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
The interest in nano-engineered cements basically brought by the demands on increasing the performance of concrete structures in challenging applications,such as extreme thermal,electrical,and electromagnetic environm...The interest in nano-engineered cements basically brought by the demands on increasing the performance of concrete structures in challenging applications,such as extreme thermal,electrical,and electromagnetic environments.Traditional cementitious materials lack technological capabilities regarding thermal conductivity,electrical resistivity,mechanical strength,and electromagnetic shielding.Such limitations prevent their application in highperformance and multifunctional concrete structures,which are increasingly required in modern construction.The highdimensional complexity of multivariable optimization of multiple properties makes most of the current approaches unsuitable,and so requires new ways to predict,model,and optimize the performance of such advanced materials.In the present contribution,we introduce a holistic approach to the optimization of nano-engineered cements composites incorporating epoxy resin,nano titanium dioxide,carbon nanotubes,and portland cement.Advanced techniques of machine learning used include random forest regressor for multi-output property prediction and eXtreme gradient boosting,an implementation of the gradient-boosting algorithm that proved particularly useful in multi-objective optimization,for electrical codes’thermal and mechanical properties.Bayesian optimization is also employed to decrease the experimental trials and fine-tune the processing parameters;high-dimensional input space is reduced using principal component analysis to attain optimal model performance.Graph neural networks are utilized for modeling structureproperty relations,and Gaussian processes serve as a surrogate model mimicking the outcome of finite element analysis simulation effectively reducing delays at the computational level.The model yields noteworthy improvements:resistivity decreases by 30%–40%,thermal conductivity increases by 25%–30%,and tensile strength increases by 15%–20%.These enhancements make nano-engineered cements composites highly promising for multifunctional applications such as vibration absorption and electromagnetic shielding,which presents the need for smart,high-performance concrete structures for advanced applications in construction.展开更多
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
基金funded by the Ministry of Education,Culture,Research,and the Technology,Indonesia for Matching Fund (Kedaireka)Scheme in 2022 with Contract No.155/E1/KS.06.02/2022.
文摘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.
文摘The interest in nano-engineered cements basically brought by the demands on increasing the performance of concrete structures in challenging applications,such as extreme thermal,electrical,and electromagnetic environments.Traditional cementitious materials lack technological capabilities regarding thermal conductivity,electrical resistivity,mechanical strength,and electromagnetic shielding.Such limitations prevent their application in highperformance and multifunctional concrete structures,which are increasingly required in modern construction.The highdimensional complexity of multivariable optimization of multiple properties makes most of the current approaches unsuitable,and so requires new ways to predict,model,and optimize the performance of such advanced materials.In the present contribution,we introduce a holistic approach to the optimization of nano-engineered cements composites incorporating epoxy resin,nano titanium dioxide,carbon nanotubes,and portland cement.Advanced techniques of machine learning used include random forest regressor for multi-output property prediction and eXtreme gradient boosting,an implementation of the gradient-boosting algorithm that proved particularly useful in multi-objective optimization,for electrical codes’thermal and mechanical properties.Bayesian optimization is also employed to decrease the experimental trials and fine-tune the processing parameters;high-dimensional input space is reduced using principal component analysis to attain optimal model performance.Graph neural networks are utilized for modeling structureproperty relations,and Gaussian processes serve as a surrogate model mimicking the outcome of finite element analysis simulation effectively reducing delays at the computational level.The model yields noteworthy improvements:resistivity decreases by 30%–40%,thermal conductivity increases by 25%–30%,and tensile strength increases by 15%–20%.These enhancements make nano-engineered cements composites highly promising for multifunctional applications such as vibration absorption and electromagnetic shielding,which presents the need for smart,high-performance concrete structures for advanced applications in construction.
基金Qujing Normal University Student Innovation and Entrepreneurship Training Project,No.S202310684035.
文摘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.
