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
Satellite observation scheduling plays a significant role in improving the efficiency of satellite observation systems.Although many scheduling algorithms have been proposed,emergency tasks,characterized as importance...Satellite observation scheduling plays a significant role in improving the efficiency of satellite observation systems.Although many scheduling algorithms have been proposed,emergency tasks,characterized as importance and urgency（e.g.,observation tasks orienting to the earthquake area and military conflict area）,have not been taken into account yet.Therefore,it is crucial to investigate the satellite integrated scheduling methods,which focus on meeting the requirements of emergency tasks while maximizing the profit of common tasks.Firstly,a pretreatment approach is proposed,which eliminates conflicts among emergency tasks and allocates all tasks with a potential time-window to related orbits of satellites.Secondly,a mathematical model and an acyclic directed graph model are constructed.Thirdly,a hybrid ant colony optimization method mixed with iteration local search（ACO-ILS） is established to solve the problem.Moreover,to guarantee all solutions satisfying the emergency task requirement constraints,a constraint repair method is presented.Extensive experimental simulations show that the proposed integrated scheduling method is superior to two-phased scheduling methods,the performance of ACO-ILS is greatly improved in both evolution speed and solution quality by iteration local search,and ACO-ILS outperforms both genetic algorithm and simulated annealing algorithm.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 Natural Science Foundation of China (61104180)the National Basic Research Program of China(973 Program) (97361361)
文摘Satellite observation scheduling plays a significant role in improving the efficiency of satellite observation systems.Although many scheduling algorithms have been proposed,emergency tasks,characterized as importance and urgency（e.g.,observation tasks orienting to the earthquake area and military conflict area）,have not been taken into account yet.Therefore,it is crucial to investigate the satellite integrated scheduling methods,which focus on meeting the requirements of emergency tasks while maximizing the profit of common tasks.Firstly,a pretreatment approach is proposed,which eliminates conflicts among emergency tasks and allocates all tasks with a potential time-window to related orbits of satellites.Secondly,a mathematical model and an acyclic directed graph model are constructed.Thirdly,a hybrid ant colony optimization method mixed with iteration local search（ACO-ILS） is established to solve the problem.Moreover,to guarantee all solutions satisfying the emergency task requirement constraints,a constraint repair method is presented.Extensive experimental simulations show that the proposed integrated scheduling method is superior to two-phased scheduling methods,the performance of ACO-ILS is greatly improved in both evolution speed and solution quality by iteration local search,and ACO-ILS outperforms both genetic algorithm and simulated annealing algorithm.
