Nitrogen-rich heterocyclic energetic compounds(NRHECs)and their salts have witnessed widespread synthesis in recent years.The substantial energy-density content within these compounds can lead to potentially dangerous...Nitrogen-rich heterocyclic energetic compounds(NRHECs)and their salts have witnessed widespread synthesis in recent years.The substantial energy-density content within these compounds can lead to potentially dangerous explosive reactions when subjected to external stimuli such as electrical discharge.Therefore,developing a reliable model for predicting their electrostatic discharge sensitivity(ESD)becomes imperative.This study proposes a novel and straightforward model based on the presence of specific groups(-NH_(2) or-NH-,-N=N^(+)-O^(-)and-NNO_(2),-ONO_(2) or-NO_(2))under certain conditions to assess the ESD of NRHECs and their salts,employing interpretable structural parameters.Utilizing a comprehensive dataset comprising 54 ESD measurements of NRHECs and their salts,divided into 49/5 training/test sets,the model achieves promising results.The Root Mean Square Error(RMSE),Mean Absolute Error(MAE),and Maximum Error for the training set are reported as 0.16 J,0.12 J,and 0.5 J,respectively.Notably,the ratios RMSE(training)/RMSE(test),MAE(training)/MAE(test),and Max Error(training)/Max Error(test)are all greater than 1.0,indicating the robust predictive capabilities of the model.The presented model demonstrates its efficacy in providing a reliable assessment of ESD for the targeted NRHECs and their salts,without the need for intricate computer codes or expert involvement.展开更多
Discernment of seismic soil liquefaction is a complex and non-linear procedure that is affected by diversified factors of uncertainties and complexity.The Bayesian belief network(BBN)is an effective tool to present a ...Discernment of seismic soil liquefaction is a complex and non-linear procedure that is affected by diversified factors of uncertainties and complexity.The Bayesian belief network(BBN)is an effective tool to present a suitable framework to handle insights into such uncertainties and cause–effect relationships.The intention of this study is to use a hybrid approach methodology for the development of BBN model based on cone penetration test(CPT)case history records to evaluate seismic soil liquefaction potential.In this hybrid approach,naive model is developed initially only by an interpretive structural modeling(ISM)technique using domain knowledge(DK).Subsequently,some useful information about the naive model are embedded as DK in the K2 algorithm to develop a BBN-K2 and DK model.The results of the BBN models are compared and validated with the available artificial neural network(ANN)and C4.5 decision tree(DT)models and found that the BBN model developed by hybrid approach showed compatible and promising results for liquefaction potential assessment.The BBN model developed by hybrid approach provides a viable tool for geotechnical engineers to assess sites conditions susceptible to seismic soil liquefaction.This study also presents sensitivity analysis of the BBN model based on hybrid approach and the most probable explanation of liquefied sites,owing to know the most likely scenario of the liquefaction phenomenon.展开更多
In this paper, the structure characteristics of open complex giant systems are concretely analysed in depth, thus the view and its significance to support the meta synthesis engineering with manifold knowledge models...In this paper, the structure characteristics of open complex giant systems are concretely analysed in depth, thus the view and its significance to support the meta synthesis engineering with manifold knowledge models are clarified. Furthermore, the knowledge based multifaceted modeling methodology for open complex giant systems is emphatically studied. The major points are as follows: (1) nonlinear mechanism and general information partition law; (2) from the symmetry and similarity to the acquisition of construction knowledge; (3) structures for hierarchical and nonhierarchical organizations; (4) the integration of manifold knowledge models; (5) the methodology of knowledge based multifaceted modeling.展开更多
文摘Nitrogen-rich heterocyclic energetic compounds(NRHECs)and their salts have witnessed widespread synthesis in recent years.The substantial energy-density content within these compounds can lead to potentially dangerous explosive reactions when subjected to external stimuli such as electrical discharge.Therefore,developing a reliable model for predicting their electrostatic discharge sensitivity(ESD)becomes imperative.This study proposes a novel and straightforward model based on the presence of specific groups(-NH_(2) or-NH-,-N=N^(+)-O^(-)and-NNO_(2),-ONO_(2) or-NO_(2))under certain conditions to assess the ESD of NRHECs and their salts,employing interpretable structural parameters.Utilizing a comprehensive dataset comprising 54 ESD measurements of NRHECs and their salts,divided into 49/5 training/test sets,the model achieves promising results.The Root Mean Square Error(RMSE),Mean Absolute Error(MAE),and Maximum Error for the training set are reported as 0.16 J,0.12 J,and 0.5 J,respectively.Notably,the ratios RMSE(training)/RMSE(test),MAE(training)/MAE(test),and Max Error(training)/Max Error(test)are all greater than 1.0,indicating the robust predictive capabilities of the model.The presented model demonstrates its efficacy in providing a reliable assessment of ESD for the targeted NRHECs and their salts,without the need for intricate computer codes or expert involvement.
基金Projects(2016YFE0200100,2018YFC1505300-5.3)supported by the National Key Research&Development Plan of ChinaProject(51639002)supported by the Key Program of National Natural Science Foundation of China
文摘Discernment of seismic soil liquefaction is a complex and non-linear procedure that is affected by diversified factors of uncertainties and complexity.The Bayesian belief network(BBN)is an effective tool to present a suitable framework to handle insights into such uncertainties and cause–effect relationships.The intention of this study is to use a hybrid approach methodology for the development of BBN model based on cone penetration test(CPT)case history records to evaluate seismic soil liquefaction potential.In this hybrid approach,naive model is developed initially only by an interpretive structural modeling(ISM)technique using domain knowledge(DK).Subsequently,some useful information about the naive model are embedded as DK in the K2 algorithm to develop a BBN-K2 and DK model.The results of the BBN models are compared and validated with the available artificial neural network(ANN)and C4.5 decision tree(DT)models and found that the BBN model developed by hybrid approach showed compatible and promising results for liquefaction potential assessment.The BBN model developed by hybrid approach provides a viable tool for geotechnical engineers to assess sites conditions susceptible to seismic soil liquefaction.This study also presents sensitivity analysis of the BBN model based on hybrid approach and the most probable explanation of liquefied sites,owing to know the most likely scenario of the liquefaction phenomenon.
文摘In this paper, the structure characteristics of open complex giant systems are concretely analysed in depth, thus the view and its significance to support the meta synthesis engineering with manifold knowledge models are clarified. Furthermore, the knowledge based multifaceted modeling methodology for open complex giant systems is emphatically studied. The major points are as follows: (1) nonlinear mechanism and general information partition law; (2) from the symmetry and similarity to the acquisition of construction knowledge; (3) structures for hierarchical and nonhierarchical organizations; (4) the integration of manifold knowledge models; (5) the methodology of knowledge based multifaceted modeling.
