compressive strength of concrete

This paper proposes probabilistic models for predicting the yield stress of reinforcing steel bars and the compressive strength of concrete used in Iran’s construction industry. The importance of this research stems from the recognition that the strength of construction materials is one of the main parameters in performance-based design, in the calibration of load and resistance factor design (LRFD) provisions, and in risk and resilience analysis of civil infrastructure. Moreover, due to the common practice of on-site casting of the concrete and a large number of rolling mill companies producing reinforcing steel bars, there is a considerable amount of uncertainty in the compressive strength of concrete and the yield stress of steel bars. In this paper, first an extensive database is compiled from concrete and steel laboratory tests. One key field of data for developing the concrete strength model is the nominal design strength of concrete, which was unavailable for a notable portion of the collected data. The database was augmented to account for the missing data using classification algorithms of k-nearest neighbors (KNN) and RBF-Kernel based on machine learning. Next, a probabilistic model is developed using Bayesian linear regression using the Rtx software to predict the compressive strength of concrete as a function of its nominal strength, curing duration, and the quality grade of the concrete manufacturer. The models are subsequently diagnosed for the quality of prediction, heteroskedasticity, and normality of the errors to ensure they are statically sound and well represent the underlying data. Next, a model reduction procedure is implemented to discard the inconclusive predictors from the model and to eliminate high correlations among the model parameters to achieve the final model form. Finally, the yield stress of reinforcing steel of Grades A-III and A-IV are modeled using Bayesian random variables whose distribution parameters are also random and are inferred from the collected data. Bayesian inference enables the quantification of epistemic uncertainties in the model parameters and hence, makes it possible to update the model using Bayesian updating as new data emerge.

The performance of concrete structures is affected by the transfer of force between rebar and concrete, which depends on factors such as concrete strength, yield stress, rebar geometry, rebar diameter, concrete cover and splice length. Also, the use of reinforced polymer materials has become popular due to their successful application in structure. In this study, the effect of various factors on the bond strength of glass fiber-reinforced polymer bars, used in concrete beams, is evaluated and the accuracy of the equations proposed by different regulations and researchers is investigated. To investigate the effect of different factors on bond strength of glass fiber-reinforced polymer, the results of 43 tests conducted by different researchers have been used. Factors studied on the bond strength include the effect of the surface shape of the reinforcement, concrete strength, the amount of concrete cover on the bar, the splice length and transverse reinforcement. After examining the effect of various factors, the accuracy of the equations provided by regulations and researchers in determining the bond resistance for these laboratory results, the accuracy and standard deviation of each equation is evaluated. The results showed that increasing the compressive strength of concrete in samples without stirrup increases the bond strength, and in samples with stirrup, it has no effect on the bond strength. Also, the majority of existing equations estimate the amount of bond strength more than the values obtained from experiment results.

The fly ash obtained from the extraction of exhaust gases from coal and silt furnaces, is non-plastic and fine, which is a different combination based on natural coal fuel. Fly ash is one of the waste materials in thermal power plants. The use of waste materials such as fly ash in the concrete industry offers an alternative and valuable solution to create an environmentally friendly environment. However, experimental work is time-consuming and expensive, and the use of soft computing techniques can speed up the process of predicting concrete's resistance properties. In this study, artificial neural network (ANN) was used to predict the compressive strength of fly ash-based high-performance concrete. A number of 471 experimental data were extracted from valid sources and parameters such as cement, fly ash, water, superplasticizer, fine and coarse aggregate and age of the samples were considered as input parameters and compressive strength of samples was considered as output parameters. Among the networks with different number of neurons, a network which has the best correlation coefficient values obtained from training, evaluation and testing data and also has the lowest mean square error (MSE) value was selected as the optimal network. In this study, the network with the number of 6 neurons provided the best results. Also, the effect of each parameter in different numerical ranges on the compressive strength of concrete was investigated and presented, and it was found that the age of the samples and the amount of cement, fly ash and water have the greatest relative importance.

Nowadays, intelligent methods inspired from nature are implemented to resolve complex problems, there are very popular too. The most common one is artificial neural network; they are capable to collect huge amount of complex information through experiments and tests. With increasing population and a rise in construction and also due to limited resources and consumable materials, demand for hot rolled earthquake-resistant materials in the construction industry has increased. The purpose of this research, by considering concrete mix design parameters as input, the Static neural network and Time-series modeling to predict the compressive strength of concrete will be used. Mixing fly ash and silica fume various designs with different percentages (1%, 5%, 7%, 10%, 12%, 15%, 18%) and mixed with silica fume, fly ashes identical percentages (% 1% 1% 3 and 3%, 5% and 5%, 7% and 7%, 9% and 9%, 10% and 10%) as a percentage of the weight of cement, to evaluate the performance of the models in question were applied. It turned out that neural network models for predicting time series with 5 neurons performance concrete compressive strength is accurate and reliable.

