"Artificial neural network modeling of stress in concrete under step-lo" by Marcus Karyl C. Soberano

Artificial neural network modeling of stress in concrete under step-loading using non-linear ultrasonic test results

Date of Publication

11-2016

Document Type

Master's Thesis

Degree Name

Master of Science in Civil Engineering

Subject Categories

Civil Engineering

College

Gokongwei College of Engineering

Department/Unit

Civil Engineering

Thesis Adviser

Jason Maximino C. Ongpeng

Defense Panel Chair

Cheryl Lyne Capiz Roxas

Defense Panel Member

Ronaldo S. Gallardo
Ma. Klarissa G. Martinez

Abstract/Summary

This study deals with the prediction of stress in concrete using the non-linear ultrasonic test and artificial neural network (ANN). Data were obtained from 36 cube specimens and 27 beam specimens subjected to step loading. For the cubes, the ordinary concrete (ORC), fiber reinforced concrete (FRC) and concrete with varying size of aggregate were studied. For the reinforced concrete beams, 7 types of concrete were investigated with varying amount of reinforcements and water to cement ratio (w/c). The fundamental harmonic amplitude (A1), second harmonic amplitude (A2), third harmonic amplitude (A3), strain/neutral axis (NA) and peak to peak amplitude (PPA) were found to be the significant input parameters for ANN based on the results of the Spearman’s rank order correlation. The optimum models were determined based on the Pearson correlation coefficient (R), mean square error (MSE) and soundness of the behavior of the input parameter with the stress of the concrete. The Daponte’s amplitude sensitivity (DA) was used in analyzing the result of the parametric study. Results of the sensitivity analysis show that for the ORC and FRC, the A2 and strain were long range sensitive for all w/c. The A3 decreased its sensitivity as the water to cement ratio was increased. For the size of aggregate study, PPA and A3 decreased their sensitivity as the size of aggregate increases. A1 was only sensitive for small aggregate concrete. The A2 and strain were long range sensitive and varying the size of aggregate did not affect their sensitivity. In the study of reinforced concrete beams, the PPA was sensitive for all types of concrete except WC40B. The A1 was very sensitive to the load having long range sensitivity for all types of concrete except WC40A. The A2 and NA were sensitive for all types of concrete. Lastly, A3 decreased its sensitivity as the water to cement ratio increased. The specific damping capacity (S) of concrete was also investigated and ANN models were produced using the load, neutral axis and loading branch as the input parameters to predict the damping in concrete using the energy method. The highest magnitude for S occurs at the first load of the concrete. S would decrease in magnitude when repetitive load was applied. When a higher load was introduced, a new peak was observed. The results of this study highlight and improve the non-destructive evaluation capabilities of non-linear ultrasonic test.

Abstract Format

html

Language

English

Format

Electronic

Accession Number

CDTG007057

Shelf Location

Archives, The Learning Common's, 12F Henry Sy Sr. Hall

Physical Description

1 computer optical disc; 4 3/4 in.

Keywords

Concrete—Testing; Neural networks (Computer science); Nonlinear mechanics

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Embargo Period

11-8-2024

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