Properties Of High Performance Concrete Research Articles

ANN-Powered Models for Predicting Shrinkage and Creep Properties of High-Performance Concrete Using Supplementary Cementitious Materials

ANN-Powered Models for Predicting Shrinkage and Creep Properties of High-Performance Concrete Using Supplementary Cementitious Materials

Compressive strength prediction of high-performance concrete using MLP model

ABSTRACT The remarkable mechanical properties of high-performance concrete (HPC) make it indispensable in a wide range of engineering applications. Predicting HPC’s compressive strength with precision is essential to guaranteeing its structural stability and longevity. The present work introduces a novel method for predicting HPC compressive strength (CS) by utilizing the Multi-layer Perceptron (MLP) model in conjunction with three distinct optimizers, namely Northern Goshawk Optimization (NGO), Manta Ray Foraging Optimization (MRFO), and Atom Search Optimization (ASO). An effective method for capturing complex relationships between input and output variables is the multi-layer perceptron (MLP) in artificial neural networks. Developing a dependable, robust predictive model is the goal of training the MLP model on a variety of datasets of HPC mixtures and CS. As evidenced by the MLNG model’s R2 value of 0.994 and RMSE of 1.3572, the joint efforts of MRFO, NGO, and ASO during the optimization phase improve the MLP model and produce promising results. These results clarify how different optimizers play a crucial role in improving the accuracy of HPC compressive strength predictions made with the MLP model. This information helps make better decisions regarding design and construction methods for HPC.

Enhancing high-performance concrete properties through partial replacement of fine aggregate with crusher run dust: A comprehensive experimental investigation

Enhancing high-performance concrete properties through partial replacement of fine aggregate with crusher run dust: A comprehensive experimental investigation

Flexural and fracture behavior of high-performance concrete using notch specimen

This study investigated the mechanical properties of high-performance concrete using notched specimens. Allflexural specimens had an identical geometry of 40×40×160 mm3 with a span length of 120 mm. Four notchto-depth ratios were as follows: 0 (N0 series), 0.125 (N1 series), 0.250 (N2 series), and 0.375 (N3 series), whichwere designed at the specimen bottom of midspan section. The investigation focused on the four parametersof high-performance concrete, including load carrying, deflection capacity, flexural strength, and the criticalstress intensity factor. The results showed that the load-carrying and deflection capacities decrease with anincrease in the notch-to-depth ratios. Furthermore, the flexural strength was ranked as follows: N0 series > N3series > N2 series > N1 series, whereas ranking of the regarding critical stress intensity factor was opposite:N1 series > N2 series > N3 series.

Effect of hybrid lead-PVA fibers on microstructure and radiation shielding properties of high-performance concrete

With the widespread application of nuclear technology in the medical and energy fields, the demand for efficient radiation shielding materials is increasing. Employing only lead or polyvinyl alcohol (PVA) fiber reinforcement cannot achieve improvements in the brittleness of high-performance concrete (HPC) while also enhancing radiation shielding efficiency and mechanical properties. This study develops a novel HPC for radiation attenuation, incorporating magnetite as the aggregate and reinforced with hybrid lead-PVA fibers (RSHPC). The synergistic effects of various proportions of hybrid lead-PVA fibers on the mechanical properties, three-dimensional microstructure, pore structure, and interfacial transition zone (ITZ) were investigated. In addition, the radiation attenuation capacities of RSHPC with different hybrid lead-PVA fiber proportions were evaluated through experimental and simulation analysis. The results show that hybrid lead-PVA fibers notably improve the air-void structure of RSHPC. The incorporation of PVA fibers, by improving the ITZ of matrix, effectively mitigates the negative impact of lead fibers on mechanical performance. The refinement of pore structure and the introduction of light and heavy nuclides lead to a significant enhancement in the gamma-ray and neutron radiation attenuation capabilities of RSHPC. Utilizing X-ray computed tomography (X-CT) for three-dimensional reconstruction further indicates the optimization impact of hybrid lead-PVA fibers on the microstructure, notably in the improvement of pore distribution and fiber dispersion, thus enhancing the overall properties and radiation attenuation performance of RSHPC.

