Compressive Strength Of Concrete Research Articles

Multiscale experimental and simulation analysis on the synergistic modification mechanism of nano-SiO₂/EVA at the recycled concrete interface

ABSTRACT To enhance the reuse of construction waste in structural materials and improve the interfacial transition zone (ITZ) in recycled concrete (RC), this study employed two modification strategies and conducted a multiscale analysis to compare the effects of combined Nano-SiO2 (NS)/ethylene-vinyl acetate copolymer (EVA) modification and EVA-only modification on the mechanical properties, hydration characteristics, and microstructure of RC. The aim was to better overcome the interface defects in recycled concrete through synergistic modification. Experimental results show that the incorporation of EVA and NS significantly improves the mechanical properties of RC, with the combined modification outperforming the single modification. Specifically, when EVA content is 5% and NS is 2%, the 28-day compressive, shear, and flexural strengths of concrete increased by 15.7%, 17.6%, and 18.5%, respectively. Microscopic tests, including scanning electron microscopy (SEM), X-ray diffraction (XRD), and Fourier-transform infrared spectroscopy (FTIR), revealed that the cement matrix became denser, the porosity decreased, and the content of hydrated calcium silicate (C-S-H) increased. Molecular dynamics (MD) simulations indicate that EVA-only modification enhances interfacial bonding through hydrogen and ionic bonds, while the combined modification further strengthens the interface by forming additional Si-O-Si bonds, thereby improving the overall cohesion between the new and old concrete.

Advanced Machine Learning Techniques for Predicting Concrete Compressive Strength

Advanced Machine Learning Techniques for Predicting Concrete Compressive Strength

The Influence of Rice Husk Ash Incorporation on the Properties of Cement-Based Materials.

Rice husk ash is a kind of biomass material. Its main component is silicon dioxide, with a content of up to 80%. It has high pozzolanic activity and can react with hydroxide in cement. When treating rice husks, rice husk ash with high volcanic ash activity and a good microaggregate filling effect can be obtained by selecting a suitable incineration environment. These advantages make rice husk ash an ideal concrete admixture, replacing the traditional admixture such as fly ash and slag in concrete. This paper summarizes the preparation methods and physical and chemical properties of rice husk ash, as well as the physical and chemical properties of rice husk ash concrete, such as mechanical properties, temperature resistance, freezing resistance, permeability resistance and chemical erosion resistance. The results show that using 20% rice husk ash as a substitute material for cement improves the resistance strength, compressive strength, flexural strength, and permeability of concrete. In short, the incorporation of rice husk ash can effectively improve the performance of cement-based materials, which will be conducive to the green development of the building material industry and the implementation of the two-carbon strategy.

Finite Element Analysis of PCCP Curling and Roughness

Smoothness specifications for newly built Portland Cement Concrete Pavements (PCCP) in Kansas have evolved over the last two decades through applications and a number of revisions. However, some PCCP sections built under current specifications in the smoothness bonus range experience rapid loss of smoothness with time. Sometimes this happens even before the sections are opened to traffic. It is believed that this loss results from curling due to the temperature differential between the pavement top and bottom surfaces. In this study, threedimensional simulation of curling of PCCP has been presented using a finite element (FE) software, ANSYS. A number of FE models were built to simulate several newly built PCCP sections in Kansas. The sections were modeled as three-layer systems with cement-treated base and lime-treated subgrade. Layer materials were modeled as linear elastic and the properties were obtained from the tests conducted during construction. Pavement layers and steel dowel bars were modeled using 3- dimensional solid (brick) elements. The complex interaction of the slab with the dowel bars was modeled as a contact problem. Regression models were developed for the curling deflection and International Roughness Index (IRI), a roughness statistics resulting from the curled profiles, based on different simulation parameters. The results obtained from the simulations show that the curling deflection and IRI calculated from the deflected slab profiles are affected by the slab thickness, compressive strengths of the concrete slab and base layers, and the temperature differential between the pavement top and bottom surfaces. Both curling deflection and IRI increase with an increase in temperature differential and compressive strength of the stabilized base layer. Higher slab thickness and concrete compressive strength would result in lower curling deflection and IRI values.

