Strength Of Concrete Research Articles

Experimental Study of The Effect of Activator Molarity on The Compressive Strength of Fly Ash-Based Geopolymer Concrete from Bengkayang Regency Power Plant

Geopolymer concrete is a form of concrete where Portland cement is substituted with alternative materials like fly ash, rice husk ash, and other silica- and aluminum-rich sources as binders. The research employed an experimental method by preparing geopolymer concrete test specimens using fly ash, NaOH solution, Na₂SiO₃, fine aggregate, and coarse aggregate. Tests conducted included physical properties (slump test and unit weight) and mechanical properties (compressive strength, split tensile strength, and modulus of elasticity). The results showed a slump range of 130–190 mm, an average unit weight between 2249.39 kg/m³ and 2336.39 kg/m³, a 28-day average compressive strength between 12.32 MPa and 15.92 MPa, an average split tensile strength between 1.46 MPa and 1.56 MPa, and an average modulus of elasticity (Chord Modulus) between 3011.67 MPa and 3881.8 MPa. The optimal variation was achieved with an NaOH solution concentration of 10 M, resulting in a slump of 160 mm, a 28-day average compressive strength of 15.92 MPa, an average split tensile strength of 1.56 MPa, and an average modulus of elasticity (Chord Modulus) of 3881.48 MPa. Therefore, fly ash-based geopolymer concrete demonstrates potential as a viable substitute for cement as a binder material.

Flexural Behavior of Thin Concrete Slabs Reinforced with Surface Embossed Grid-Type Carbon-Fiber Composites

Fiber-reinforced polymers (FRPs) are being increasingly used to replace rebars as reinforcements for concrete. In this study, the flexural behavior of one-way concrete slabs reinforced with a grid-type carbon FRP (CFRP) (carbon grid), in the form of strands with embossed surfaces, was experimentally investigated. The experimental variables included the effective depth, number of carbon grid layers, and concrete compressive strength. The results exhibit that the surface embossing of the CFRP strands effectively improves their bonding with concrete based on the crack formation pattern. Concrete specimens reinforced with carbon grids exhibited an increased maximum load and stiffness as the effective depth, number of carbon grid layers, and concrete compressive strength increased. Among the experimental variables, the effective depth exhibited the greatest influence on the flexural behavior of the carbon-grid-reinforced concrete specimen. Furthermore, the ratios of the experimental to calculated flexural strength values for all carbon-grid-reinforced concrete specimens ranged from 0.74 to 1.22. Based on the results, a trilinear load–deflection curve was proposed to simulate the flexural behavior of carbon-grid-reinforced concrete members, considering the bond property between the concrete and the carbon grid. The proposed trilinear load–deflection curve reasonably simulated the flexural behavior of the specimens reinforced with carbon grids.

Study on the Axial Compressive Behavior of Steel Fiber Reinforced Concrete Confined with High-Strength Rectangular Spiral Stirrup

Monotonic axial compression tests were carried out on 16 steel fiber-reinforced concrete (SFRC) columns confined by rectangular spiral stirrups. The impacts of stirrup spacing, stirrup strength, concrete strength, and cross-sectional aspect ratio on the peak load, ductility, and failure mode of these columns were analyzed. The test results demonstrate that steel fibers significantly mitigate the spalling of the concrete column’s protective layer through their bridging effect. Small spacing and high-strength spiral stirrups effectively confine the core concrete, enhancing the bearing capacity and ductility of concrete columns. Concrete strength exhibits a positive correlation with the confinement effect. However, as concrete strength increases, the rate of improvement in the confinement effect decreases. At peak compressive stress, the high-strength stirrup may not reach its yield state. Based on the test results, a method for calculating stirrup stress under the peak stress of confined concrete is proposed. A “coupling restraint coefficient” is proposed, and a constitutive model for HRSS confined steel fiber reinforced concrete is developed, considering the coupled effect of effective confinement forces in different directions. Comparative analysis shows that the constitutive model established in this paper agrees well with the experimental results and demonstrates good applicability.

