ﺑﻬﻴﻨﻪ ﺳﺎزی ﻃﺮح ﻣﺨﻠﻮط ﺑﺘﻦ ﺧﻮدﺗﺮاﻛﻢ اﻟﻴﺎﻓﻲ ﺣﺎوی ﭘﻠﻲ ﭘﺮوﭘﻠﻴﻦ ﺑﺎ اﺳﺘﻔﺎده از اﻟﮕﻮرﻳﺘﻢ ﻫﺎی ﻓﺮا اﺑﺘﻜﺎری ﺟﺴﺘﺠﻮی ﻛﻼغ و ژﻧﺘﻴﻚ

اﻳﻦ ﻣﻄﺎﻟﻌﻪ ﺑﻪ ﺗﺤﻘﻴﻖ در ارﺗﺒﺎط ﺑﺎ ﻃﺮﺣﻲ ﺑﻬﻴﻨﻪ ﺑﺘﻦ ﺧﻮدﺗﺮاﻛﻢ اﻟﻴﺎﻓﻲ ﺣﺎوی ﭘﻠﻲ ﭘﺮوﭘﻴﻠﻦ ﺑﺮای ﺳﺎﺧﺖ در ﺻﻨﻌﺖ ﺳﺎﺧﺘﻤﺎن ﻣﻲ ﭘﺮدازد. ﺑﺘﻦ ﻣﺎده ای ﺗﺮﻛﻴﺒﻲ اﺳﺖ ﻛﻪ از ﻣﻮاد ﺳﻴﻤﺎﻧﻲ ، آب، ﺳﻨﮕﺪاﻧﻪ و ﻣﻮاد اﻓﺰودﻧﻲ ﺗﺸﻜﻴﻞ ﺷﺪه اﺳﺖ ﻛﻪ ﻣﺠﻤﻮﻋﻪ اﻳﻦ ﻣﻮاد ﻋﻨﺎﺻﺮ اﺻﻠﻲ ﺗﺸﻜﻴﻞ دﻫﻨﺪه ﺑﺘﻦ ﻣﻲ ﺑﺎﺷﻨﺪ. ﺑﺎ ﺗﻮﺳﻌﻪ اﺳﺘﻔﺎده از اﻳﻦ ﻣﻮاد در ﻣﻬﻨﺪﺳﻲ ﺳﺎﺧﺖ وﺳﺎزﻣﺨﻠﻮطﻫﺎی ﺑﺘﻨﻲ ﺑﻴﺸﺘﺮﻳﻦ ﻣﻮارد اﺳﺘﻔﺎده را در ﺻﻨﻌﺖ ﺳﺎﺧﺖ و ﺳﺎز در ﺳﺮﺗﺎﺳﺮ دﻧﻴﺎ داﺷﺘﻪ اﺳﺖ. ﻣﻘﺎوﻣﺖ ﻓﺸﺎری ﻣﻌﻢ ﺗﺮﻳﻦ ﻣﺸﺨﺼﻪ در ﺑﺘﻦ ﻣﻲ ﺑﺎﺷﺪ. ﺑﺎ اﻳﻦ ﺣﺎل ﻧﻘﺎﻳﺼﻲ در ﺑﺘﻦ ﻣﺎﻧﻨﺪ ﺗﺮک در ﺑﺘﻦ، ﭼﻐﺮﻣﮕﻲ ﭘﺎﻳﻴﻦ و ﻣﻘﺎوﻣﺖ ﻛﺸﺸﻲ وﺟﻮد داﺷﺘﻪ ﻛﻪ ﻧﻴﺎز ﺑﻪ ﺗﻘﻮﻳﺖ ﺑﺘﻦ در ﺑﺮاﺑﺮ اﻳﻦ ﻋﻴﺐ را ﺑﺮﺟﺴﺘﻪ ﻛﺮده اﺳﺖ. در ﺳﺎﻟﻴﺎن اﺧﻴﺮ اﺳﺘﻔﺎده از ﺑﺘﻦ ﻣﺤﺘﻮی اﻟﻴﺎف ﺑﻌﻨﻮان ﻳﻚ ﻣﺎدهی ﺳﺎﺧﺘﻤﺎﻧﻲ ﻣﻬﻢ و ﺑﺎ ﺧﻮاص ﻣﻜﺎﻧﻴﻜﻲ ﻣﻨﺎﺳﺐ ﺟﻬﺖ ﺳﺎﺧﺖ و ﺳﺎز اﻧﻮاع ﺳﺎزهﻫﺎ اﺳﺘﻔﺎده ﻣﻲﺷﻮد. ﻫﺪف اﺻﻠﻲ در اﻳﻦ ﺗﺤﻘﻴﻖ، ﻃﺮاﺣﻲ ﺑﺘﻦ ﺗﻮاﻧﻤﻨﺪ ﺧﻮدﺗﺮاﻛﻢ اﻟﻴﺎﻓﻲ ﺑﺎ اﺳﺘﻔﺎده از