Compressive Strength Of Concrete Research Articles

Predicting the compressive strength of high-performance concrete using an interpretable machine learning model.

Reliable predictions of concrete strength can reduce construction time and labor costs, providing strong support for building construction quality inspection. To enhance the accuracy of concrete strength prediction, this paper introduces an interpretable framework for machine learning (ML) models to predict the compressive strength of high-performance concrete (HPC). By leveraging information from a concrete dataset, an additional six features were added as inputs in the training process of the random forest (RF), AdaBoost, XGBoost and LightGBM models, and the optimal hyperparameters of the models were determined using 5-fold cross-validation and random search methods. Four interpretable ML models for predicting the compressive strength of HPC, including the RF, AdaBoost, XGBoost and LightGBM models, which combine feature derivation and random search, were constructed. In addition, the SHapley Additive exPlanations (SHAP) method was applied to analyze the effects of the input features on the prediction results of the LightGBM model, which combines feature derivation and random search. The results showed that input features such as age, water/cement ratio, slag, and water were the key influences for predicting the compressive strength of HPC. Input features such as the superplastic/cement ratio, slag/cement ratio, and ash/cement ratio had nonsignificant impacts on the predicted compressive strength.

An experimental study of the effect maximum coarse aggregate size and material orientation on concrete compressive strength

An experimental study of the effect maximum coarse aggregate size and material orientation on concrete compressive strength

Enhanced Predictive Ensemble Learning Models for Compressive Strength of Concrete Modified with Bentonite Clay

This study addresses the logistical challenges of testing bentonite plastic concrete in the field by developing predictive models for compressive strength (CS) using Python-based machine learning techniques. The models, including random forest, adaptive boosting, extreme gradient boosting, and gradient boosting regression tree, were enhanced with forensic-based investigation optimization. A dataset of 285 CS records was used to train and validate these models. SHAP analysis highlighted the impact of various inputs like gravel, bentonite, curing time, and cement on CS. Results show that the GBRT-FBIO model outperformed the others in CS prediction, indicating its superior effectiveness for material science applications.

Impact-Driven Penetration of Multi-Strength Fiber Concrete Pyramid-Prismatic Piles

The article focuses on studying the impact-driven penetration of multi-strength fibroconcrete pyramid-prismatic piles. The research object includes multi-strength pyramid-prismatic piles with varying types of reinforcement and different levels of concrete compressive strength. The aim of the study is to experimentally investigate the enhancement of pile impact resistance through the differentiated selection of concrete strength based on the dynamic stresses in the pile shaft caused by impact forces. As a result of the experimental studies on the piles, it was found that the difference in energy costs for driving them does not exceed 3.7–4.1%, proving the insignificant influence of the type of reinforcement and fiber concrete strength on the energy expenditure during driving. At the same time, it was established that the type of reinforcement and the type of fiber significantly affect the strength and impact-resistant properties of the pile shaft, ensuring defect-free driving. For example, the defectiveness (e.g., chips, cracks, potholes, spalling) of the head of the steel fiber concrete (SFC) pile reaches 57.5%, while for the polypropylene fiber concrete (PFC) pile it does not exceed 5.2%, demonstrating the advantages of using polypropylene fiber under impact conditions.

Experimental Test and Analytical Calculation on Residual Strength of Prestressed Concrete T-Beams After Fire

