Evaluation of the fire performance of concrete containing graphene oxide (GO)

dc.contributor.advisorWeerasinghe, TGPL
dc.contributor.authorAlahakoon, AMYD
dc.date.accept2026
dc.date.accessioned2026-08-18T05:38:16Z
dc.date.issued2026
dc.description.abstractConcrete stands as one of the most widely used construction material in Sri Lanka, as in most other countries. But, still, its performance under elevated temperatures remains a critical concern, particularly regarding loss of strength and spalling. Incorporation of graphene based materials (e.g., GO) has emerged as a more effective approach to improve the thermo-mechanical performance of concrete. Sri Lankan graphene is recognized globally for its high purity, making it a valuable candidate for producing GO-based concrete with potential export value. Despite extensive studies shows GO’s impact on structural integrity under ambient conditions, limited research has addressed its fire performance. The study evaluates the role of synthesized graphene oxide (GO) in mitigating thermal degradation, specifically assessing its effects on residual mechanical characteristics and spalling prevention in heated concrete. Concrete mixes were prepared with 0%, 0.01%, 0.02%, and 0.04% GO as a percentage weight of cement. Samples were grouped and heated to 200°C, 400°C, 600°C, and 700°C, and two cooling methods were used for all the cubes: passive cooling and water quenching. The residual compressive strengths were evaluated, and microstructural observations were examined using Scanning Electron Microscope (SEM). Results revealed that the inclusion of GO refined the pore structure and limited crack propagation, leading to improved residual strength and enhanced spalling resistance up to 700°C. The optimal performance was observed at a GO content of 0.02%. In 0.02% GO mixed specimens at 2000C shows a 55% improvement over the control specimen. Similarly, at 4000C, 6000C and 7000C temperatures the improvement percentages are 36.8%, 36.32% and 25.85% respectively. In addition, a Convolutional Neural Network (CNN) model was developed to recognize and classify surface cracks on fire exposed specimens automatically. The CNN model demonstrated high accuracy (Around 0.9) in distinguishing crack patterns based on the GO content and exposed temperature. It provided a quantitative means of assessing damage intensity. The study confirmed that Sri Lankan GO can significantly enhance fire resilience and post fire structural integrity of concrete, highlighting its potential for sustainable and high performance construction applications.
dc.identifier.accnoTH6176
dc.identifier.citationAlahakoon, A.M.Y.D. (2026). Evaluation of the fire performance of concrete containing graphene oxide (GO) [Master’s theses, University of Moratuwa]. Institutional Repository University of Moratuwa. https://dl.lib.uom.lk/handle/123/25488
dc.identifier.degreeMSc (Major Component Research)
dc.identifier.departmentDepartment of Civil Engineering
dc.identifier.facultyEngineering
dc.identifier.urihttps://dl.lib.uom.lk/handle/123/25488
dc.language.isoen
dc.subjectCONCRETE CONSTRUCTION-Fire Resistance
dc.subjectCONCRETE CONSTRUCTION-Thermal Shock Resistance
dc.subjectGRAPHENE OXIDE
dc.subjectSCANNING ELECTRON MICROSCOPES
dc.subjectCONVOLUTIONAL NEURAL NETWORKS
dc.subjectDEEP LEARNING
dc.subjectMSc (MAJOR COMPONENT RESEARCH)-Dissertations
dc.subjectCIVIL ENGINEERING-Dissertations
dc.subjectMSc (Major Component Research)
dc.titleEvaluation of the fire performance of concrete containing graphene oxide (GO)
dc.typeThesis-Abstract

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