A NEW STATISTICAL MODEL FOR THE ESTIMATION OF AUTOCLAVE EXPANSION OF PORTLAND CEMENT

This paper presents the predictive functional control of an autoclave expansion of Portland cement, using non-linear regression equation. This is in order to save time and expense also for quality control assurance for produced cement (in cement factories). The autoclave expansion test (ASTM C151- 05) is one of the internationally used tests in detecting the unsoundness of Portland cement. The factors affecting test results were reviewed. A statistical analysis was built and based on 50 different cement samples taken from 8 different Iraqi cement factories. Thirty three of the samples were ordinary Portland cement while the other seventeen samples were sulfate resisting Portland cement. The model examines different variables such as; chemical composition (phase composition and oxides percentages), and physical properties such as fineness. Regression analysis was performed to establish a mathematical formula. According to the analysis the model provide good estimation of autoclave expansion and yielded good correlations with the data used in this study. It was found that the multiple linear regressions are very suitable for predicting the autoclave expansion of Portland cement. Study results indicate that the correlation coefficient may reach 0.9797, indicating that the proposed method has referential value. The model was tested with collected new raw data and the predictions were highly correlation to the experimental results (R2=0.9535)

Compressive Strength and Elastic Modulus of Slurry Infiltrated Fiber Concrete (SIFCON) at High Temperature

SIFCON is a special type of fiber reinforced concrete (FRC) with an unattached fiber matrix that gives the composite matrix important tensile properties and, due to its high fiber content, SIFCON also has distinctive and unique ductility and energy absorption properties. Higher temperature resistance is one of the most important parameters affecting the durability and service life of the material. In this research, the compression strength and elastic modulus of Slurry Infiltrated Fiber Concrete (SIFCON) were tested both before and after exposure to high temperatures. Two fire exposure durations of 2 and 3 hours are examined. In addition to room temperatures, three temperature ranges of 400 ° C, 600 ° C and 900 ° C have been introduced. The results of the experiment showed that the compressive strength and elastic modulus decreased after exposure to high temperatures. The drastically reduction of compressive strength took place with increasing temperature above 600 °C. While, the reduction in elastic modulus values is more significant than the decrease in compressive strength at the same fire flame temperatures. The residual compressive strength and elastic modulus at 900 °C were in the range of (52.1% to 59.6%) and (30.6% to 34.1%) respectively

Behavior of Reactive Powder Concrete Columns with or without Steel Ties

In this paper, an experimental work was carried out to investigate the behavior of reactive powder concrete (RPC) columns with or without steel ties. The main objective of this work is to investigate experimentally the behavior of RPC columns, to search the effect of the experimental variables, type of concrete (RPC and NSC), percentage of micro steel fibers and spacing between steel ties. Twelve RPC columns and five NSC columns were cast and tested under concentric axial compression load up to failure and the results are reported herein. The experimental results showed that RPC column specimens failed in a controlled manner without observing spalling of concrete cover or buckling of the longitudinal reinforcement to well beyond the peak load due to the inclusion of steel fibers in RPC. Also, the space and amount of steel ties affect the load carrying capacity of columns by increasing the load carrying capacity with decreasing spacing of lateral ties. Keywords: Reactive powder concrete, Steel ties, Crack width, Load carrying capacity

Numerical modeling of the experimental test for shear strengthened of fire damaged high strength lightweight RC beams with SIFCON jacket

The behaviour of concrete structures when exposed to fire is essential because fire represents an excessive loading and critical structural case for any concrete structure. This paper discusses the adopted procedure for modelling and simulation of concrete beams that experimentally tested in the lab. Through using of ABAQUS program, a finite model of lightweight reinforced concrete beams was carried out under realistic fire circumstances. 14 Beam specimens with lightweight high strength types, before and after firing up to 600°C, and improvement with SIFCON jacketing technique, were simulated and compared with the experimental results. For both the displacement at mid span and the maximum load carrying capacity of the specimens, the absolute error between experimental and numerical values was determined. From the results, it can be seen that the minimum and maximum determined absolute error for specimens’ load carrying capacity was about 0.521% and 15.61%, respectively. on the other side, minimum and maximum determined absolute error for specimens’ displacement corresponded to the max load were 0.42% and 11.42%, respectively. accordingly, and since the estimated errors less than 15%, it can be said that the performed simulation process was accurate and successful when compared with other researchers’ studies. As a result, it was found that there is an agreement between the practical and theoretical findings of the study, and a helpful tool for predicting failure in the event of a fire occurrence is provided