Optimizing High-Performance Concrete Properties Containing Blast Furnace Slag and Marble Powder

Abstract Building and industrialization-related environmental harm is becoming an increasingly serious concern. In an effort to create an eco-friendly high-performance concrete (HPC), this paper addresses the idea of partially replacing cement with recyclable industrial waste. The current study will experimentally examine the slump, the strengths of compressive (SC) and porosity (P) of fifteen HPC mixtures manufactured from locally available resources. Hence, the effects of utilizing marble powder (MP) as a mineral additive in binary mixes and ternary with cement (PC) and granulated ground blast furnace slag (GGBFS) on the HPCs properties were studied in order to develop statistical models based on mixture design. Highly accurate prediction graphs and models were created for HPC workability, P at 28-day, SC at 7 and 28-day. All responses have satisfactory coefficients of correlation (R2 ≥ 0.76). Replacing cement with GGBFS causes a rise in slump in mixtures. Nevertheless, that only remains relevant when the mixtures have a small MP percentage (≤ 25%). A minor decrease in SC can be attributed by an increase of GGBFS. After 28-day, using GGBFS alone caused a little drop in SC; however, when GGBFS and PC were mixed, SC increased, in comparison with reference composition, and the porosity was reduced. Conversely, SC is superior with lower porosity when a small amount of MP is utilized. The best combination is HPC14, containing 5% GGBFS; it offers an optimal equilibrium among the three qualities; with HPC4’s (15%GGBFS+5%MP) qualities, being almost identical to those of reference HPC15, a lower amount of cement may also be utilized. Findings encourage the use of MP and GGBFS to partially replace cement to produce eco-friendly and cost-effective HPC. An extremely high correlation coefficient indicated a strong relationship between P and SC.

Effects of Holes and the Confining Pressure on the Mechanical Properties of High-Performance Concrete

Effects of Holes and the Confining Pressure on the Mechanical Properties of High-Performance Concrete

Experimental study on hybrid fiber reinforced high performance concrete properties: Investigatingcomprehensive energy consumption capacity

The brittleness of concrete in tension increases with higher compressive strength, necessitating the study of cementitious composites that combine high strength and ductility. This study investigated the fresh and mechanical properties of high-strength concrete (HPC) incorporating fibers: steel, basalt, and polyethylene (PE). Hybrid fiber reinforced high performance concrete (HyFHPC) was created by tailoring mix proportions, with eight series of specimens cast, each carrying different fiber combinations alongside plain HPC as a reference. Compressive, tensile, and flexural tests were conducted, and results were analyzed. The electron microscopy techniques were employed to observe the microstructural morphology and perform elemental analysis of the composites. Findings revealed a dense HPC matrix with 1.5 % total fiber content, exhibiting uniform dispersion and ideal bonding. Digital image correlation (DIC) technique was utilized to analyze strain distribution and crack development of specimens in tension and bending, proving effective in tracking the strain and crack evolution. Statistical analysis of five influencing elements—cubic compressive strength, axial compressive strength, tensile energy dissipation, tensile fracture energy, and energy release rate—revealed Series F9 (S0.5P1) as optimal for comprehensive energy consumption performance. HyFHPC demonstrated enhanced strength, ductility, and toughness compared to HPC with mono fiber or none. The hybridization achieved positive synergy and improved reinforcing efficiency while conserving resources.

Investigation of mechanical properties of high-performance concrete via multi-method of regression tree approach

Concrete's workability and durability are influenced by its mechanical properties, including Tensile and Compressive strength. High-performance concrete exhibits non-linear relationships between its compressive and tensile strength characteristics and the proportions of its constituent materials, such as water, aggregate, cement, and admixtures. The investigation of the relations between the constituents of concrete and its mechanical properties has posed a challenging issue. This paper seeks to develop a range of single, hybrid, and ensemble models to resolve the problem of accurately estimating the mechanical properties of concrete according to its constituent components as input variables. In this regard, various frameworks incorporating a machine learning technique called decision tree and four metaheuristic optimization algorithms were utilized in model development to emulate the values of tensile and compressive strength. The findings indicated that the decision tree-dynamic arithmetic optimization algorithm hybrid model produced results with an correlation of determination of 0.9919 in compressive strength and 0.9933 in tensile strength, which were on average 1–3 % higher than that of the decision tree-dandelion optimizer algorithm, decision tree-arithmetic optimization algorithm, and decision tree-coot optimization algorithm, indicating superior optimization performance of dynamic arithmetic optimization algorithm. Additionally, the powerful prediction capability of the decision tree + dynamic arithmetic optimization algorithm + dandelion optimizer algorithm + coot optimization algorithm + arithmetic optimization algorithm ensemble model was noticeable with R2 of 0.9882 and 0.9936 in compressive and tensile strength estimation reliable to be used for various data produced in the future. In general, the utilization of coupling techniques to generate hybrid and ensemble models can enhance the accuracy of predictions while reducing the time and costs associated with experimental procedures.

