The current research’s purpose is to examine how Ultra-High Performance Fiber Concrete (UHPFC) holds up in terms of strength and durability for strengthening purposes. For this reason, the experimental and the theoretical studies in this research attempted to assess different fresh and hardened properties of a variety of ultra-high performance combinations. Steel fibers were utilized to differentiate all of the program's combinations at percentages of 0.25 %, 0.5 %, 0.75 %, 1%, and 1.25 % by volume. Mini flow slump, compressive and flexural strength, ultrasonic pulse velocity, water absorption, and porosity tests were all used to examine the performance of the strength and durability of the material. The findings of this study's trials showed that steel fibers increased the strength of UHPFC. The steel fiber ratio of 1% gave the maximum compressive strength, whereas 1.25 percent yielded the highest flexural strength. Because the fibers function as a bridge, preventing internal breaking, the tensile test results were improved as the proportion of steel fiber rises. Through the use of the multi-objective optimization approach, the optimal ratio of fibers was chosen at the end of the laboratory work since it has the best durability and strength characteristics. Statistical software (Minitab 2018) was used to find the optimal combination of UHPFC that meets all of the requirements. The theoretical selected optimum ratio of 0.77% of fibers obtained from the optimization was evaluated and validated experimentally. The optimized mix provided 90.28 MPa, 14.6 MPa, and 20.2 MPa for compressive, splitting tensile and flexural tests respectively with better durability performance compared to other mixes prepared in this investigation.
This research presents an efficient strategy to find optimum analysis and shape design for arch dams. Where the design geometry is built using (Solid Work Program), which is considered as one of important programs for analysis and design of complex structures. A finite element method is used to analyze the arch dam body, which is proved to be an important method for analysis and gives accurate results according to previous researches. The design of the basic shape of the dam has been done by using horizontal curve and vertical curve equations. After conducting the analysis and design of the initial model by (SolidWork) program, it was transferred to the second phase. This is the shape optimization process by using (Genetic Algorithm) in (Matlab) program. This method is an efficient method for all optimization problems in different branches. The objective function in this research is the minimum volume of the dam, which leads to minimum weight design. There are many constraint controls the selecting of optimum shape. In this work, geometrical and structural constraints are considered. At this stage, to calculate the volume of the dam body, integration method is used to convert the volume in terms of the design variables (tc1, tc2, and tc3) which represent the thickness of the dam at three levels. Then this equation has been moved to (Genetic Algorithm tools) using (m-file) to complete the optimization process. The results show that the best design shape of the dam is with thicknesses (5.5m, 13.3m, and 19.8m) with a final optimal volume of53.75% less than the initial model and the stress is still less than the allowable limits
AbstractConstruction of concrete structures involves at least two different main materials: concrete and steel. Design of these structures should be based on cost rather than weight minimization. In this work, least cost design of singly and doubly reinforced beams is done by applying of the Lagrangian multipliers method (LMM) under ultimate design constraint beside other constraints. Cost objective functions and moment constraints are derived and implemented within the optimization method. The optimum solution comparisons with conventional design methods are performed and the result reported, showing that the LMM can be successfully applied to the minimum cost deign of reinforced concrete beams without need for iterative trials. Optimum design solution surfaces have been developed. Good and reliable results have been obtained and confirmed by using standard design procedures. The artificial neural networks (ANN) has been trained with design data obtained from optimal design formulas. After successful trials, the model predicted the optimum depth of the beam sections and optimum areas of steel required for the problems with accuracy satisfying all design constraints.
In recent years, a number of researchers have adopted the wet packing (WP) approach to design different types of concrete mixes. Particle grading is a key to the optimization of the wet compactness density; for that reason, all empty spaces that exist in between large-size particles need to be completely filled with particles of smaller size. Previously-conducted studies in this field have been focused on measuring the particle size distribution’s packing density (PD) of the of granular matrices is the purpose of investigating how to increase the PD of cementitious materials. Thus, literature lacks models capable of predicting the optimal PD value. The current study collected and analyzed 216 datasets in order to construct a model for accurate prediction of PD. The main datasets were organized into two categories: modeling datasets and validation datasets. To configure the model in the best way, a hybrid gravitational search algorithm-artificial neural network (GSA-ANN) was also developed in this study. The findings confirmed ANN as an effective alternative for measuring the ultimate PD of cementitious pastes. ANN provided high levels of accuracy, practicality, and effectiveness in the process of predicting the PD value. Based on the final results, the implementation of the hybrid GSA-ANN technique causes a significant decrease in the number of tests conducted on experimental samples, which results in not only saving time and money, but also reducing the CO2 emission volume.