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ÖgeModeling and sensitivity analysis the thermal behaviour of mass concrete with finite volume method(Graduate School, 20230616) Danaei, Farzad ; Akkaya, Yılmaz ; 501201013 ; Structural EngineeringConcrete is one of the most widely used materials in the world, second only to water. As the population grows and available land becomes limited, there is a growing need for large structures such as dams and bridges and towers to meet the demands of water management, transportation, and accommodation. To ensure the strength and durability of these structures, highperformance concrete is often used. However, a major challenge in such concrete structures is thermal cracking, which occurs due to temperature gradients within the concrete. Concrete has low thermal conductivity, meaning that heat does not dissipate quickly throughout the material. As a result, the outer layer of concrete cools faster than the inner layer, creating thermal gradients. These temperature differences cause differential thermal expansion, if there is no restriction for these movements, there is no problem. But as soon as these movements are stopped by internal or external restrictions, the development of stress will start. When these stresses exceed the tensile strength of the concrete, cracks form. These cracks can result in issues such as water penetration, reduced structural integrity, durability problems (such as corrosion of embedded reinforcement), and aesthetic concerns. Different standards define limitations on the maximum temperature reached within concrete and the maximum temperature gradient within concrete elements, in Turkish standards (TS 13515 ) these limitations are 65 C and 25 C respectively. These specifications are designed to minimize the risk of thermal cracking by ensuring that concrete structures are maintained within safe temperature ranges throughout their service life. The finite volume modeling technique is used in the current model, which was developed in Python. The concrete element is separated into nodes in this manner, and for each node, a control volume according to its location is considered. Convection and conduction are taken into account as boundary conditions in the model, with the flexibility to include other heat transfer processes such as radiation and solar loads. Additionally, an equivalent convection coefficient is derived and employed in the model to account for the impact of formwork and insulation using the analogy of electrical resistance. The governing equation, which is developed from energy balance principles, is then applied to each node. This energy balance takes into consideration all of the energy that enters, is produced, is lost, and is stored inside the concrete. The present model is capable of accepting the ambient temperature using a predictive method, or it may also take actual temperaturetime histories as input to improve its accuracy and dependability. The model incorporates the concept of maturity and calculates the heat generated during cement hydration using the Arrhenius maturity function. To simulate the heat generation, Schindler's Sshaped function is employed, requiring curve fitting techniques to determine two important hydration parameters: the slope parameter and time parameter. Unlike previous models that use a single set of hydration parameters, which fails to capture the behavior of blended cement, the current model addresses this limitation by utilizing the superposition of two Sshaped functions. This approach accurately catch all the points on the released heat curve for blended cement. By considering the behavior of blended cement, the model effectively captures the heat generation characteristics. From the Sshaped function, the generated heat rate function can be easily obtained. Additionaly, the model has the capability to accept the generated heat rate as an input. Accurately determining the generated heat rate function is crucial in simulating the thermal behavior of mass concrete, ensuring that the model accurately represents the actual heat generation process. During the experimental phase, data obtained from the Bursa Beton factory was employed. In Chapter 3 of the thesis, the experiment setup is described in detail, and the resulting outcomes are presented. This particular chapter focuses on investigating the impact of insulation on the temperature gradient within the concrete, as well as the influence of different concrete mixtures. The analysis of the collected data revealed noteworthy findings. When a thick layer of insulation was applied around the concrete, the temperature development recorded by the thermocouples placed inside the concrete exhibited similar behavior on both the right and left sides. However, in cases where no insulation was present and the concrete samples were exposed to the environment, distinct temperature profiles were observed between the right and left side sensors. This disparity in temperature can be attributed to the microclimate effect, which includes factors such as wind speed and solar loading on the concrete surface. It is worth noting that this effect has often been overlooked in previous models. The model has been developed in both 2D and 3D. The 3D version is validated by simulating Bursa Beton samples and comparing the findings with experimental data, whereas the 2D model is validated by comparing its results with the Ballims model. The 3D model is additionally validated using data from a study carried out at West Virginia University. The model constantly exhibits sufficient accuracy in every validation