Utilization of marble waste as fine aggregate in the composition of high performance concrete containing mineral additions

This study investigates the effect of adding marble waste as fine aggregate on the properties of high-performance concrete. To achieve this objective, 10%, 20%, 30%, and 40% of marble waste fine aggregate were substituted for ordinary fine aggregate in the composition of high-performance concrete mixtures containing silica fume and ground blast furnace slag. The fresh properties were studied using slump, fresh density and air content tests, while the hardened properties were assessed using compressive and flexural strengths, ultrasonic pulse velocity, water absorption, and chemical acid attack tests. The results obtained revealed that the addition of marble waste fine aggregate significantly improved the properties of high-performance concrete and increased its resistance to chemical acid attacks. The composition-based ground blast furnace slag containing 40% marble waste fine aggregate outperformed all other compositions, increasing compressive strength by 30.98%.

An experimental study of the relationship between modulus of elasticity, Poisson's ratio, and the mechanical properties of high-performance concrete

This study investigated the mechanical properties of high-performance concrete (HPC) with the percentage of mineral admixtures. HPC is a good choice for multi-story buildings because it has many advantages. As HPC becomes more popular, it is important to research its properties. This study used the P.C. Aitcin method to design HPC mixes with compressive strengths of 60 to 80 MPa. Fly ash is used for M60 and M70 grade HPC and Silica Fume is used for M80 grade HPC is used as mineral admixture as a 20% replacement for cement. In this research paper, the study of properties of the different ingredients used to make HPC is carried out. The testing of three concrete mixes for mechanical properties such as compressive strength, split tensile strength, modulus of elasticity, and flexural strength, as well as other important properties is done. This study explores High-Performance Concrete (HPC) by precisely controlling the water-to-cement ratio and incorporating mineral and chemical admixtures, achieving strengths from 60 to 80 MPa. It investigates key mechanical properties, establishes empirical equations, and compares findings with codes and literature. The results showed that a lower water-to-binder ratio (w/b) led to better mechanical properties. The experimental program includes the casting of 39 cubes, 39 cylinders, 39 beams and 81 columns to test the physical properties of different concrete grades. The use of a compression testing machine with a load capacity of 3000 kN to test the HPC cubes and a loading frame with a load capacity of 2000 kN to test the columns. The average compressive strength for HPC M60 to M80 was 65.40 MPa, 73.45 MPa, and 82.64 MPa respectively. The modulus of elasticity values ranged from 40 to 50 GPa. The findings of the study showed that an increase in concrete strength correlated with a decrease in the average value of Poisson's ratio. Overall, the study suggests that HPC is a good choice for high-rise buildings because it has many advantages. As HPC becomes more popular, it is important to research its mechanical properties. This study provides valuable results about the mechanical properties of HPC in comparison with IS 456:2000. From the experimental results of tested columns, it was observed that as the load increased, the deflection decreased for both uniaxially and biaxially loaded columns, highlighting the novelty of this finding. Additionally, the columns subjected to biaxial loading displayed greater sensitivity compared to their axial and uniaxial loading.

The Properties of High-Performance Concrete with Manganese Slag under Salt Action.

