Estimation Of Compressive Strength Research Articles

High-Performance Concrete compressive property prediction via deep hybrid learning

The vast usage of concrete made it the second most used material after water. This volume of concrete consumes an enormous number of natural sources and chronically enhances environmental pollution by CO2 emission. Cementitious supplementary materials such as fly ash and micro silica help decrease the usage of cohesive materials in the concrete and improve concrete’s properties, specifically compressive strength. In addition, due to being the by-product materials of other industries, applying these materials contribute to the decline of environmental pollution. On the other hand, fly ash and micro silica decrease the ratio of water to cement and increase the compressive strength (CS) of concrete. High-Performance Concrete (HPC) is one of the types of concrete used in dams, bridges, etc. In order to achieve the compressive strength of HPC, it is necessary to conduct laboratory tests, which are not economical in terms of time and cost. For this reason, in the present study, the prediction of the CS of the mentioned concrete can be done based on soft-based and artificial intelligence. Furthermore, various mixed designs of HPC, such as fly ash and silica fume coupled with different percentages of plasticizers, are considered the base dataset for developing the prediction models. Neural network-based model hybridized with antlion optimization algorithm and biography-based optimization algorithm developed for compressive strength estimation. The result showed that the AMLP-I model with R2 and RMSE values of 0.9879 and 1.9003 accurately predicted compressive strength and can be referred to as the most qualitative prediction model compared to the BMLP model.

Calibrating equations to predict the compressive strength of FRP-Confined columns using optimized neural network model

A procedure based on machine learning algorithms is used to develop design equations for the compressive strength of FRP confined columns. The procedure involved training and optimizing an Artificial Neural Network (ANN), and using the parametric variation of each input of the ANN to investigate nonlinear relationships between input parameters and model output. Results of the parametric study were illustrated using Partial Dependence Plots (PDP), and a polynomial regression model (PRM) was calibrated to offer a simple equation to calculate compressive strength of columns confined with FRP sheets. The ANN and PRM developed using this method provided more accurate and precise estimates of compressive strength of retrofitted columns than other models found in the literature. Moreover, investigating the relationships between each input variable and the output of the ANN model and coupling effects between pairs of input variables on the output of the ANN model using Partial Dependent and contour plots revealed that confined compressive strength was insensitive to the elastic modulus of the composite layers with the thicknesses of 2 mm or less. A new equation was recalibrated for specimens with composite layer thickness greater than 2 mm, which provided very accurate estimates of confined compressive strength for this empirical subset.

Early estimation of 28-day compressive strength of mortars using regression and neural network-based models

Concrete, the most commonly used construction material, presents a challenge in determining its compressive strength (CS) after 28 days of manufacture. This study aims to predict the compressive strength of conventionally cured (CC) mortar specimens using data from accelerated microwave-cured (MC) specimens. Two hundred and seventy-six specimens with different mixes and admixtures were divided into two groups: one half was subjected to MC and the other to CC. The CSs of the CC specimens were determined after 28 days, while the MC specimens were subjected to additional tests such as ultrasonics pulse velocity and three-point bending after microwave curing. Regression and artificial neural network models (ANN) were developed using the obtained data. While the data of the specimens subjected to MC were used as independent variables of the developed models, the compressive strength of the conventionally cured specimens (CC) was used as the dependent variable. In the models, 60% of the data were used for training, 20% for validation, and 20% for testing. The predictive capabilities of the developed models were compared using performance statistics such as root mean square error (RMSE), root mean absolute error (MAE), and Nash-Sutcliffe efficiency coefficient (NSE). As a result of the study, it was found that compared to all models, the highest performance values were obtained by the ANN-based models. Among the regression-based models, the multivariate adaptive regression splines method (MARS) obtained the highest performance values. Considering the calculated NSE values, it was determined that the ANN-based model gave similar results to the MARS model for the training and validation data sets but provided performance values approximately 25% higher than the MARS model for the test data set. In addition, the results obtained from the modeling studies showed that the CSs of concrete could be predicted very accurately and well in advance.