اﻟﮕﻮرﻳﺘﻢﻫﺎی ﻓﺮا اﺑﺘﻜﺎری ﺑﺎ ﭘﻴﺎده ﺳﺎزی در ﻧﺮم اﻓﺰار ﻣﺘﻠﺐ ﻣﻲ ﺑﺎﺷﺪ. ﺑﺮای ﺑﻬﻴﻨﻪ ﺳﺎزی ﻣﺒﺘﻨﻲ ﺑﺮ راﻫﻜﺎرﻫﺎی ﻓﺮا اﺑﺘﻜﺎری، اﻟﮕﻮرﻳﺘﻢ ﺟﺴﺘﺠﻮی ﻛﻼغ csa و اﻟﮕﻮرﻳﺘﻢ ژﻧﺘﻴﻚ ga ﺑﻪ ﻋﻨﻮان راﻫﻜﺎرﭘﺮدازﺷﻲ ﻣﺤﺎﺳﺒﺎﺗﻲ ﺗﻮﺳﻌﻪ داده داده ﺷﺪه اﺳﺖ. ﺑﺮای اﻳﻦ ﻣﻨﻈﻮر، 67 ﻃﺮح اﺧﺘﻼط ﺑﺘﻦ ﺧﻮدﺗﺮاﻛﻢ اﻟﻴﺎﻓﻲ ﺷﺎﻣﻞ آب 137/2 – 195 ﻛﻴﻠﻮﮔﺮم ﺑﺮ ﻣﺘﺮﻣﻜﻌﺐ ، ﺳﻴﻤﺎن 325/5 – 520 ﻛﻴﻠﻮﮔﺮم ﺑﺮ ﻣﺘﺮﻣﻜﻌﺐ ، درﺷﺖ داﻧﻪ 722 – 920 ﻛﻴﻠﻮﮔﺮم ﺑﺮ ﻣﺘﺮﻣﻜﻌﺐ ، رﻳﺰداﻧﻪ 804/9 – 960 ﻛﻴﻠﻮﮔﺮم ﺑﺮ ﻣﺘﺮﻣﻜﻌﺐ ، ﻧﺎﻧﻮﺳﻴﻠﻴﺲ 0 – 49/6 ﻛﻴﻠﻮﮔﺮم ﺑﺮ ﻣﺘﺮﻣﻜﻌﺐ ، درﺻﺪ ﺣﺠﻤﻲ اﻟﻴﺎف 0 – 0/9 درﺻﺪ ،ﭘﻮدرﺳﻨﮓ آﻫﻚ 0 – 288/9 ﻛﻴﻠﻮﮔﺮم ﺑﺮ ﻣﺘﺮﻣﻜﻌﺐ و ﻓﻮق روان ﻛﻨﻨﺪه 1/75 – 10/5 ﻛﻴﻠﻮﮔﺮم ﺑﺮ ﻣﺘﺮﻣﻜﻌﺐ ﺑﺮای ﻃﺮاﺣﻲ ﻣﺨﻠﻮط ﺑﻬﻴﻨﻪ ﻣﻮرد اﺳﺘﻔﺎده ﻗﺮار ﮔﺮﻓﺖ. در اﻳﻦ ﻣﻄﺎﻟﻌﻪ ﺑﺮای ﻓﺮﻣﻮﻟﻪ ﺷﺪن ﻣﺴﺌﻠﻪ ﺑﻬﻴﻨﻪ ﺳﺎزی، ﺗﺎﺑﻊ ﻫﺪف ﻣﻘﺎوﻣﺖ ﻓﺸﺎری ﺑﺘﻦ ﺑﺮﭘﺎﻳﻪ روش رﮔﺮﺳﻴﻮن ﭼﻨﺪﮔﺎﻧﻪ ﺧﻄﻲ ﺗﻮﺳﻌﻪ داده ﺷﺪ. ﻫﻤﭽﻨﻴﻦ ﻗﻴﺪﻫﺎی ﺑﺮرﺳﻲ ﺷﺪه در اﻳﻦ ﻣﻄﺎﻟﻌﻪ ﻧﺴﺒﺖ ﻣﻘﺎدﻳﺮ ﻃﺮح اﺧﺘﻼط و ﺣﺠﻢ ﻣﻄﻠﻖ ﻣﻘﺎدﻳﺮ ﻃﺮح اﺧﺘﻼط ﺑﺮای ﻃﺮاﺣﻲ ﻣﺨﻠﻮﻃﻲ ﺑﺎ ﻣﻘﺎوﻣﺖ ﺑﻬﻴﻨﻪ و ﻣﻘﺮون ﺑﻪ ﺻﺮﻓﻪ ﺑﻪ ﻋﻨﻮان ﻣﺤﺪودﻳﺖ ﻫﺎی