High temperatures during a fire can lead to the evaporation of moisture and the degradation of hydration products within concrete, consequently compromising its mechanical properties. This paper thoroughly investigates the effect of fire-induced high temperatures on the residual load-bearing capacity of concrete structures, with a focus on prestressed concrete T-beams. By conducting constant temperature tests and residual load-bearing capacity tests, complemented by finite element modeling, this study examines the degradation of mechanical properties in prestressed concrete T-beams due to fire exposure and its impact on post-fire residual load-bearing capacity. Additionally, an equivalent concrete compressive strength method was employed to propose a calculation method for concrete material degradation under high temperatures and a corresponding concrete strength reduction factor. Simplified calculations were also performed for the high-temperature damage to reinforcement and prestressed tendons, leading to the derivation of a simplified formula for the residual load-bearing capacity of post-fire prestressed concrete T-beams. The results indicate that in prestressed concrete T-beams exposed to fire, an increase in holding time results in more severe damage modes, accelerated crack propagation, and wider crack widths during bending failure. Under the same load, a longer holding time corresponds to a more pronounced reduction in deflection. At holding times of 60 min, 120 min, and 180 min, the prestress losses were 48.17%, 85.16%, and 93.26%, respectively. The cracking load decreased by 15%, 27%, and 42%, while the residual load-bearing capacity decreased by 11%, 21%, and 28%. Comparison with experimental data demonstrates that both the finite element model and the simplified calculation formula exhibit high accuracy, offering a reliable reference for the performance evaluation of post-fire prestressed concrete T-beams.

Predictive modeling for compressive strength of blended cement concrete using hybrid machine learning models

Predictive modeling for compressive strength of blended cement concrete using hybrid machine learning models

Effect of magnetization conditions on the slump and compressive strength of magnetized water concrete

Magnetized water (MW) technology uses a magnetic field to affect the dipole moment of water molecules, changing the hydrogen bonding between them. As a result, the physical and chemical properties of water are altered. MW has the potential to reduce the need for admixtures and cement while preserving the desired strength and workability of concrete. However, the exact mechanisms by which different magnetization conditions improve concrete properties remain unclear. This study explored the effects of magnetic field intensity and water flow velocity on the workability and compressive strength of concrete using a custom-built magnetization device. Twelve distinct MWs were prepared under varying magnetic field intensities (210, 230, 250, and 270 mT) and water flow velocities (0.5, 0.6, and 0.7 m/s), and the performance of the concrete was assessed by slump and compressive strength tests. Additionally, the physicochemical properties of the MW were examined. The research results indicated the presence of significant interactions between the magnetization parameters. When the water was magnetized at a flow velocity of 0.7 m/s, and combined with magnetic field intensities of 210 and 230 mT, both physicochemical properties of MW and the mechanical performance of the MW concrete were optimally enhanced. Under the conditions of a magnetic field intensity of 210 mT and flow velocity of 0.7 m/s, the workability and compressive strength of the concrete prepared with MW improved by 30.4 % and 13.7 %, respectively. These findings highlighted the importance of considering the combined effects of magnetization parameters when optimizing the mechanical properties of MW concrete.

Early age bond-slip behavior of reinforcing steel bars in concrete subjected to uniaxial lateral pressure: An experimental study

The bond between reinforcing bars and concrete is a crucial factor concerning the serviceability of reinforced concrete structures, particularly for those during construction in some developing areas where plain bars were still used as reinforcements. Despite numerous studies on early age bond behavior between steel bars and concrete, little research has been conducted to quantify the effect of uniaxial lateral pressures on this bond. This study involved testing a total of 378 pullout specimens to evaluate the early age bond-slip behavior of plain bars in concrete under uniaxial lateral pressure. The main test parameters included the concrete ages (1, 3, 7, 14 and 28 days), lateral pressures (0, 0.1, 0.2, 0.4 and 0.6 times the compressive strengths of concrete), concrete strength grades (C20, C35 and C45), and bar diameters (12, 16 and 20 mm). Based on the experimental results, a series of analytical expressions were developed for bond strength, maximum bond slip and residual bond stress predictions considering the effects of early age and lateral pressure. The results demonstrate that degradation of bond strength at early ages can be effectively mitigated by uniaxial lateral pressures. As the age decreases from 28 to 1 days, the bond strength exhibits a significant decrease of up to 51.59 %, while this adverse effect can be reduced by around 10 % in the presence of lateral pressures. Increasing the pressure level from 0 to 0.4 results in a significant increased early age bond strength by 112.6 % to 199.0 % for different bar diameters. However, the residual-to-maximum bond stress ratio remains nearly constant at 0.49 regardless of age and lateral pressure, as the pressure primarily contributes to frictional forces at the bar-concrete interface. An age-dependent bond-slip constitutive model considering the effect of uniaxial lateral pressures was finally proposed and verified with the test results. The paper concludes that given the concrete compressive strength at 28 days, the proposed bond strength and bond stress-slip models can predict the early age bond responses of plain bars in concrete under uniaxial lateral pressures with reasonable accuracy.

