This paper presents the effect of aggregate type on high temperature resistance of self-compacting mortars (SCM) produced with normal and lightweight aggregates like expanded perlite and pumice. Silica fume (SF) and fly ash (FA) were used as mineral additives. Totally 13 different mixtures were designed according to the aggregate rates. Mini slump flow, mini V-funnel and viscometer tests were carried out on the fresh mortar. On the other hand, bulk density, porosity, water absorption and high temperature tests were made on the hardened SCM. After being heated to temperatures of 300, 600 and $900^{\circ}C$, respectively, the tensile strength in bending and compressive strength of mortars determined. As a result of the experiments, the increase in the use of lightweight aggregate increased total water absorption and porosity of mortars. It is observed that, the increment in the usage of lightweight aggregate decreased tensile strength in bending and compressive strengths of mortar specimens exposed to high temperatures but the usage of up to 10% expanded perlite in mortar increased the compressive strength of specimens exposed to $300^{\circ}C$.

This paper presents a generalized formulation for optimizing the design of concrete beam reinforced with glass fiber reinforced polymer bar. The optimization method is formulated to find the design variables leading to the minimum weight of concrete beam with constraints imposed based on ACI code provisions. A simple genetic algorithm is utilized to solve the optimization task. The weights of concrete and glass fiber reinforced polymer bar are included in the formulation of the objective function. The ultimate limit states and the serviceability limit states are included in formulation of constraints. The results of illustrated example demonstrate the efficiency of the proposed method to reduce the weight of beam as well as to satisfy the above requirement. The application of the optimization based on the most economical design concept have led to significant savings in the amount of the component materials to be used in comparison to classical design solutions.

Two-stage concrete (TSC), also known as pre-placed aggregate concrete, is characterized by its unique placement technique, whereby the coarse aggregate is first placed in the formwork, then injected with a special grout. Despite its superior sustainability and technical features, TSC has remained a basic concrete technology without much use of modern chemical admixtures, new binders, fiber reinforcement or other emerging additions. In the present study, an experimental database for TSC was built. Different types of cementitious binders (single, binary, and ternary) comprising ordinary portland cement, fly ash, silica fume, and metakaolin were used to produce the various TSC mixtures. Different dosages of steel fibres having different lengths were also incorporated to enhance the mechanical properties of TSC. The database thus created was used to develop fuzzy logic models as predictive tools for the grout flowability and mechanical properties of TSC mixtures. The performance of the developed models was evaluated using statistical parameters and error analyses. The results indicate that the fuzzy logic models thus developed can be powerful tools for predicting the TSC grout flowability and mechanical properties and a useful aid for the design of TSC mixtures.

This paper has numerically investigated the changes of loading-induced stress in concrete with the corrosion time in the sulfate-containing environment. Firstly, based on Fick's law and reaction kinetics, a diffusion-reaction equation of sulfate ion in concrete is proposed, and it is numerically solved to obtain the spatial and temporal distribution of sulfate ion concentration in concrete by the finite difference method. Secondly, by fitting the existed experimental data of concrete in sodium sulfate solutions, the chemical damage of concrete associated with sulfate ion concentration and corrosion time is quantitatively presented. Thirdly, depending on the plastic-damage mechanics, while considering the influence of sulfate attack on concrete properties, a simplified chemo-mechanical damage model, with stress-based plasticity and strain-driven damage, for concrete under axial loading and sulfate attack is determined by introducing the chemical damage degree. Finally, an axially compressed concrete prism immersed into the sodium sulfate solution is regarded as an object to investigate the time-varying stress in concrete subjected to the couplings of axial loading and sulfate attack.

In this study, the strength properties of alkali-activated silica fume (SF) mortars were investigated. The crushed limestone sand with maximum size of 0-5 mm and the sodium meta silicate ($Na_2SiO_3$) used to activate the binders were kept constant in the mortar mixtures. The mortar specimens using the replacement ratios of 0, 25, 50, 75 and 100% SF by weight of cement together with $Na_2SiO_3$ at a constant rate were produced in addition to the control mortar produced by only cement. Moreover, the mortar specimens using the replacement ratio of 4% titanium dioxide ($TiO_2$) by weight of cement in the same mixture proportions were produced. The prismatic specimens produced from eleven different mixtures were de-moulded after a day, and the wet or dry cure was applied on the produced specimens at laboratory condition until the specimens were used for flexural strength ($f_{fs}$) and compressive strength ($f_c$) measurement at the ages of 7, 28 and 56 days. The $f_{fs}$ and $f_c$ values of mortars applied the wet or dry cure were compared with the results of control mortar. The findings revealed that the $f_c$ results of the alkali activated 50% SF mortars were higher than that of mortar produced with Portland cement only. It was found that the $f_{fs}$ and $f_c$ of alkali-activated SF mortars cured in dry condition was averagely 4% lower than that of alkali-activated SF mortars cured in wet condition.

Chloride induced reinforcement corrosion is widely accepted to be the most frequent mechanism causing premature degradation of reinforced concrete structures. The electrochemical impedance of reinforcing steel in diatomite- and zeolite-containing concrete exposed to sodium chloride was assessed. Chemical, physical and mineralogical properties of three concrete samples (20% diatomite, 20% zeolite, and a reference containing neither) were correlated with corrosion investigations. The steel-reinforced samples were exposed to 3.5% NaCl solution for 500 days, and measured every 15 days via EIS method. Results indicated that porosity and capillary spaces increase the diffusion rate of water and electrolytes throughout the concrete, making it more susceptible to cracking. Reinforcement in the reference concrete was the most corroded compare to the zeolite and the diatomite samples.