Promoting the suitability of rice husk ash concrete in the building sector via contemporary machine intelligence techniques

Eco-friendly concrete is in great demand, and as a consequence, the necessity to find sustainable alternatives to ordinary cement has become critical. In recent years, there has been considerable interest in the potential benefits of utilizing rice husk ash (RHA) as a cement substitute in concrete. Evaluating concrete properties in a laboratory setup like compressive strength (CS) is a time-consuming and expensive process. The development of trustworthy and precise models for CS estimation might be a better strategy. To determine the CS of RHA concrete, this research used boosting ensemble algorithms, namely gradient boosting (GB), AdaBoost regressor (ABR), and extreme gradient boosting (XGB), over a vast literature database. The estimation models were validated using statistical measures, error distribution by plotting violin graphs, and k-fold analysis. Furthermore, SHapley Additive exPlanations (SHAP) analysis was utilized to assess the importance of contributing elements. The results showed that XGB had the greatest performance in estimating the CS of RHA concrete out of the three methods tested. While the GB and ABR models both had R2 values of 0.90 and 0.94, respectively, the XGB model achieved a value of 0.96. The violin graphs indicated that the average absolute error values for the GB, ABR, and XGB were 5.82, 4.65, and 3.53 MPa, implying the higher precision of the XGB model in estimating the CS of RHA concrete. The SHAP study demonstrated that the three most influential factors in increasing the strength were cement, specimen age, and rice husk ash. The construction sector may benefit from the application of such technologies by facilitating the development of quick and low-cost methods for identifying material qualities and the influence of input parameters.

Estimation of compressive strength of concrete cement using random vector functional link networks: a case study

Estimation of compressive strength of concrete cement using random vector functional link networks: a case study

Estimation of compressive strength and slump of HPC concrete using neural network coupling with metaheuristic algorithms

The compressive strength and slump of concrete have highly nonlinear functions relative to given components. The importance of predicting these properties for researchers is greatly diagnosed in developing constructional technologies. Such capacities should be progressed to decrease the cost of expensive experiments and enhance the measurements’ accuracy. This study aims to develop a Radial Basis Function Neural Network (RBFNN) to model the hardness features of High-Performance Concrete (HPC) mixtures. In this function, optimizing the predicting process via RBFNN will be aimed to be accurate, as the aim of this research, conducted with metaheuristic approaches of Henry gas solubility optimization (HGSO) and Multiverse Optimizer (MVO). The training phase of models RBHG and RBMV was performed by the dataset of 181 HPC mixtures having fly ash and superplasticizer. Regarding the results of hybrid models, the MVO had more correlation between the predicted and observed compressive strength and slump values than HGSO in the R2 index. The RMSE of RBMV (3.7 mm) was obtained 43.2 percent lower than that of RBHG (5.3 mm) in the appraising slump of HPC samples, while, for compressive strength, RMSE was 3.66 MPa and 5 MPa for RBMV and RBHG respectively. Moreover, to appraise slump flow rates, the R2 correlation rate for RBHG was computed at 96.86 % while 98.25 % for RBMV in the training phase, with a 33.30% difference. Generally, both hybrid models prospered in doing assigned tasks of modeling the hardness properties of HPC samples.

Non-destructive estimation of uniaxial compressive strength of deteriorated carbonate building stones by frost action

Non-destructive estimation of uniaxial compressive strength of deteriorated carbonate building stones by frost action

Local buckling and axial compressive behavior of stainless-clad bimetallic steel-plate composite walls

