Prediction Of Concrete Strength Research Articles

Soft computing models for prediction of bentonite plastic concrete strength

Bentonite plastic concrete (BPC) is extensively used in the construction of water-tight structures like cut-off walls in dams, etc., because it offers high plasticity, improved workability, and homogeneity. Also, bentonite is added to concrete mixes for the adsorption of toxic metals. The modified design of BPC, as compared to normal concrete, requires a reliable tool to predict its strength. Thus, this study presents a novel attempt at the application of two innovative evolutionary techniques known as multi-expression programming (MEP) and gene expression programming (GEP) and a boosting-based algorithm known as AdaBoost to predict the 28-day compressive strength ( ) of BPC based on its mixture composition. The MEP and GEP algorithms expressed their outputs in the form of an empirical equation, while AdaBoost failed to do so. The algorithms were trained using a dataset of 246 points gathered from published literature having six important input factors for predicting. The developed models were subject to error evaluation, and the results revealed that all algorithms satisfied the suggested criteria and had a correlation coefficient (R) greater than 0.9 for both the training and testing phases. However, AdaBoost surpassed both MEP and GEP in terms of accuracy and demonstrated a lower testing RMSE of 1.66 compared to 2.02 for MEP and 2.38 for GEP. Similarly, the objective function value for AdaBoost was 0.10 compared to 0.176 for GEP and 0.16 for MEP, which indicated the overall good performance of AdaBoost compared to the two evolutionary techniques. Also, Shapley additive analysis was done on the AdaBoost model to gain further insights into the prediction process, which revealed that cement, coarse aggregate, and fine aggregate are the most important factors in predicting the strength of BPC. Moreover, an interactive graphical user interface (GUI) has been developed to be practically utilized in the civil engineering industry for prediction of BPC strength.

Compressive Strength Prediction of Concrete Under Sulfate Attack Using Coupled Machine Learning Methods

Compressive Strength Prediction of Concrete Under Sulfate Attack Using Coupled Machine Learning Methods

Comparative analysis of cement grade and cement strength as input features for machine learning-based concrete strength prediction

Machine learning (ML) has gained recognition as a valuable tool for predicting concrete properties. This study investigated the influence of input features related to cement strength on the performance of ML models. Four datasets with various features were prepared, and for each dataset, cement grade and cement strength were alternately applied as input features. ML models such as Random Forest, Extreme Gradient Boosting, and Multilayer Perceptron Neural Network were utilized to predict concrete strength for each dataset. The results showed a tendency for the prediction performance to improve when cement properties were used as input features, with the extent of improvement varying across the datasets. Permutation importance analysis indicated that cement strength often had a greater influence on the ML models than cement grade, positively enhancing prediction performance. Therefore, considering cement strength as an input feature is expected to be beneficial for constructing more accurate ML models.

Tensile strength prediction of crumb rubber concrete based on mesoscale modelling

ABSTRACT This study presents an experimental study and a mesoscale model for evaluating the tensile performance of crumb rubber concrete (CRC). In the experimental study, indirect tensile strengths of CRC with 6%, 12% 18% rubber contents were tested. Then the tested CRC samples were modelled as a five-phase material containing rubber, coarse aggregate, mortar, aggregate-mortar interface layer, and rubber-mortar interface layer. Numerical results from the mesoscale model agreed well with experimental tests. Parameter analysis was carried out to investigate the content, size, shape of rubber particle, thickness of interface layer on the tensile strength of CRC. Numerical results also indicated that rubber content was the governing influencing factor on the tensile strength reduction. As comparison, the influence from rubber size and rubber shape was relatively low. For a specimen with constant rubber content and size, changes in the distribution of rubber particles resulted in slight changes in the tensile strength of CRC as well. The effect of rubber particles on the tensile properties of concrete was similar to that of pores. Finally, tensile strength prediction formula was developed based on the pores model proposed in this study.

Experimental research and theoretical prediction on mechanical properties for recycled GFRP fiber reinforced concrete

Recycled GFRP fiber has increasingly been reused in concrete, necessitating an improvement in its recycling process and an investigation into its effects on the mechanical properties of concrete to achieve higher recycling values. To this end, this study proposed a novel GFRP recycling method that integrates mechanical recycling, water-vibratory separation, and secondary sieving to efficiently remove impurities from GFRP waste. Three different sizes of recycled GFRP fibers (SF, MF, and LF) were sorted from decommissioned GFRP wind turbine blades using the novel recycling method, and their effects of on the mechanical properties of concrete were investigated. The results show that all three types of recycled GFRP fibers increase the strength and toughness of concrete. With equivalent fiber content, the largest recycled GFRP fiber (LF) yields the most substantial reinforcement effect on the mechanical properties of concrete. Specifically, incorporating 1.0 % LF increases the compressive strength, splitting tensile strength, and flexural strength of concrete by 14.0 %, 16.8 %, and 37.0 %, respectively, while also increasing flexural toughness by 2.9 times. In addition, the mechanical properties of recycled GFRP fiber reinforced concrete were theoretically analyzed in this study, and two strength prediction models for recycled GFRP fiber reinforced concrete were established to achieve rapid prediction of concrete strength.