Optimizing high-strength concrete compressive strength with explainable machine learning

This study leverages machine learning to enhance the prediction of high-strength concrete (HSC) compressive strength, addressing the limitations of conventional methods, which are often tedious, less reliable, and time-consuming. Extreme Gradient Boosting (XGB) serves as the primary model, with hyperparameter optimization via metaheuristic algorithms such as Cuckoo Search (CSA), Water Strider (WS), Leopard Seal (LS), Harris Hawk (HH), Invasive Weed (IW), and Forest Optimization (FO). A total of 681 data sets were collected from existing literature. The models underwent tenfold cross-validation, with the LS-XGB model achieving an almost ideal performance in testing sets. Other models, including CSA-XGB, WS-XGB, HH-XGB, IW-XGB, and FO-XGB, also demonstrated strong performance, each with R2 > 0.96. For model explainability, Shapley's Additive Explanation (SHAP) analysis has been applied to the best-performing LS-XGB model. The analysis revealed that cement and superplasticizer (SP) are the most crucial features contributing to HSC development, with optimal ranges identified at 600–900 kg/m3 for cement and 8–10 kg/m3 for SP. The study demonstrates on how feature interactions contribute to concrete materials compressive strength, providing better and above all sustainable constructions. Furthermore, the LS-XGB model's optimal performance depicts the strongly nonlinear nature of HSC materials, validated through a set of derived graphs. Additionally, 30 concrete cubes were prepared for experimental validation, and the datasets demonstrated an accuracy of 92% showcasing the ability of models to make well informed decision.

Utilizing waste materials in concrete: A review on mechanical and sustainable performance

The construction industry consumes more natural resources than any other sector, necessitating sustainability practices. Utilizing waste materials in concrete addresses disposal challenges while enhancing sustainability. Many waste materials possess pozzolanic properties, improving concrete performance. This review aims to examine sustainable concrete incorporating waste materials such as fly ash (FA), silica fume (SF), recycled concrete aggregate (RCA), slag (S), waste perlite powder (WPP), groundnut husk ash (GHA), and nano silica (NS). The methodology involves a comprehensive analysis of life cycle assessment (LCA) studies, country-specific waste production, and concrete performance focusing on environmental and economic impacts. Results show that FA, SF, S, WPP, and NS enhance concrete's compressive strength, with SF yielding a 41% increase at a 12% substitution rate. Additionally, substituting FA can reduce carbon dioxide (CO2) emission by 68% and combining 5% SF and 15% FA lower CO2 emission by 16% compared to reference concrete. Furthermore, combining WPP and RCA results in a 17% cost reduction compared to reference concrete, while GHA substitution decreases cost by up to 30%. These findings highlight the potential of utilizing waste materials to improve mechanical performance, reduce environmental impact, and lower construction costs. The review suggests future research directions to further improve performance.

Performance Analysis of Ronier Fibers (Borassus Aethiopum) with Silica Fume on the Mechanical Properties of Concrete

This research examines how Silica Fume (SF) and Ronier Fibers (RF) (Borassus aethiopum) affect concrete's mechanical and durability properties. Natural fibers are sustainable and have the potential to improve concrete performance. Thus, their inclusion into concrete has attracted considerable research. This study used SF as an additional cementitious material at a replacement rate of 10%. RF were utilized at 0.5%, 1%, and 1.5% by weight of cement, including Untreated (UN) and Treated (TRT) forms. An alkali treatment was utilized to increase the adherence of TRT fibers to the cement matrix. In addition to the durability traits, like Water Absorption (WA) and resistance to chemical attack, the mechanical qualities, including compressive strength, tensile strength, and flexural strength, were measured. The findings showed that while SF increased the composite's strength and durability, the addition of RF, especially in the TRT form, significantly increased the concrete's tensile and flexural strengths. The ideal mechanical strength-to-durability ratio was found to be 1% TRT fiber and 10% SF content. Moreover, fiber treatment strengthened the fiber-matrix bond by decreasing the WA and enhancing the resilience to harsh environmental factors. This study’s results revealed that using SF along with RF offers a viable homogenous and compact concrete matrix, making it appropriate for use in environmentally friendly construction projects.

A Parametric Study of GFRP Composite Beams with Encased I-Section using 3D Finite Element Modeling

Glass Fiber Reinforced Polymer (GFRP) materials play a crucial role in the construction industry due to their lightweight properties, corrosion resistance, and high strength. Furthermore, the GFRP reinforcement ratio is a significant factor in the strength design philosophy that governs the design of flexible members. This study presents a parametric investigation of the performance of concrete composite beams reinforced and encased with pultruded GFRP. This study investigates the effect of concrete compressive strength and GFRP reinforcement ratio on the structural behavior of composite beams with encased GFRP sections under static loads. To achieve this objective, five simply supported models were numerically simulated using the Abaqus software. The reference model comprised normal concrete with a 30 MPa compressive strength, 0.42% GFRP longitudinal reinforcing ratio, and transverse steel rebars, with the GFRP I-section encased in the center of the cross-section. The other models maintained similar properties and geometries but varied in reinforcement ratio (0.85% and 1.2%) and compressive strength (25 MPa and 20 MPa). The results showed that increasing the reinforcement ratio in composite beams with encased GFRP sections improved the ultimate capacity by approximately 29% and 41% for 0.85% and 1.2% ratios, respectively, compared to the reference beam. Conversely, reducing compressive strength below 30 MPa decreased maximum load by about 16% and 23% for 25 MPa and 20 MPa values, respectively, in relation to the reference beam.

