Compressive Strength Of Lightweight Concrete Research Articles

Machine learning-based compressive strength estimation in nanomaterial-modified lightweight concrete

Abstract The development of nanotechnology has led to the creation of materials with unique properties, and in recent years, numerous attempts have been made to include nanoparticles in concrete in an effort to increase its performance and create concrete with improved qualities. Nanomaterials are typically added to lightweight concrete (LWC) with the goal of improving the composite’s mechanical, microstructure, freshness, and durability qualities. Compressive strength is the most crucial mechanical characteristic for all varieties of concrete composites. For this reason, it is essential to create accurate models for estimating the compressive strength (CS) of LWC to save time, energy, and money. In addition, it provides useful information for planning the construction schedule and indicates when the formwork should be removed. To predict the CS of LWC mixtures made with or without nanomaterials, nine different models were proposed in this study: the gradient-boosted trees (GBT), random forest, tree ensemble, XGBoosted (XGB), Keras, simple regression, probabilistic neural networks, multilayer perceptron, and linear relationship model. A total of 2,568 samples were gathered and examined. The most significant factors influencing CS during the modeling process were taken into account as input variables, including the amount of nanomaterials, cement, water-to-binder ratio, density, the content of lightweight aggregates, type of nano, fine and coarse aggregate content, and water. The performance of the suggested models was assessed using a variety of statistical measures, including the coefficient of determination (R 2), scatter index, mean absolute error, and root-mean-squared error (RMSE). The findings showed that, in comparison to other models, the GBT model outperformed the others in predicting the compression strength of LWC mixtures enhanced with nanomaterials. The GBT model produced the best results, with the greatest value of R 2 (0.9) and the lowest value of RMSE (5.286). Furthermore, the sensitivity analysis showed that the most important factor influencing the prediction of the CS of LWC enhanced with nanoparticles is the water content.

Experimental study on the effect of basalt fiber reinforced expanded polystyrene foams on the compressive strength of lightweight concrete

Reducing the structural load and operational energy consumption of buildings is a trend in the development of the construction industry, and lightweight concrete has become a research hotspot because of its dual characteristics of low density and high strength. In this paper, a three-step method is used to prepare lightweight concrete composites. In the first step, basalt fiber powder was adhered to polystyrene foam ball expanded polystyrene spheres (EPSs) by the “ball rolling method” to obtain basalt fiber reinforced epoxy composite balls. In the second step, the epoxy resin composite ball and the configured cement matrix are filled into a prefabricated mold. Finally, the lightweight concrete specimens were overlaid, pressed into shape, and cured for 28 days. By changing the volume ratio, size, and wall thickness of the epoxy composite balls, as well as the length and content of basalt fibers, the lightweight concrete was exhibited a reduction in density and increase in strength. The results showed that the smaller the epoxy composite spheres and the larger the wall thickness are, the higher the compressive strength of the material for a certain volume ratio of epoxy composite spheres will be. The addition of appropriate hollow glass beads can reduce the number of pores in the matrix, but excessive hollow glass beads will reduce the bonding force of the concrete matrix. Basalt fibers not only improve the strength of EPSs but also serve to connect matrix–matrix and matrix–epoxy composite spheres.

The Effect of Kevlar Fibers on the Mechanical Properties of Lightweight Perlite Concrete

Fiber-reinforced concrete contains a fibrous material that increases its structural cohesion. The use of separate short fibers distributed in a random direction improves the strength of Lightweight Concrete (LWC) without exceeding its upper-density limit, improving its high fragility and mechanical properties compared to Natural-Weight Concrete (NWC). This study investigated the effect of adding Kevlar 49 fibers with three percentages of cement weight, 0.5, 1, and 1.5%, on the workability, dry density, and tensile and compressive strength of LWC. The use of Kevlar fibers in different proportions improves mechanical properties, significantly increases durability, and reduces the workability of LWC. The increase in compressive strength when adding 0.5% fibers was 19 and 15% and when adding 1% was 10 and 6%, after 7 and 28 days, respectively. At 1.5%, after 7 and 28 days, there was a decrease in compressive strength due to fiber agglomeration. Additionally, increasing the fiber dose from the optimal value caused a sharp decrease in workability by 37-40%.

Preparation and Compression Resistance of Lightweight Concrete Filled with Lightweight Calcium Carbonate Reinforced Expanded Polystyrene Foam.