Manganese slag (MS) containing a certain amount of active hydration substances may be used as a kind of cementitious material. In the present study, we measured the mass, the relative dynamic modulus of elasticity (RDME), and the flexural and compressive strengths of MS high-performance concrete (MS-HPC) with added basalt fibers exposed to NaCl freeze-thaw cycles (N-FCs), NaCl dry-wet alternations (N-DAs), and Na2SO4 dry-wet alternations (NS-DAs). Scanning electron microscope energy-dispersive spectrometer (SEM-EDS) spectra, thermogravimetric analysis (TG) curves, and X-ray diffraction spectroscopy (XRD) curves were obtained. The mass ratio of MS ranged from 0% to 40%. The volume ratio of basalt fibers varied from 0% to 2%. We found that, as a result of salt action, the mass loss rate (MLR) exhibited linear functions which were inversely correlated with the mass ratio of MS and the volume ratio of basalt fibers. After salt action, MLR increased by rates of 0~56.3%, but this increase was attenuated by the addition of MS and basalt fibers. Corresponding increases in RDME exhibited a linear function which was positively correlated with MS mass ratios in a range of 0~55.1%. The addition of MS and basalt fibers also led to decreased attenuation of mechanical strength, while the addition of MS led to increased levels of flocculent hydration products and the elements Mn, Mg, and Fe. CaClOH and CaSO4 crystals were observed in XRD curves after N-DA and NS-DA actions, respectively. Finally, the addition of MS resulted in increased variation in TG values. However, the opposite result was obtained when dry-wet actions were exerted.

Study on the fracture mechanical properties of high performance concrete (HPC) with rapid cooling after high temperature

In practical engineering, concrete structures typically undergo high temperatures and fire spray cooling during the process of fire, and high temperatures will cause changes in the microstructure of concrete. Therefore, studying the fracture mechanical properties of concrete after rapid cooling at high temperatures is more in line with the actual engineering situation. This paper sets five temperature conditions (20°C, 200°C, 400°C, 600°C, and 800°C), and after rapid cooling in a water bath, a hydraulic servo machine is used to conduct three-point bending beam fracture tests on plain concrete (PC) and high performance concrete (HPC). The fracture process and mechanism are analyzed using digital image correlation (DIC) and acoustic emission (AE). The research results indicate that the three-point bending unstable fracture load of PC and HPC decreases with the increase of temperature, with the maximum reduction after 800 ℃ high temperature, reaching 87.5% and 82.2%, respectively. After high temperature, the fracture energy of PC and HPC both showed a trend of first increasing and then decreasing. The ductility index of PC and HPC increases with the increase of temperature. The unstable fracture toughness and initial fracture toughness of PC and HPC decrease with the increase of temperature. By combining DIC and AE technologies, the development and fracture mode of cracks in PC and HPC after rapid cooling at high temperature are analyzed. The results showed that the softening effect of concrete is more significant after high temperature, the crack initiation stage is earlier, and the bearing capacity is lower. Furthermore, modern testing techniques are employed to reveal the mechanism of the impact of high temperature and cooling methods on the mechanical properties of HPC from a microscopic perspective. The research results have important reference value for practical engineering.

Investigation of mechanical properties of high-performance concrete via optimized neural network approaches

In this paper, an artificial intelligence approach has been employed to analyze the slump and compressive strength (CS) of high-performance concrete (HPC), focusing on its mechanical properties. The importance of assessing these critical concrete characteristics has been widely acknowledged by experts in the field, leading to the development of innovative methods for estimating parameters that typically require laboratory testing. These intelligent techniques improve the accuracy of mechanical property predictions and reduce the resource-intensive and costly nature of experimental work. The radial basis function neural network (RBFNN) is the foundational model for predicting the mechanical attributes of various HPC mixtures. To fine-tune the RBFNN’s performance in replicating the mechanical properties of HPC samples, two optimization algorithms, namely the Golden Eagle Optimizer (GEO) and Dynamic Arithmetic Optimization Algorithm (DAOA), have been employed. In this manner, both RBGE and RBDA models were trained using a dataset comprising 181 HPC samples that included superplasticizers and fly ash. The results show that DAOA has significantly improved the base model’s predictive capability, achieving a higher correlation with a value R2 of 0.936 when estimating slump. Furthermore, RBDA exhibited a more favorable root mean square error (RMSE) in predicting compressive strength compared to RBGE, with a notable 16% difference. Ultimately, both integrated models demonstrated their effectiveness in accurately modeling the mechanical properties of HPC.

