Properties Of High Performance Concrete Research Articles

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.

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.

Influence of nano zirconia on the mechanical and durability properties of high-performance concrete containing nano-silica

High Performance Concrete (HPC) was prepared by reinforcing with hybrid nanoparticles such as nano-SiO2 and nano-ZrO2 and the flowability, strength and durability of the concrete was investigated. Slump study showed that the prepared HPC displayed a slump loss with the addition of hybrid nanoparticles due to the absorption of water by hybrid nanoparticles. Experimental results showed that the inclusion of 10% nano-SiO2 and 5% nano-ZrO2 hybrid nanoparticles (mix code M5) enhanced the compressive strength of concrete and exhibited a maximum of 50.12 MPa and 76.4 MPa at 7 days and 28 days of curing due to the reduction of pore volume. Split tensile strength followed the same trend of variation as compressive strength and the mix code M5 showed the maximum split tensile strength of 5.33 MPa and 7.08 MPa cured at 7 days and 28 days respectively. Flexural study resulted that the mix code M5 exhibited a maximum flexural strength of 5.73 MPa and 7.38 MPa cured at 7 days and 28 days respectively owing to the restriction of crack ignition by reinforced hybrid nanoparticles. Rheological study reported that all the prepared concretes lost the weight by less than 1% and the chloride attack displayed maximum weight loss of 4% whereas alkaline attack showed less weight of concrete.

Effect of Dolomite and Metakaolin on the Properties of High-Performance Concrete

Cement is one of the extensively used civil engineering material in the construction industry due to its strength characteristics. The use of cement-based materials and the development of infrastructure are rapidly increasing. The production of cement in large quantity leads to environmental degradation and emission of CO2. For making the construction process in more sustainable way, the pursuit for new binders has started and stimulated innovative opportunities to use industrial by product as binders. This paper studies the effect of dolomite and metakaolin on the properties of high-performance concrete. For the study, M70 grade of concrete taken and optimum percentage of metakaolin found out by replacing cement by metakaolin in varying percentages of 10%, 20% and 30%. Effect of metakaolin and dolomite in high performance concrete is found out by replacing cement by dolomite in varying percentages of 5%, 7.5%, 10% and 12.5% and obtained optimum percentage of metakaolin. Properties such as fresh, mechanical, and loading behavior of RCC beam were found out. To study the cost effectiveness of using metakaolin and dolomite in high performance concrete, cost analysis was carried out. The test results reveals that the compressive strength, flexural strength and split tensile strength for a curing period of 28 days shows an increase of 4.99%,15.78% and 5.89% respectively. Flexural behavior of RCC beam with optimum percentage shows an increase of 23.8 % compared to the control beam. Cost of saving of cement by using metakaolin and dolomite is about 10% and it can be used as an alternative of cement economically.

Predicting Strength Properties of High-Performance Concrete Modified with Natural Aggregates and Ferroslag under Varied Curing Conditions

High-performance concrete (HPC) is obtained by inclusion of mineral admixtures like silica fumes and fly ash to the normal concrete. Consumption of natural materials such as sand, natural aggregates, and limestone produces environmental degradation. Similarly, industrial by-products such as fly ash, silica fume, and ferro slag need to be safely disposed of without negatively impacting the environment. The problem being addressed in this study is the need to develop high-performance concrete (HPC) that is durable and environmentally friendly. In recent years, the use of natural aggregates and ferro slag as partial replacements for traditional aggregates has gained attention as a sustainable alternative in the production of concrete. However, there is limited research on the effect of these materials on the mechanical and durability properties of HPC under varied curing conditions. In this current research, high-performance concrete of M60 grade with partial substitution of coarse aggregate with ferro slag aggregate was formed as per the recommendations of the American Concrete Institute with the inclusion of fly ash and silica fume. Natural coarse aggregate was partly substituted by ferro slag aggregate in proportions from 0% to 40%. Partial substitution of cement was made with 15% of fly ash and 10% of silica fumes. Specimens of normal concrete mix (MF0) and modified ferro slag aggregate concrete mix (MF20, MF30, and MF40) were prepared and subjected to acid test, sulphate test, and alternate wet and drying tests to assess the compressive strength of the concrete mixes. Central composite design (CCD) of RSM modelling was adopted to recommend a regression model to forecast the compressive strength of concrete under wetting drying test, acid test, and sulphate attack. Further, natural aggregate, ferro slag, and duration of curing were considered as basic variables to suggest the model. Regression models for response data were evaluated using analysis of variance (ANOVA) and Pareto charts. The results show that the mix MF30 (30% substitution of natural aggregate by ferro slag aggregate) had higher compressive strength. The residual compressive strength at 270 days under alternate wetting and drying, acid attack, and sulphate attack was obtained as 62 MPa, 62.50 MPa, and 66.50 MPa, respectively. Similarly, the percentage loss of weight was obtained as 12.92%, 12.22%, and 6.60% for alternate wetting and drying, acid attack, and sulphate attack, respectively. The findings of the analysis of variance (ANOVA) indicate that the most significant factors influencing the variables C S W D , C S A T , and C S S T are natural aggregate, ferro slag, and curing period. The regression models for C S W D , C S A T , and C S S T are extremely significant, as shown by the ANOVA and Pareto chart analyses.