Lightweight concrete is widely used in the construction industry due to its low density and high strength. In this paper, lightweight concrete was prepared by a simple two-step method. Firstly, the light calcium carbonate reinforced epoxy macrospheres (LCR-EMS) material was obtained by adhering calcium lighter carbonate powder to the expanded polystyrene foam spheres (EPS) material using the "balling method". In the second step, the LCR-EMS was mixed with water, cement, and the hollow glass microspheres (HGMS) material using the "molding method" to obtain lightweight concrete. The combination of macroscopic photographs and microscopic morphology shows that the LCR-EMS material itself is uniformly encapsulated and well bonded to the matrix. Test results show that the density of the lightweight concrete decreases with an increase in the volume fraction of stacked LCR-EMS, the diameter, and the proportion of HGMS in the matrix, but it decreases with a decrease in the number of layers of LCR-EMS. The compressive strength of lightweight concrete exhibits a completely opposite trend. When three layers of LCR-EMS were used as filler material, the density and compressive strength of the concrete were 1.246 g/cm3 and 8.19 MPa, respectively. The density and maximum compressive strength of lightweight concrete were 1.146 g/cm3 and 6.37 Mpa, respectively, when filled with 8-9 mm-2L-90 svol% of LCR-EMS and 40 wt% of HGMS in the matrix. Compared with lightweight concrete filled with 90% EPS, the density increased by 20% while the compressive strength increased by 300%.

Prediction of the compressive strength of lightweight concrete containing industrial and waste steel fibers using a multilayer synthetic neural network

The use of waste and industrial steel fibers as part of the materials used in concrete can increase resistance and reduce cost and air pollution. It also saves energy. One of the important measures for technical inspections and assessment of the existing condition of structures, especially bridges, which is the most important communication factor, is to check the compressive strength. Considering that the calculation of compressive strength in the laboratory is done with the intervention of human power and is undoubtedly affected by human error, we decided to use it through.Predicting the mechanical properties of concrete reinforced with steel fibers based on artificial neural network models without the need to conduct any laboratory studies will save money in construction projects. Unlike classical methods in statistical theories, neural networks do not require any specific model or function along with limiting assumptions to linearize problems.For this purpose, this research was done with the aim of compensating this problem and with the aim of building a neural network with high accuracy that can make the desired predictions with the least error. In this research, this modeling was done using artificial neural networks (ANN) and Levenberg algorithm. The data used to train the neural network was collected from 45 different mixing schemes. Then the compressive strength of the sample is determined experimentally. The parameters considered for the ANN inputs are the values of steel fiber, water, water-cement ratio, cement and superlubricant. The objective data of this study included the compressive strength of each of these mixing designs at the ages of 7, 28 and 60 days. Then, to design the neural network, 75% of the data were considered as training data, 15% as target data and 15% as validation data. The compressive strength of concrete samples made from waste steel fibers increases. One of the reasons for this result is the placement and uniform distribution of fibers in the cement matrix, or in other words, the optimal amount of desired fibers in concrete. For experimental information and data, results can be seen with the help of neural network in data analysis. It was observed that the validation is correlated with a correlation coefficient above 99% and the constructed neural network has sufficient accuracy and validity.

The effect of fly ash as coating powder on compressive strength of lightweight concrete

Expanded polystyrene (EPS) has been used and studied for decades in lightweight concrete. Because EPS beads are incredibly light, using these beads in concrete will cause the particles to segregate. Furthermore, the hydrophobic nature of EPS materials weakens the bond between them and cement paste. This research will investigate the compressive strength of lightweight concrete using EPS beads as a coarse aggregate replacement, focusing on the effect of fly ash coating with a binder made from a mixture of Polyvinyl Acetate (PVA) and water. A comparison of density and compressive strength between normal concrete and concrete made from EPS beads was conducted in this study. EPS beads will replace 100% of coarse aggregate. Coated EPS beads that were being produced in this research have an average weight per volume of 246–251 kg/m3. The concrete that uses EPS beads coated with fly ash showed positive results for an increase in compressive strength of 8%, 20%, 38%, and 27% at the age of 7, 14, 28 and 56 days of concrete with EPS beads without modification.