instance, creating trust in its abilities and allowing sensitivity analysis to be carried out. Additionally, a sensitivity analysis is carried out to examine the impact of different variables on the temperature profile of mass concrete. The initial temperature of the concrete, the size of the concrete element, and the usage of supplemental cementitious materials (SCMs) in place of cement in blended cement are among the changes that have been taken into consideration. The results of the analysis show that the final temperature profile is significantly influenced by both the initial temperature and the size of the concrete part. However, the addition of SCMs to the concrete mixture lessens this sensitivity, especially when fly ash is used instead of some part of cement. Furthermore, when considering the utilization of different replacement levels of supplementary cementitious materials (SCMs), the findings demonstrate significant reductions in both the maximum temperature and maximum temperature gradient within the concrete. The results indicated that the usage of fly ash led to a greater reduction in the maximum temperature and temperature gradient compared to using GGBFS. Additionally, the presence of GGBFS resulted in a delay in the time required for the concrete to reach its maximum temperature. This suggests that adding fly ash to mass concrete lessens its sensitivity to changes in size while at the same time reducing the maximum temperature and thermal gradient inside the concrete. Furthermore, the degree of hydration affects the thermal properties of concrete, including its thermal conductivity and specific heat capacity. As the hydration reaction advances, the amount of available water or moisture inside the concrete drops, which causes a decrease in thermal conductivity and specific heat capacity. In earlier models, these thermal properties were frequently assumed to have constant values. A sensitivity analysis comparing the modeling results with constant thermal properties to those considering variations with hydration reveals that assuming constant specific heat capacity significantly impacts the final results. However, assuming a constant thermal conductivity does not cause substantial changes. In conclusion, the developed model offers a simple yet effective approach to predict the temperature distribution within concrete elements using the finite volume method. It accounts for various boundary conditions, considers the generation of heat during cement hydration, and incorporates the behavior of blended cement using a superposition of Sshaped functions. The model's accuracy is validated through comparisons with experimental data and existing models. Sensitivity analysis provides insights into the influence of different parameters and variations on the temperature profile. By addressing the thermal cracking issue, the model contributes to ensuring the safety, durability, and costeffectiveness of concrete structures.

ÖgePrediction of earlyage mechanical properties of high strength concrete with pozzolans by using statistical methods(Graduate School, 20220614) Dalgıç, Muzaffer Umur ; Akkaya, Yılmaz ; 501181029 ; Structure EngineeringThe developments in concrete technology are becoming more important and effective with the help of innovative approaches on materials and computer sciences and their applications. With advanced calculation methods, computing programs/softwares and supercomputers, the mechanical behavior of concrete is better understood in many aspects, today. In addition, the materials used in concrete technology are now much more diverse, more useful, and much more effective than in the past by the opportunities provided from the industry. On the other hand, this level of development and effectiveness still depends on specific needs of concrete. However, this natural limitation does not prevent performance improvement, durability, sustainability, environmental and budgetfriendly expectations of concrete in a planned service life. Accordingly, while cement types, aggregates, moisture contents of aggregates, and air contents in concrete mixtures maintain their importance, the concrete mixture designs can be rearranged by weight and/or concrete mixing ratios according to the relevant pioneer test results, and new concrete matrices can be obtained by using fly ash, micro silica, nano silica, ground blast furnace slag, fiber, glass, wood, etc. Moreover, recyclable materials such as water, aggregate, glass, fiber, wood, etc. and even living organic materials are the topics that the concrete industry has recently focused on. In this context, the idea of using new construction materials may arise depending on relevant test results of special concretes produced for special projects. However, willing to change the concrete mixture designs and/or building materials based on test results can be quite difficult, because of time and budget concerns. For this reason, the most used type of concrete in the ready mixed concrete world is normal weight concrete (NWC), which is adapted by the concrete industry. Considering this fact, despite all the possibilities, determining a right concrete mixture design still differs in many ways depending on time, material, and external factors. In this idea, in general, specimens of hardened concrete in the form of cubes, cylinders, and rectangular prisms are tested at an early age to obtain results of mechanical properties such as compressive strength, splitting tensile strength, and modulus of elasticity so that further investigations and predictions of the concrete can be made. According to these test results, statistical