This study investigated the local buckling and axial compressive behavior of stainless-clad bimetallic steel-plate composite (SC) walls. Stainless-clad bimetallic steel is a high-performance steel that combines the merits of both the substrate and cladding materials, showing good mechanical properties. An experimental database was compiled to evaluate the effect of normalized faceplate slenderness ratio on the local buckling and axial compressive behavior of SC walls. Experimental investigations were conducted on six stainless-clad bimetallic steel-plate composite walls with parameter of normalized faceplate slenderness ratios. Finite element models were developed and benchmarked for parametric studies. Test results and finite element analysis results were incorporated into the database. The enhanced database were utilized to evaluate the application of the current design provisions in calculating the axial compressive strength. It was found that CECS:546–2018, Eurocode 4, and AISC 360–22 cannot provide reasonable estimation of axial compression strength. Finally, a simplified equation was suggested for estimating the axial compressive strength of SC walls. The design method was conservative for both non-slender and slender steel faceplates.

Estimation of unconfined compressive strength of marine clay modified with recycled tiles using hybridized extreme gradient boosting method

An accurate evaluation of the clay's properties when mixed with recyclable materials is the end objective of many geotechnical experimental efforts. However, experimental studies need time, cost, and laborious efforts, which could be eliminated using alternative methods. This study concentrated on the prediction of unconfined compressive strength (UCS) of marine clay (MC) amended with recycled tiles (RT). For this aim, four novel hybridized models were developed for estimating UCS of MC modified with RT, with the integration of Adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), random forest (RF), and extreme gradient boosting (XGB) with Aquila optimizer (AO) algorithm. The results illustrate that all four models contain reliable precision in UCS prediction procedure. Between all models, AO-XGB model has the smallest values of scatter index (SI) than others (Excellent performance), with SITrain=0.0247 and SITest=0.02483. It was clear that the AO-XGB model contains the least uncertainty, with 9.735 and 1.44 for the testing and training dataset, which depicts its greater generalization capability compared to others. All in all, by considering the explanations of results and comparison with published literature, the hybridized AO-XGB model could outperform other models to be practiced in practical usage.

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.

Data-Driven Model for Predicting the Compressive Strengths of GFRP-Confined Reinforced Concrete Columns

This paper focuses on the compressive strength of Glass fiber reinforced polymer (GFRP)-confined reinforced concrete columns. Data from 114 sets of GFRP-confined reinforced concrete columns were collected to evaluate the researchers’ and proposed model. A data-driven machine learning model was used to model the compressive strength of the GFRP-confined reinforced concrete columns and investigate the importance and sensitivity of the parameters affecting the compressive strength. The results show that the researchers’ model facilitates the study of the compressive strength of confined columns but suffers from a large coefficient of variation and too high or conservative estimation of compressive strength. The back propagation (BP) neural network has the best accuracy and robustness in predicting the compressive strength of the confined columns, with the coefficient of variation of only 14.22%, and the goodness of fit for both the training and testing sets above 0.9. The parameters that have an enormous influence on compressive strength are the concrete strength and FRP thickness, and all the parameters, except the fracture strain of FRP, are positively or inversely related to the compressive strength.

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.

Evaluating the effectiveness of waste glass powder for the compressive strength improvement of cement mortar using experimental and machine learning methods

This study utilized both experimental testing and machine learning (ML) strategies to assess the effectiveness of waste glass powder (WGP) on the compressive strength (CS) of cement mortar. The cement-to-sand ratio was kept 1:1 with a water-to-cement ratio of 0.25. The superplasticizer content was 4% by cement mass, and the proportion of silica fume was 15%, 20%, and 25% by cement mass in three different mixes. WGP was added to cement mortar at replacement contents from 0 to 15% for sand and cement with a 2.5% increment. Initially, using an experimental method, the CS of WGP-based cement mortar at the age of 28 days was calculated. The obtained data were then used to forecast the CS using ML techniques. For CS estimation, two ML approaches, namely decision tree and AdaBoost, were applied. The ML model's performance was assessed by calculating the coefficient of determination (R2), performing statistical tests and k-fold validation, and assessing the variance between the experimental and model outcomes. The use of WGP enhanced the CS of cement mortar, as noted from the experimental results. Maximum CS was attained by substituting 10% WGP for cement and 15% WGP for sand. The findings of the modeling techniques demonstrated that the decision tree had a reasonable level of accuracy, while the AdaBoost predicted the CS of WGP-based cement mortar with a higher level of accuracy. Utilizing ML approaches will benefit the construction industry by providing efficient and economic approaches for assessing the properties of materials.