The correct estimation of compressive strength of concrete plays a key role in evaluating the performance of reinforced concrete (RC) structures under gravity and seismic loads. According to the provisions of Chapter 9 of the Iranian National Building Regulations, sampling and performing compressive strength tests are required to determine the average compressive strength of concrete. In this study, by statistically studying the results derived from analyzing the archive of the compressive strength of concrete samples extracted from reinforced concrete (RC) buildings constructed over the past ten years in Tabriz, the values of statistical parameters affecting the average compressive strength of concrete such as the coefficient of variation (COV) and the standard deviation were calculated. The probability density function (PDF) and the cumulative distribution function (CDF) of the above-mentioned statistical parameters were determined for the purpose of comparison with the values provided in the Iranian National Building Regulations and other international standards as well as for use in future studies. The results show that in 30% of the buildings studied, the standard deviation of compressive strength of concrete exceeds the amounts presented in Chapter 9 of the Iranian National Building Regulations. A similar result is obtained in 18% of the high-rise buildings studied. The coefficient of variation of compressive strength of concrete in approximately 22% of the buildings examined is higher than the allowable values of FEMA356 (2000) and this problem was observed in a higher percentage of the low-rise buildings.

Compressive strength test of concrete is one of the most important tests for controlling the quality of concrete in construction structures and, moulds with different shapes and dimensions (cubic and cylindrical) are used to provide experimental specimens based on the workshop conditions and the regulations of the relevant codes and standards. The purpose of this research is to investigate and determine changes in the conversion factors for the strength of specimens with different shapes and dimensions for C35 and C45 concretes made with different types of cement. In this research, 425-1, 2, and 5 cements were used for the preparation of specimens. A total of 162 cubic and cylindrical specimens with dimensions (200 × 200 × 200) mm, (150 × 150 × 150) mm and (150x300) mm at the age of 3, 7 and 28 days have been tested for compressive strength. The results of this study show that in addition to the shape and size of the samples, the type of cement used also affects the strength and conversion factors for the strength of the specimens with various shapes and sizes. The results also show that the difference in conversion factor for strength obtained with the corresponding conversion factors provided by the Iranian National Building Code, Part 9 at the age of 3 and 7 days of the specimens is maximum and is minimum at the age of 28 days.

Under acid rain conditions, physical and chemical reactions occur in the concrete structure, which leads to a change in pH. As these reactions continue, the concrete begins to lose its mechanical strength, leading to cracking, weight loss, and eventual structural damage. Since the control of acid rain and its effects on the environment is inevitable in some cases, researchers have done many studies on this topic and have proposed solutions to eliminate or control its effects. One of the new solutions in this field is the use of nanoparticles. In recent years, studies have focused on silica nanoparticles, with the aim of using this material to further enhance the properties of concrete. The addition of nanosilica to concrete reduces the water permeability of the concrete as well as higher resistance to chemical attacks. In this paper, the mechanical properties and durability of nanosilica-containing concrete including weight loss, compressive strength, electrical resistance and water absorption under acidic conditions are investigated. By increasing nanosilica to concrete under normal conditions, the mechanical properties of concrete are improved, but according to the observed results, when the samples are placed in acidic conditions, the nanosilica in the samples reacts with acid and the performance of nanosilica improves the properties. The mechanical properties of the concrete deteriorate due to the mechanical replacement of the percentage of cement with nanosilica.

Aim of this paper is studying the effect of mineral addictive and type of them, on the processing of cement produce and quality of it. For this purpose, preparation of samples of cement clinker with additives of pozzolan, perlite, pumice and also abrasive organic compounds have been investigated their grinding efficiency. Then, concrete blocks with dimensions of 10 × 10 cm were constructed, than tested compressive strength at different ages of 2, 3, 7 and 28 days. Based on the results, the use of mineral additives has led to an increase in the number of Blaine, in other words, additives have played the role of abrasive aid. The mixing plan of 85% of clinker and 15% of pozzolan resulted in the highest amount of blast cement (4546 m2/kg). The use of additives at the age of 2 and 3 days of concrete has reduced the compressive strength. At the age of 28 days, the presence of additives increases its compressive strength compared to the non-use of the additive. The combination of organic abrasives and mineral additives in all cases has led to an increase in concrete bulk and compressive strength. The combination of 15% by weight of pozzolan, 85% clinker, 4% gypsum and 3 g of abrasive organic compound, is the optimum combination of cement.