Influence of alkali molarity on compressive strength of high-strength geopolymer concrete using machine learning techniques based on curing regimes and temperature

The compressive strength behavior of high-strength geopolymer concrete (HSGPC) has been studied in this research work with varying alkali concentration using the novel machine learning techniques. The alkali concentration in the activation solution plays a significant role in the geopolymerization process and affects the resulting compressive strength. In this research work, the range between 4 M and 16 M for alkali molarity (M), 18 kg/m3 and 160 kg/m3 for NaOH and 41 kg/m3 and 229 kg/m3 for NaSi was collected from literature and used in the various design mixes of this exercise. This was necessary because higher alkali concentrations promote a more efficient dissolution and activation of the aluminosilicate compounds, leading to increased geopolymerization and the formation of more calcium silicate hydrate (C-S-H) gel. The increased C-S-H gel content contributes to improved strength development. However, there is an optimal alkali concentration range for the sustainable production of geopolymer concrete, and exceeding this range can have a negative impact on compressive strength and ecofriendly handling of concrete. A total of fifty-three records were collected from literature and deployed in modeling the compressive strength (Fc) considering various curing regimes. Three symbolic machine learning techniques such as genetic programming (GP), evolutionary polynomial regression (EPR), and the artificial neural network (ANN) are used in this research model. The relative importance values for each input parameter were also evaluated, which indicated that all factors have significant impacts on (Fc), but Age (i.e., curing regime) has the most influence compared to FA, NaOH, and CAg then the other inputs. From the model relations between the calculated and predicted values, it can be shown that the decisive model, ANN produced line of parametric equation of y = 0.995x, and produced performance indices; MAE of 2.13 MPa, RMSE of 2.86 MPa and R-squared of 0.981, which makes the ANN the most reliable model in agreement with previous applications of the technique. These are against the poor performance of the EPR and GP, which produced R-squared less than 0.8 with higher error rates. The Taylor chart and the variance distribution, which further compares the accuracy and variances of the developed models support the outcomes. Generally, alkali molarity has shown its potential in the production of HSGPC due to its role in the reactivity phases of the concrete formulation; hydration, activation, pozzolanic, and geopolymerization reactions producing the gel needed for the strength gain in HSGPC.

Performance of Molasses Waste as a Cement Replacement to Study Concrete Compressive Strength

To reduce the negative environmental impact of cement production and preserve natural resources, an experimental investigation was conducted to study the performance of concrete specimens at different curing ages and to determine the compressive strength of these specimens by replacing cement with molasses. Experimentation was carried out on the concrete specimens at a temperature range of 25 °C to 30 °C; six specimens were cast for each replacement ratio, except for 0.75% wt. of cement (86.55 g) and 1% wt. of cement (113.6 g), where five samples were considered for each ratio. The average 28-day compressive strength of the conventional concrete specimens came out to be 29 MPa, but increased to 40 MPa with the addition of 0.25% wt. of cement molasses (28.85 g). It was observed that as the percentage of molasses waste in the concrete mix was further increased by replacing the cement, the compressive strength of the concrete specimens increased gradually and then significantly decreased. The findings shed light on the prospect of using molasses waste instead of cement in the concrete mix. Also, it is worth mentioning that about 30% of the cost–benefit was obtained with reference to that of conventional admixtures available in the market for the production of concrete. However, it is notable that a long-term durability study needs to be conducted before making it viable. This work not only addresses a sustainable and innovative method of waste management (SDG12), but also contributes to low carbon emissions (SDG13). The novelty of this work lies in the fact that no such kind of study has been conducted in Pakistan so far, in addition to the very limited international literature available, and, in particular, no evidence on the compressive strength results at higher molasses dosages, i.e., 1% wt. of cement (113.6 g) and 2% wt. of cement (230.8 g).