Machine Learning-Based Predictive Modeling of Sustainable Lightweight Aggregate Concrete

Nowadays, lightweight aggregate concrete is becoming more popular due to its versatile properties. It mainly helps to reduce the dead loads of the structure, which ultimately reduces design load requirements. The main challenge associated with lightweight aggregate concrete is finding an optimized mix per requirements. However, the conventional material design of this composite is quite costly, time-consuming, and iterative. This research proposes a simplified methodology for the mix designing of structural and non-structural lightweight aggregate concrete by incorporating machine learning. For this purpose, five distinct machine learning algorithms, support vector machine (SVM), artificial neural network (ANN), decision tree (DT), Gaussian process of regression (GPR), and extreme gradient boosting tree (XGBoost) algorithms, were investigated. For the training, testing, and validation process, a total of 420 data points were collected from 43 published journal articles. The performance of models was evaluated based on statistical performance indicators. Overall, 11 input parameters, including ingredients of the concrete mix and aggregate properties were entertained; the only output parameter was the compressive strength of lightweight concrete. The results revealed that the GPR model outperformed the remaining four machine learning models by attaining an R2 value of 0.99, RMSE of 1.34, MSE of 1.79, and MAE of 0.69. In a nutshell, these simplified modern techniques can be employed to make the design of lightweight aggregate concrete easy without extensive experimentation.

Compressive behavior of lightweight concrete using aerogel-reinforced expanded polystyrene foams

The continuous development of the construction industry is also pushing concrete to change. Lightweight and high-strength concrete is becoming more and more important. Aerogel reinforced Epoxy Macrospheres (AR-EMS) were obtained by the ball rolling method. The lightweight concrete was prepared by mixing AR-EMS, cement, and hollow glass beads into the mold by the molding method. In this experiment, the influencing factors such as stacking volume fraction, inner diameter, wall thickness of AR-EMS and different types of hollow glass microspheres (HGMS), different content and length of glass fibers (GF) in matrix were studied. The comprehensive microstructure and performance characterization results show that AR-EMS binds well to the concrete matrix, and the addition of long fibers effectively improves the compressive strength of lightweight concrete. The lightweight concrete can meet the requirements of both high strength (11.46 MPa) and low density (0.897 g/cm³) when the matrix is filled with 2 wt% of 12 mm GF and AR-EMS with 9–10 mm-2layers-90%. Compared to the concrete without GF and the EPS-filled concrete, the maximum compressive strength was increased by 69% and 398%, respectively.

Promulgation of sustainable palm oil clinker sand in the development of structural and non‐structural lightweight foamed concrete

AbstractThe excessive exploitation of natural resources, such as mining or river sand in concrete, has resulted in several catastrophic disasters worldwide. Furthermore, the low compressive strength of lightweight concrete (LFC) has limited its use as structural concrete. In this research, the feasibility of replacing natural mining sand (MS) as fine aggregates in the development of LFC with local agro‐industrial waste material, namely palm oil clinker (POC), which can be considered a sustainable material, at different replacement ratios of 0%, 25%, 50%, 75%, and 100% was investigated. Because of the porous structure of POC, a volume‐based method was employed in the mix design of using the specific gravities to produce LFC with three different target densities of 1100, 1300, and 1500 kg/m3. The investigated parameters include workability, compressive strength, splitting tensile strength, flexural strength, ultrasonic pulse velocity (UPV), and scanning electron microscope (SEM) images. Overall, 100% replacement of MS volume with POC with the sieve size below 0.6 mm resulted in a 90‐day compressive strength of 26.7 MPa in the LFC with a density of 1500 kg/m3. Similar to the compressive strength, the utilization of the sustainable POC as fine aggregates slightly improved the tensile and flexural strengths of LFC. On the other hand, replacing 100% of MS with POC with the sieve size below 0.6 mm reduced the flow test results of LFC from 405 to 350 mm. The use of POC in LFC improved the bonding between cementitious material and fine aggregate, according to the UPV results.

Performance of Palm Oil Clinker Lightweight Aggregate Concrete Comprising Spent Garnet as Fine Aggregate Replacement

The increase in building activity as a result of population expansion has resulted in an overexploitation of aggregate, with disastrous environmental consequences. Simultaneously, the disposal of spent garnet by the shipbuilding industry and palm oil clinker by palm oil mills harms the environment and needs a greater amount of landfill space. Therefore, the purpose of this study was to determine the influence of spent garnet as a fine aggregate substitute on the fresh, mechanical, and durability properties of palm oil clinker concrete. Concrete mixes were created using various percentages of spent garnet as a fine aggregate substitute, including 0%, 10%, 20%, 30%, and 40%. The workability, compressive strength, flexural strength, splitting tensile strength, modulus of elasticity, water absorption, and acid resistance of the water cured concrete were all determined. It was determined that using 20% spent garnet increased the compressive strength of lightweight concrete. The positive filler effect of spent garnet resulted in a densely packed internal structure of concrete, allowing it to have the lowest percentage of water absorption. The same mixtures exhibited the least mass change and strength reduction when exposed to acid solution. The results established that ecologically friendly concrete may be manufactured by including considerable volumes of waste from the shipbuilding and palm oil sectors.

