Date of Publication

7-10-2024

Document Type

Master's Thesis

Degree Name

Master of Science in Civil Engineering

Subject Categories

Civil Engineering | Structural Engineering

College

Gokongwei College of Engineering

Department/Unit

Civil Engineering

Thesis Advisor

Bernardo A. Lejano

Defense Panel Chair

Richard M. De Jesus

Defense Panel Member

Lessandro Estelito O. Garciano
Andres Winston C. Oreta

Abstract/Summary

Advances in technology are leading to the automation of structural health monitoring. Current attempts to attach sensors or detectors onto concrete structures for monitoring damages are problematic due to high cost, low durability, and limited sensing capabilities among other issues. Self-sensing concrete addresses these issues by acting as both the sensor and the structure by adding conductive fillers such as graphene or carbon fiber, but one of the barriers that prevent it from widespread market use is the need for optimal mix designs that maximize functionality while minimizing cost. This study investigates the optimal graphene content as conductive powder, recycled carbon fiber content as conductive fiber, W/C ratio, and superplasticizer content to maximize the workability, compressive strength, and electrical conductivity while minimizing the cost. A Response Surface Methodology (RSM) with a Central Composite Design (CCD) was employed through the use of the statistical analysis software Design-Expert to determine the optimal amount of each variable. A total of 25 experimental runs were used with different levels being studied for each factor. Graphene was studied at levels ranging from 0.06% to 0.14%, while recycled carbon fiber was studied at levels ranging from 0.6% to 1.4%. The W/C ratio was studied at levels ranging from 0.4 to 0.6, and the superplasticizer was studied at levels ranging from 0.6% to 1.4%. The results of the RSM yielded an optimum mix design of 0.10% graphene content, 0.6% recycled carbon fiber content, 0.4 W/C, and 1.4% superplasticizer content. The combined effects of these variables showed a predicted slump of 129.37 mm, a compressive strength of 26.6 MPa, a fractional change in resistivity (FCR) of 114.43%, and cost of 6.77 Php/kg or 16,200 Php/m3. An FCR of 114.43% indicates a sensitivity level almost double than those reported in previous literature, allowing for higher resolution structural health monitoring capable of inferring small changes in the concrete internal state without compromising mechanical performance.

Abstract Format

html

Language

English

Format

Electronic

Keywords

Concrete—Additives; Building materials

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Embargo Period

10-14-2024

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