Concrete Compression Research Articles

Eurocode 2 design of recycled aggregate concrete elements under compression: Evaluation through model uncertainties from tests on columns

AbstractRecycled aggregate concrete contributes to the recovery of construction and demolition waste, which is a growing societal concern. The technical feasibility of recycled aggregate concrete has been extensively tested and validated and the revisions of codes are already including clauses for recycled aggregate concrete specification and design. However, there are still doubts regarding its structural behavior. This article assesses the suitability of Eurocode 2 design clauses for concrete elements under compression when used for recycled aggregate concrete design. This is done through the analysis of model uncertainties that are estimated from a database collected from laboratory experiments on columns. Both the current version of Eurocode 2 (with and without taking confinement into account) and the upcoming (second generation) Eurocode 2 are considered. A database was collected with clearly defined criteria and includes coarse recycled aggregates produced from concrete waste, since columns made with other types of recycled aggregates are scarcely tested and do not represent the current status for structural recycled aggregate concrete design and production. The statistics of the model uncertainty for natural and recycled concrete columns were compared and it was observed that the mean model uncertainty tends to decrease slightly when recycled aggregates are used. However, a preliminary assessment of whether a partial factor for recycled aggregate concrete compression design should be used concluded that this partial factor is not necessary to ensure reliable design and the design of columns may be carried out using the same formulae used for conventional concrete. The format proposed may be used for other resistance models, provided that the partial factor is calibrated for each resistance model.

Axial Compression Behavior of Elliptical Concrete-Filled Steel Tube Composite Short Columns with Encased Steel Considering Spherical-Cap Gap

This study explored the axial compression behavior of elliptical concrete-filled steel tubes with encased steel considering spherical-cap gap (GSECFST) composite short columns. We designed 25 composite column specimens by varying the steel tube yield strength (fty), steel skeleton yield strength (fsy), concrete cubic compression strength (fcu), steel tube thickness (t), steel skeleton sectional area (As), the long and short half-axis ratio (a/b), the gap ratio (Xsg), and the slenderness ratio (λ). Based on the nonlinear constitutive models of the materials and the nonlinear contact effect among materials, the ABAQUS 6.20 finite element software established the refined finite element models of these composite short columns. Also, the rationality of the finite element modeling with a spherical-cap gap was verified by comparing it with the existing experimental results. The influence regularity of various parameters on the load (N)-displacement (Δ) curves, bearing capacity, initial stiffness, and ductility of the composite short columns was obtained. In addition, the failure modes, N-Δ process, sectional strain distribution, and gap feature index of the constraint partition model for GSECFST axial compression short columns were revealed. The results showed a weakened interaction between the elliptical steel tube and concrete. Also, the axial compression bearing capacity, initial stiffness, and core concrete ductility were reduced because of the spherical-cap gap. As fty, fsy, fc, and Asy increased, the axial bearing capacity, initial stiffness, and ductility of GSECFST composite short columns improved significantly but decreased with increasing of a/b, Xsg, and λ. When the gap ratio of the spherical crown was less than 4%, the outer steel tubes in the mid-span area of the GSECFST composite short columns buckled in the direction of the elliptical short axis under axial compression, and the concrete expanded outward and crushed. The failures were similar to those of the specimens without the spherical-cap gap. Based on the sectional constraint partition model, we propose the calculation formula of axial compression bearing capacity for GSECFST composite short columns. Consequently, this study is a reference for the elastic-plastic analysis of frame systems with similar composite columns.

Experimental Investigation of Compressive Concrete with Different Immersion Times and Its Stochastic Damage Model

Continuous large amounts of precipitation can lead to a rapid increase in the water content of concrete in village building foundations, which can adversely affect the mechanical properties, such as the compressive strength of concrete. There are few experimental studies on the compressive stochastic mechanical properties of concrete in the wet state after considering different soaking times (different water contents and saturations), but there is no corresponding stochastic damage principal structure model. In this study, the mechanical properties of concrete under different immersion times were tested to obtain the mechanical properties of the concrete degradation law, and the random damage intrinsic model of wet concrete was established. The results of this paper were compared with the classical test results from the literature to verify the validity of the model. The results show that the proposed stochastic damage model is able to consider both the effects of the saturation and the damage behavior of wet concrete under compression, which is beneficial to the structural design and maintenance protection of village buildings in areas with abundant precipitation.

