Water Absorption Of Concrete Research Articles

Ceramsite Foam Concrete Insulation Block

This paper studied the lightweight ceramsite, thickener and waterproofing agent dosage on the influence of the foam concrete water absorption and strength, and further researched into the amount of foaming agent on the influence of the density and strength. The results show that adding 20% ceramic, 0.03% thickening agent and 1% waterproof agent prepared the best performance of foam concrete.

Deterioration of dynamic mechanical property of concrete with mineral admixtures under fatigue loading

The dynamic mechanical property of concrete is one of the key parameters, which greatly influences durability of infrastructures subjected to continuous heavy loading, such as girder and track slab of high-speed railway foundation structure. This paper reports serials of experiments designed to investigate the deterioration of dynamic mechanical properties of different concretes under fatigue loading condition. Four parameters including relative dynamic elastic modulus (RDEM), relative dynamic shear modulus (RDSM), relative compressive strength (RCS) and water absorption (WA) of concrete were evaluated to assess the dynamic properties and microstructures of concretes. Results show that the fatigue stress levels and fatigue cycle durations significantly influence the dynamic mechanical properties of concrete including dynamic elastic modulus and dynamic shear modulus. Addition of proper mineral admixture can improve the dynamic mechanical characteristics of concrete and increase its resistance against the fatigue loading effect. Keeping the amount of mineral admixture in concrete constant, its dynamic mechanical property with fly ash is lower than that with fly ash and silica fume. The water absorption in concrete, which is an indirect parameter reflecting capillary porosity, increases evidently after bearing fatigue-loading. There is a close correlation between the deterioration of dynamic mechanical property and the increasing of water absorption of concrete. This indicates that the damage of microstructure of concrete subjected to fatigue loading is the indispensable reason for the decay of its dynamic mechanical performance.

Capillary absorption of concrete after mechanical loading

Microcracks, which are always present in concrete, play a critical role in the durability of concrete because they provide additional pathways for aggressive agents such as chlorides to penetrate into the material. A thorough understanding of the transport properties of cracked/damaged concrete is thus essential for predicting its long-term durability. In addition, for concrete in an unsaturated state, water acts as the main medium for the ingress of chloride ions into concrete. This paper presents an experimental investigation into the capillary absorption of concrete after being subjected to compressive or tensile loading. Sorptivity is chosen as a parameter to describe the rate of water penetrating into concrete since it can characterise the tendency of cementitious materials to absorb and transmit water by the capillary mechanism. A series of water absorption experiments were conducted on concrete samples after being subjected to uniaxial compressive or tensile loading. Three load levels were considered – 70%, 80% and 90% of the corresponding compression and tension loading capacity. An improved gravimetrical test device was used to measure the cumulative water absorbed by concrete at the given time of exposure. The results show that mechanical loading, compression or tension, has an important influence on the water absorption of concrete. The rate of capillary absorption of load-damaged concrete can be up to double that of sound concrete samples for the ranges of load level studied.

Water absorption control of ternary blended concrete with nano-SiO2 in presence of rice husk ash

In the current study, the effects of SiO2 nanoparticles as additive with two different sizes of 15 and 80 nm on water absorption of rice husk ash (RHA) blended concrete have been investigated. Concrete samples were prepared by replacing 10, 15 and 20 wt% of cement with RHA and 0.5, 1.0, 1.5 and 2.0% of cement with SiO2 nanoparticles followed by curing in lime solution for 7, 28 and 90 days. The results indicated that the resistance to water absorption of Portland cement–nano SiO2–rice husk ash (PC–NS–RHA) ternary blended concrete was considerably improved with respect to the control concrete. This improvement was observed at all curing ages and replacement levels but the optimal point was reached for 20% of RHA incorporating 2% of 80 nm SiO2 particles at 90 days of curing. Fast formation of C–S–H gel in the presence of ultra high active nano-sized SiO2 and micron level RHA particles together with their high filler effect may result in a continuous cement paste with the lowest weak zones. It has been concluded that the use of novel ternary blended concrete (PC–NS–RHA) provides significant reduction in the water absorption of concrete.

Study on silane impregnation for protection of high performance concrete

Steel corrosion caused by chloride penetration is the main reason for the damage of concrete structures in marine environment. By silane impregnation, the corrosion of chloride on concrete can be inhibited effectively, and the water absorption of concrete can also be decreased. Silane gel and silane solution was used as protection treatment on concrete surface respectively. Effect of silane type and painting amount of silane on the mechanical performance, capillary water absorption and chloride ion permeability of concrete was studied in the present investigation. The experimental results showed that the two silanes can penetrate to the depth of about 2∼6mm. Compared with untreated samples, the reduction of water absorption and chloride absorption of treated concrete was higher than 90%. The chloride diffusion coefficient and electric flux decreased obviously. The protection effect of silanes on concrete both improves with the increase of painting amount. The protection of silane gel on concrete was better than that of silane solution.