Interaction effect of recycled aggregate type, moisture state, and mixing process on the properties of high-performance concretes

This paper presents the results of a laboratory study and statistical analysis to determine the effect of the combination of three key parameters on the properties of high-performance concretes with recycled aggregates: mixing procedure, aggregate moisture states, and aggregate type. The study included evaluation of slump, compressive, flexural, and splitting tensile strength, modulus of elasticity, and drying shrinkage of concretes with a water/cementitious ratio of 0.45. Statistical analysis of the experimental data included one-way analysis of variance (ANOVA) and post hoc test. The results showed that it is possible to produce high quality concretes using recycled coarse aggregates by manipulating the above mentioned parameters, which is a technical, economical, and sustainable alternative to natural aggregate concretes. The two-stage mixing approach had a positive effect on workability, strength, and drying shrinkage. Aggregates in the pre-saturated state showed higher performance than those in the oven-dried and saturated surface-dried states.

Strength, shrinkage, heat evolution, and microstructure of high performance concrete containing high proportions of ground bottom ash blended with fly ash

This study examines the use of ground bottom ash incorporating fly ash in high performance concrete. A self-compacting concrete was considered in this study as to maximize the properties of high performance concrete. Bottom ash was processed to a fine consistency by oven-drying, sieving, and grinding. Ordinary Portland cement (OPC), sieved and ground bottom ash (GBA), and fly ash (FA) were used for producing high performance concrete at a W/B ratio of 0.27. Replacement of 50% cement by GBA produced 101.0 MPa concrete at 28 days: 21.6% stronger than OPC concrete. The 50% GBA mixture needed a greater quantity of superplasticizer to satisfy slump flow requirements for self-compacting concrete, but that dosage was reduced with a partial replacement of GBA by FA. Elastic moduli of all blended concretes matched OPC concrete. However, the blended concretes experienced much less autogenous shrinkage. Pastes containing GBA and FA have less portlandite than OPC paste. In addition, replacing OPC by GBA–FA substantially reduced cumulative heat evolution.

Prediction of Mechanical Properties of High-Performance Concrete (HPC) Reinforced with Steel Fibers

Prediction of Mechanical Properties of High-Performance Concrete (HPC) Reinforced with Steel Fibers

Influence of hybrid coir-steel fibres on the mechanical behaviour of high-performance concrete: Step towards a novel and eco-friendly hybrid-fibre reinforced concrete

In this study, an effort has been extended to analyse the properties of high-performance concrete (HPC) reinforced with hybrid combinations of coconut fibre (CF) (at 0.1, 0.2, and 0.3% volume fractions) and steel fibre (SF) (at 0.5 and 1% volume fractions). The compressive strength, load–deflection behaviour, and splitting tensile strength of fibre-reinforced HPC were evaluated. Moreover, ultrasonic pulse velocity and water absorption characteristics were also examined. The results revealed that the hybridization of CF and SF notably increased the mechanical strength and flexural behavior of HPC. A maximum increase in the compressive strength of HPC was noticed with the hybridization of 0.1–0.3 vol% of CF and 0.5 vol% SF. However, the tensile and flexural strength of HPC increased proportionally with the increase in CF and SF content. The use of 0.1–0.2 vol% CF by volume fraction is recommended for optimum mechanical performance without compromising the quality and permeability-resistance of plain and SF-reinforced HPC. This study provides insight into the development of eco-friendly and cheap hybrid fibre-reinforced HPC using CF.

