The recently developed SCCDS composite tube,a novel variant of the pipe-in-pipe(PIP)structure,demonstrates strong potential for subsea pipeline applications.However,theoretical research regarding its structural behavi...The recently developed SCCDS composite tube,a novel variant of the pipe-in-pipe(PIP)structure,demonstrates strong potential for subsea pipeline applications.However,theoretical research regarding its structural behavior under compression-torsion loading and bearing capacity calculations remains limited,particularly concerning the influence of dual hydraulic pressures during operation.This study examines the impact of dual hydraulic pressures on the compressive-torsional behavior of SCCDS composite tubes.A finite element(FE)model was developed and validated against experimental results,comparing failure modes,full-range loading curves,and bearing capacity to elucidate the working mechanism under dual pressures.A parametric study was then conducted to examine the effects of geometric-physical parameters.Results demonstrate that dual pressures substantially enhance the bearing capacity of sandwich concrete by increasing the normal contact stress at the interface.Increasing concrete strength(f_(c))provides minimal enhancement to torsional resistance compared to the yielding strengths of outer tube(f_(yo))and inner tube(f_(yi)).Higher diameter-to-thickness ratios of outer tube(D_(o)/t_(o))and inner tube(D_(i)/t_(i))significantly reduce torsional capacity.At 1000 m water depth,increasing the D_(o)/t_(o)ratio from 27.5 to 36.67,55,and 110 reduces bearing capacity by 11.17%,23.08%,and 36.14%respectively.Strict measures should be implemented to prevent substantial reductions in strength and ductility for SCCDS composite tubes with large hollow ratios(e.g.,χ=0.849)or high axial compression ratios(e.g.,n=0.8).The study proposes a modified calculation method for determining N-T curves that incorporates dual hydraulic pressure effects,providing guidance for performance evaluation of novel SCCDS composite tubes in deep-sea engineering.展开更多
This research aims to develop an advanced deep learning-based ensemble algorithm,utilizing environmental temperature and solar radiation as feature factors,to conduct hourly temperature field predictions for steel-con...This research aims to develop an advanced deep learning-based ensemble algorithm,utilizing environmental temperature and solar radiation as feature factors,to conduct hourly temperature field predictions for steel-concrete composite decks(SCCDs).The proposed model comprises feature parameter lag selection,two non-stationary time series decomposition methods(empirical mode decomposition(EMD)and time-varying filtering-based empirical mode decomposition(TVFEMD)),and a stacking ensemble prediction model.To validate the proposed model,five machine learning(ML)models(random forest(RF),support vector regression(SVR),multilayer perceptron(MLP),gradient boosting regression(GBR),and extreme gradient boosting(XGBoost))were tested as base learners and evaluations were conducted within independent,mixed,and ensemble frameworks.Finally,predictions are made based on engineering cases.The results indicate that consideration of lag variables and modal decomposition can significantly improve the prediction performance of learners,and the stacking framework,which combines multiple learners,achieves superior prediction results.The proposed method demonstrates a high degree of predictive robustness and can be applied to statistical analysis of the temperature field in SCCDs.Incorporating time lag features helps account for the delayed heat dissipation phenomenon in concrete,while decomposition techniques assist in feature extraction.展开更多
基金supported by the High-level Talent Research Startup Fund(Grant No.1608722024)of Xi’an University of Archit-ectureTechnology and the Shaanxi Province High-level Youth Talents Program(Grant No.Z20240589).
文摘The recently developed SCCDS composite tube,a novel variant of the pipe-in-pipe(PIP)structure,demonstrates strong potential for subsea pipeline applications.However,theoretical research regarding its structural behavior under compression-torsion loading and bearing capacity calculations remains limited,particularly concerning the influence of dual hydraulic pressures during operation.This study examines the impact of dual hydraulic pressures on the compressive-torsional behavior of SCCDS composite tubes.A finite element(FE)model was developed and validated against experimental results,comparing failure modes,full-range loading curves,and bearing capacity to elucidate the working mechanism under dual pressures.A parametric study was then conducted to examine the effects of geometric-physical parameters.Results demonstrate that dual pressures substantially enhance the bearing capacity of sandwich concrete by increasing the normal contact stress at the interface.Increasing concrete strength(f_(c))provides minimal enhancement to torsional resistance compared to the yielding strengths of outer tube(f_(yo))and inner tube(f_(yi)).Higher diameter-to-thickness ratios of outer tube(D_(o)/t_(o))and inner tube(D_(i)/t_(i))significantly reduce torsional capacity.At 1000 m water depth,increasing the D_(o)/t_(o)ratio from 27.5 to 36.67,55,and 110 reduces bearing capacity by 11.17%,23.08%,and 36.14%respectively.Strict measures should be implemented to prevent substantial reductions in strength and ductility for SCCDS composite tubes with large hollow ratios(e.g.,χ=0.849)or high axial compression ratios(e.g.,n=0.8).The study proposes a modified calculation method for determining N-T curves that incorporates dual hydraulic pressure effects,providing guidance for performance evaluation of novel SCCDS composite tubes in deep-sea engineering.
基金National Natural Science Foundation of China(No.52278235)Science and Technology Program of Hunan Provincial Department of Transportation(No.202309),China.
文摘This research aims to develop an advanced deep learning-based ensemble algorithm,utilizing environmental temperature and solar radiation as feature factors,to conduct hourly temperature field predictions for steel-concrete composite decks(SCCDs).The proposed model comprises feature parameter lag selection,two non-stationary time series decomposition methods(empirical mode decomposition(EMD)and time-varying filtering-based empirical mode decomposition(TVFEMD)),and a stacking ensemble prediction model.To validate the proposed model,five machine learning(ML)models(random forest(RF),support vector regression(SVR),multilayer perceptron(MLP),gradient boosting regression(GBR),and extreme gradient boosting(XGBoost))were tested as base learners and evaluations were conducted within independent,mixed,and ensemble frameworks.Finally,predictions are made based on engineering cases.The results indicate that consideration of lag variables and modal decomposition can significantly improve the prediction performance of learners,and the stacking framework,which combines multiple learners,achieves superior prediction results.The proposed method demonstrates a high degree of predictive robustness and can be applied to statistical analysis of the temperature field in SCCDs.Incorporating time lag features helps account for the delayed heat dissipation phenomenon in concrete,while decomposition techniques assist in feature extraction.