Contribution to the design of prestressed hollow core purlins without transverse reinforcement

AbstractThe hollow core purlin is a structural element resulting from the sectioning of hollow core slabs into two ribs. This component can be classified as a prestressed hollow core beam without transverse reinforcement. Although this solution offers potential for increasing the productivity of precast concrete element factories, it currently lacks normative guidelines due to the high risk of shear failure. The aim of this study is to highlight an equation to underpin the design of hollow core purlins based on experimental verification. To achieve this objective, six samples of hollow core purlins were analyzed. Initially, the manufacturing characteristics and cross‐sectional geometry of each piece were observed to gather data on the production process. Subsequently, positive bending tests were conducted to characterize stiffness and determine the effective mean tensile strength. Later, shear strength tests were performed to identify the failure mechanisms of the elements. In parallel, the prestressing forces, stiffness, tensile strength, cracking moment, calculated shear strength using the flexural‐shear mechanism, and verification of shear strength due to diagonal tension were computed based on concrete compressive strength values and data provided by the manufacturer. Finally, the experimental values obtained were compared with the theoretical calculated values to establish a correlation that highlights the calculation procedure that best represents the behavior of the studied elements.

Predictive Modeling of the Long-term Effects of Combined Chemical Admixtures on Concrete Compressive Strength Using Machine Learning Algorithms

Predictive Modeling of the Long-term Effects of Combined Chemical Admixtures on Concrete Compressive Strength Using Machine Learning Algorithms

Predicting and optimizing maximum flexural strength of planar composite plate shear walls – concrete filled with the application of LR and RSM

Traditional theoretical models, while conservative, overlook the complex, nonlinear interactions between material and geometric parameters that influence wall flexural strength. Additionally, the extent to which these models maintain their conservative assumptions across varying parameters remains uncertain. This study aims to develop advanced predictive models for the flexural strength of planar composite plate shear walls—concrete filled (C-PSW/CF) by employing Linear Regression (LR) and Response Surface Methodology (RSM). Central Composite Design (CCD) was utilized to design numerical experiments to analyze the effects of varying parameters, including steel plate yield strength, concrete compressive strength, wall depth, depth-to-thickness ratio, and reinforcement ratio. A validated complex finite element modeling (FEM) procedure was used to analyze numerical experiments involving varying parameters of the selected variables. Both LR and RSM were applied to the generated dataset to formulate predictive equations for the maximum moment capacity of walls. Interaction plots were developed to reveal the nonlinear relationships between the variables and identify the most effective parameters influencing the wall strength. Comparisons between theoretical predictions, RSM, and LR models revealed that RSM provided the most accurate predictions, closely aligning with FEM results and effectively capturing the nonlinear interactions between wall parameters. The findings of this paper contribute to the development of more practical, accurate and optimized design methods for C-PSW/CF, ultimately improving both structural safety and efficiency.

Predicting the compressive strength of fiber-reinforced self-consolidating concrete using a hybrid machine learning approach

Predicting the compressive strength of fiber-reinforced self-consolidating concrete using a hybrid machine learning approach

A Study on a New Method for Simultaneous Internal Curing and hydrophobic in Concrete