Computer-aided Prediction of the ZrO2 Nanoparticles' Effects on Tensile Strength and Percentage of Water Absorption of Concrete Specimens

In the present paper, two models based on artificial neural networks and genetic programming for predicting split tensile strength and percentage of water absorption of concretes containing ZrO2 nanoparticles have been developed at different ages of curing. For building these models, training and testing using experimental results for 144 specimens produced with 16 different mixture proportions were conducted. The data used in the multilayer feed forward neural networks models and input variables of genetic programming models were arranged in a format of eight input parameters that cover the cement content, nanoparticle content, aggregate type, water content, the amount of superplasticizer, the type of curing medium, age of curing and number of testing try. According to these input parameters, in the neural networks and genetic programming models, the split tensile strength and percentage of water absorption values of concretes containing ZrO2 nanoparticles were predicted. The training and testing results in the neural network and genetic programming models have shown that two models have strong potential for predicting the split tensile strength and percentage of water absorption values of concretes containing ZrO2 nanoparticles. It has been found that neural network (NN) and gene expression programming (GEP) models will be valid within the ranges of variables. In neural networks model, as the training and testing ended when minimum error norm of network gained, the best results were obtained and in genetic programming model, when 4 genes were selected to construct the model, the best results were acquired. Although neural network have predicted better results, genetic programming is able to predict reasonable values with a simpler method rather than neural network.

Combined effects of fly ash and waste ferrochromium on properties of concrete

Cement and water-to-binder ratio were kept constant as 400kg/m3 and 0.40, respectively. Cement was replaced with fly ash at the ratio of 10, 20 and 30wt.%. Coarse limestone aggregates were replaced with coarse ferrochromium aggregate at the ratio of 25, 50 and 75wt.%. On fresh concretes, slump, air content and unit weight tests were performed. On the hardened concretes, compressive strength, splitting tensile strength, elasticity modulus, abrasion resistance, freeze–thaw resistance, porosity and water absorption were determined. The use of fly ash lowers values several properties: compressive strength, splitting tensile strength, elasticity modulus and wear resistance; however freeze–thaw durability increases. Usage of ferrochromium aggregates increases strength of concrete and also abrasive wear resistance. Effect of ferrochromium aggregates on porosity and water absorption of concrete is insignificant while fly ash enhances these properties. A highly accurate equation relating compressive strength to splitting tensile strength is provided. An equation relating wear to compressive strength and fly ash volume fraction is also defined.

Efficacy of ASTM Saturation Techniques for Measuring the Water Absorption of Concrete

The aim of this study was to compare the efficacy of three ASTM saturation techniques for measuring the water absorption of concrete. The water absorption of two ordinary concretes was determined based on the cold water, boiling water and vacuum saturation techniques. The concretes were produced with water to cement (w/c) ratios of 0.50 and 0.60 and tested at 7 and 28 days. The vacuum saturation was more effective than the cold water and boiling water saturation methods. The vacuum saturation technique produced up to 13.1% more water absorption than the boiling water saturation method and up to 20.2% more water absorption than the cold water saturation method. Thus the present study suggests that the vacuum saturation technique is more efficient than both cold water and boiling water saturation methods to measure the water absorption of concrete.

RETRACTED ARTICLE: Computer-aided prediction of the Al2O3 nanoparticles’ effects on tensile strength and percentage of water absorption of concrete specimens

In the present paper, two models based on artificial neural networks and genetic programming for predicting split tensile strength and percentage of water absorption of concretes containing Al2O3 nanoparticles have been developed at different ages of curing. For purpose of building these models, training and testing using experimental results for 144 specimens produced with 16 different mixture proportions were conducted. The data used in the multilayer feed-forward neural networks models and input variables of genetic programming models are arranged in a format of eight input parameters that cover the cement content, nanoparticle content, aggregate type, water content, the amount of superplasticizer, the type of curing medium, Age of curing and number of testing try. According to these input parameters, in the neural networks and genetic programming models, the split tensile strength and percentage of water absorption values of concretes containing Al2O3 nanoparticles were predicted. The training and testing results in the neural network and genetic programming models have shown that every two models have strong potential for predicting the split tensile strength and percentage of water absorption values of concretes containing Al2O3 nanoparticles. It has been found that NN and GEP models will be valid within the ranges of variables. In neural networks model, as the training and testing ended when minimum error norm of network gained, the best results were obtained, and in genetic programming model, when 4 gens was selected to construct the model, the best results were acquired. Although neural network have predicted better results, genetic programming is able to predict reasonable values with a simpler method rather than neural network.