Estimation of compressive strength of various stones based on non-destructive techniques

Estimation of compressive strength of various stones based on non-destructive techniques

A unified design equation for square and rectangular concrete-filled steel tubular short columns

This paper develops a new unified design equation to estimate the axial compressive strength of square and rectangular concrete-filled steel tubular (CFST) short columns. The following four steps are involved: (i) compiling an experimental database consisting of 348 square and 113 rectangular CFST short columns, (ii) conducting 245 parametric analyses using benchmarked finite element (FE) models to address the gaps in the database, (iii) evaluating existing design equations and identifying their insufficiencies, and (iv) proposing and validating the new design equation. As compared with the existing design equations, the proposed design equation has the following advantages: (i) allowing a fast and convenient estimation of the axial compressive strength of square and rectangular CFST short columns, (ii) getting rid of the calculation of the section slenderness, and (iii) extending the codified material strength limits of the concrete infill and steel tube, i.e., it is suitable to square and rectangular CFST short columns with the compressive strength of the concrete infill up to 150 MPa and yield stress of steel tube up to 1000 MPa.

Evaluation of the Potential of Metakaolin, Electric Arc Furnace Slag, and Biomass Fly Ash for Geopolymer Cement Compositions.

The widespread use of geopolymer cement (GPC) has been hindered by a lack of scientific knowledge that still exists regarding its synthesis process. Key points, such as the release of aluminosilicate species from the raw materials and its link to the properties of GPC, have still not been completely studied. As a result, most of the GPC formulations covered in the literature are based on precursors' elemental analysis using XRF (X-ray Fluorescence), or other equivalent analysis methods, and consider that the total aluminosilicate content of the precursors is available for participating in the geopolymerization process, which seems very unlikely. In this study, the amounts of aluminate and silicate species released from metakaolin (MK), electric arc furnace slag (EAFS), and biomass fly ash (BFA) in alkaline dissolution tests were determined by simple spectrophotometric methods. It was found that MK yields the highest aluminosilicate dissolution amount, about 2.1 mmol of silicate + aluminate per gram of MK, while EAFS and BFA yield about 0.53 and 0.32 mmol/g precursor, respectively. These results were used to estimate the total amounts of dissolved aluminosilicates in a series of GPC mortars prepared from these raw materials, which were thereafter subjected to mechanical tests. It was shown that the mortars' compressive strength (which ranged from 1 to 63 MPa) is linearly correlated with their estimated total amount of dissolved aluminosilicates, with the best linear fit yielding a coefficient of determination above 0.99. It was concluded that by using the results of the dissolution tests, the estimation of compressive strength is greatly improved when compared to using the elemental analysis obtained by XRF, which yields a coefficient of determination of 0.88 and a larger dispersion of data points. The results reveal the usefulness of this simple method for evaluating the potential of inorganic industrial waste streams as precursors for GPC.

Condition and characterization analysis of a twentieth century cultural heritage through non-destructive testing (NDT) methods: the case of the Sivas industry school ironworking atelier in Turkey