The high hydrophilicity and permeability of concrete caused by many pores and hydroxyl groups produced by cement hydration are the main factors leading to dry shrinkage, cracking, and poor corrosion resistance of concrete. Giving concrete internal curing and hydrophobicity is an effective measure to solve the above problems. In this paper, a new idea and preparation method for an organic combination of hydrophilic and hydrophobic materials are proposed to improve the service life of concrete structures. Furthermore, the combined effects of hydrophilic and hydrophobic amphoteric materials (HAM) on the mechanical properties, internal curing properties, and hydrophobic properties of concrete were comprehensively investigated. In addition, the physical and chemical action mechanisms were analyzed in conjunction with microscopic tests. The results indicated that HAM rapidly released water within 3-7 days before concrete curing, and then slowly released water accompanied by the release of hydrophobic materials, gradually from hydrophilic to hydrophobic properties; The addition of HAM did not reduce the mechanical properties of concrete, which solved the problem of low compressive strength of integral hydrophobic concrete; The addition of HAM reduced the drying shrinkage of concrete by 154.12%, increased the contact angle to 96°, and decreased the water absorption rate by 36%, allowing the concrete to achieve better internal curing and a hydrophobic effect. The preparation of this material can open up a new way to solve the hydrophilic and hydrophobic properties required by concrete at the same time, and fill the gap that concrete can not balance hydrophilic and hydrophobic properties.

Data-driven models for predicting compressive strength of 3D-printed fiber-reinforced concrete using interpretable machine learning algorithms

3D printing technology is growing swiftly in the construction sector due to its numerous benefits, such as intricate designs, quicker construction, waste reduction, environmental friendliness, cost savings, and enhanced safety. Nevertheless, optimizing the concrete mix for 3D printing is a challenging task due to the numerous factors involved, requiring extensive experimentation. Therefore, this study used three machine learning techniques, including Gene Expression Programming (GEP), Multi-Expression Programming (MEP), and Decision Tree (DT), to forecast the compressive strength of 3D printed fiber-reinforced concrete (3DP-FRC). The dataset comprises 299 data points with sixteen variables gathered from experimental research studies. For training the model, 70 % of the dataset was used, while the remaining 30 % was reserved for model testing. Several statistical metrics were utilized to evaluate the accuracy and applicability of the models. In addition, SHapley Additive exPlanations (SHAP), partial dependence plots, and individual conditional expectations approach were employed for the interpretability of the models. The proposed GEP, MEP, and DT models indicated enhanced efficacy, exhibiting correlation coefficient (R) scores of 0.996, 0.987, and 0.990, with mean absolute errors (MAE) of 1.029, 4.832, and 2.513, respectively. Overall, the established GEP model demonstrated exceptional performance compared to MEP and DT, showcasing high prediction precision in assessing the strength of 3DP-FRC. Moreover, a simple empirical formulation has been devised using GEP to predict the compressive strength, offering a simplified and efficient approach for predicting 3DP-FRC strength. The SHAP approach identified water, silica fume, fiber diameter, curing age, and loading directions as leading controlling parameters in predicting strength of 3DP-FRC. In summary, the proposed models can potentially minimize both the computational workload and the need for experimental trials in formulating the mixed design of 3D-printed concrete.

Soft Computing Techniques to Model the Compressive Strength in Geo-Polymer Concrete: Approaches Based on an Adaptive Neuro-Fuzzy Inference System

Media visual sculpture is a landscape element with high carbon emissions. To reduce carbon emission in the process of creating and displaying visual art and structures (visual communication), geo-polymer concrete (GePC) is considered by designers. It has emerged as an environmentally friendly substitute for traditional concrete, boasting reduced carbon emissions and improved longevity. This research delves into the prediction of the compressive strength of GePC (CSGePC) employing various soft computing techniques, namely SVR, ANNs, ANFISs, and hybrid methodologies combining Genetic Algorithm (GA) or Firefly Algorithm (FFA) with ANFISs. The investigation utilizes empirical datasets encompassing variations in concrete constituents and compressive strength. Evaluative metrics including RMSE, MAE, R2, VAF, NS, WI, and SI are employed to assess predictive accuracy. The results illustrate the remarkable precision of all soft computing approaches in predicting CSGePC, with hybrid models demonstrating superior performance. Particularly, the FFA-ANFISs model achieves a MAE of 0.8114, NS of 0.9858, RMSE of 1.0322, VAF of 98.7778%, WI of 0.9236, R2 of 0.994, and SI of 0.0358. Additionally, the GA-ANFISs model records a MAE of 1.4143, NS of 0.9671, RMSE of 1.5693, VAF of 96.8278%, WI of 0.8207, R2 of 0.987, and SI of 0.0532. These findings underscore the effectiveness of soft computing techniques in predicting CSGePC, with hybrid models showing particularly promising results. The practical application of the model is demonstrated through its reliable prediction of CSGePC, which is crucial for optimizing material properties in sustainable construction. Additionally, the model’s performance was compared with the existing literature, showing significant improvements in predictive accuracy and robustness. These findings contribute to the development of more efficient and environmentally friendly construction materials, offering valuable insights for real-world engineering applications.

