Discovering causal models for structural, construction and defense-related engineering phenomena
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) pertaini...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-01-01
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| Series: | Defence Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214914724000849 |
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| author | M.Z. Naser |
| author_facet | M.Z. Naser |
| author_sort | M.Z. Naser |
| collection | DOAJ |
| description | 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 GRaSP (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. |
| format | Article |
| id | doaj-art-1bf19d5fa3e14c88bbb7aa11d3637b9e |
| institution | Kabale University |
| issn | 2214-9147 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Defence Technology |
| spelling | doaj-art-1bf19d5fa3e14c88bbb7aa11d3637b9e2025-01-23T05:26:45ZengKeAi Communications Co., Ltd.Defence Technology2214-91472025-01-01436079Discovering causal models for structural, construction and defense-related engineering phenomenaM.Z. Naser0School of Civil & Environmental Engineering and Earth Sciences (SCEEES), Clemson University, Clemson, South Carolina 29634, USA; The Artificial Intelligence Research Institute for Science and Engineering (AIRISE), Clemson University, Clemson, South Carolina 29634, USA; Corresponding author.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 GRaSP (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.http://www.sciencedirect.com/science/article/pii/S2214914724000849CausalityCausal discoveryDirected acyclic graphsMachine learningMetrics |
| spellingShingle | M.Z. Naser Discovering causal models for structural, construction and defense-related engineering phenomena Defence Technology Causality Causal discovery Directed acyclic graphs Machine learning Metrics |
| title | Discovering causal models for structural, construction and defense-related engineering phenomena |
| title_full | Discovering causal models for structural, construction and defense-related engineering phenomena |
| title_fullStr | Discovering causal models for structural, construction and defense-related engineering phenomena |
| title_full_unstemmed | Discovering causal models for structural, construction and defense-related engineering phenomena |
| title_short | Discovering causal models for structural, construction and defense-related engineering phenomena |
| title_sort | discovering causal models for structural construction and defense related engineering phenomena |
| topic | Causality Causal discovery Directed acyclic graphs Machine learning Metrics |
| url | http://www.sciencedirect.com/science/article/pii/S2214914724000849 |
| work_keys_str_mv | AT mznaser discoveringcausalmodelsforstructuralconstructionanddefenserelatedengineeringphenomena |