Assessment of mechanical and durability performance of silica fume and metakaolin as cementitious materials in high-performance concrete

AbstractThe present study aims to determine the effects of blending cementitious materials on the mechanical and durability properties of high-performance concrete (HPC). Densified silica fume and fine-grounded metakaolin are used as supplementary cementitious materials (SCMs). A total of 16 mixes containing both binary and ternary blending of SCMs were chosen for w/b ratios of 0.4 and 0.3 respectively. The hardened properties tested for the HPC mixes were compressive strength at 7, 28, and 90 days, flexural strength at 28 days, and modulus of elasticity at 28 days. Maximum strength gains up to 15%, 38%, and 23% for compression, flexure, and elastic modulus were observed in ternary mixes compared to binary mixes. Stress-strain behaviour of ternary mixes indicates increased tolerance of stress for the least amount of strain in the specimens. Based on the experimental results, empirical relations were developed and checked with the existing codes and by earlier researchers. The durability properties tested for HPC were water absorption at 28 days, acid attack, and sulphate attack at 28, 56, and 90 days. Ternary mixes improved the pore structure of HPC, resulting in a 56% reduction in water absorption and a 34% reduction in compressive strength loss due to immersion in 5% H2SO4 at 90 days. The findings of the study endorse that ternary blending of SF and MK can improve the engineering properties of HPC, and a mix containing SF 10% and MK 10% is recommended for the best results.

Comparative analysis of environmental, social, and mechanical aspects of high-performance concrete with calcium oxide-activated slag reinforced with basalt, and recycled PET fibers

This paper studied the effects of steel, basalt, and recycled PET fibers on the environmental, social, and mechanical properties of high-performance concrete (HPC) with calcium oxide-activated slag (HPC-CAS). The energy consumption, greenhouse gas emissions, and human health impacts of concrete production were determined using resources, climate change, and human health indexes. The compressive strength, tensile and flexural strengths, modulus of elasticity, electrical resistance, and durability of HPC-CAS with different fiber types and contents were also measured and determined. Furthermore, the microstructure of HPC-CAS was examined by scanning electron microscope (SEM). The results revealed that 3 % recycled PET or basalt fibers increased the HPC-CAS tensile strength by 89 % and 55 %, respectively, and the flexural strength by 28 % % and 1.2 times, respectively, compared to specimens without fibers. The specimens with fibers also showed higher ductility and lower electrical resistance than those without fibers. The SEM analysis showed that basalt fibers had a better bond with the geopolymer matrix than recycled PET fibers. Basalt fibers had the lowest environmental impact and recycled PET fibers had the lowest social impact among the three types of fibers. However, recycled PET fibers also had lower mechanical performance and durability than basalt fibers. It was concluded, therefore, that the choice of fiber type for HPC-CAS depends on the balance between social, environmental, and technical criteria.

Applying machine learning techniques in the form of ensemble and hybrid models to appraise hardness properties of high-performance concrete

Accurately estimating concrete mechanical parameters using artificial intelligence-based methods can save time and energy. Existing nonlinear relationships between concrete components have entered uncertainty in the estimation of hardness properties of the slump and compressive strength as one of the most important parameters in concrete design. Employing regular approaches to use AI models individually in estimating dependent variables has been adopted in many studies. Therefore, the current study has aimed to develop predictive models in two categories of ensemble and hybrid frameworks to predict the hardness properties of high-performance concrete (HPC). In this regard, models based on Support Vector Regression, Decision Tree, and AdaBoost Machine learning were coupled with a metaheuristic optimization algorithm Chaos game optimizer (CGO). Linking three predictive models as well as tuning their internal settings via optimization algorithm could generate various types of hybrid and ensemble models. By assessing the results of the proposed models for compressive strength, the performance of ADA-CGO hybrid models was calculated higher than the ensemble model of SVR-ADA-DT, with 1.22% and 166% percent difference in terms of R2 and RMSE, respectively. Also, for predicting Slump, other hybrid models appeared with weaker performance than the ensemble model, with an average difference of 40.66% in terms of the MAE index. Generally, using advanced types of individual models, including ensemble and hybrid, indicated boosted performance accompanied by low-cost modeling processes.