Developing Artificial Neural Network and Multiple Linear Regression Models to Predict the Ultimate Load Carrying Capacity of Reactive Powder Concrete Columns

The study focuses on development a model to predict the ultimate load carrying capacity of Reactive Powder Concrete (RPC) columns. Two different statistical methods regression techniques (RT) and the artificial neural network (ANN) methods were used for determining the RPC columns ultimate load carrying capacity. The data is collected from three experimental studies the first used to develop the model and the other two used as a case study. Experimental results used as input data to develop prediction models. Two different techniques adopted to develop the models the first was Artificial Neural Network (ANN) and the second was multi linear regression techniques (RT). The models use to predict the ultimate load carrying capacity of RPC columns. To predict the ultimate load carrying capacity of RPC columns four input parameters were identified cross-section, micro steel fiber volume fraction content, compressive strength and main steel reinforcement area. Both models build with assistance of MATLAB software. The results exhibit that the cross section area has most significant effect on ultimate load carrying capacity. The performance of ANNs with different architecture was considered to adopt the pest ANN. An ANN with one layer consist of 7 neurons provide the best prediction. The results of this investigation indicate that ANNs have strong potential as statistical method for prediction the ultimate load carrying capacity of RPC columns. Keywords: Reactive powder concrete, artificial neural network, multiple linear regressions, ultimate load carrying capacity, Statistical analysis

Strengthening of Fire Damaged, Light Weight, High Strength Reinforced Concrete Beam Using SIFCON Jacket

This study aims to extrapolate the behavior of lightweight (LECA) high strength concrete beams subjected to high temperatures. the LECA aggregate was utilized as coarse fraction in the reference mixture. a post development process in terms of jacketing the fire damaged beams with SIFCON materials layer was also investigated. In addition to the reference samples, various parameters of concrete beams and conditioning were conducted, namely, fire duration exposure, concrete cover, and SIFCON layer thickness. In details, two concrete cover thickness, half and one-hour fire duration exposure, and two SIFCON layer thicknesses were the main parameters in this study. the thermal gradient through the beam cross section was captured through installing thermocouples sensors embedded inside at various location. The physical and chemical properties were tested for all used materials in this study. Overall, fourteen concrete beam samples were tested for all the three phases (normal or reference, fire damaged samples, and post enhancement with SIFCON jacket). the level of comparison for the tested samples was focused on several parameters are; maximum shear load capacity and corresponded displacement, ductility index, cracking load, initial and secant stiffness, and energy absorption. The experimental test results under the scope of this research have shown significant improvement for the strengthened beams were observed compared with the damaged samples. Moreover, the results have cleared that the strengthened beams, in term of the mentioned indices were recovered as and comparable to the undamaged (reference beam), except the absorption energy. Where further studies and efforts have to be paid to overcome such issue

Studying of Some Mechanical Properties of Reactive Powder Concrete Using Local Materials

This research aims to investigate and evaluate a reactive powder concrete (RPC) cast using economical materials. Its mechanical properties were investigated and evaluated by studying the effects of using different cement and silica fume contents and locally steel fibers aspect ratios as reinforcement for this concrete. A compressive strength of about 155.2MPa, indirect tensile strength of 16.0MPa, modulus of elasticity of 48.7GPa, flexural strength of 43.5MPa, impact energy of 3294.4kN.m and abrasion loss 0.59% have been achieved for reinforced RPC contains 910 kg/m3 cement content, silica fume content 185 kg/m3 of cement weight and fiber volume fraction 2%. The water absorption values were 1.5 times higher for the normal strength concrete in comparison with the reactive powder concrete