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Prestressed concrete i-girder optimization via genetic algorithm
Adibaskoro T.a, Suarjana M.a
a Faculty of Civil and Environmental Engineering, Institut Teknologi Bandung, Bandung, 40132, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2019 Published by ITB Journal Publisher.Prestressed concrete has been gaining popularity in the construction industry because of its many advantages, which include reduced dead load due to less material used and overall cost savings. Nonetheless, a single prestressed concrete I-girder as a structural element in highway bridges is still significantly costly and massive, so optimization can yield a significant amount of cost savings as well as reduced material consumption. In this study, prestressed concrete I-girder optimization was carried out by implementing a genetic algorithm (GA), a method inspired by nature’s evolution and natural selection. This study evaluates a number of aspects of applying a genetic algorithm for optimization of material cost of a prestressed concrete I-girder design. A new method for calculating the fitness value is proposed, which was proven to be essential for the application developed in this study. The best solution that resulted from the optimization process is presented, defined by being the least costly solution while still maintaining compliance with the AASHTO LRFD 2007 design code, which includes ultimate strength, service stresses and deflection, detailing requirements, geometrical feasibility, etc. Lastly, a sensitivity analysis was carried out, discussing the influence of the starting conditions on the output of the optimization process.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Fitness values,I-girders,Material cost,Natural selection,Overall costs,Reduced material consumption,Structural elements,Ultimate strength[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Genetic algorithm,Highway bridges,I-girder,Optimization,Prestressed concrete[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.5614/j.eng.technol.sci.2019.51.2.2[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]