Mechanical and rheological properties of High-Performance concrete (HPC) incorporating waste glass as cementitious material and fine aggregate

The disposal of waste glass was a huge challenge for the sustainable development of the city. Its usage in High-Performance concrete (HPC) was a perspective way to improve the working and mechanical properties. In this paper, waste glass powder (WGP) and waste glass sands (WGS) were introduced in the HPC and used to partly replace cement and river sands, respectively. The individual effects of WGP or WGS on flowability, rheology, thixotropy, alkali-silica reaction, mechanical properties, and microstructure were investigated. Furthermore, the central composite face-centered design (CCF) method was adopted to assess the synergistic effect of the mixtures blending combined WGP and WGS on rheological and mechanical properties. The results showed that the workability, thixotropy, and mechanical properties of HPC with WGP incorporation were improved, while the addition of WGS had a negative effect on mechanical properties. Meanwhile, no obvious expansion hazard was observed after ASR testing in the presence of WGP or WGS. 20% WGP (20% binder weight) and 10% WGS (by aggregate weight) exhibited better synergistic effect on the properties of HPC, especially the highest comprehensive evaluation result score ɑP20S10 of 3.24 was obtained under this replacement, exhibiting optimal utilization waste glass in HPC.

Influence of Graphene Oxide and Ground Granulated Blast Furnace Slag on Engineering Properties of High-Performance Concretes

Abstract Concrete structures immersed in sea water suffer severe corrosion, and the application of nanomaterials to concrete presents an innovative approach to enhancing both its physical and mechanical properties. In this paper, the effects of combined admixtures of graphene oxide nanoplatelets (GONPs) and ground granulated blast furnace slag (GGBFS) on concrete were studied, and mechanical and physical tests were performed. It was known that the GGBFS admixture improves the durability of concrete, but in concrete samples containing both GONPs and GGBFS, the compressive strength increased by nearly 43 % and 46 % at 28 and 90 days of curing, respectively, compared with ordinary portland cement (OPC) concretes. The same admixing regime caused an increase in flexural strength of up to 58 % and 59 % during the same time spans. This was accompanied with a significant reduction in chloride permeability, which was appraised by a 64 % drop in electrical conductivity measurements. The GONPs additive, even at the very low concentrations, accelerated the formation of calcium silicate hydrate, thereby enhancing the mechanical and flexural strengths that directly affect the durability and water and chloride absorption. The overall durability and rebar corrosion resistance that were shown through electrochemical tests are consequences of the combined improvements in the physico-mechanical concrete properties. OPC concrete is thus both stronger and significantly more chloride ion resistant with additions of GONPs in the parts per million range and by replacing it with 50 percent by weight GGBFS. The enhanced pozzolanic reactions in concrete help to fill the porosity and significantly contribute to the curtailment mechanism.

Experimental studies on mechanical and durability properties of high performance concrete with partial replacement of fine aggregate with crushed Quartzite and binding material by fly ash and silica fume

The high performances concrete’s demand growing day by day due to its mechanical properties like compressive strength and it’s durability characteristics therefore the Research on high performance concrete on its verge. The availability of natural fine aggregates are diminishing day by day there by it is the time to search for alternative. Many researches are using utilizing industrial waste in High performance Concrete. In the present study, M60 grade of the concrete was used and designed as per the mix design standards of American Concrete Institute (ACI, 234R-96). The mechanical properties of HPC, such as compressive strength, split tensile strength, and modulus of elasticity, as well as durability properties, such as acid resistance test, sulphate resistance test, chloride resistance test, ultrasonic pulse velocity test, and alternate wet and dry test, were determined using specimens of regular concrete mix and modified Crushed Quartzite concrete mixes. According to the experimental findings, HPC has good mechanical and durability features when 30% crushed quartzite is substituted for fine aggregate, and the amount of binder is determined by the compressive strength of concrete when varied amounts of fly ash and silica fume are used. Optimization of admixture arrived as Fly ash 15% and silica fume 10% to the weight of cement content.

Modeling the hardness properties of high-performance concrete via developed RBFNN coupling matheuristic algorithms

Examining the properties of High-Performance Concrete (HPC) has been a big challenge due to the highly heterogeneous relationships and coherence among several constituents. The employment of silica fume and fly ash as eco-friendly components in mixtures benefits the concrete to improve its physical features. Although machine learning approaches are utilized broadly in many studies solitarily to estimate the mechanical features of concrete, causing to reduce accuracy and lift the cost and complexities of computational networks. Consequently, current research aims to develop a Radial Basis Function Neural Network (RBFNN) integrating with optimization algorithms in order to precisely model the mechanical characteristics of HPC mixtures including compressive strength (CS) and slump (SL). Feeding the dataset of HPC samples to hybrid models will result to reproduce the given CS and SL factors simultaneously. The results of the models showed that the maximum rate of correlation between estimated values and measured ones was obtained at 98.3% while the minimum rate of RMSE was calculated at 3.684 mm (and MPa) in the testing phase. Employing such soft-oriented approaches has been benefiting us to reduce costs and increase the result accuracy.