Tarbiat Modares University Modares Civil Engineering Journal 2476-6763 20 3 2020 10 22 Optimization of Mixture Proportions of Self-compacted Fiber Reinforced Concrete incorporating Polypropylene using Genetic and Crow search Algorithms Optimization of Mixture Proportions of Self-compacted Fiber Reinforced Concrete incorporating Polypropylene using Genetic and Crow search Algorithms 1 12 12346 FA MOHAMMAD JAVAD TAHERI AMIRI Assistant Professor, Department of Civil Engineering, Higher Education Institute of Pardisan Ali Ashrafian MSc in Civil Engineering, Tabari University of Babol 0000-0002-9134-6722 Javad Berenjian Assistant professor, Babol University of Technology Fatemeh Asghari Tilaki MSc student in Civil Engineering, Tabari University of Babol Journal Article The utilization of concrete Incorporating with fibers is one of the proper issues of construction industry in last years. The main focus of this research to design a high performance self-compacted fiber reinforced concrete (SCFRC) by using an evolutionary algorithm, which is implemented in MATLAB. Crow Search Algorithm (CSA) and Genetic Algorithm (GA) are statistical ways which are developed by optimization based meta-heuristic solutions. A total of 67 concrete mixtures were considered by varying the levels of key factors affecting concrete strength of concrete, namely, water content (137.2-195 kg/m3), cement content (325.5-520 kg/m3), coarse aggregate content (722-920 kg/m3), fine aggregate content (804.9-960 kg/m3), nano silica content (0-49.6 kg/m3),percentage of volumetric of fibers (0-0.9 %), lime stone powder content (0-288.9 kg/m3) and superplasticizer content (1.75-10.5 kg/m3) were developed to design optimized mixture proportions. The objective function called maximizing concrete strength was formulated as an optimization problem on the basis of Multiple Linear Regression (MLR) method. The constrains including ratio of mixture proportions and absolute volume of mixture design were utilized to obtain an optimal-strength and cost-effective design. The concrete technological constraints were identified as the factors of experimental design for concrete production. The evolutionary implementation of results reached incorporating mixture proportions having strengths in range of 30 - 88.7 MPa. Five numerical examples for optimum mixture design of SCFRC were considered to evaluate the capability and efficiency of CSA and GA algorithm. These results were compared and concluded that CSA (3.38-14.49 % of mean error) performed better than GA (7.95-15.52 % of mean error) for this application. Also, the proposed evolutionary CSA and GA algorithms are found to be reliable and robustness tools to solve and optimize engineering and concrete technological problem. The utilization of concrete Incorporating with fibers is one of the proper issues of construction industry in last years. The main focus of this research to design a high performance self-compacted fiber reinforced concrete (SCFRC) by using an evolutionary algorithm, which is implemented in MATLAB. Crow Search Algorithm (CSA) and Genetic Algorithm (GA) are statistical ways which are developed by optimization based meta-heuristic solutions. A total of 67 concrete mixtures were considered by varying the levels of key factors affecting concrete strength of concrete, namely, water content (137.2-195 kg/m3), cement content (325.5-520 kg/m3), coarse aggregate content (722-920 kg/m3), fine aggregate content (804.9-960 kg/m3), nano silica content (0-49.6 kg/m3),percentage of volumetric of fibers (0-0.9 %), lime stone powder content (0-288.9 kg/m3) and superplasticizer content (1.75-10.5 kg/m3) were developed to design optimized mixture proportions. The objective function called maximizing concrete strength was formulated as an optimization problem on the basis of Multiple Linear Regression (MLR) method. The constrains including ratio of mixture proportions and absolute volume of mixture design were utilized to obtain an optimal-strength and cost-effective design. The concrete technological constraints were identified as the factors of experimental design for concrete production. The evolutionary implementation of results reached incorporating mixture proportions having strengths in range of 30 - 88.7 MPa. Five numerical examples for optimum mixture design of SCFRC were considered to evaluate the capability and efficiency of CSA and GA algorithm. These results were compared and concluded that CSA (3.38-14.49 % of mean error) performed better than GA (7.95-15.52 % of mean error) for this application. Also, the proposed evolutionary CSA and GA algorithms are found to be reliable and robustness tools to solve and optimize engineering and concrete technological problem. https://mcej.modares.ac.ir/article_12346_a3590023df66ac92ae35e3316026d17d.pdf