Effect of sample conditioning on the water absorption of concrete

ASTM C1585 is commonly used to determine the absorption and rate of absorption of water in unsaturated hydraulic cement concretes. Unfortunately, a wide range of relative humidities can exist in the samples after this relatively short conditioning period and such variation may considerably influence the test results. Three main variables were studied in this program: the water to cement ratio, the paste volume fraction, and the effect of sample conditioning. The results confirm that water absorption testing is considerably influenced by sample preparation. Samples conditioned at 50% relative humidity can show up to six times greater total absorption than similar samples conditioned at 80% relative humidity. Samples that were conditioned in the oven at 105 °C do not appear to follow a similar trend when compared with specimens conditioned in chambers at lower temperatures for a longer duration. The absorption is also influenced by the volume of paste in the samples. The experiments show that a lack of control on moisture content or lack of consideration of the material composition may lead to a misunderstanding of the actual absorption behavior.

Effect of metakaolin and foundry sand on the near surface characteristics of concrete

This paper deals with the effect of foundry sand (FS) and metakaolin (MK) on the near surface characteristics of concrete. A control concrete having cement content 450 kg/m 3 and w/c of 0.45 was designed. Cement was replaced with three percentages (5%, 10%, and 15%) of metakaolin weight, and fine aggregates were replaced with 20% foundry sand. Tests were conducted for initial surface absorption, sorptivity, water absorption and compressive strength at the ages of 35, 56, and 84 days. Test results indicated that with the increase in MK content from 5% to 15%, there was a decrease in the initial surface absorption, decrease in the sorptivity till 10% metakaolin replacement. But at 15% MK replacement an increase in sorptivity was observed. All mixtures showed low water absorption characteristic i.e. less than 10%. Compressive strength shared an inverse relation with sorptivity. Higher MK replacements of 15% are not helpful in improving inner core durability, even though it helps in improving surface durability characteristics. Inclusion of foundry sand resulted in reduction in compressive strength. This aspect cements the findings that addition of FS causes permeability of concrete to increase causing in an increase in sorptivity and water absorption of concrete.

Computer-aided design of the effects of Fe 2O 3 nanoparticles on split tensile strength and water permeability of high strength concrete

In the present paper, two models based on artificial neural networks and genetic programming for predicting split tensile strength and percentage of water absorption of concretes containing Fe 2O 3 nanoparticles have been developed. To build these models, training and testing of the network by using experimental results from 144 specimens produced with 16 different mixture proportions were conducted. The data used in the multilayer feed forward neural networks models and input variables of genetic programming models have been arranged in a format of eight input parameters that cover the cement content, nanoparticle content, aggregate type, water content, the amount of superplasticizer, the type of curing medium, age of curing and number of testing try. According to these input parameters, in the two models, the split tensile strength and percentage of water absorption values of concretes containing Fe 2O 3 nanoparticles were predicted. The training and testing results in the neural network and genetic programming models have shown that every two models are of strong potential for predicting the split tensile strength and percentage of water absorption values of concretes containing Fe 2O 3 nanoparticles. Although neural network has predicted better results, genetic programming is able to predict reasonable values with a simpler method rather than neural network.

RETRACTED: Prediction of Physical and Mechanical Properties of High Strength Concrete Containing CuO Nanoparticles by Artificial Neural Network and Genetic Programming

In this article, two models based on artificial neural networks and genetic programming for predicting flexural strength and percentage of water absorption of concretes containing CuO nanoparticles have been developed at different ages of curing. For the purpose of building these models, training and testing using experimental results for 144 specimens produced with 16 different mixture proportions were conducted. The data used in the multilayer feed-forward neural networks models and input variables of genetic programming models are arranged in a format of eight input parameters that cover the cement content (C), nanoparticle content (N), aggregate type (AG), water content (W), the amount of superplasticizer (S), the type of curing medium (CM), Age of curing (AC), and number of testings tried (NT). According to these input parameters, in the neural networks and genetic programming models, the flexural strength and percentage of water absorption values of concretes containing CuO nanoparticles were predicted. The training and testing results in the neural network and genetic programming models have shown that every two models have strong potential for predicting the flexural strength and percentage of water absorption values of concretes containing CuO nanoparticles. Although neural network have predicted better results, genetic programming is able to predict reasonable values with a simpler method rather than neural network.

