and ZnO as antimicrobial agents.

Prediction of compressive strength of high-performance concrete via automated machine learning models

Prediction of compressive strength of high-performance concrete via automated machine learning models

Prediction of compressive strength of high-performance concrete (HPC) using machine learning algorithms

Prediction of compressive strength of high-performance concrete (HPC) using machine learning algorithms

Application of deep learning in civil engineering: boosting algorithms for predicting strength of concrete

Machine learning (ML) has been applied in civil engineering to predict the compressive strength of concrete with high accuracy. In this paper, five boosting ensemble algorithms, i.e., XGBoost, AdaBoost, GBDT, LightGBM, and CatBoost, were used to predict the compressive strength of high-performance concrete (HPC). The models were evaluated using performance indicators such as R2, root mean square error (RMSE), and mean absolute error (MAE). The results showed that the CatBoost model had the highest accuracy with a R2 (0.970) and a RMSE (2.916). The prediction accuracy of the model was increased through hyperparameter optimization, which got a higher with a R2 (0.975) and a RMSE (2.863). Meanwhile, the SHapley Additive exPlanations (SHAP) method was used to explain the output results of the optimal model (CatBoost), which generated explainable insights that further revealed the complex relationship between the prediction model parameters. The results showed that AGE, W/B, and W/C had the most impact on high-performance concrete compressive strength (HPCCS) prediction, which was similar to the results of sensitivity analysis. This study provided a theoretical basis and technical guidance for developing the mix design of a new high-performance concrete (HPC) system. In the future, the interpretable results of the model output should be iteratively checked and validated in the actual laboratory in order to provide guidance for engineering practice.

Developing aquila optimization-based fuzzy system to predict the mechanical properties of the improved HPC

High-performance concrete performs better than normal concrete because of using additional components than usual concrete components. Several artificially based analytics were used to evaluate the compressive strength (CS) of high-performance concrete (HPC) made with fly ash and blast furnace slag. In the present research, the Aquila optimizer (AO) was used to find the best values for the determinants of the adaptive neuro-fuzzy inference system (ANFIS), and radial basis function neural network (RBFNN) that may be changed to enhance performance. The suggested approaches were established using 1030 tests, eight inputs (a primary component of mix designs, admixtures, aggregates, and curing age), and the CS as the forecasting objective. The results of the outperformed model were then contrasted with those found in the existing scientific literature. Calculation results point to the potential benefit of combining AO-RBFNN and AO-ANFIS study. The AO-ANFIS demonstrated significantly higher R2 (Train: 0.9862, Test: 0.9922) and lower error metrics (such as: RMSE at 2.1434 (train) and 1.2763 (Test)) when compared to the AO-RBFNN and previously published articles. In summation, the proposed method for determining the CS of HPC supplemented with blast furnace slag and fly ash may be established using the suggested AO-ANFIS analysis.

The effect of water absorption distribution of recycled coarse aggregate on the compressive strength distribution of high-performance concrete

High water absorption is a typical characteristic of recycled coarse aggregate and is often used to explain the loss of performance of concrete when replacing natural coarse aggregate with recycled coarse aggregate. Extensive attention has been paid to the mean value of the water absorption of recycled coarse aggregate, but not to the standard deviation. This paper aims to assess whether recycled coarse aggregates with the same mean water absorption but different standard deviations will perform equally in high-performance concrete (HPC). The resulting HPC mixtures exhibited very similar compressive strength. Even so, it was hypothesised that as the standard deviation of the water absorption of recycled coarse aggregate increases over a wide range, the compressive strength of HPC will first increase due to local variations in the water/cement ratio, then decrease due to the presence of weak particles, and finally remain constant due to the role of the surrounding new mortar.

Prediction of compressive strength of high-performance concrete via coupled meta-heuristic random forest regression techniques

Prediction of compressive strength of high-performance concrete via coupled meta-heuristic random forest regression techniques

An explanatory machine learning model for forecasting compressive strength of high-performance concrete

An explanatory machine learning model for forecasting compressive strength of high-performance concrete

Estimating high-performance concrete compressive strength with support vector regression in hybrid method

Estimating high-performance concrete compressive strength with support vector regression in hybrid method

Predicting the compressive strength of High-performance concrete by using Radial basis function with optimization Improved Grey Wolf optimizer and Dragonfly algorithm

This paper uses two optimizers (Improved Gray Wolf Optimizer (I_GWO) and Dragonfly Optimization Algorithm (DA)) for the sensitivity and robustness of artificial intelligence (AI) techniques, namely radial basis functions (RBFs). The purpose is to evaluate and analyze the predictive strength of high-performance concrete (HPC). 170 samples were collected for this purpose. This includes eight input parameters, cement, silica fume, fly ash, water, coarse aggregate, total aggregate, high water reducing agent, concrete age, and one output parameter, the compressive strength, to produce Increase learning and validation data sets. The proposed AI model was validated against several standard criteria: coefficient of determination (R2), root mean square error (RMSE), scatter index (SI), RMSE-observations standard deviation ratio (RSR), and coefficient of persistence (CP), n10_index. Many runs were performed to analyze the sensitivity and robustness of the model. The results show that I_GWO using RBF performs better than DA. Furthermore, sensitivity analysis indicated that cement content and HPC test age are the most essential and sensitive factors for predicting the compressive strength of HPC, according to the evaluations performed on the models, it was seen that the IGWO_RBF model provided better results compared to other models and can be introduced as the practical model for the prediction of HPC’s CS. In conclusion, this study can help to select appropriate AI models and suitable input parameters to accurately and quickly estimate the compressive strength of HPC.

The influence of fly ash and blast furnace slag on the compressive strength of high-performance concrete (HPC) for sustainable structures

The influence of fly ash and blast furnace slag on the compressive strength of high-performance concrete (HPC) for sustainable structures

Comparative study of machine learning methods to predict compressive strength of high-performance concrete and model validation on experimental data

Comparative study of machine learning methods to predict compressive strength of high-performance concrete and model validation on experimental data

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

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

Development of a radial basis neural network for the prediction of the compressive strength of high-performance concrete

Development of a radial basis neural network for the prediction of the compressive strength of high-performance concrete

Incorporation of radial basis function with Gorilla Troops Optimization and Moth-Flame Optimization to predict the compressive strength of high-performance concrete

Incorporation of radial basis function with Gorilla Troops Optimization and Moth-Flame Optimization to predict the compressive strength of high-performance concrete

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

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