MIXTURE RATIO DESIGN METHOD FOR POROUS CONCRETE PERMEABLE BASE

As a new kind of material for permeable base, mixture ratio design of porous concrete should ensure both the porosity and the mechanical strength. Based on Talbol Formula and foreign experiences, four kinds of gradation of porous concrete are designed and effective particle size and uniform coefficient are taken as the effective descriptive targets of aggregate gradation. Three factors of cement dosage, water cement ratio and aggregate gradation are considered in an orthogonality test with four levels adopted in every factor. Through variance analysis of the tests results, it can be drawn that when confidence probability is 95%, the factors that evidently affect 7d compressive strength are gradation and cement aggregate ratio, and the factors evidently affect effective porosity are gradation, cement aggregate ratio and water cement ratio. While when confidence probability is 90%, gradation, cement dose and water cement ratio all have evident influence on 7d compressive strength and effective porosity. Based on the test results, a series of regression relationships of 7d compressive strength and effective porosity of porous concrete are derived. Finally, the empirical equation method of mixture ratio design for porous concrete is proposed.

Developing a brain inspired multilobar neural networks architecture for rapidly and accurately estimating concrete compressive strength

Concrete compressive strength is a critical parameter in construction and structural engineering. Destructive experimental methods that offer a reliable approach to obtaining this property involve time-consuming procedures. Recent advancements in artificial neural networks (ANNs) have shown promise in simplifying this task by estimating it with high accuracy. Nevertheless, conventional ANNs often require deep networks to achieve acceptable results in cases with large datasets and where generalization is required for a variety of mixtures. This leads to increased training durations and susceptibility to noise, causing reduced accuracy and potential information loss in deep networks. In order to address these limitations, this study introduces a novel multi-lobar artificial neural network (MLANN) architecture inspired by the brain’s lobar processing of sensory information, aiming to improve the accuracy and efficiency of estimating concrete compressive strength. The MLANN framework employs various architectures of hidden layers, referred to as “lobes,” each with a unique arrangement of neurons to optimize data processing, reduce training noise, and expedite training time. Within the study context, an MLANN is developed, and its performance is evaluated to predict the compressive strength of concrete. Moreover, it is compared against two traditional cases, ANN and ensemble learning neural networks (ELNN). The study results indicated that the MLANN architecture significantly improves the estimation performance, reducing the root mean square error by up to 32.9% and mean absolute error by up to 25.9% while also enhancing the A20 index by up to 17.9%, ensuring a more robust and generalizable model. This advancement in model refinement can ultimately enhance the design and analysis processes in civil engineering, leading to more reliable and cost-effective construction practices.

Strength development and coefficient of thermal expansion of high-strength concrete using silica fume

Silica fume as a partial replacement for cement in high-strength concrete has been the focus of numerous studies. However, the impact of substituting cement with silica fume in concrete mixtures on the mechanical and thermal properties of high-strength concrete remains insufficiently explored. Silica fume, characterized by its high pozzolanic activity and ultra-fine particles, is incorporated into concrete mixtures to enhance their mechanical properties and durability. The research examines the influence of varying silica fume content on the compressive strength and CTE of high-strength concrete. In the present study, concrete specimens with a water-cement ratio of 0.32 were prepared, with 5%, 10%, and 15% of the cement replaced by silica fume. Experimental results demonstrate that silica fume significantly improves compressive strength, particularly at early ages, starting from 7 days. However, the CTE of these mixtures is not significantly affected, with the average values varying slightly, ranging from 8.95 to 9.93 × 10⁻⁶/°C. This study contributes to further clarifying the role of silica fume in concrete mixtures and its effect on the CTE

Damage Evolution Characteristics of Steel-Fiber-Reinforced Cellular Concrete Based on Acoustic Emission

In order to investigate the steel fiber parameters on the damage characteristics and crack evolution of cellular concrete materials, uniaxial compression–acoustic emission combined tests were carried out on steel-fiber-reinforced cellular concrete (SFRCC) with different steel fiber contents (0%, 0.5%, 1%, 1.5%, and 2%) and different porosities (10% and 20%). The material damage evolution characteristics were analyzed by acoustic emission parameters and IB values, and the crack types were identified using Gaussian mixture clustering method (GMM) pairs. The results show the following: the inclusion of steel fibers increased the compressive strength of cellular concrete by 19.8~46.3% at 10% porosity, and by 37.1~102.2% at 20% porosity; the addition of steel fibers significantly increased the density and intensity of the acoustic emission signals; the decreasing tendency of the IB value at the peak stress slowed down with the increase in the amount of steel fibers, and the steel fibers could effectively inhibit the crack development; crack classification results show that the proportion of shear cracks in all stages of cellular concrete increased significantly after the addition of steel fibers.

