The rapid advancement of three-dimensional printed concrete(3DPC)requires intelligent and interpretable frameworks to optimize mixture design for strength,printability,and sustainability.While machine learning(ML)mode...The rapid advancement of three-dimensional printed concrete(3DPC)requires intelligent and interpretable frameworks to optimize mixture design for strength,printability,and sustainability.While machine learning(ML)models have improved predictive accuracy,their limited transparency has hindered their widespread adoption in materials engineering.To overcome this barrier,this study introduces a Random Forests ensemble learning model integrated with SHapley Additive exPlanations(SHAP)and Partial Dependence Plots(PDPs)to model and explain the compressive strength behavior of 3DPC mixtures.Unlike conventional“black-box”models,SHAP quantifies each variable’s contribution to predictions based on cooperative game theory,which enables causal interpretability,whereas PDP visualizes nonlinear and interactive effects between features that offer practical mix design insights.A systematically optimized random forest model achieved strong generalization(R2=0.978 for training,0.834 for validation,and 0.868 for testing).The analysis identified curing age,Portland cement,silica fume,and the water-tobinder ratio as dominant predictors,with curing age exerting the highest positive influence on strength development.The integrated SHAP-PDP framework revealed synergistic interactions among binder constituents and curing parameters,which established transparent,data-driven guidelines for performance optimization.Theoretically,the study advances explainable artificial intelligence in cementitious material science by linking microstructural mechanisms to model-based reasoning,thereby enhancing both the interpretability and applicability of ML-driven mix design for next-generation 3DPC systems.展开更多
The porkchop plot is the porkchop-shaped,computer-generated contour plot that displays the launch date and arrival date characteristics of an interplanetary flight path for a given launch opportunity between two celes...The porkchop plot is the porkchop-shaped,computer-generated contour plot that displays the launch date and arrival date characteristics of an interplanetary flight path for a given launch opportunity between two celestial bodies.We found that,when the gravity assistance of a planet is taken into account of an interplanetary transfer trajectory,the classical porkchop plot cannot give enough information for the interplanetary mission.In this regard,we have generalized the classical porkchop plot into the 3D porkchop plot that gives the launch,flyby,and arrival opportunity of the interplanetary mission.This paper presents an alternative opportunity for the Psyche asteroid mission with Mars’s gravity assist.Psyche is a largest M-type asteroid in the solar system and may be a good source of Platinum group metals.This paper also presents the analytical solution of the gravity assist model,which is useful for obtaining the optimal flyby radius and the optimal thrust impulse during the planetary gravity assist.展开更多
基金supported by the Ongoing Research Funding Program(Grant No.ORFFT-2025-025-4)at King Saud University,Riyadh,Saudi Arabia.The grant was awarded to Yassir M.Abbas。
文摘The rapid advancement of three-dimensional printed concrete(3DPC)requires intelligent and interpretable frameworks to optimize mixture design for strength,printability,and sustainability.While machine learning(ML)models have improved predictive accuracy,their limited transparency has hindered their widespread adoption in materials engineering.To overcome this barrier,this study introduces a Random Forests ensemble learning model integrated with SHapley Additive exPlanations(SHAP)and Partial Dependence Plots(PDPs)to model and explain the compressive strength behavior of 3DPC mixtures.Unlike conventional“black-box”models,SHAP quantifies each variable’s contribution to predictions based on cooperative game theory,which enables causal interpretability,whereas PDP visualizes nonlinear and interactive effects between features that offer practical mix design insights.A systematically optimized random forest model achieved strong generalization(R2=0.978 for training,0.834 for validation,and 0.868 for testing).The analysis identified curing age,Portland cement,silica fume,and the water-tobinder ratio as dominant predictors,with curing age exerting the highest positive influence on strength development.The integrated SHAP-PDP framework revealed synergistic interactions among binder constituents and curing parameters,which established transparent,data-driven guidelines for performance optimization.Theoretically,the study advances explainable artificial intelligence in cementitious material science by linking microstructural mechanisms to model-based reasoning,thereby enhancing both the interpretability and applicability of ML-driven mix design for next-generation 3DPC systems.
文摘The porkchop plot is the porkchop-shaped,computer-generated contour plot that displays the launch date and arrival date characteristics of an interplanetary flight path for a given launch opportunity between two celestial bodies.We found that,when the gravity assistance of a planet is taken into account of an interplanetary transfer trajectory,the classical porkchop plot cannot give enough information for the interplanetary mission.In this regard,we have generalized the classical porkchop plot into the 3D porkchop plot that gives the launch,flyby,and arrival opportunity of the interplanetary mission.This paper presents an alternative opportunity for the Psyche asteroid mission with Mars’s gravity assist.Psyche is a largest M-type asteroid in the solar system and may be a good source of Platinum group metals.This paper also presents the analytical solution of the gravity assist model,which is useful for obtaining the optimal flyby radius and the optimal thrust impulse during the planetary gravity assist.