RETRACTED ARTICLE: Computer-aided design of the effects of Cr2O3 nanoparticles on split tensile strength and water permeability of high strength concrete

In the present paper, two models based on artificial neural networks and genetic programming for predicting split tensile strength and percentage of water absorption of concretes containing Cr2O3 nanoparticles have been developed at different ages of curing. For purpose of building these models, training and testing using experimental results for 144 specimens produced with 16 different mixture proportions were conducted. The data used in the multilayer feed forward neural networks models and input variables of genetic programming models are arranged in a format of 8 input parameters that cover the cement content, nanoparticle content, aggregate type, water content, the amount of superplasticizer, the type of curing medium, age of curing and number of testing try. According to these input parameters, in the neural networks and genetic programming models the split tensile strength and percentage of water absorption values of concretes containing Cr2O3 nanoparticles were predicted. The training and testing results in the neural network and genetic programming models have shown that every two models have strong potential for predicting the split tensile strength and percentage of water absorption values of concretes containing Cr2O3 nanoparticles. It has been found that NN and GEP models will be valid within the ranges of variables. In neural networks model, as the training and testing ended when minimum error norm of network was gained, the best results were obtained and in genetic programming model, when 4 genes were selected to construct the model, the best results were acquired. Although neural network has predicted better results, genetic programming is able to predict reasonable values with a simpler method rather than neural network.

Effect of using Wastewater on the Properties of High Strength Concrete

This paper investigates the effect of using wastewater on the properties of high strength concrete. Wastewater samples were collected from three car washing station in Muscat area. The collected wastewater samples were mixed together and chemical analysis was carried out. Four water samples, including a controlled potable (tap) water were analysed for pH, total dissolved solids (TDS), chloride, hardness, alkalinity, and sulfates. Chemical analysis results showed that although the chemical compositions of wastewater were much higher than those parameters found in tap water, the water composition was within the ASTM standard limits for all substance indicating that the wastewater produced can be used satisfactorily in concrete mixtures.High strength concrete mixtures were prepared using different proportions of wastewater and water-to-cement ratio of 0.35. The percentage of wastewater replaced ranged between 25–100% of tap water used in concrete. For each concrete mixture, Six 150mm×150mm×150mm cubes, three 300mm×150mm dia. cylinders and three 100mmx100mmx500mm prisms were cast. Slump, compressive, tensile and flexural strengths were determined at 28-day of curing. Cube compressive strength was also determined at 7-day of curing. Also, initial surface absorption test was conducted at 28-day of curing in order to assess the durability of concrete. Results indicated that the strength of concrete of the mixtures prepared using wastewater was comparable with the strength of the control mixture. Also, the water absorption of concrete is not affected when wastewater was used.

RETRACTED: Prediction split tensile strength and water permeability of high strength concrete containing TiO2 nanoparticles by artificial neural network and genetic programming

Abstract In the present paper, two models based on artificial neural networks (ANN) and genetic programming (GEP) for predicting split tensile strength and percentage of water absorption of concretes containing TiO 2 nanoparticles have been developed at different ages of curing. For purpose of building these models, training and testing using experimental results for 144 specimens produced with 16 different mixture proportions were conducted. The data used in the multilayer feed forward neural networks models and input variables of genetic programming models are arranged in a format of eight input parameters that cover the cement content (C), nanoparticle content (N), aggregate type (AG), water content (W), the amount of superplasticizer (S), the type of curing medium (CM), Age of curing (AC) and number of testing try (NT). According to these input parameters, in the neural networks and genetic programming models the split tensile strength and percentage of water absorption values of concretes containing TiO 2 nanoparticles were predicted. The training and testing results in the neural network and genetic programming models have shown that every two models have strong potential for predicting the split tensile strength and percentage of water absorption values of concretes containing TiO 2 nanoparticles. It has been found that NN and GEP models will be valid within the ranges of variables. Although neural network have predicted better results, genetic programming is able to predict reasonable values with a simpler method rather than neural network.