Concreto reforçado por fibras metálicas helicoidais

The purpose of adding metallic fibers (filiform elements) to cementitious matrices is to increase the toughness of these brittle materials. The lateral confinement technique, using spiral reinforcement or stirrups, is capable of increasing the compressive strength of the confined core of cement matrices. Thus, this work aimed to use helical metal fibers (two-dimensional elements) with the aim of increasing both the toughness and the compressive strength of concrete. So, an experimental program was carried out in order to determine the compressive strength and residual strength (toughness index) of five types of concrete, varying the volume fraction of helical metal fibers from 0 (reference concrete) to 2%, every 0.5%. The results obtained indicated that the helical metal fibers were able to increase the residual strength of concrete by up to 30% (concrete with 2% of helical fibers), these increases being linear and directly proportional to the amount of fibers added, but they did not have a significant influence on the compressive strength of the final products. However, more tests need to be carried out, varying the helical fiber parameters, to confirm these preliminary results.

Analysis of the Effect of Brick Waste on Concrete Compressive Strength

Analysis of the Effect of Brick Waste on Concrete Compressive Strength

Study on the Influence of Fibers to the Physical Mechanical Properties of Foam Concrete

Foam concrete has been widely used in building, infrastructure and fire prevention fields because of its lightweight, excellent heat and sound insulation performance. However, due to its high porosity, the compressive strength of foam concrete is usually low, which limits its application in some high-strength structures. In order to solve this problem, researchers improve the mechanical properties of foam concrete by adding different types of fiber materials. This paper summarizes the research progress of fiber on the performance of foam concrete, and analyzes the influence of different types of fiber (such as polypropylene fiber, basalt fiber, polyvinyl alcohol fiber, etc.) on the performance of foam concrete. The research shows that adding proper amount of fiber into foam concrete can effectively improve the mechanical, durability, dry shrinkage and other properties of foam concrete.

Collision Milling of Oil Shale Ash as Constituent Pretreatment in Concrete 3D Printing

Concrete is an essential construction material, and infrastructures, such as bridges, tunnels, and power plants, consume large quantities of it. Future infrastructure demands and sustainability issues necessitate the adoption of non-conventional supplementary cementitious materials (SCMs). At the same time, global labor shortages are compelling the conservative construction sector to implement autonomous and digital fabrication methods, such as 3D printing. This paper thus investigates the feasibility of using oil shale ash (OSA) as an SCM in concrete suitable for 3D printing, and collision milling is examined as a possible ash pretreatment. OSA from four different sources was collected and analyzed for its physical, chemical, and mineralogical composition. Concrete formulations containing ash were tested for mechanical performance, and the two best-performing formulations were assessed for printability. It was found that ash extracted from flue gases by the novel integrated desulfurizer has the greatest potential as an SCM due to globular particles that contain β-calcium silicate. The 56-day compression strength of concrete containing this type of ash is ~60 MPa, the same as in the reference composition. Overall, collision milling is effective in reducing the size of particles larger than 10 μm but does not seem beneficial for ash extracted from flue gasses. However, milling bottom ash may unlock its potential as an SCM, with the optimal milling frequency being ~100 Hz.

Fatigue Life Prediction of FRP-Strengthened Reinforced Concrete Beams Based on Soft Computing Techniques.

This paper establishes fatigue life prediction models using the soft computing method to address insufficient parameter consideration and limited computational accuracy in predicting the fatigue life of fiber-reinforced polymer (FRP) strengthened concrete beams. Five different input forms were proposed by collecting 117 sets of fatigue test data of FRP-strengthened concrete beams from the existing literature and integrating the outcomes from Pearson correlation analysis and significance testing. Using Gene Expression Programming (GEP), the effects of various input configurations on the accuracy of model predictions were examined. The model prediction results were also evaluated using five statistical indicators. The GEP model used concrete compressive strength, the steel reinforcement stress range ratio to the yield strength, and the stiffness factor as input parameters. Subsequently, using the same input parameters, the Multi-Objective Genetic Algorithm Evolutionary Polynomial Regression (MOGA-EPR) method was then employed to develop a fatigue life prediction model. Sensitivity analyses of the GEP and MOGA-EPR models revealed that both could precisely capture the fundamental connections between fatigue life and multiple contributing variables. Compared to existing models, the proposed ones have higher prediction accuracy with a coefficient of determination reaching 0.8, significantly enhancing the accuracy of fatigue life predictions for FRP-strengthened concrete beams.

