Identifying and estimating causal effects of bridge failures from observational data

被引:0
|
作者
Çiftçioğlu A.Ö. [1 ]
Naser M.Z. [2 ,3 ]
机构
[1] Department of Civil Engineering, Manisa Celal Bayar University
[2] School of Civil & Environmental Engineering and Earth Sciences (SCEEES), Clemson University
[3] Artificial Intelligence Research Institute for Science and Engineering (AIRISE), Clemson University
关键词
Bridges; Causal inference; Counterfactuals; Failure; Hazards; Vulnerability;
D O I
10.1016/j.iintel.2023.100068
中图分类号
TB3 [工程材料学];
学科分类号
0805 ; 080502 ;
摘要
This paper presents a causal analysis aimed at identifying and estimating causal effects with regard to bridge failures under extreme events. Observational data on about 299 bridge incidents were used to conduct this causal investigation and examine bridges’ performance. As causal investigations can also deliver counterfactual assessments of parallel worlds, a causal analysis can serve as a high-merit methodology to evaluate the performance of critical bridges. Our findings quantify the causal impacts of various factors spanning the characteristics of bridges, traffic demands, and incident type (i.e., fire, high wind, scour/flood, earthquake, and impact/collision). More specifically, our analysis reveals high causal effects related to the used structural system, construction materials, and demand served. © 2023 The Authors
引用
收藏
相关论文
共 50 条
  • [31] BREEDING AND GENETICS SYMPOSIUM: Inferring causal effects from observational data in livestock
    Rosa, G. J. M.
    Valente, B. D.
    JOURNAL OF ANIMAL SCIENCE, 2013, 91 (02) : 553 - 564
  • [32] Predicting causal effects in large-scale systems from observational data
    Marloes H Maathuis
    Diego Colombo
    Markus Kalisch
    Peter Bühlmann
    Nature Methods, 2010, 7 : 247 - 248
  • [33] Joint estimation of causal effects from observational and intervention gene expression data
    Rau, Andrea
    Jaffrezic, Florence
    Nuel, Gregory
    BMC SYSTEMS BIOLOGY, 2013, 7
  • [34] Estimating causal effects from multiple cycle data in studies of in vitro fertilization
    Hogan, JW
    Scharfstein, DO
    STATISTICAL METHODS IN MEDICAL RESEARCH, 2006, 15 (02) : 195 - 209
  • [36] Estimating causal effects from panel data with dynamic multivariate panel models
    Helske, Jouni
    Tikka, Santtu
    ADVANCES IN LIFE COURSE RESEARCH, 2024, 60
  • [37] A Causal Dirichlet Mixture Model for Causal Inference from Observational Data
    Lin, Adi
    Lu, Jie
    Xuan, Junyu
    Zhu, Fujin
    Zhang, Guangquan
    ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY, 2020, 11 (03)
  • [38] Estimating Social Influence from Observational Data
    Sridhar, Dhanya
    De Bacco, Caterina
    Blei, David
    CONFERENCE ON CAUSAL LEARNING AND REASONING, VOL 177, 2022, 177
  • [39] Estimating heterogeneous causal effects in observational studies using small area predictors
    Ranjbar, Setareh
    Salvati, Nicola
    Pacini, Barbara
    COMPUTATIONAL STATISTICS & DATA ANALYSIS, 2023, 184
  • [40] MAKING CAUSAL INFERENCES FROM OBSERVATIONAL DATA - REPLY
    KEMPTHORNE, O
    BIOMETRICS, 1978, 34 (04) : 714 - 714