Prediction of high-performance concrete strength using machine learning with hierarchical regression

Prediction of high-performance concrete strength using machine learning with hierarchical regression

Empowering Structural Integrity: YOLO-Based Crack Detection and ML-Driven Concrete Strength Prediction of Critical Infrastructure Caused due to Mining Operation.

In this paper, YOLO algorithm is used for detecting crack by incorporating machine learning approach utilized for predicting concrete strength and possible crack in critical infrastructure caused during mining operation. Over time, those fissures become more noticeable until they finally collapse. Big data is accumulating huge amount of data by utilizing remote sensing, earth penetrating RADAR, LiDAR, GPS, and satellite image data for large-scale operations. The performance of the proposed approach is compared with other state-of-the-art algorithms, such as, YOLO V3 and YOLO V8. To investigate the performance, four performances metric like mean absolute error (MAE), root mean square error (RMSE), relative standard error (RSE) and root relative squared error (RRSE) has considered. It is found that our approach is giving superior results in term of the compared performance metrics. Moreover, non-parametric statistical testing, such as, signed test, Wilcoxon Rank test and Friedman's test has been conducted to verify the dominance of proposed approach over others.

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.

Benchmarking AutoML solutions for concrete strength prediction: Reliability, uncertainty, and dilemma

Building precise machine learning and deep learning models has traditionally required a combination of mathematical skills and hands-on experience to meticulously adjust hyperparameters that significantly impact the learning process. As datasets continue to expand across various engineering domains, researchers increasingly turn to machine learning methods to uncover hidden insights that may elude classic regression techniques. This surge in adoption raises concerns about the adequacy of resultant meta-models and the interpretation of findings. In response to these challenges, automated machine learning (AutoML) emerges as a promising solution, aiming to construct machine learning models with minimal intervention or guidance from human experts. This paper benchmarks AutoML solutions by providing an overview of their principles and applying them to predict the most important mechanical properties of different concrete datasets, i.e., compressive strength. Nine datasets from various concrete types, sample sizes, and features are utilized, with a detailed discussion on the benchmark dataset from high-performance concrete, applying best practices to the other eight datasets. For each case, the importance of hyperparameter tuning is discussed, alongside the ensemble and stacking models. Tree-based models are employed for each dataset to develop SHAP plots, interpret results, and understand the contribution of each component in the mix design to the overall strength of the concrete. This paper further explores three unique aspects of benchmarking AutoML in material science: (1) “reliability” by contrasting the benchmark dataset’s error metric with literature collected over the past 20 years, (2) “uncertainty” by quantifying the variability in the mean and standard deviation of the error metric from different datasets and its correlation with the sample-to-feature ratio, and (3) “dilemma” by discussing the shortcomings of AutoML in specific concrete datasets.

Metaheuristic optimization of machine learning models for strength prediction of high-performance self-compacting alkali-activated slag concrete

Metaheuristic optimization of machine learning models for strength prediction of high-performance self-compacting alkali-activated slag concrete

Review of a concrete strength prediction model using machine learning

Review of a concrete strength prediction model using machine learning

Research on the strength prediction for pervious concrete based on design porosity and water-to-cement ratio

Abstract The strength prediction of pervious concrete is hard to implement for the mix design due to the porous structure. This work studied the influence of the water-to-cement ratio on the fluidity, viscosity, and mechanical properties of cement paste. Then, the porosity, permeability, and compressive strength of the pervious concrete with various porosities were investigated, and the test results were fitted and analyzed. The result indicates that as the water-to-cement ratio increases, the viscosity of the cement paste reduces and the fluidity increases. The water-to-cement ratio has a negative linear relationship with net slurry strength. The porosity and permeability of pervious concrete fluctuate in accordance with the same rule as the water-to-cement ratio changes. The compressive strength of pervious concrete with varying design porosities increases initially, then declines as the water-to-cement ratio rises. According to the linear fitting analysis, when the water-to-cement ratio is constant, the permeability and compressive strength of pervious concrete have a positive and negative linear relationship with the design porosity, respectively. By analyzing the fitting results and combining the volume method of pervious concrete, a calculation method for mix proportion design is proposed to predict the strength of pervious concrete.

