Modeling the ultimate confined compressive strength and increase in strength of carbon-reinforced concrete columns: An artificial neural network approach
Date of Publication
2008
Document Type
Bachelor's Thesis
Degree Name
Bachelor of Science in Civil Engineering with Spec in Construction Technology & Management
College
Gokongwei College of Engineering
Department/Unit
Civil Engineering
Thesis Adviser
Jason Maximino Co Ongpeng
Defense Panel Chair
Andres Winston C. Oreta
Defense Panel Member
Bernardo A. Lejano
Jonathan R. Dungca
Abstract/Summary
Carbon Fiber Reinforced Polymer (CFRP) has proven to be a method of increasing the ultimate confined compressive strength. This paper utilizes the MATLAB software to apply artificial neural network (ANN) modeling through Self Organizing Map (SOM) to classify data sets with similarities observed by the program, and Back-Propagation (BP) to predict the increase in compressive strength of CFRP.
Various parameters were considered in the ANN models such as volumetric ratio of steel (us), volumetric ratio of carbon fiber (ucfrp), diameter (D), Length (L), Ultimate confined compressive strength (fcc), Unconfined compressive strength (fco).
SOM was used to group data with similar behavior. Each group classified through SOM was observed, analyzed and screened by the proponents of the study to be trained and tested. In order to obtain the best model, organized groups of data that showed evident relationship among its parameters were then used for back-propagation.
Back-propagation was applied to determine the output values from the organized data. Through linear regression the R-value which reflects the extent of the linear relationship between target and output was determined. MAE as well as MSE were utilized as error analyses. A parametric study was done to determine the behavior of the model. Chosen models were also compared with the models of the 10 authors of the gathered data.
The researchers further enhanced the study by testing the model determined by back propagation. For each group, a combination of two specific parameters was considered as the varying variables while the other remaining parameters were held constant. Results were plotted in 3D through MATLAB and analyses were conducted to find out the relationship of the varying variables to the strength of carbon fiber.
Abstract Format
html
Language
English
Format
Accession Number
TU16909
Shelf Location
Archives, The Learning Commons, 12F, Henry Sy Sr. Hall
Physical Description
104 leaves : ill.(some col.) ; 30 cm.
Recommended Citation
Chan, K. O., Manalansan, E., & Trespeses, M. M. (2008). Modeling the ultimate confined compressive strength and increase in strength of carbon-reinforced concrete columns: An artificial neural network approach. Retrieved from https://animorepository.dlsu.edu.ph/etd_bachelors/11876