Artificial neural network modeling of confinement of carbon fiber reinforced polymer as retrofitting materials in columns

Date of Publication

2011

Document Type

Bachelor's Thesis

Degree Name

Bachelor of Science in Civil Engineering with Spec in Construction Technology & Management

Subject Categories

Civil Engineering

College

Gokongwei College of Engineering

Department/Unit

Civil Engineering

Thesis Adviser

Jason Maximino C. Ongpeng

Defense Panel Chair

Andres Winston C. Oreta

Defense Panel Member

Ronaldo S. Gallardo
Alden Paul D. Balili

Abstract/Summary

The utilization of Carbon Fiber Reinforced Polymer (CFRP) as a retrofitting material has proven its strengthening effects on existing circular, square and rectangular concrete columns. To further develop the use CFRP in the field of civil engineering, theoretical models are needed to predict the effectiveness of CFRP confinement in columns with regards to its compressive strength.

The Self-Organizing Map (SOM) toolbox was used to classify data according to the parameters that have similarities which is observed by the toolbox. Each grouping classified through SOM that showed evident relationship among its parameters was observed, analyzed and was related to the ultimate confined compressive strength and increase in strength of the confined columns. From the analysis of the best SOM models, parameters which have significant effect on the CFRP-confinement were then chosen to be used for the back-propagation models. These back-propagation models were then compared to existing models by different authors to verify its accuracy.

Three artificial neural networks consisting of circular and non-circular data were developed to predict the ultimate confined compressive strength (fcc). The parameters that were considered in the back-propagation model are volumetric ratio of carbon fiber (Pcfrp), volumetric ratio of steel (Ps), unconfined compressive strength (fco), and the columns geometrical properties (b, h, and L). The models performed better than some models with regard to their correlation (R).

Abstract Format

html

Language

English

Format

Print

Accession Number

TU15889

Shelf Location

Archives, The Learning Commons, 12F, Henry Sy Sr. Hall

Physical Description

xxi, 174 leaves : ill. (some col.) ; 30 cm.

Keywords

Neural networks (Computer science); Carbon fibers; Columns, Concrete--Reinforcement

Embargo Period

12-14-2021

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