Improving the Mechanical Properties of High-Performance Concrete using Alccofine and Glass Powder

The CO2 emissions from cement have had significant negative consequences on the environment, necessitating the substitution of cement in the production of concrete. This research tends to cover the mechanical characteristics and microstructural studies of concrete formed by replacing some of the cement with Alccofine (AL) by percentages of 5%, 10%, 15%, 20%, 25% and 30% and some of fine aggregate with Glass Powder (GP) by percentages of 10%, 20%, 30%, 40% and 50% in the concrete of the M40 grade. A pozzolanic substance called alccofine and high silica content material called glass powder was extensively utilized in the building industry. When the mechanical properties of concrete were tested at various time periods, the optimal partial cement and fine aggregate substitution was determined to be 20% Alccofine and 50% of Glass Powder. When compared to conventional concrete of the same ratio, the compressive strength of the concrete has been enhanced at 20 percent replacement of AL and 50 percent replacement of GP. Moreover, concrete was subjected to a microstructural examination to observe the hydration process at various time intervals, and the results clearly demonstrated how pores and C-S-H (calcium-silicate hydrate) gel are created in both early ages and later ages. According to the study's findings, cement may be replaced in part without impairing its properties and can help lessen the impact of CO2 on the environment.

Effect of manufactured sand, micro silica, and GGBS on properties of high-performance concrete

The most often used building material for engineering constructions is concrete. The rapid urbanization has necessitated the need for High Strength (HS) and High -Performance Concrete (HPC) for specialized constructions, such as high rise/tall structure and other important structures. Greater cement content may be required for concrete with higher performance like strength and durability, but code does not permit it, because higher cement content increases the heat of hydration, which leads to the development of thermal cracks in concrete, reduces its structural performance and damage environment by producing CO2 during production of Ordinary Portland Cement (OPC). This study tries to replace OPC by Micro Silica (MS) and Ground Granulated Blast Furnace Slag (GGBS) by some percentage to find our effect of these materials on properties of concrete HPC.In this work, the influence of Manufactured Sand (M-Sand), Micro Silica (MS) and Ground Granulated Blast Furnace Slag (GGBS) on the mechanical and durability qualities of High - Performance Concrete (HPC) is examined experimentally. Ordinary Portland cement (OPC) is used to create the HPC mixtures and then 40%,45%, 50% by weight of OPC is replaced with GGBS and 2.5% and 5% by weight of OPC is replaced with MS respectively. Additionally, OPC is replaced by 40%GGBS+ 2.5% MS, 40% GGBS+5% MS, 45%GGBS+ 2.5% MS, 45% GGBS+5% MS, and 50%GGBS+ 2.5%MS, 50%GGBS+5% MS. For each percentage replacement workability test, compressive test at the age of 28,56 and 90 days, water permeability test at 28 days and 90 days, Scanning Elector Microscope (SEM) test after 90 days are carried out. The investigative study’s findings showed that partial replacement of OPC with GGBS, MS, and combinations of GGBS and MS produces more consistent outcomes for strength and durability when compared to control mix.

Estimating the mechanical properties of high-performance concrete via automated and ensembled machine learning methods

Environmentally friendly concrete is needed to reduce increased concrete consumption’s environmental and climate impacts. One of the approaches to achieve this goal is using fly ash (FA) and silica fume (SF) in concrete mixes. The mentioned admixtures can enhance the workability and mechanical properties of concrete. Therefore, testing to predict the strength of concrete can be time-consuming and expensive. Thus, operating artificial intelligence methods, especially machine learning (ML), can simplify and speed up the procedure. Therefore, this investigation proposes to explore the possibility of using the ML models to predict the compressive strength (CS) and Slump (SL) of High-performance concrete (HPC). For this task, experimental results of HPC samples are used to develop the ML models, including Light Gradient Boosting Machine (LGBM) and Decision Tree (DT). In addition, some meta-heuristic algorithms have been used to improve the accuracy of presented models, which contain the Zebra Optimization Algorithm and Tunicate Swarm Algorithm. Extensive calculational tests were performed to assess the performance of the deemed models by using results achieved through experimental tests by statistical indicators. The LGZO model stands out from the other proposed models, exhibiting superior performance with impressively low RMSE values of 1.859 and 5.234 for CS and SL, respectively. These results prove the model’s exceptional accuracy and robust predictive capability. Research findings suggest that utilizing predictive techniques instead of conducting costly and time-consuming tests could reduce costs and save time.