Data Driven Framework with Graphical User Interface for Predicting Flexural Behavior of FRCM Strengthened RC Beams

In this study, the flexural capacity of reinforced concrete (RC) beams strengthened with fiber-reinforced cementitious matrices (FRCM) was computed using machine learning (ML) algorithms including: (i) adaptive neuro-fuzzy inference system (ANFIS), (ii) artificial neural network (ANN), and (iii) extreme gradient boosting (XGBoost). A total of 198 pertinent experimental datasets were compiled and included six types of FRCM composites (PBO, carbon, glass, basalt, coated carbon, and combined glass and carbon). The considered input parameters comprise the beam cross-sectional details, area of tensile and compressive steel reinforcement, mechanical properties of FRCM composite, and concrete compressive strength. To assess the reliability of ML models, four existing analytical models and one established standard guideline were used for comparison. Moreover, six statistical metrics were employed, along with an overfitting analysis, to determine the best-fitting model. Graphical fitting of the optimal model was depicted using the Taylor diagram, violin plot, as well as multi-panel histogram plot. Based on both graphical and statistical metrics, the XGBoost model attained the highest precision compared to all analytical and ML-based models. The correlation coefficient and MAPE of the XGBoost model were 0.9977 and 2.98%, respectively. To interpret the influence of individual parameters on the flexural strength of the FRCM-strengthened RC beams, a feature importance plot based on SHAP explanatory theory was deployed. Ultimately, a user-friendly graphical interface was developed and made accessible to aid practicing engineers in estimating the flexural strength of FRCM-strengthened RC beams, offering an effective alternative to complex design procedures.

Prediction of compressive strength of nano concrete used in construction under freeze-thaw cycles

Prediction of compressive strength of nano concrete used in construction under freeze-thaw cycles

Transfer learning framework for modelling the compressive strength of ultra-high performance geopolymer concrete

Transfer learning framework for modelling the compressive strength of ultra-high performance geopolymer concrete

The influence of water cement ratio and sand ratio on the compressive strength of ceramic aggregate concrete at room temperature

The influence of water cement ratio and sand ratio on the compressive strength of ceramic aggregate concrete at room temperature

Effect of model architecture and input parameters to improve performance of artificial intelligence models for estimating concrete strength using SonReb

The use of Artificial Intelligence (AI) with the non-intrusive SonReb method, which combines Ultrasonic Pulse Velocity (UPV) and Rebound Number (RN) to predict concrete compressive strength, has attracted increasing attention in recent years. This study introduces a novel approach to improve AI models for predicting concrete strength, making them more suitable for future practical applications. One of the key challenge in AI models is the number of input parameters; while more inputs often improve accuracy, they are typically impractical for most applications dealing with existing structures (e.g., requiring detailed concrete mix design information that is often unavailable). SonReb AI-based models which use only two input parameters (UPV and RN) have shown reasonable accuracy, but their general use is limited by adoption of different testing standards which precludes the development of large databases. This study aims to improve two-parameter SonReb-based AI models through the addition of a binary input variable that represents the type of the specimen geometry (cube or cylinder) and investigates the effect of model architecture by comparing three different AI algorithms: Artificial Neural Networks (ANN), Deep Neural Networks (DNN), and Adaptive Neuro-Fuzzy Inference Systems (ANFIS). Six AI models were developed using 514 data points from experimental tests and collected data, and an unbiased data splitting method was applied for training and testing. The results showed that including specimen geometry improved model accuracy across all AI algorithms. The results of this study show that regardless of AI architecture, the proposed novel approach not only improves the accuracy of models, but also enables the use of larger databases containing both cubic and cylindrical specimens.

High Initial Concrete Compressive Strength with Variations of Superplasticizer and Silica Fume Additions

High Initial Concrete Compressive Strength with Variations of Superplasticizer and Silica Fume Additions

Full Probability Conversion Model for Predicting Concrete Compressive Strength Using the Rebound Method

Full Probability Conversion Model for Predicting Concrete Compressive Strength Using the Rebound Method