Compressive strength prediction of ternary blended geopolymer concrete using artificial neural networks and support vector regression

Compressive strength prediction of ternary blended geopolymer concrete using artificial neural networks and support vector regression

Strength properties prediction of RCA concrete via hybrid regression framework

High-performance concrete (HPC) is commonly utilized in the construction industry because of its strength and durability. The mechanical properties of HPC, specifically its compressive and tensile strength, are crucial indicators. Accurate prediction of concrete strength is crucial for optimizing the design as well as the performance of concrete structures. In this investigation, a novel approach for strength prediction of HPC is proposed, employing the Support Vector Regression (SVR) algorithm in conjunction with three optimizers: the Slime Mold Algorithm (SMA), Adaptive Opposition Slime Mold Algorithm (AOSM), and Equilibrium Slime Mold Algorithm (ESMA). The SVR algorithm is a robust machine-learning technique that has displayed promising results in various prediction tasks. The utilization of SVR allows for the effective modeling and prediction of the complex relationship between the strength properties of HPC and the influencing factors. To achieve this, a dataset comprising 344 samples of high-performance concrete was collected and utilized to train and assess the SVR algorithm. However, the choice of suitable optimization algorithms becomes crucial to enhance prediction accuracy and convergence speed. Through extensive experimentation and comparative analysis, the proposed framework’s performance is evaluated using real-world HPC strength data. The results demonstrate that combining SVR with AOSM, ESMA, and SMA outperforms traditional prediction accuracy and convergence speed optimization methods. The suggested framework provides an effective and reliable solution for accurately predicting the compressive strength (CS) of HPC, enabling engineers and researchers to optimize the design and construction processes of HPC structures.

Compressive Strength Prediction of High-Performance Hydraulic Concrete using a Novel Neural Network Based on the Memristor

Compressive Strength Prediction of High-Performance Hydraulic Concrete using a Novel Neural Network Based on the Memristor

Ultrasonic pulse velocity and artificial neural network prediction of high-temperature damaged concrete splitting strength

To examine the integrity of any structure following a fire, assessments of the impact of high temperatures on concrete are essential, particularly its decreased in tensile strength. Destructive examinations, such as the extraction of concrete cores, can pose significant cost and safety challenges, particularly when applied to structures that have already sustained damage. Consequently, for assessing damaged concrete, non-destructive in-situ tests are the favored approach. This study aims to develop an artificial neural network model utilizing data from ultrasonic pulse velocity measurements. The model's purpose is to assess the tensile splitting strength of concrete subjected to elevated temperatures, ranging from 200 to 800 °C. The splitting strength investigation showed that increasing the exposure temperature from 200 to 800°C results in splitting strength reduction of 15 to 75% respectively. Also, the ultrasonic pulse velocity experienced a reduction of 85% when the exposure temperature reaches 800 °C. In addition, the results of the artificial neural network model indicated that ultrasonic pulse velocity and temperature data were sufficient to reasonably forecast the tensile splitting strength of concrete. The developed artificial neural network model has a coefficient of determination (R2) of 0.943, a mean absolute relative error (MARE) of 5.028, and an average squared error (ASE) of 0.000907.

Review of researches on strength model of foamed concrete

Compared with ordinary concrete, foamed concrete has the advantages of light weight, high fluidity, good thermal and sound insulation properties. It has been initially applied in the construction industry. As the most basic research content of foamed concrete, the strength prediction model has received widespread attention from scholars. In this paper, the existing foamed concrete strength prediction models were summarized. The development status of the traditional empirical model based on theory or experiment and the artificial intelligence model based on computer technology were analyzed, and the advantages and disadvantages of each model were described comprehensively. The following conclusions can be drawn: ①Porosity is an important factor affecting the density and compressive strength of foamed concrete. Considering pore structure and volume composition will be more convenient and accurate to predict the strength. ② As for gel-space ratio model, considering the contribution of different hydration products produced by the cementitious materials to the strength is helpful to improve the accuracy of the prediction model. ③ The artificial intelligence model overcomes the shortcomings of less factors in the empirical model and large amount of experiments with high cost, which makes the strength prediction more effective and precise. This paper provides a train of thought for further research on the strength prediction model of foamed concrete. The development of foamed concrete strength prediction model can provide theoretical guidance for the design of foamed concrete, and is of great significance for promoting the application of foamed concrete in various occasions.

Strength Prediction of Non-Sintered Hwangto-Substituted Concrete Using the Ultrasonic Velocity Method.