Compressive strength prediction of structural lightweight concrete by applying the recurrent neural network‐fractional order bat optimization algorithm

AbstractNeural networks methods produce an analysis in various fields. The Neural Network is frequently employed for developing statistical methods for nonlinear systems since, the Neural Networks use the simulation benefits of complicated behavior in several problems. In this study, the Recurrent Neural Network and Recurrent Neural Network based on Fractional Order Bat Optimization Algorithm is utilized for predicting the compressive strength of Lightweight Concrete mixes in 3, 7, 14, and 28 days curing. Recurrent Neural Network has been used for having a comparison. The inputs to the network for the compressive strength are made of eight variables: sand, lightweight fine aggregate, water to cement ratio, silica fume utilized in solution, Lightweight course aggregate, silica fume utilized supplementary to cement, curing period, and superplasticizer. According to the results, the Recurrent Neural Network based on Fractional Order Bat Optimization Algorithm has predicted precise outcomes and trained so fast in comparison with RNN method.by considering the outcomes, the Neural Networks methods are satisfactory mechanisms to evaluate the LWC compressive strength. This can decrease the price and reduce the time of processes in these problems.

Compressive Strength Prediction of Lightweight Concrete: Machine Learning Models

Concrete is the most commonly used construction material. The physical properties of concrete vary with the type of concrete, such as high and ultra-high-strength concrete, fibre-reinforced concrete, polymer-modified concrete, and lightweight concrete. The precise prediction of the properties of concrete is a problem due to the design code, which typically requires specific characteristics. The emergence of a new category of technology has motivated researchers to develop mechanical strength prediction models using Artificial Intelligence (AI). Empirical and statistical models have been extensively used. These models require a huge amount of laboratory data and still provide inaccurate results. Sometimes, these models cannot predict the properties of concrete due to complexity in the concrete mix design and curing conditions. To conquer such issues, AI models have been introduced as another approach for predicting the compressive strength and other properties of concrete. This article discusses machine learning algorithms, such as Gaussian Progress Regression (GPR), Support Vector Machine Regression (SVMR), Ensemble Learning (EL), and optimized GPR, SVMR, and EL, to predict the compressive strength of Lightweight Concrete (LWC). The simulation approaches of these trained models indicate that AI can provide accurate prediction models without undertaking extensive laboratory trials. Each model’s applicability and performance were rigorously reviewed and assessed. The findings revealed that the optimized GPR model (R = 0.9803) used in this study had the greatest accuracy. In addition, the optimized SVMR and GPR model showed good performance, with R-values 0.9777 and 0.9740, respectively. The proposed model is economic and efficient, and can be adopted by researchers and engineers to predict the compressive strength of LWC.

Effect of crushed cockle shell as partial fine aggregate replacement on workability and strength of lightweight concrete

Dumping of cockle shell waste by cockle trade emits stinking smell and pollutes the environment. This study investigates the workability and compressive strength of lightweight concrete containing crushed cockle shell as fine aggregate substitution. Four concrete mixes were prepared by integrating diverse content of crushed cockle shell as partial fine aggregate substitution by weight of sand. Workability and compressive strength of concrete were determined via slump test and compressive strength test. Concrete mix become less workable as larger content of crushed cockle shell is integrated. Utilization of crushed cockle shell up to 10% enhances the concrete’s compressive strength.

Experimental Study of Compressive Strength of Lightweight Concrete Using Wood Charcoal

The use of lightweight concrete materials in Indonesia, especially in the Merauke Regency area can be an alternative amid the rapid development of the housing sector. In this experimental study, the author took the initiative to replace coarse aggregate with wood charcoal as light coarse aggregate. The purpose of this study was to determine the value of compressive strength and to determine whether the wood charcoal material met the standard of lightweight concrete coarse aggregate. Planning the proportion of lightweight concrete mixture in this study using a volume ratio between cement, sand and wood charcoal of 1: 2, 1: 2: 2.5 and 1: 2: 5 with variations of test days at 3, 7, 14, 21 and 28 day. From the results of the compressive strength test of lightweight concrete, the use of wood charcoal aggregate as coarse aggregate in concrete causes the value of the compressive strength of concrete to decrease.