Investigation of the Gaussian Process with Various Kernel Functions for the Prediction of the Compressive Strength of Concrete

The Compressive Strength of Concrete (CSC) is a critical parameter for evaluating the quality of concrete used in various construction projects, including buildings, bridges, and roads. The primary objective of this study is to examine the efficacy of a Gaussian Process (GP) Machine Learning (ML) model employing two kernel functions: Radial Basis Function (RBF) and Polynomial (POL), for predicting the CSC, considering readily quantifiable parameters. Based on these kernel functions, two models were created for this prediction, GP-RBF and GP-POL. The modeling process employed a total of 369 concrete sample data, including compressive strength values and eleven other physico-mechanical properties, collected from the Cua Luc bridge project in Vietnam. This dataset was partitioned into a training set (70%) and a testing set (30%) for model training and validation. Various validation metrics, including R2, Root Mean Square Error (RMSE), and Mean Absolute Error (MAE), were used to evaluate and compare the models. The findings of this study demonstrated that both models GP-RBF and GP-POL exhibited strong performance in predicting CSC, with GP-POL demonstrating marginal superiority over GP-RBF. Consequently, it can be concluded that POL is more efficacious than RBF in training the GP model for CSC prediction.

AI-driven Modeling for the Optimization of Concrete Strength for Low-Cost Business Production in the USA Construction Industry

The need to develop ecologically friendly sustainable building materials is made apparent by the worldwide construction industry's substantial contribution to global greenhouse gas emissions. The use of supplemental materials in concrete is one potential solution to lessen the environmental footprint. Thus, the purpose of this work is to use Machine Learning (ML) algorithms to forecast and create an empirical formula for the Compressive Strength (CS) of concrete with supplemental materials. Six distinct ML models—XGBoost, Linear Regression, Decision Tree, k-Nearest Neighbors, Bagging, and Adaptive Boosting—were trained and tested using a dataset that included 359 experimental data of varying mix proportions. The most significant factors used as input parameters are cement, aggregates, water, superplasticizer, silica fume, ambient curing, and supplemental material. Several statistical measures, such as Mean Absolute Error (MAE), coefficient of determination (R2), and Mean Square Error (MSE), were used to evaluate the models. XGBoost model outperformed the other models with R2 values of 0.99 at the training stage. To ascertain how the input parameters affected the outcome, feature importance analysis using Shapely Additive exPlanations (SHAP) was conducted. It was demonstrated that curing age and cement type significantly affected the strength of concrete with high SHAP values. By eliminating experimental procedures, reducing the demand for labor and resources, increasing time efficiency, and offering insightful information for enhancing sustainable manufacturing of concrete, this research advances the low-cost production of concrete in the USA construction industry.

Effect of CO2 curing on the strength and microstructure of composite waste glass concrete

Effect of CO2 curing on the strength and microstructure of composite waste glass concrete

Prediction of Thermal Cracking During Construction of Massive Monolithic Structures

The problem of early crack formation caused by temperature stresses in hardening concrete is very relevant for massive monolithic reinforced concrete structures. The aim of the work is to develop a method for thermal cracking risk prediction during the construction of massive monolithic reinforced concrete structures. The innovation of the research consists in taking into account the dependence of the concrete elastic modulus and strength on the time and temperature of hardening. The significance of the study lies in analysis of methods for reducing the risk of early cracking using the example of a real structure. The object of the study is a fragment of a massive monolithic dock wall. The analysis is performed by the finite element method using a program developed by the authors in the MATLAB environment. Verification of the developed software was performed by comparison with the solution in ANSYS using a linear elastic model without time dependence of the elastic modulus. Next, various options were used to set the dependence of the mechanical characteristics of concrete on the time and temperature of hardening. An analysis was conducted of the possibility of reducing the risk of early cracking by reducing the length of the concrete block, correcting the heat exchange conditions of the surfaces, and reducing the heat generation of concrete.

