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Optimization of Mortar Compressive Strength Prepared with Waste Glass Aggregate and Coir Fiber Addition Using Response Surface Methodology
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作者 Cut Rahmawati Lia Handayani +6 位作者 Muhtadin Muhammad Faisal Muhammad Zardi S.M.Sapuan Agung Efriyo Hadi Jawad Ahmad haytham f.isleem 《Journal of Renewable Materials》 EI 2023年第10期3751-3767,共17页
Waste Glass(WGs)and Coir Fiber(CF)are not widely utilized,even though their silica and cellulose content can be used to create construction materials.This study aimed to optimize mortar compressive strength using Resp... Waste Glass(WGs)and Coir Fiber(CF)are not widely utilized,even though their silica and cellulose content can be used to create construction materials.This study aimed to optimize mortar compressive strength using Response Surface Methodology(RSM).The Central Composite Design(CCD)was applied to determine the optimization of WGs and CF addition to the mortar compressive strength.Compressive strength and microstructure testing with Scanning Electron Microscope(SEM),Fourier-transform Infrared Spectroscopy(FT-IR),and X-Ray Diffraction(XRD)were conducted to specify the mechanical ability and bonding between the matrix,CF,and WGs.The results showed that the chemical treatment of CF produced 49.15%cellulose,with an average particle size of 1521μm.The regression of a second-order polynomial model yielded an optimum composition consisting of 12.776%WGs and 2.344%CF with a predicted compressive strength of 19.1023 MPa.C-S-H gels were identified in the mortars due to the dissolving of SiO_(2) in WGs and cement.The silica from WGs increased the C-S-H phase.CF plays a role in preventing,bridging,and branching micro-cracks before reaching maximum stress.WGs aggregates and chemically treated CF are suitable to be composited in mortar to increase compressive strength. 展开更多
关键词 CELLULOSE response surface methodology waste glass coir fiber composite
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Proposed numerical and machine learning models for fiber-reinforced polymer concrete-steel hollow and solid elliptical columns
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作者 Tang QIONG Ishan JHA +3 位作者 Alireza BAHRAMI haytham f.isleem Rakesh KUMAR Pijush SAMUI 《Frontiers of Structural and Civil Engineering》 SCIE EI CSCD 2024年第8期1169-1194,共26页
This study employs a hybrid approach,integrating finite element method(FEM)simulations with machine learning(ML)techniques to investigate the structural performance of double-skin tubular columns(DSTCs)reinforced with... This study employs a hybrid approach,integrating finite element method(FEM)simulations with machine learning(ML)techniques to investigate the structural performance of double-skin tubular columns(DSTCs)reinforced with glass fiber-reinforced polymer(GFRP).The investigation involves a comprehensive examination of critical parameters,including aspect ratio,concrete strength,number of GFRP confinement layers,and dimensions of steel tubes used in DSTCs,through comparative analyses and parametric studies.To ensure the credibility of the findings,the results are rigorously validated against experimental data,establishing the precision and trustworthiness of the analysis.The present research work examines the use of the columns with elliptical cross-sections and contributes valuable insights into the application of FEM and ML in the design and evaluation of structural systems within the field of structural engineering. 展开更多
关键词 elliptical column fiber-reinforced polymer machine learning finite element method ABAQUS
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