methods come to the fore in many cases in terms of time and cost efficiency, and deep analysis to predict results of concrete performance depending on time and material to decide whether these concrete mixture designs comply with standards and regulations. Because, in regression analysis, which is one of these statistical methods, it is possible to predict a mechanical property of concrete without using destructive or nondestructive methods with enough concrete samples. In this way, the gains are obtained in terms of space, time, and cost. As a further step from the regression analysis, the use of machine learning methods such as Neural Net Fitting (NNF) to predict a data has become quite common today in the concrete world. Before statistical estimation of a data set, the concrete mixture designs should be cared for their validations. Furthermore, the atmospheric conditions at work sites where the concrete is casted are very important to obtain realistic test results from the concrete casting process. Therefore, the experiments such as slump, flow, unit weight, air content, ambient temperature, bleeding, adiabatic process, setting time etc. for fresh concrete samples can be carried out in the work fields. For this thesis, fresh concrete samples were taken for 33 different concrete mixture designs in 150X300 mm cylindrical sample containers in the numbers allowed by national standards and regulations. Besides, two distinct types of fine aggregates (FA) and three diverse types of coarse aggregates (CA) were used in these mixture designs with fly ash (FA) + micro silica (MS), ground granulated blast furnace slag (GGBS), and five different cement (C) types were used as binding material for these designs. The samples prepared within this framework were also kept in safe places in the worksites for the first setting process of the concrete, right after the sampling process was completed. Subsequently, the concrete samples, when the initial setting process were completed, were transferred to the laboratory environment for the hardened concrete tests in the international standards for 0.5, 1, 2, 3, 7, 14 and 28 days. And, the samples were prepared for the compressive strength, splitting tensile strength and modulus of elasticity tests for statistical analysis and estimations. In this thesis, as one of the statistical analysis models, regression analysis based on convergence of the obtained estimation results to real data (drawing curves) are used. The properties such as age of concrete samples (time), unit weights of mixture components, unit volumes of mixture components, mixing ratios and/or coefficients of an estimation methods etc. were analyzed individually and cumulatively. Accordingly, the relations of the predicted data with the concrete mixture designs are studied with linear or nonlinear equations in univariate and multivariate regression models. In addition to the equations used for the estimation of the test results, other statistical results such as R (Correlation of Coefficient), R² (Coefficient of Determination), R²adj (Adjusted Correlation of Determination), Sum of Squared of Errors (SSE), Mean Square Error (MSE), and Root Mean Square Error (RMSE) were obtained. The relationships between the actual test results, and predicted results were examined at the end. Due to the nature of the models used in the univariate regression analysis, only one variable was considered, and the results were estimated accordingly. The number of variables taken into consideration was analyzed individually for each mixture design. Although such individual analyzes were possible, many sequential studies on the actual, and estimated results had been the cost of time. Therefore, predicting the actual results required more complex analyzes like the multivariate regression analysis in this study. Before the more complex analyses, the variables were studied onebyone and/or in combinations for the multiple regression analyses. The substantial number of these combinations let the study to the machine learning process, and the effect of hidden layers between the input (mixture designs) values and the target (test) values four output values (algorithm results) were observed in the machine learning process. Although it was really complicated to detect these hidden layers by the individual calculations, only the input values, and target data values were chosen in the machine learning procedure without stepping directly into the hidden layers. On the other hand, it was understood that increasing the number of hidden layers deviated the estimation results from the target values. Therefore, to obtain more accurate results, the number of samples in the machine learning algorithms were changed as much as possible, while the number of hidden layers was increased. Yet, it was revealed that increasing the number of samples and/or hidden layers at the same time caused undesirable estimation results. It was also determined that an infinite number of experiments could be made with the machine learning to predict the target values. But, since it was not possible to conduct an infinite number of trials onebyone, all trials were recorded first, and then evaluated from the best to the worst and/or in the LevenbergMarquardt (LM) algorithm form the NNF machine learning process. In addition to this, R and MSE values in the NNF machine learning process, training, validation, test, and all correlation results were displayed in the x  y planes. Finally, in this framework, the best results were shared in association with the statistical results with physical meanings specific to mixture designs.