Determining Compressed Concrete Element Limit States Based on the Widths and Depths of Cracks Caused by Transverse Deformations.

In the modern theory of compressed concrete elements, the most attention is paid to longitudinal deformations, whereas transverse ones are rarely considered and just within Poisson's coefficient limits (i.e., elastic concrete behavior in the transverse direction). However, transverse deformations significantly develop beyond the limits corresponding to Poisson's coefficient, where they lead to longitudinal crack initiation and development. In-depth experimental and numerical investigations of transverse deformations in the inelastic stage showed that it is necessary to consider crack propagation. The present study proposes simultaneous consideration of longitudinal and transverse deformations, as well as the appearance of cracks and their widths and depths. This allowed us to obtain a complete compressed concrete element behavior pattern at all performance stages in two types of limit states (based on longitudinal and transverse deformations). Consequently, new ultimate limit states by the depth and width of cracks caused by transverse deformations are proposed to be included in modern design practices and codes.

Probabilistic models of dynamic increase factor (DIF) for reinforced concrete structures: A Bayesian approach

The response of structures under rapidly varying loads can be affected by strain rate sensitivity generally expressed using Dynamic Increase Factor (DIF). Current models for estimating the DIF in Reinforced Concrete (RC) structures are generally deterministic and have restricted applicability due to their dependence on limited experimental data resulting in bias. This paper overcomes these limitations by proposing three probabilistic models that quantify compressive and tensile concrete and steel DIF, accounting for the relevant uncertainties. The proposed models are based on existing deterministic models with the addition of probabilistic correction terms. Bayesian updating is employed to estimate the unknown model parameters using observational data from a large collection of experimental observations. The models incorporate model uncertainties stemming from assumed model form and (potential) missing variables through a model error term. The proposed probabilistic models are used to evaluate the reliability of RC structures under dynamic loads. As an illustration, the proposed probabilistic models are used to estimate the reliability of an example RC column under combined dynamic axial force and moment, and a RC column or beam under dynamic bending moments resulting in cracking. In the two examples, we consider the ACI 318-19 requirements for Ultimate Limit State (ULS) and Serviceability Limit States (SLS). In comparison to deterministic DIF models, the proposed probabilistic models yield enhanced predictive accuracy, presenting a practical and robust approach to assess the structural reliability under impact and blast loads.

The Effect of Soaking K-300 Concrete Using Seawater on Concrete Quality

Concrete is a mixture of cement, fine aggregate, coarse aggregate, and water with or without additional ingredients to form a solid mass, its use is very common in building structures, but the final quality of the strength of the concrete depends on many factors, including the implementation of its manufacture. and immersion. The research aimed to determine changes in the quality of k-300 concrete and the maximum strength of concrete immersed in salt water. The research was carried out in the Laboratory of the Faculty of Engineering, University of 17 August 1945 Samarinda for 2 months. The stages of research activities are starting, literature study, preparation of materials and tools, testing concrete building materials in the laboratory, designing concrete mixes, making test objects/samples, evaluation, testing concrete compressive strength, conclusions, and reporting. Tests carried out in the laboratory include: (1) checking wear/abrasion of coarse aggregate using the Los Angeles method; (2) examination of coarse aggregate and fine aggregate sieve analysis; (3) examination of specific gravity and SSD absorption of coarse aggregate and fine aggregate; (4) checking water content; (5) checking mud and clay levels; (6) checking the bulk density of coarse aggregate, fine aggregate and mixed aggregate; (7) inspection of concrete slump experiments; (8) soaking concrete using seawater and freshwater; and (9) checking concrete compression strength. The research results show that: (1) The compressive strength of K-300 concrete soaked in seawater, namely 304,915 f'c, is lower than that soaked in fresh water, namely 308,580 f'c; and (2) differences in concrete compressive strength occur at old concrete ages (21 days and 28 days) but at young concrete ages (3, 7 and 14 days) there are no differences in compressive strength.