Performance of circular CFST columns strengthened with CFRP grid-reinforced ECC under axial compression: Numerical modelling and design

This paper describes a detailed numerical investigation using finite element models validated against test results from an experimental investigation on concrete-filled steel tubular (CFST) columns strengthened with carbon fiber-reinforced polymer (CFRP) textile grid-reinforced engineered cementitious composite (ECC). The three-dimensional model incorporates the confinement effects provided by the ECC, CFRP grid layers, and the circular tube. The validated model is used to evaluate the performance of strengthened CFST short columns through parametric assessments covering different geometric and material properties. Range analysis is applied in an orthogonal design to evaluate the relative significance of key parameters influencing the ultimate axial strength of the column member. The findings reveal a nearly 24 % increase in ultimate strength and over a 42 % enhancement in the residual strength of the concrete core due to the strengthening configuration. Additionally, the composite action of the CFRP grid-reinforced ECC leads to a 13 % increase in ultimate axial strength and over a 28 % improvement in the residual strength of the CFST column. The tube diameter is shown to have the greatest impact on the column axial resistance, followed by ECC thickness, ECC compressive strength, core concrete compressive strength, tube yield strength, tube thickness, and the number of CFRP grid layers. Finally, a simplified design model, which is supported by the experimental and numerical results, is introduced for the prediction of the compressive capacity of strengthened circular CFST columns.

Comparative use of different AI methods for the prediction of concrete compressive strength

Comparative use of different AI methods for the prediction of concrete compressive strength

3D mapping of the stiffness evolution of SAP concrete through elastic wave tomography

ABSTRACT Early-stage phenomena during concrete curing greatly influence its final mechanical properties, making hydration monitoring crucial. Non-destructive techniques have, therefore, gained great attention for early-age concrete assessment. This study examines the elastic wave velocity and stiffness development of hardening concrete cubes using elastic wave tomography (EWT) at different curing stages. Simultaneously, ultrasonic pulse velocity (UPV) measurements with a pseudorandom noise (PRN) pulse are performed and compared to a standardized UPV device. Monitoring begins 7 hours after mixing and continues until 70 hours. A three-dimensional (3D) velocity map compares conventional concrete with concrete containing superabsorbent polymers (SAPs), a shrinkage-reducing admixture. SAPs reduce the compressive strength of concrete by increasing the porosity, affecting the elastic wave velocity evolution. However, internal curing by SAPs promotes hydration product formation, partially offsetting strength loss. EWT provides global insights into SAP uniformity across specimens, contrasting with traditional single-path UPV measurements. Furthermore, early-age stiffness evolution, linked to UPV, is a good indicator of final mechanical properties. Results show that SAP concrete exhibits slower 3D stiffness development than conventional concrete but supports hydration over a longer period. Continuous wavelet transform (CWT) analysis of PRN signals shows prolonged signal transmission development, indicating a more thorough hydration evolution in SAP concrete.

Enhancing concrete strength through precision vibration engineering: Aggregate settlement and pore stats

Enhancing concrete strength through precision vibration engineering: Aggregate settlement and pore stats

Elucidating the Memory Effects of Magnetic Water Treatment via Precipitated Phase Changes of Calcium Carbonate

Research on the effects of magnetic fields on water and aqueous solutions has produced various findings, such as the suppression of scale formation in pipes and boilers, inhibition of metal corrosion, enhancement of concrete strength, and changes in properties like viscosity and electrical conductivity. However, the challenges in quantifying these effects, the issues with reproducibility affected by trace elements in the water used in the experiments, and the involvement of complex parameters and mechanisms have led to ongoing debates, with some questioning the very existence of magnetic field effects. The “memory effect”, where the impact of magnetic exposure persists for a certain period, further complicates explanations of these phenomena. To fully elucidate and enable practical applications of these effects, further research is essential. In this study, we aimed to investigate the magnetic field effects on water, including memory effects, where the quantification and elucidation potentially lead to various applications, including environmentally friendly solutions on scale suppression and life science issues. The results revealed that the vaterite phase precipitation ratio significantly increased in magnetically treated water, reaching up to 51%, from 26% without the treatment, which is high reproducibility; furthermore, a reduction in mean particle size was observed when using magnetically treated water, suggesting that it may help prevent scaling. Furthermore, when solutions of calcium carbonate, calcium chloride, and sodium bicarbonate were individually subjected to magnetic treatment, the most notable increase in the vaterite phase precipitation ratio was observed when calcium chloride and sodium bicarbonate solutions were magnetically treated separately and then reacted to precipitate calcium carbonate.