ﺗﻜﻨﻮﻟﻮژﻳﻜﻲ از ﻓﺎﻛﺘﻮرﻫﺎی آزﻣﺎﻳﺸﮕﺎﻫﻲ ﺗﻮﻟﻴﺪ ﺑﺘﻦ ﻣﻮرد ﺗﻮﺟﻪ ﻗﺮار ﮔﺮﻓﺘﻪ اﺳﺖ. ﭘﻴﺎده ﺳﺎزی اﻟﮕﻮرﻳﺘﻤﻴﻚ روشﻫﺎی ﻓﺮا اﺑﺘﻜﺎری در ﻣﺤﺪوده 30 – 88/7 ﻣﮕﺎﭘﺎﺳﻜﺎل ﺗﺎ رﺳﻴﺪن ﺑﻪ ﻣﺨﻠﻮط ﺑﺎ ﻣﻘﺎدﻳﺮ ﺑﻬﻴﻨﻪ اداﻣﻪ ﭘﻴﺪا ﻛﺮده و در ﻧﻬﺎﻳﺖ 5 ﻧﻤﻮﻧﻪ از ﻣﺨﻠﻮط ﺑﻬﻴﻨﻪ ﺗﻮﺳﻌﻪ داده ﺷﺪه ﺑﺎ اﺳﺘﻔﺎده از اﻟﮕﻮرﻳﺘﻢﻫﺎی csa و ga ﺟﻬﺖ ﺑﺮرﺳﻲ ﻗﺎﺑﻠﻴﺖ و ﺑﻬﺮه وری اﻟﮕﻮرﻳﺘﻢﻫﺎ ﮔﺰارش ﮔﺮدﻳﺪ. ﻧﺘﺎﻳﺞ اراﺋﻪ ﺷﺪه در اﻳﻦ ﻣﻄﺎﻟﻌﻪ ﻧﺸﺎن داده اﺳﺖ ﻛﻪ ﻋﻤﻠﻜﺮد اﻟﮕﻮرﻳﺘﻢ csa ﺑﺎ ﻣﺤﺪوده ﺧﻄﺎی ﻣﻴﺎﻧﮕﻴﻦ 3/38 – 14/49 درﺻﺪ در ﻣﻘﺎﻳﺴﻪ ﺑﺎ اﻟﮕﻮرﻳﺘﻢ ga ﺑﺎ ﻣﺤﺪوده ﺧﻄﺎی ﻣﻴﺎﻧﮕﻴﻦ 7/95 – 15/52 ﻧﺘﺎﻳﺞ ﻗﺎﺑﻞ ﺗﻮﺟﻪ در دﻗﺖ و ﻫﻤﮕﺮاﻳﻲ ﺟﻮابﻫﺎ اراﺋﻪ ﻧﻤﻮده اﺳﺖ. از اﻳﻦ ﻫﻤﭽﻨﻴﻦ، اﻟﮕﻮرﻳﺘﻢﻫﺎی ﻣﻮرد اﺳﺘﻔﺎده ﺑﻪ ﻋﻨﻮان اﺑﺰار ﻗﺎﺑﻞ اﻃﻤﻴﻨﺎن در ﺣﻞ ﻣﺴﺎﻳﻞ ﺑﻬﻴﻨﻪ ﺳﺎزی در ﻣﺴﺎﻳﻞ ﻣﻬﻨﺪﺳﻲ ﺑﻮﻳﮋه ﺗﻜﻨﻮﻟﻮژی ﺑﺘﻦ ﻗﺎﺑﻞ ﺗﻮﺟﻪ ﻣﻲ ﺑﺎﺷﺪ.

optimization of mixture proportions of self-compacted fiber reinforced concrete incorporating polypropylene using genetic and crow search algorithms

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.