Machine Learning Technique for the Prediction of Blended Concrete Compressive Strength

Predicting concrete strength is complex due to the high non-linearity involved in strength development, especially when using supplementary cementitious materials (SCMs) such as fly ash, silica fume, and GGBS. In this paper, an artificial neural network has been used to predict the compressive strength of concrete for four cases, namely concrete without cement replacement, and binary, ternary, and quaternary cement concretes corresponding to one, two and three different SCMs in the mix. To predict the strength accurately, a total of 1013 data were collected from 37 literature and trained using two training algorithms namely Levenberg-Marquardt (LM) and Bayesian Regularization (BR). The best predictions were achieved using one hidden layer with 14 and 15 neurons for LM and BR algorithms respectively. A high accuracy has been achieved with a correlation factor of 0.97 and 0.966 using the BR and LM algorithms respectively, with a20-index of 83%. Generally, the BR algorithm gives a better overall performance, while underestimating the compressive strength compared to LM. Sensitivity analysis has also been investigated using linear and quadratic regressions. The findings showed that the highest contributors to concrete strength were cement and water, while the lowest contributor was coarse aggregate.

An investigation of the effect of high temperature on the strength compression and ultrasonic pulse velocity of self-compacting concrete

One of the most significant mechanisms of deterioration in self-compacting concrete (SCC) structures over the course of their service life is exposure to elevated temperatures. For this reason, the effects of elevated temperatures on the residual mechanical properties of SCC made of various cementitious additions (silica fume (SF), limestone filler (LF), and crushed dune sand (SD)) was investigated via partially substituting amount of Portland cement in the SCC. The SCC was formed and heated with the coupling procedure of heating-cooling at ambient temperatures ranging from 20, 150, 400, 600, and 800 °C. The compressive strength (CS) and ultrasonic pulse velocity (UPV) were measured after cooling at room temperature of 20 °C. The findings show that CS and UPV exhibit intriguing outcomes for all self-compacting concretes. Otherwise, the UPV decreases with temperature in the range of 250 and 800 °C. In addition, at 600 °C, SCC-SF and SCC-SD have the highest residual compressive strengths, 31.20 and 23.80 MPa, respectively.

Research on Concrete Compressive Strength Detection Technology Based on Intelligent Machine Vision

Abstract Concrete is the most common and important building material nowadays. Its compressive strength plays a crucial role in the result and safety of the building. To improve the efficiency of concrete compressive strength detection, this study combines intelligent machine vision technology to design a concrete compressive strength detection system. The features of concrete are extracted using the edge detection method. Then the extracted features are classified using the random forest method to complete the identification and localization of concrete. Based on this basis, the compressive strength of concrete is calculated and detected based on the conversion relationship between uniaxial compressive strength and point load strength. Finally, after testing the performance of the system, the practical effects of the system are examined. According to the results, the system’s detection rate is between 0.058 and 0.072 seconds, and the recognition accuracy and classification accuracy of the four different types of concrete detection exceed 80%. The relative error values for the detected compressive strength were 5.87% and 3.52%, respectively, and they passed the compressive strength detection of retardation diagrams in complex situations. The excellent performance of this study in real concrete detection meets the demand for concrete compressive detection in reality.

Compressive Strength Prediction Model of High Strength Concrete by Destructive and Nondestructive Technique

Concrete’s compressive strength can be tested in a laboratory before construction begins. Since concrete is a natural material and cannot be destroyed, it is not possible to determine its compressive strength through destructive testing. Rebound hammers are typically used in the field to evaluate the structural elements’ ability to withstand hardened concrete. As part of the current study, a comparison was made between concrete’s compressive strength measured by destructive testing and its surface hardness measured by rebound hammering. Tests were conducted on laboratory-made concrete cubes in this study to determine destructive and non-destructive behavior. Minitab software was used for regression analysis. Schmidt rebound hammer tests, a type of nondestructive testing (NDT), were shown to have very strong relationships with concrete destructive compression tests. Schmidt rebound hammers are commonly used to measure the surface hardness of concrete, since the hammer rebound number and concrete strength are theoretically correlated. Utilising a Schmidt hammer, it was applied. Standard concrete cubes with crushing strengths between 20 and 30 MPa were created using various mix proportions. Using regression analysis, destructive and non-destructive values are correlated. The linear regression equation is well suited for obtaining the compressive strength using rebound value by using linear regression equation.