COMPUTER-AIDED PREDICTION OF PHYSICAL AND MECHANICAL PROPERTIES OF HIGH STRENGTH CEMENTITIOUS COMPOSITE CONTAINING Cr2O3 NANOPARTICLES

In the present paper, two models based on artificial neural networks (ANN) and genetic programming (GEP) for predicting flexural strength and percentage of water absorption of concretes containing Cr2O3 nanoparticles have been developed at different ages of curing. For purpose of building these models, training and testing using experimental results for 144 specimens produced with 16 different mixture proportions were conducted. The data used in the multilayer feed forward neural networks models and input variables of genetic programming models are arranged in a format of eight input parameters that cover the cement content (C), nanoparticle content (N), aggregate type (AG), water content (W), the amount of superplasticizer (S), the type of curing medium (CM), Age of curing (AC) and number of testing try (NT). According to these input parameters, in the neural networks and genetic programming models the flexural strength and percentage of water absorption values of concretes containing Cr2O3 nanoparticles were predicted. The training and testing results in the neural network and genetic programming models have shown that every two models have strong potential for predicting the flexural strength and percentage of water absorption values of concretes containing Cr2O3 nanoparticles. Although neural network have predicted better results, genetic programming is able to predict reasonable values with a simpler method rather than neural network.

A study of the permeability and acid attack of corn cob ash blended cements

The durability of concrete made with corn cob ash (CCA) blended cement was investigated in this study. Permeability and chemical attack involving H 2SO 4 and HCl were the key parameters considered. Nine classes of CCA blended cements were employed with the CCA content ranging from 0% to 25%. The 0% CCA replacement involved the use of normal ordinary Portland cement and it served as the control. The water absorption of blended cement concrete was performed using 100 mm cube specimens of mix proportions 1:1½:3, 1:2:4 and 1:3:6 with 0.5, 0.6 and 0.7 water-to-binder ratios, respectively. The chemical attack test was carried out using 50 × 50 × 15 mm mortar specimens of mix proportions 1:1, 1:2 and 1:3 with water-to-binder ratio ranging between 0.26 and 0.29. The results indicated that the use of CCA blended cement reduces the water absorption of concrete specimens. Optimal reduction occurred at 10% CCA replacement for 1:1½:3 and 1:2:4 mix proportions and at 15% CCA replacement for 1:3:6 mix proportion. The resistance to chemical attack was improved as the addition of CCA up to 15% replacement level, caused a decrease in permeability and reduction in weight loss due to reaction of the specimens with HCl and H 2SO 4 acid water.

Assessment of the recycling potential of fresh concrete waste using a factorial design of experiments

Recycling of industrial wastes and by-products can help reduce the cost of waste treatment prior to disposal and eventually preserve natural resources and energy. To assess the recycling potential of a given waste, it is important to select a tool capable of giving clear indications either way, with the least time and work consumption, as is the case of modelling the system properties using the results obtained from statistical design of experiments. In this work, the aggregate reclaimed from the mud that results from washout and cleaning operations of fresh concrete mixer trucks (fresh concrete waste, FCW) was recycled into new concrete with various water/cement ratios, as replacement of natural fine aggregates. A 3(2) factorial design of experiments was used to model fresh concrete consistency index and hardened concrete water absorption and 7- and 28-day compressive strength, as functions of FCW content and water/cement ratio, and the resulting regression equations and contour plots were validated with confirmation experiments. The results showed that the fresh concrete workability worsened with the increase in FCW content but the water absorption (5-10 wt.%), 7-day compressive strength (26-36 MPa) and 28-day compressive strength (32-44 MPa) remained within the specified ranges, thus demonstrating that the aggregate reclaimed from FCW can be recycled into new concrete mixtures with lower natural aggregate content.

Concretes and mortars recycled with water treatment sludge and construction and demolition rubble

There are about 7500 water treatment plants in Brazil. The wastes these plants generate in their decantation tanks and filters are discharged directly into the same brooks and rivers that supply water for treatment. Another serious environmental problem is the unregulated disposal of construction and demolition rubble, which increases the expenditure of public resources by degrading the urban environment and contributing to aggravate flooding and the proliferation of vectors harmful to public health. In this study, an evaluation was made of the possibility of recycling water treatment sludge in construction and demolition waste recycling plants. The axial compressive strength and water absorption of concretes and mortars produced with the exclusive and joint addition of these two types of waste was also determined. The ecoefficiency of this recycling was evaluated by determining the concentration of aluminum in the leached extract resulting from the solubilization of the recycled products. The production of concretes and mortars with the joint addition of water treatment sludge and recycled concrete rubble aggregates proved to be a viable recycling alternative from the standpoint of axial compression strength, modulus of elasticity, water absorption and tensile strength by the Brazilian test method.