Compressive strength test of concrete is one of the most important tests to control the quality of concrete in construction structures and based on the workshop conditions and rules of the relevant regulation and standards, the control specimens with different dimensions and shapes (cubic and cylindrical) are used. In this research for concrete grade C25 and using different types of cement, variations the conversion factors for strength of specimens with different shapes and dimensions was experimentally investigated. In this research with use the cement types 1-425, 2 and 5, totally 81 cubic and cylindrical concrete specimens with different size and dimensions (200×200×200) mm, (150×150×150) mm and (150×300mm) were made and tested at ages 3, 7 and 28 days. The result of this study show that in addition to the shape and size of the specimens, the type of cement used also affect the strength and conversion factor of the strength of the specimens with different shapes and sizes.

Using FRP material, due to their excellent features like high strength to weight ratio, resistance to corrosion, convenience of transportation and installation, is developing rapidly. Using FRP bar instead of steel rebar significantly prevents corrosion in concrete members specifically for the seashore concrete structures. In this research, in order to investigate the benefits of replacing steel rebar with FRP bars in concrete columns, some column specimens were modeled in finite element computer program and the effects of parameters like FRP bar ratio and compressive strength of concrete on the flexural capacity and ductility of column were discussed. The investigations on the concrete columns strengthened with CFRP bar and steel rebar, indicate that the FRP bar ratio is significantly less than the steel rebar ratio to achieve the acceptable strength.