Deep Learning based Concrete Compressive Strength Prediction with Modified Resilient Backpropagation Training

Deep Learning based Concrete Compressive Strength Prediction with Modified Resilient Backpropagation Training

A Theoretical Model for Predicting Axial Compressive Strain of FRP-Confined Concrete

The axial compressive strength and strain of structural elements made of reinforced concrete are enhanced by the external confinement provided by fiber-reinforced polymer (FRP) sheets. There is still a need for more research into estimating axial compressive strain even though numerous studies have suggested analytical approaches to predict the axial compressive strength of concrete structural elements. This is a result of earlier strain models’ inadequate accuracy. Furthermore, rudimentary modelling techniques and small, noisy databases were used in the development of these models. To suggest a more realistic strain model and compare it with earlier models, a more rigorous methodology is therefore required. The goal of this study is to present a strain model for FRP-confined concrete members by analytical modeling based on a large database containing 570 sample points. When the models were assessed using statistical parameters, it was discovered that the estimations of the freshly proposed models were more accurate than those of the previous models. The estimations’ relative study provides significant support for the recommended analytical model’s applicability and accuracy in forecasting the axial-strain of CFRP-confined concrete compression members.

Experimental investigation of Carbon FRCM confinement effect on concrete cylinders under monotonic loading

In recent years, fabric reinforced cementitious matrix (FRCM) became a desirable material in the rehabilitation of concrete structures. The FRCM system features some advantages such as compatibility with the substrate, easier and non-expensive installation on a wet concrete, and ultraviolet degradation resistance. As FRCM is a new material, the existing literature provides limited knowledge on the effectiveness of FRCM in confining concrete. This paper incorporated an experimental program where plain concrete cylinders were confined with FRCM and were tested under uniaxial compressive loading. The experimental testing parameters included concrete grade, number of FRCM layers and the fibres direction. The test results showed that FRCM was effective in confining plain concrete as the compressive strength increased and a ductile behaviour was developed. Providing more FRCM layers did not enhance the confining action. The application of longitudinally oriented fibres had an insignificant effect on the concrete compression behaviour.

Prediction of Geopolymer Concrete Compressive Strength Using Convolutional Neural Networks

Prediction of Geopolymer Concrete Compressive Strength Using Convolutional Neural Networks

Application of Artificial Neural Networks for Predicting Axial Strain of FRP-Confined Concrete

Multiple research studies have developed frameworks to forecast the ability of concrete structural elements to withstand compression along their length. However, further exploration is required to refine predictions for the axial compressive strain, as existing strain models lack precision. The earlier models were created with restricted and noisy data sets and basic modelling methods, underscoring the necessity for a more meticulous approach to introduce a more accurate strain model and to evaluate its forecasts against those of current models.This study wants to fill in the gap by creating models for how much concrete reinforced with fiber-reinforced polymer (FRP) can stretch using computer simulations called artificial neural networks (ANN). This approach is based on a substantial database comprising 570 sample points. The comprehensive investigation of these estimates robustly validates the accuracy and practicality of the suggested ANN models for predicting the axial strain of FRP -confined concrete compression members.

Detection and Application of Concrete Compressive Strength with Machine Vision Technology

Abstract This paper proposes research on concrete compressive strength detection based on machine vision technology in response to the problems of traditional concrete compressive strength detection methods. The original image is directly input into the network for training. There will be too much memory occupation. The efficiency is too low, it needs to be visually preprocessed to meet the requirements of the dataset to establish a high-performance concrete compressive strength detection network model. Then, the processed high-performance concrete compressive strength feature images are inputted into the Mask R-CNN network. The compressive strength features are extracted and trained by the convolutional neural network. Then, the extracted features are further processed with a bilinear difference to ensure their integrity, and finally, the construction of the high-performance concrete detection model based on Mask R-CNN is completed. Simulation experiments are used to analyze the detection and application of high-performance concrete using the theory of machine vision. The data show that the training and validation accuracies of the Mask R-CNN model are 96.75% and 96.52%, respectively, which are 5% and 3.77% higher than that of the migration learning network model (91.75%, 92.75%). In addition, the predictions of the Mask R-CNN model have a relative error of less than 0.05 compared to the actual values. The research presented in this paper is applicable to field strength testing of common concrete structures and can provide theoretical references for the study of construction materials.