Performance evaluation of pigmented fair-faced concrete under various environments: mechanical properties, apparent properties, and color stability

Fair-faced concrete is green concrete that is directly decorated by its texture. For a more stunning visual impact, pigmented fair-faced concrete with more colors on the surface is prepared. However, the effects of different pigments and service environments on pigmented fair-faced concrete need to be explored. Therefore, four iron oxide inorganic pigments were employed to prepare pigmented fair-faced concrete, and the changes in its fresh properties and mechanical properties were studied through slump, slump flow, and compressive strength tests. Meanwhile, camera analysis and colorimetry were utilized to evaluate the changes in its apparent properties and color stability under four different distinct service environments in this experiment. Results indicate that incorporating pigments reduces both the slump and slump flow of the fresh mixture, and its fresh properties gradually decrease as the pigment content increases. The slump and slump flow of concrete with 6% iron oxide yellow (IOY) were 30.00% and 44.00% lower than the control. Adding IOY and iron oxide red (IOR) can improve the 7-d and 28-d compressive strength of concretes. The 28-d compressive strength of concrete with 2%-6% IOY increased by 7.72%, 11.78% and 15.09% over the control. Meanwhile, the surface pore area of concrete with IOY is the lowest and that of concrete containing 2% IOY is 3.97 cm2/m2. Additionally, the color stability of concrete performs best in water immersion environments and worst in natural environments. It is noteworthy that the concrete added with IOY exhibits excellent color stability under all four service environments. This study aims to evaluate the effects of different pigments on concrete properties and investigate the color stability of four types of concrete under diverse service conditions. The findings will promote the application of pigmented fair-faced concrete in construction and provide technical insights for future research.

Predicting the Residual Compressive Strength of Concrete Exposed to Elevated Temperatures Using Interpretable Machine Learning

Predicting the Residual Compressive Strength of Concrete Exposed to Elevated Temperatures Using Interpretable Machine Learning

Compressive behavior of manufactured sand recycled coarse aggregate concrete-filled steel tubular short columns

To study the mechanical properties of manufactured sand recycled aggregate concrete-filled steel tubular (RACFST) columns, a total of 81 square short RACFST column specimens with different types of manufactured sand and natural sand were created and subjected to axial compression tests. The variables included sand type, core concrete strength, slenderness ratio, and steel tube thickness. The failure mode, axial load-displacement curve, axial compressive capacity, and deformation capacity of the specimens were analyzed in detail. The results indicate that the failure mode of the specimen is generally waist drum-type failure and shear-type failure, and both of them are local buckling. The load-displacement curve of the manufactured sand specimen is similar to that of the natural sand specimen, but the steel tube of the manufactured sand specimen yields later than that of the natural sand specimen. Generally, the limestone manufactured sand specimen has the highest axial compressive capacity and the best ductility ratio, while the pebble manufactured sand specimen has an axial compressive capacity similar to the natural sand specimen but a lower ductility ratio. The effects of concrete compressive strength, slenderness ratio, and steel tube thickness on specimens with manufactured sand are similar to those with natural sand. Based on the results of this experimental study, a formula for calculating the axial compressive capacity of square RACFST columns is provided.