I‌n t‌h‌e p‌r‌e‌s‌e‌n‌t p‌a‌p‌e‌r, t‌h‌e d‌e‌t‌a‌i‌l‌e‌d m‌e‌s‌o‌s‌t‌r‌u‌c‌t‌u‌r‌e o‌f c‌o‌n‌c‌r‌e‌t‌e i‌s g‌e‌o‌m‌e‌t‌r‌i‌c‌a‌l‌l‌y g‌e‌n‌e‌r‌a‌t‌e‌d a‌n‌d i‌t‌s c‌o‌m‌p‌r‌e‌s‌s‌i‌v‌e s‌t‌r‌e‌n‌g‌t‌h i‌s n‌u‌m‌e‌r‌i‌c‌a‌l‌l‌y e‌s‌t‌i‌m‌a‌t‌e‌d u‌s‌i‌n‌g t‌h‌e 3D f‌i‌n‌i‌t‌e e‌l‌e‌m‌e‌n‌t m‌e‌t‌h‌o‌d. T‌h‌e m‌o‌d‌e‌l‌s c‌o‌n‌t‌a‌i‌n t‌w‌o p‌h‌a‌s‌e‌s o‌f m‌o‌r‌t‌a‌r a‌n‌d c‌o‌a‌r‌s‌e a‌g‌g‌r‌e‌g‌a‌t‌e‌s. T‌h‌e F‌E m‌o‌d‌e‌l‌s o‌f c‌o‌n‌c‌r‌e‌t‌e a‌r‌e c‌u‌b‌i‌c i‌n s‌h‌a‌p‌e, w‌i‌t‌h a s‌i‌d‌e l‌e‌n‌g‌t‌h o‌f 80 m‌m. A‌g‌g‌r‌e‌g‌a‌t‌e‌s a‌r‌e a‌s‌s‌u‌m‌e‌d t‌o b‌e s‌p‌h‌e‌r‌i‌c‌a‌l a‌n‌d b‌e‌h‌a‌v‌e i‌n a l‌i‌n‌e‌a‌r e‌l‌a‌s‌t‌i‌c m‌a‌n‌n‌e‌r. T‌h‌e f‌a‌m‌o‌u‌s F‌u‌l‌l‌e‌r f‌o‌r‌m‌u‌l‌a i‌s u‌t‌i‌l‌i‌z‌e‌d f‌o‌r t‌h‌e a‌g‌g‌r‌e‌g‌a‌t‌e g‌r‌a‌d‌i‌n‌g c‌u‌r‌v‌e, a‌n‌d t‌h‌e s‌i‌m‌p‌l‌e s‌e‌q‌u‌e‌n‌t‌i‌a‌l i‌n‌h‌i‌b‌i‌t‌i‌o‌n (S‌S‌I) t‌e‌c‌h‌n‌i‌q‌u‌e i‌s e‌m‌p‌l‌o‌y‌e‌d t‌o f‌i‌l‌l t‌h‌e c‌o‌n‌c‌r‌e‌t‌e c‌u‌b‌e‌s w‌i‌t‌h t‌h‌e p‌a‌r‌t‌i‌c‌l‌e‌s. O‌n‌l‌y a‌g‌g‌r‌e‌g‌a‌t‌e‌s b‌i‌g‌g‌e‌r t‌h‌a‌n 4.75 m‌m i‌n d‌i‌a‌m‌e‌t‌e‌r (g‌r‌a‌v‌e‌l) a‌r‌e m‌o‌d‌e‌l‌e‌d, i.e., t‌h‌e p‌a‌r‌t‌i‌c‌l‌e‌s s‌m‌a‌l‌l‌e‌r t‌h‌a‌n 4.75 m‌m i‌n d‌i‌a‌m‌e‌t‌e‌r (s‌a‌n‌d) a‌r‌e n‌o‌t c‌o‌n‌s‌i‌d‌e‌r‌e‌d i‌n‌d‌i‌v‌i‌d‌u‌a‌l‌l‌y a‌n‌d a‌s‌s‌u‌m‌e‌d t‌o b‌e p‌a‌r‌t o‌f t‌h‌e h‌o‌m‌o‌g‌e‌n‌i‌z‌e‌d n‌o‌n‌l‌i‌n‌e‌a‌r c‌e‌m‌e‌n‌t p‌a‌s‌t‌e. A m‌o‌d‌i‌f‌i‌e‌d v‌e‌r‌s‌i‌o‌n o‌f t‌h‌e p‌l‌a‌s‌t‌i‌c-d‌a‌m‌a‌g‌e m‌o‌d‌e‌l, p‌r‌o‌p‌o‌s‌e‌d b‌y L‌e‌e a‌n‌d F‌e‌n‌v‌e‌s [J. L‌e‌e, G.L. F‌e‌n‌v‌e‌s, I‌n‌t‌e‌r‌n‌a‌t‌i‌o‌n‌a‌l J‌o‌u‌r‌n‌a‌l f‌o‌r N‌u‌m‌e‌r‌i‌c‌a‌l M‌e‌t‌h‌o‌d‌s i‌n E‌n‌g‌i‌n‌e‌e‌r‌i‌n‌g 50 (2001) 487-506], h‌a‌s b‌e‌e‌n a‌d‌o‌p‌t‌e‌d t‌o s‌i‌m‌u‌l‌a‌t‌e t‌h‌e i‌n‌e‌l‌a‌s‌t‌i‌c r‌e‌s‌p‌o‌n‌s‌e o‌f t‌h‌e m‌o‌r‌t‌a‌r. T‌h‌i‌s c‌o‌n‌s‌t‌i‌t‌u‌t‌i‌v‌e m‌o‌d‌e‌l i‌n‌c‌o‌r‌p‌o‌r‌a‌t‌e‌s t‌w‌o i‌n‌d‌e‌p‌e‌n‌d‌e‌n‌t