Causality,the science of cause and effect,has made it possible to create a new family of models.Such models are often referred to as causal models.Unlike those of mathematical,numerical,empirical,or machine learning(M...Causality,the science of cause and effect,has made it possible to create a new family of models.Such models are often referred to as causal models.Unlike those of mathematical,numerical,empirical,or machine learning(ML)nature,causal models hope to tie the cause(s)to the effect(s)pertaining to a phenomenon(i.e.,data generating process)through causal principles.This paper presents one of the first works at creating causal models in the area of structural and construction engineering.To this end,this paper starts with a brief review of the principles of causality and then adopts four causal discovery algorithms,namely,PC(Peter-Clark),FCI(fast causal inference),GES(greedy equivalence search),and GRa SP(greedy relaxation of the sparsest permutation),have been used to examine four phenomena,including predicting the load-bearing capacity of axially loaded members,fire resistance of structural members,shear strength of beams,and resistance of walls against impulsive(blast)loading.Findings from this study reveal the possibility and merit of discovering complete and partial causal models.Finally,this study also proposes two simple metrics that can help assess the performance of causal discovery algorithms.展开更多
The learning algorithms of causal discovery mainly include score-based methods and genetic algorithms(GA).The score-based algorithms are prone to searching space explosion.Classical GA is slow to converge,and prone to...The learning algorithms of causal discovery mainly include score-based methods and genetic algorithms(GA).The score-based algorithms are prone to searching space explosion.Classical GA is slow to converge,and prone to falling into local optima.To address these issues,an improved GA with domain knowledge(IGADK)is proposed.Firstly,domain knowledge is incorporated into the learning process of causality to construct a new fitness function.Secondly,a dynamical mutation operator is introduced in the algorithm to accelerate the convergence rate.Finally,an experiment is conducted on simulation data,which compares the classical GA with IGADK with domain knowledge of varying accuracy.The IGADK can greatly reduce the number of iterations,populations,and samples required for learning,which illustrates the efficiency and effectiveness of the proposed algorithm.展开更多
This study employs a data-driven methodology that embeds the principle of dimensional invariance into an artificial neural network to automatically identify dominant dimensionless quantities in the penetration of rod ...This study employs a data-driven methodology that embeds the principle of dimensional invariance into an artificial neural network to automatically identify dominant dimensionless quantities in the penetration of rod projectiles into semi-infinite metal targets from experimental measurements.The derived mathematical expressions of dimensionless quantities are simplified by the examination of the exponent matrix and coupling relationships between feature variables.As a physics-based dimension reduction methodology,this way reduces high-dimensional parameter spaces to descriptions involving only a few physically interpretable dimensionless quantities in penetrating cases.Then the relative importance of various dimensionless feature variables on the penetration efficiencies for four impacting conditions is evaluated through feature selection engineering.The results indicate that the selected critical dimensionless feature variables by this synergistic method,without referring to the complex theoretical equations and aiding in the detailed knowledge of penetration mechanics,are in accordance with those reported in the reference.Lastly,the determined dimensionless quantities can be efficiently applied to conduct semi-empirical analysis for the specific penetrating case,and the reliability of regression functions is validated.展开更多
隐藏社区检测有助于揭示网络深层次功能和结构特征,是一个具有挑战性的研究领域。隐藏社区由弱关系连接而成,受具有较强连接关系的显性社区影响,在网络中不易被检测到。当前的隐藏社区发现算法对节点属性信息和全局拓扑结构的综合利用...隐藏社区检测有助于揭示网络深层次功能和结构特征,是一个具有挑战性的研究领域。隐藏社区由弱关系连接而成,受具有较强连接关系的显性社区影响,在网络中不易被检测到。当前的隐藏社区发现算法对节点属性信息和全局拓扑结构的综合利用仍显不足,为解决这一问题,提出了一种基于双重图卷积神经网络(GCN)联合优化隐藏社区发现算法——HCDGCN(hidden community detection based on dual GCN)。HCDGCN融合节点局部和全局结构特征,通过两个GCN共同迭代优化一个损失函数,并逐步削弱权重,使得弱关系社区变得清晰可见,实现了隐藏社区发现。在真实数据集上的实验结果表明,HCDGCN在隐藏社区发现方面优于现有基准方法,实现了更快的收敛速度和更优的社区划分。展开更多
文摘Causality,the science of cause and effect,has made it possible to create a new family of models.Such models are often referred to as causal models.Unlike those of mathematical,numerical,empirical,or machine learning(ML)nature,causal models hope to tie the cause(s)to the effect(s)pertaining to a phenomenon(i.e.,data generating process)through causal principles.This paper presents one of the first works at creating causal models in the area of structural and construction engineering.To this end,this paper starts with a brief review of the principles of causality and then adopts four causal discovery algorithms,namely,PC(Peter-Clark),FCI(fast causal inference),GES(greedy equivalence search),and GRa SP(greedy relaxation of the sparsest permutation),have been used to examine four phenomena,including predicting the load-bearing capacity of axially loaded members,fire resistance of structural members,shear strength of beams,and resistance of walls against impulsive(blast)loading.Findings from this study reveal the possibility and merit of discovering complete and partial causal models.Finally,this study also proposes two simple metrics that can help assess the performance of causal discovery algorithms.