Water pressure test operation is used before the grouting to determine the rate of penetrability, the necessity and estimations related to grouting, by the penetration of water into the borehole. One of the parameters which have the highest effect is pressure of water penetration since the application of excessive pressure causes the hydraulic fracture to occur in the rock mass, and on the other hand, it must not be so small that prevents from seeing mechanical weaknesses and the rate of permeability. Mathematical modeling is used for the first time in this study to determine the optimum pressure. Thus, the joints that exist in the rock mass are simulated using cylindrical shell model. The joint surroundings are also modeled through Pasternak environment. To obtain equations governing the joints and the surroundings, energy method is used accompanied by Hamilton principle and an analytical solution method is used to obtain the maximum pressure. In order to validate the modeling, the pressure values obtained by the model were used in the sites of Seymareh and Aghbolagh dams and the relative error rates were measured considering the differences between calculated and actual pressures. Modeling in the sections of Seymareh dam showed 4.75, 3.93, 4.8 percent error rates and in the sections of Aghbolagh dam it rendered the values of 22.43, 5.22, 2.6 percent. The results indicate that this modeling can be used to estimate the amount of pressure for hydraulic fracture in water pressure test, to predict it and to prevent it.

Concrete shear walls are one of the major structural lateral resisting systems in buildings. In some cases, due to the change in the occupancy of the structure or functional requirements like architectural and even mechanical ones, openings need to be provided and installed in structural walls after their construction. Providing these openings may significantly influence the structural behavior of the constructed wall. This paper considers the results of a nonlinear finite element analysis of shear walls with opening strengthened by carbon fiber reinforced polymer (CFRP) strips with different configurations. Details of bond-slip constitutive model of link elements to simulate the connections of FRP strips to concrete surface is presented. The proposed model in this research has been validated using experimental results available in the literature. The results indicated that the proposed configuration of CFRP strips significantly improved the lateral resistance and deformation capacity of the shear walls with opening.

External pre-stressing is often used in strengthening or retrofitting of steel-concrete composite beams. In this way, a proper numerical model should be able to trace the completely nonlinear response of these structures at service and ultimate loads. A three dimensional finite element model based on shell elements for representing the concrete slab and the steel beam are used in this work. Partial interaction at the slab-beam interface can be taken into account by using special beam-column elements as shear connectors. External pre-stressed tendons are modeled by using one-dimensional catenary elements. Contact elements are included in the analysis to represent the slipping at the tendon-deviator locations. Validation of the numerical model is established by simulating seven pre-stressed steel-concrete composite beams with experimental results. The model predictions agree well with the experimental results in terms of collapse loads, path failures and cracking lengths at negative moment regions due to service loads. Finally, the accuracy of some simplified formulas found in the specialized literature to predict cracking lengths at interior supports at service loading and for the evaluation of ultimate bending moments is also examined in this work.

The purpose of this paper is to evaluate the settlement of lightweight coarse aggregate of self-compacting lightweight concrete (SCLC) after placement of concrete on its final position. To investigate this issue, sixteen samples of concrete mixes were made. The water to cementitious materials ratios of the mixes were 0.35 and 0.4. In addition to the workability tests of self-compacting concrete (SCC) such as slump flow, V-funnel and L-box tests, a laboratory experiment was made to examine the segregation of lightweight coarse aggregate in concrete. Because of the difficulties of this test, the image processing technique of MATLAB software was used to check the segregation above too. Moreover, the fuzzy logic technique of MATLAB software was utilized to improve the clarity of the borders between the coarse aggregate and the paste of the mixtures. At the end, the results of segregation tests and software analyses are given and the accuracy of the software analyses is evaluated. It is worth noting that the minimum and maximum differences between the results of laboratory tests and software analyses were 1.2% and 9.19% respectively. It means, the results of image processing technique looks exact enough for estimating the segregation of lightweight coarse aggregate in SCLC.

Crack on concrete surface allows more rapid penetration of chlorides. Crack width and depth are dominant parameters for chloride behavior, however their effects on chloride penetration are difficult to quantify. In the present work, the previous anisotropic (1-D) model on chloride diffusion in concrete with single crack is improved considering crack shape and roughness. In the previous model, parallel-piped shape was adopted for crack shape in steady-state condition. The previous model with single crack is improved considering wedge shape of crack profile and roughness. For verifying the proposed model, concrete samples for nuclear power plant are prepared and various crack widths are induced 0.0 to 1.2 mm. The chloride diffusion coefficients in steady-state condition are evaluated and compared with simulation results. The proposed model which can handle crack shape and roughness factor is evaluated to decrease chloride diffusion and can provide more reasonable results due to reduced area of crack profile. The roughness effect on diffusion is evaluated to be 10-20% of reduction in chloride diffusion.

This paper summarizes the results of experimental research, and artificial intelligence methods focused on determination of compressive strength of lightweight cement mortar with silica fume and fly ash after sulfate attack. The artificial neural network and the support vector machine were selected as artificial intelligence methods. Lightweight cement mortar mixtures containing silica fume and fly ash were prepared in this study. After specimens were cured in $20{\pm}2^{\circ}C$ waters for 28 days, the specimens were cured in different sulfate concentrations (0%, 1% $MgSO_4^{-2}$, 2% $MgSO_4^{-2}$, and 4% $MgSO_4^{-2}$ for 28, 60, 90, 120, 150, 180, 210 and 365 days. At the end of these curing periods, the compressive strengths of lightweight cement mortars were tested. The input variables for the artificial neural network and the support vector machine were selected as the amount of cement, the amount of fly ash, the amount of silica fumes, the amount of aggregates, the sulfate percentage, and the curing time. The compressive strength of the lightweight cement mortar was the output variable. The model results were compared with the experimental results. The best prediction results were obtained from the artificial neural network model with the Powell-Beale conjugate gradient backpropagation training algorithm.