Influence of Concrete Compressive Strength on CFRP Strengthening and Repairing of RC Two‐Way Slabs

In this research, the feasibility of strengthening and repairing two‐way reinforced concrete slabs and the effect of compressive strength on the properties of strengthened and repaired slabs using CFRP sheets were studied. The experimental program includes testing twelve samples. Four samples served as control panels, while four samples were strengthened using CFRP sheets to study the effect of carbon sheets on the ultimate load capacity. Also, the four pretested control panels were also taken and rerepaired using CFRP sheets and retested for the purpose of evaluating their properties after repair. The ultimate load capacity, energy dissipation capacity, initial stiffness, and failure pattern were explained by the experimental results obtained. The experimental results revealed that the use of CFRP strips led to an improvement in the average ultimate loading capacity by 16.2% and 6.45% for the strengthening and repairing system, respectively. Also, the utilization of the CFRP strips as a strengthening system significantly improved the stiffness and the cracking load of RC two‐way slabs. It was also noted that the ultimate loading capacity was proportionally related to the concrete strength for strengthened slabs with an increase of 23.3% for slabs with the highest concrete strength (C: 70).

Hybrid of AdaBoost and Optimized LSTM Using Modified Bald Eagle Search in Predicting Concrete Compressive Strength

Hybrid of AdaBoost and Optimized LSTM Using Modified Bald Eagle Search in Predicting Concrete Compressive Strength

Optimizing Sustainability of Concrete Structures Using Tire-Derived Aggregates: A Performance Improvement Study

Tire-derived aggregate concrete (TDAC), or rubberized concrete, is gaining ground as an eco-friendly option in civil engineering. By substituting traditional coarse aggregates with recycled rubber tires, TDAC offers a greener choice with excellent energy absorption capabilities. This leads to robust structures and reduced upkeep expenses. Nonetheless, TDAC’s lower strength than regular concrete requires a delicate balance between energy absorption and strength. This study investigates two enhancements to TDAC performance: (a) the impact of sodium hydroxide (NaOH) solution pretreatment and SikaLatex bonding agent addition on TDAC’s compressive strength, and (b) the use of varying water–cement ratios and superplasticizer to enhance TDAC’s mechanical properties. This study involves concrete cylinder compression tests and the creation of strength estimation equations. Results show that NaOH-treated tire-derived aggregate (TDA) boosts workability, increasing slump by 4.45 cm (1.75 in), yet does not significantly enhance compressive strength, causing a 34% reduction. Conversely, combining NaOH pretreatment with Sikalatex bonding agent enhances workability by 28% and boosts compressive strength by 21% at the same water-cement ratio. To optimize performance, it is advised to employ modified TDA concrete with a water–cement ratio under 0.34 and superplasticizer. These findings highlight the potential of modified TDA concrete in sustainable and seismic-resistant designs.

Mesoscopic discrete modeling of compression and fracture behavior of concrete: Effects of aggregate size distribution and interface transition zone

This paper presents a mesoscopic discrete modeling approach for studying compression and fracture behavior of concrete. A modified piecewise linear sieve curve was proposed to generate irregularly shaped aggregates following specific particle size distributions, and then a random particle model and the parallel bond model were implemented within a 3D framework of discrete element model (DEM). To characterize the interface transition zone (ITZ) which is generally the weak region in concrete, a reduction factor was employed to adjust the strength and consider the effect of ITZ on the mechanical response of concrete. The numerical modeling was calibrated and validated by comparing simulation results with experimental data from compression, splitting tensile, and dog-bone tests. The results demonstrate that the random aggregate model provides accurate representations of the concrete mesostructure, and the discrete model can effectively capture the effects of aggregate size distribution and ITZ on mechanical properties and crack propagation of concrete.