This paper presents and investigates the properties of concrete in which a portion of the cement is substituted with non-sintered Hwangto (NSH), a readily available building material in Asia. Given the inactive nature of NSH, this study aimed to determine the optimal cement replacement ratio and quantitative strength of the material. The unit weight, compressive strength, ultrasonic pulse velocity (UPV), and stress-strain of the NSH concrete (NSHC) were evaluated. Additionally, we developed a predictive model for determining compressive strength based on the regression analysis of compressive strength and UPV. The water-to-binder ratio was set to 0.41, 0.33, and 0.28, and the NSH replacement rates in the cement were set to 0%, 15%, 30%, and 45% for evaluating various strength ranges. The mechanical property measurements indicated reductions of 5.35% in unit weight, 35.62% in compressive strength, and 6.34% in UPV as the NSH was replaced. Notably, the smallest deviation from plain concrete was observed at a replacement rate of 15%. The scanning electron microscopy analysis results showed that the plain concrete exhibited a crystalloid structure; however, as the NSH replacement rate increased, the amorphous structure and pores increased while unreacted NSH particles were also observed. The X-ray diffraction analysis results demonstrate that the peak intensities for kaolinite and mullite increased as the NSH replacement rate increased, while those of C-S-H gel and CaO showed low peak intensities. Furthermore, the regression analysis concluded that an exponential function was suitable. Consequently, a compressive strength prediction model was developed, and in the error test, the NSHC model demonstrated an average error of <10%, with fewer errors at the lower compressive strength boundaries.

Synergistic effects of supplementary cementitious materials and compressive strength prediction of concrete using machine learning algorithms with SHAP and PDP analyses

In order to reduce the CO2 associated with cement production, this study explored the potential of rice husk ash (RHA) and fly ash (FA) as supplementary cementitios materials for partially replacing cement in concrete production. The study aimed to analyze the synergistic effects of a cement-based mixture consisting of RHA and FA in different proportions on concrete's fresh, hardened, non-destructive and microscopic properties. In addition to the experimental work, this study successfully applied machine learning to predict the compressive strength of RHA-FA concrete using three types of algorithms: ANN (Analytical Neural Network), XGB (Extreme Gradient Boosting), and GBM (Gradient Boosting Model). A total of 138 data points were used for this prediction, and statistical and parametric analyses were performed to define the impact of input parameters on the outcome. Furthermore, both destructive and non-destructive tests were conducted on hardened concrete, including compressive strength, split tensile strength, ultrasonic pulse velocity (UPV), and rebound hammer. The scanning electron microscope (SEM) was operated to analyze the microstructural characteristics of concrete. The compressive and split-tensile strength test results showed that the mixture with a higher percentage of fly ash and a lower percentage of rice husk ash achieved maximum strength. The X-ray Fluorescence (XRF) analysis revealed that both ashes contained a significant amount of silica, which gave them excellent pozzolanic properties. After 28 days, both the UPV and the rebound hammer strength align with the destructive compressive strength results. The study also employed SHAP (SHapley Additive exPlanations) and PDP (Partial Dependence Plot) analyses to identify the optimal range for each parameter's contribution to strength improvement. The machine learning models exhibited a strong correlation with the test results, achieving an R2 value of 0.84 for the XGBoost model.

Development of a machine learning model for on-site evaluation of concrete compressive strength by SonReb

Coring or destructive testing is typically the default choice for the evaluation of concrete compressive strength in reinforced concrete (RC) structures. However, it can be impractical and/or not representative of all areas of interest in a structure. While various non-destructive test (NDT) methods can be correlated to concrete strength, the accuracy of any single NDT used for this purpose is generally low. The SonReb method, which combines ultrasonic pulse velocity readings and rebound number, has been shown to have improved accuracy over single test methods. However, empirical SonReb equations, calibrated to specific datasets using regression analysis, cannot necessarily be applied to concrete from other sources without introducing significant errors. This study presents a practical machine learning (ML) model for on-site concrete strength prediction. A large database was created from available literature along with new experimental test data. Three different ML models based on an adaptive neuro-fuzzy inference system (ANFIS) were developed along with a graphical user interface application to facilitate its use in the field. In addition to the ML models, linear and non-linear regression analyses were also conducted and compared with existing equations in the literature. The accuracy of each model was subsequently validated against core samples extracted from a reinforced concrete slab. The results show that the proposed ML model and non-linear regression provided the most reliable predictions of concrete strength of the validation specimen with a mean absolute error of less than 10 % compared with twelve core samples. The findings suggest that ML can be a potential tool to evaluate the in-place compressive strength of RC structures in combination with simple NDT methods.