h‌a‌r‌d‌e‌n‌i‌n‌g v‌a‌r‌i‌a‌b‌l‌e‌s, n‌a‌m‌e‌l‌y; e‌q‌u‌i‌v‌a‌l‌e‌n‌t t‌e‌n‌s‌i‌l‌e a‌n‌d c‌o‌m‌p‌r‌e‌s‌s‌i‌v‌e p‌l‌a‌s‌t‌i‌c s‌t‌r‌a‌i‌n‌s, a‌n‌d, t‌h‌u‌s, i‌s c‌a‌p‌a‌b‌l‌e o‌f t‌r‌a‌c‌i‌n‌g d‌a‌m‌a‌g‌e e‌v‌o‌l‌u‌t‌i‌o‌n d‌u‌e t‌o b‌o‌t‌h t‌e‌n‌s‌i‌l‌e c‌r‌a‌c‌k‌i‌n‌g a‌n‌d c‌o‌m‌p‌r‌e‌s‌s‌i‌v‌e c‌r‌u‌s‌h‌i‌n‌g. I‌n t‌h‌e f‌i‌r‌s‌t s‌t‌a‌g‌e, t‌h‌e n‌u‌m‌e‌r‌i‌c‌a‌l i‌m‌p‌l‌e‌m‌e‌n‌t‌a‌t‌i‌o‌n o‌f t‌h‌e p‌l‌a‌s‌t‌i‌c-d‌a‌m‌a‌g‌e m‌o‌d‌e‌l i‌s p‌r‌e‌s‌e‌n‌t‌e‌d a‌n‌d t‌h‌e‌n i‌t‌s v‌a‌l‌i‌d‌i‌t‌y i‌s e‌x‌a‌m‌i‌n‌e‌d i‌n a 3D F‌E e‌l‌e‌m‌e‌n‌t. N‌e‌x‌t, t‌h‌e e‌f‌f‌e‌c‌t‌s o‌f a‌g‌g‌r‌e‌g‌a‌t‌e v‌o‌l‌u‌m‌e f‌r‌a‌c‌t‌i‌o‌n, a‌g‌g‌r‌e‌g‌a‌t‌e m‌a‌x‌i‌m‌u‌m d‌i‌a‌m‌e‌t‌e‌r, a‌n‌d a‌g‌g‌r‌e‌g‌a‌t‌e e‌l‌a‌s‌t‌i‌c m‌o‌d‌u‌l‌u‌s o‌n c‌o‌n‌c‌r‌e‌t‌e c‌o‌m‌p‌r‌e‌s‌s‌i‌v‌e s‌t‌r‌e‌n‌g‌t‌h a‌r‌e s‌t‌u‌d‌i‌e‌d. I‌t i‌s s‌h‌o‌w‌n t‌h‌a‌t: (1) c‌o‌m‌p‌r‌e‌s‌s‌i‌v‌e s‌t‌r‌e‌n‌g‌t‌h r‌e‌m‌a‌i‌n‌s c‌o‌n‌s‌t‌a‌n‌t f‌o‌r s‌p‌e‌c‌i‌m‌e‌n‌s w‌i‌t‌h a‌g‌g‌r‌e‌g‌a‌t‌e v‌o‌l‌u‌m‌e f‌r‌a‌c‌t‌i‌o‌n‌s o‌f u‌p t‌o 50%, a‌n‌d t‌h‌e‌n i‌n‌c‌r‌e‌a‌s‌e‌s s‌i‌g‌n‌i‌f‌i‌c‌a‌n‌t‌l‌y w‌i‌t‌h g‌r‌a‌i‌n c‌o‌n‌t‌e‌n‌t, (2) f‌o‌r t‌h‌e r‌a‌n‌g‌e o‌f a‌g‌g‌r‌e‌g‌a‌t‌e v‌o‌l‌u‌m‌e f‌r‌a‌c‌t‌i‌o‌n‌s s‌t‌u‌d‌i‌e‌d i‌n t‌h‌i‌s p‌a‌p‌e‌r, t‌h‌e m‌a‌x‌i‌m‌u‌m a‌g‌g‌r‌e‌g‌a‌t‌e s‌i‌z‌e h‌a‌s l‌i‌t‌t‌l‌e i‌n‌f‌l‌u‌e‌n‌c‌e o‌n c‌o‌m‌p‌r‌e‌s‌s‌i‌v‌e s‌t‌r‌e‌n‌g‌t‌h, a‌n‌d (3) a‌n‌y i‌n‌c‌r‌e‌a‌s‌e i‌n t‌h‌e e‌l‌a‌s‌t‌i‌c m‌o‌d‌u‌l‌u‌s o‌f a‌g‌g‌r‌e‌g‌a‌t‌e‌s a‌c‌c‌e‌n‌t‌u‌a‌t‌e‌s s‌t‌r‌e‌s‌s c‌o‌n‌c‌e‌n‌t‌r‌a‌t‌i‌o‌n n‌e‌a‌r t‌h‌e a‌g‌g‌r‌e‌g‌a‌t‌e‌s, a‌n‌d, t‌h‌u‌s, r‌e‌d‌u‌c‌e‌s t‌h‌e c‌o‌m‌p‌r‌e‌s‌s‌i‌v‌e s‌t‌r‌e‌n‌g‌t‌h o‌f c‌o‌n‌c‌r‌e‌t‌e s‌a‌m‌p‌l‌e‌s. F‌i‌n‌a‌l‌l‌y, t‌h‌e r‌e‌s‌u‌l‌t‌s a‌r‌e s‌a‌t‌i‌s‌f‌a‌c‌t‌o‌r‌i‌l‌y c‌o‌m‌p‌a‌r‌e‌d w‌i‌t‌h t‌h‌o‌s‌e p‌r‌e‌s‌e‌n‌t‌e‌d b‌y o‌t‌h‌e‌r r‌e‌s‌e‌a‌r‌c‌h‌e‌r‌s.