The synergic influence of carbon nanotube and nanosilica on the compressive strength of lightweight concrete

Nanomaterials, owing to their extraordinary properties are known to improve the performance of concrete, are widely used in cementitious materials. Many studies have been conducted so far to understand the effects of inclusion of nanomaterials on the concrete while less studies have been conducted on Light Wight Concrete (LWC) especially on the synergic effect of nanomaterials.The influence of carbon nanotubes (CNT) and nanosilica (NS) as reinforcement fillers to increase the compressive strength of lightweight concrete using expanded clay were investigated in this study. Eight batches of samples were made by mixing CNT and NS using ultrasonication technique into the concrete mix, with one being a control system without nano reinforcement fillers to be used as a reference batch. The optimum substitution percentage of 3% for nano reinforcements was incorporated into the concrete by varying the ratio of CNT and NS. The compressive strength tests were conducted according to BS EN 12390: 2000 and BS EN 12350: 2009. From the results, it was found that incorporating 1% NS and 2% CNT, the highest compressive strength of 10.72 MPa can be achieved at 28 days, with percentage of 39% as compared to control sample. However, the incorporation of 3% CNT resulted in a negative effect on the compressive strength gain by a significant drop in compressive strength of −16% at 28 days. Field emission scanning electron microscope (FESEM) test was conducted to further observe the compressive strength behaviour for the obtained results.

Production of lightweight geopolymer concrete

Lightweight concrete has been known all around the world for decades. As a masonry material, there is an advantage: it has sufficient mechanical strength despite of its light weight. This research focuses on the production of recycled concrete based lightweight geopolymer concrete samples using glass foam aggregates. During the research geopolymer and concrete samples were made and finally their properties were investigated and compared. In the research work an alkaline silicate reaction (ASR) was also studied on the glass foam lightweight aggregates, which is very important for concrete failure. Material structural and morphological tests were obtained by scanning electron microscopy (SEM) to characterize the geopolymer concrete samples. Compressive strength of lightweight concrete and lightweight geopolymer was determined at the age of 7 days. Glass foam is suitable for use as a lightweight aggregates for workability. Based on the 7 days compressive strength results no crust typical of the ASR reaction was detected on the surface of the mixtures.

The Compressive Strength of Light Weight Concrete

The compressive strength of lightweight concrete using palm kernel shell (pks) and periwinkle shell (pws) is reported. The strength as a function of aggregates ,age, and density were investigated. The strengths were measured at interval of 7-day for 28 days. The strengths obtained were in the range of those reported in the standards for lightweight concretes. The densities were also observed to increase with age and fall within the range of those reported in the literature. The results for the compressive strength showed that the strength increases with age for both aggregates.

Shear-friction behaviour on smooth interface between high-strength and lightweight concrete

This study presents the results of ongoing research to investigate the shear-friction behaviour of the interface between high-strength (HS) concrete and lightweight (LW) concrete by conducting tests on 18 push-off specimens with a smooth interface condition. The primary parameters considered are the compressive strength of LW concrete and clamping stress through the shear interface. Together with test data from previous studies, the experimental results indicate that both LW concrete strength and clamping stress have an influence on interface shear capacity. Furthermore, an analytical model for predicting the ultimate resistance of the interface between HS and LW concrete is proposed, and the feasibility of this modified equation verified by the experimental results from related references.

In Vitro Evaluation of the Effect of SP200 Lubricant on Compressive Strength of Lightweight Concrete with Leca Aggregate and Powdered Silica

In Vitro Evaluation of the Effect of SP200 Lubricant on Compressive Strength of Lightweight Concrete with Leca Aggregate and Powdered Silica

Predicting Performance of Lightweight Concrete with Granulated Expanded Glass and Ash Aggregate by Means of Using Artificial Neural Networks.

Lightweight concrete (LWC) is a group of cement composites of the defined physical, mechanical, and chemical performance. The methods of designing the composition of LWC with the assumed density and compressive strength are used most commonly. The purpose of using LWC is the reduction of the structure’s weight, as well as the reduction of thermal conductivity index. The highest possible strength, durability and low thermal conductivity of construction materials are important factors and reasons for this field’s development, which lies largely in modification of materials’ composition. Higher requirements for construction materials are related to activities aiming at environment protection. The purpose of the restrictions is the reduction of energy consumption and, as a result, the reduction of CO2 emission. To limit the scope of time-consuming and often high-cost laboratory works necessary to calibrate models used in the test methods, it is possible to apply Artificial Neural Networks (ANN) to predict any of the concrete properties. The aim of this study is to demonstrate the applicability of this tool for solving the problems, related to establishing the relation between the choice of type and quantity of lightweight aggregates and the porosity, bulk density and compressive strength of LWC. For the tests porous lightweight Granulated Expanded Glass Aggregate (GEGA) and Granulated Ash Aggregate (GAA) have been used.