Effect of increased basalt fibre dosage on hybrid basalt fibre reinforced concrete's strength properties

Natural, eco-friendly fibers are increasingly being used as reinforcement in the creation of lightweight, low-effort polymer composites on a global scale. Because it is intelligent and has unique properties above glass fibers (GF), basalt fiber (BF) is one such material of interest that is currently in widespread use. Among these composites' clear advantages are their high specific mechanic properties, biodegradability, and non-grating nature. In this paper, basalt fibers used as reinforcement for composites are surveyed. The study also addresses how concrete's strength is affected by curing. In addition to this, an attempt is made to highlight the growing trend in research dissemination and activity in the area of basalt fibers. Further segments talk about the improvement in mechanical, warm and synthetic safe properties accomplished for applications in specific enterprises. The results of this study reveal that the optimum value of strength at 28th day of curing is obtained for a mix made with 5% of chopped basalt fiber 50% 6mm Long BF and 50% 12mm Long BF.

Research on the Seismic Performance of Concrete Compressed‐Flexural Members After Cumulative Damage From 100 Years of Design Usage

ABSTRACTThis study investigates the long‐term effects of cumulative damage on concrete members over a century, emphasizing its critical inclusion in design protocols. Employing both static and dynamic experimental approaches, the research first examines the fundamental mechanical properties of damaged concrete at the material level. Earthquake intensity data from Northeast China are then analyzed to establish the frequency of predominant intensities over a 100‐year timeframe. Subsequently, four frame columns are exposed to cumulative pseudodynamic seismic damage, followed by pseudostatic testing to assess their seismic resistance. The results demonstrate that repeated sub‐cracking‐stress damage increases concrete's uniaxial compressive strength while reducing its ultimate deformation capacity. For elements with a low axial load ratio, minor seismic damage minimally impacts ultimate deformation but enhances ultimate bearing capacity, with the steel reinforcement reaching yield at an earlier stage. In contrast, for elements with a high axial load ratio, minor seismic damage has negligible effects on both ultimate deformation and bearing capacity, though it still accelerates the onset of yield in the steel reinforcement.

Utilizing linear and nonlinear ultrasound for improved estimation of concrete strength

ABSTRACT This study investigated non-destructive concrete strength evaluation using in-situ cored samples. Data from various mix proportions and design strengths revealed inconsistent correlations between compressive strength and linear ultrasonic wave velocity (V p). This inconsistency arises because V p captures only linear elastic behaviour, whereas concrete strength reflects both linear and nonlinear behaviours. In contrast, the acoustic nonlinearity parameter (β re) exhibits superior sensitivity to microstructural conditions owing to its direct link to material nonlinearity. We proposed a new integrity index (M/β*) by combining the constrained modulus (M) derived from V p with β re to capture the entire elastic behaviour. This index demonstrated statistically significant correlation with the factor of safety (FS) across different design strengths and mix proportions. Next, we developed a risk stratification system based on M/β* using fuzzy c-means clustering, categorising concrete into three risk zones: critical (M/β* <40), moderate (40 < M/β* <75) and low (M/β* >75), providing a quantitative tool for assessing concrete quality. The results highlight the importance of integrating linear and nonlinear ultrasonic parameters for accurate strength prediction, particularly in field conditions where traditional V p–strength correlations are unreliable. This approach offers a new method for non-destructive concrete strength detection, thereby enhancing structural safety assessments.

Sustainable Optimization of Concrete Strength Properties using Artificial Neural Networks: A Focus on Mechanical Performance

Abstract In this study, a comprehensive dataset comprising 358 data points was collected from the literature, focusing on the compressive strength, split tensile strength, and modulus of elasticity of concrete incorporating recycled concrete aggregate. An Artificial Neural Network was employed as the primary machine learning algorithm to predict these mechanical properties. K-fold cross validation was utilized to validate the model’s reliability, while sensitivity analysis was performed to identify the most influential input parameters among the independent variables. The model demonstrated strong performance during training, achieving R2 values of 0.97 for compressive strength, 0.98 for split tensile strength, and 0.99 for modulus of elasticity with corresponding RMSE values of 2.55, 3.85, and 0.37, respectively. The MAE and MAPE values during training were 0.68 and 0.03 for compressive strength, 0.71 and 0.03 for split tensile strength, and 0.67 for modulus of elasticity, with RMSE values of 8.57, 5.03 and 3.83, respectively. Testing results revealed R2 values of 0.75 for compressive strength, 0.78 for split tensile strength, and 0.78 for modulus of elasticity, with RMSE values of 8.57, 5.03, and 3.83, respectively. The sensitivity analysis indicated that the cement percentage and water-to-cement ratio were the most significant parameters influencing concrete strength.