Before the conservation and restoration of many types of cultural heritage, it is necessary to perform careful examination. This study aimed to determine the original building state and deterioration by applying non-destructive testing (NDT) methods in the case of a heritage building. Another goal was to determine, via NDT methods, whether the limestones observed in this study of different forms, colours, and textures were truly different. The Sivas Industry School Ironworking Atelier, which constitutes the research object, is one of the important public buildings in the city of Sivas, Turkey. Within the scope of the study, non-destructive infrared thermography (IRT), Schmidt hammer rebound (SHR) tests, and X-ray fluorescence (XRF) spectroscopy were applied. Accordingly, through IRT, deteriorations, anomalies, and material differences were investigated, and via SHR testing, uniaxial compressive strength (UCS) estimates, strength levels and hardness classes of stones were obtained. Moreover, via XRF spectroscopy, characterization analysis of stones was conducted. The data obtained could provide information to establish a basis for subsequent conservation. The innovation of this study is that although the infrared thermography technique is typically used in the investigation of materials, it was revealed that another technique such as XRF analysis is needed to better determine whether stones that seem different based on IRT are actually different. With IRT technique, anomaly and material detorioration can be determined. In addition to these two techniques, SHR tests that are non-destructive methods are needed to think about mechanical features of the material. Therefore, when determining the conditions and for characterization analysis of a cultural heritage before restoration, different techniques should be jointly used to complement each other.

Estimation of strength, rheological parameters, and impact of raw constituents of alkali-activated mortar using machine learning and SHapely Additive exPlanations (SHAP)

One-part alkali-activated material (AAM) is a new eco-friendly developed low-carbon binder that utilizes alkaline activators in solid form. This study deals with the experimental synthesis of one-part alkali-activated mortar (AAM) based on the partial replacement of fly ash (FA) with hydraulic lime (LM) as a precursor, and machine learning-based gene expression modeling (GEP) modeling for the optimization of the developed AAM. The datasets were established by the experimental work performed during this current study. The chosen input parameters were fly ash, hydraulic lime, sodium silicate, sodium hydroxide, sand/binder, water/binder, curing age, and time after mixing. The experimental results showed greater compressive strength and rheological parameters for the specimens having a high quantity of hydraulic lime. The GEP model has shown a strong generalization capability and prediction capacity for the future estimation of compressive strength, plastic viscosity, and yield strength. All the models showed a strong correlation of 0.92, 0.89, and 0.96 for compressive strength, plastic viscosity, and yield stress respectively. SHapely Additive exPlanations (SHAP) were employed to explore the effect of each input parameter of AAM on the predicted outcomes. The results revealed a strong interaction and positive effect of LM on the YS and PV while a negative impact was observed for the compressive strength. While fly ash has shown a negative impact on three outcomes of PV, YS, and CS respectively. The addition of LM and SS-activator leads to earlier structural build-up due to the flocculation of particles caused by the faster geopolymerization reactions.

Novel Estimation of Time-Dependent Unconfined Compressive Strength of Sand Treated with Cement and Zeolite

Novel Estimation of Time-Dependent Unconfined Compressive Strength of Sand Treated with Cement and Zeolite

Genetic programming based compressive strength prediction model for green concrete

Machine Learning (ML) has transformed the workplace in all the engineering domains. Traditionally, the lab experiments are conducted to evaluate the compressive strength of concrete however, it’s have been proved to be time-consuming and labour-intensive. The high costs involved in the testing limits the number of trials and thus compromises with reliability of the construction. The study proposes state-of-art novel genetic programming (GP) based soft-computing prediction model for the estimation of the concrete compressive strength. The current focus on sustainable development goals have led to the innovations in high performance concrete from waste materials which can potentially reduce the adverse environmental impact of cement production, however, the non-linear correlation makes it difficult for the empirical relations and basic ML models to arrive at a reliable prediction model. For this purpose, a dataset of 144 trials of fly ash and silica fume concrete is taken from literature for training and testing the GP model. The GP model performs best with ten genes and a maximum tree depth of four. The population and tournament sizes have been set at 100 and 30, respectively. Crossover and mutation probabilities are 0.84 and 0.14, respectively. The GP model is authenticated using statistical parameters (R2 = 0.98 and RMSE = 0.03). The model proposes an easy-to-use equation for the prediction of compressive strength. The proposed model will save capital, labour and time along with allowing better planning since there is no need to wait for 28 long days. The models can be trained on the in-situ data and trained model can be put to use for variety of datasets in future research.