基金supported by the National Social Science Fund of China(2022-SKJJ-B-084).
文摘The learning algorithms of causal discovery mainly include score-based methods and genetic algorithms(GA).The score-based algorithms are prone to searching space explosion.Classical GA is slow to converge,and prone to falling into local optima.To address these issues,an improved GA with domain knowledge(IGADK)is proposed.Firstly,domain knowledge is incorporated into the learning process of causality to construct a new fitness function.Secondly,a dynamical mutation operator is introduced in the algorithm to accelerate the convergence rate.Finally,an experiment is conducted on simulation data,which compares the classical GA with IGADK with domain knowledge of varying accuracy.The IGADK can greatly reduce the number of iterations,populations,and samples required for learning,which illustrates the efficiency and effectiveness of the proposed algorithm.
基金supported by the National Natural Science Foundation of China(Grant Nos.12272257,12102292,12032006)the special fund for Science and Technology Innovation Teams of Shanxi Province(Nos.202204051002006).
文摘This study employs a data-driven methodology that embeds the principle of dimensional invariance into an artificial neural network to automatically identify dominant dimensionless quantities in the penetration of rod projectiles into semi-infinite metal targets from experimental measurements.The derived mathematical expressions of dimensionless quantities are simplified by the examination of the exponent matrix and coupling relationships between feature variables.As a physics-based dimension reduction methodology,this way reduces high-dimensional parameter spaces to descriptions involving only a few physically interpretable dimensionless quantities in penetrating cases.Then the relative importance of various dimensionless feature variables on the penetration efficiencies for four impacting conditions is evaluated through feature selection engineering.The results indicate that the selected critical dimensionless feature variables by this synergistic method,without referring to the complex theoretical equations and aiding in the detailed knowledge of penetration mechanics,are in accordance with those reported in the reference.Lastly,the determined dimensionless quantities can be efficiently applied to conduct semi-empirical analysis for the specific penetrating case,and the reliability of regression functions is validated.
文摘对特定领域的技术机会进行挖掘与分析,可以为企业“从0到1”的原始创新提供新参考和新建议。本文提出了一种基于超链路预测的多元技术机会发现方法。首先,基于技术间多元共现关系构建技术关系超网络,利用IPC(international patent classification)的引用信息和文本信息生成节点特征向量;其次,将超链路预测模型Hyper-SAGNN(a self-attention based graph neural network for hypergraphs)扩展到技术关系超网络中,预测未来多个技术融合形成技术机会的可能性;最后,基于新颖性、中心性、跨领域性等特征构建度量指标,发现潜在的、有价值的多元技术机会。以智能问答技术领域为例,验证了本文方法的科学性和有效性,有效挖掘出高价值的三元技术机会和四元技术机会,为企业的技术战略布局与创新策略提供了决策支持。
文摘隐藏社区检测有助于揭示网络深层次功能和结构特征,是一个具有挑战性的研究领域。隐藏社区由弱关系连接而成,受具有较强连接关系的显性社区影响,在网络中不易被检测到。当前的隐藏社区发现算法对节点属性信息和全局拓扑结构的综合利用仍显不足,为解决这一问题,提出了一种基于双重图卷积神经网络(GCN)联合优化隐藏社区发现算法——HCDGCN(hidden community detection based on dual GCN)。HCDGCN融合节点局部和全局结构特征,通过两个GCN共同迭代优化一个损失函数,并逐步削弱权重,使得弱关系社区变得清晰可见,实现了隐藏社区发现。在真实数据集上的实验结果表明,HCDGCN在隐藏社区发现方面优于现有基准方法,实现了更快的收敛速度和更优的社区划分。
