Discovering causal models for structural,construction and defense-related engineering phenomena  

作　　者：M.Z.Naser 

机构地区：[1]School of Civil&Environmental Engineering and Earth Sciences(SCEEES),Clemson University,Clemson,South Carolina 29634,USA [2]The Artificial Intelligence Research Institute for Science and Engineering(AIRISE),Clemson University,Clemson,South Carolina 29634,USA

出　　处：《Defence Technology(防务技术)》2025年第1期60-79,共20页Defence Technology

摘　　要：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.

关 键 词：CAUSALITY Causal discovery Directed acyclic graphs Machine learning Metrics 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] TU31[自动化与计算机技术—控制科学与工程] E95[建筑科学—结构工程]