Reliability Analysis of Deep Foundation Pit Using the Gaussian Copula-Based Bayesian Network Article Swipe
Urban underground space development has heightened concerns over the safety of deep foundation pit construction. This study conducted time-series monitoring of critical safety-influencing factors and applied the Gaussian copula-based Bayesian network (GCBN) model for comprehensive reliability analysis of deep foundation pit support structures. The GCBN model, integrating the multivariate data management of pair copula with Bayesian network’s uncertainty handling, found that building settlement has the greatest impact on the safety of deep foundation pit and revealed a reliability index (β) of 0.44 in an actual case, suggesting a hazardous condition. Based on the reliability index β, emergency measures were promptly taken. Compared to traditional reliability methods, the approach presented in this paper takes into account the dependence among monitoring indicators, which is more aligned with actual engineering conditions and holds significant reference value for the safety assessment of underground engineering structures.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/math12243961
- https://www.mdpi.com/2227-7390/12/24/3961/pdf?version=1734431133
- OA Status
- gold
- Cited By
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- References
- 55
- Related Works
- 10
- OpenAlex ID
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Raw OpenAlex JSON
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https://openalex.org/W4405504624Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/math12243961Digital Object Identifier
- Title
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Reliability Analysis of Deep Foundation Pit Using the Gaussian Copula-Based Bayesian NetworkWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-12-17Full publication date if available
- Authors
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Bin Tan, Qiyuan PengList of authors in order
- Landing page
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https://doi.org/10.3390/math12243961Publisher landing page
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https://www.mdpi.com/2227-7390/12/24/3961/pdf?version=1734431133Direct link to full text PDF
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2227-7390/12/24/3961/pdf?version=1734431133Direct OA link when available
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Copula (linguistics), Bayesian network, Gaussian, Computer science, Reliability (semiconductor), Bayesian probability, Artificial intelligence, Reliability engineering, Statistics, Data mining, Econometrics, Engineering, Mathematics, Physics, Quantum mechanics, Power (physics)Top concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.deep | 11, 38, 71 |
| abstract_inverted_index.into | 113 |
| abstract_inverted_index.more | 122 |
| abstract_inverted_index.over | 7 |
| abstract_inverted_index.pair | 52 |
| abstract_inverted_index.that | 60 |
| abstract_inverted_index.this | 110 |
| abstract_inverted_index.were | 98 |
| abstract_inverted_index.with | 54, 124 |
| abstract_inverted_index.Based | 90 |
| abstract_inverted_index.Urban | 0 |
| abstract_inverted_index.among | 117 |
| abstract_inverted_index.case, | 85 |
| abstract_inverted_index.found | 59 |
| abstract_inverted_index.holds | 129 |
| abstract_inverted_index.index | 78, 94 |
| abstract_inverted_index.model | 32 |
| abstract_inverted_index.paper | 111 |
| abstract_inverted_index.space | 2 |
| abstract_inverted_index.study | 16 |
| abstract_inverted_index.takes | 112 |
| abstract_inverted_index.value | 132 |
| abstract_inverted_index.which | 120 |
| abstract_inverted_index.(GCBN) | 31 |
| abstract_inverted_index.actual | 84, 125 |
| abstract_inverted_index.copula | 53 |
| abstract_inverted_index.impact | 66 |
| abstract_inverted_index.model, | 45 |
| abstract_inverted_index.safety | 9, 69, 135 |
| abstract_inverted_index.taken. | 100 |
| abstract_inverted_index.account | 114 |
| abstract_inverted_index.aligned | 123 |
| abstract_inverted_index.applied | 25 |
| abstract_inverted_index.factors | 23 |
| abstract_inverted_index.network | 30 |
| abstract_inverted_index.support | 41 |
| abstract_inverted_index.Bayesian | 29, 55 |
| abstract_inverted_index.Compared | 101 |
| abstract_inverted_index.Gaussian | 27 |
| abstract_inverted_index.analysis | 36 |
| abstract_inverted_index.approach | 107 |
| abstract_inverted_index.building | 61 |
| abstract_inverted_index.concerns | 6 |
| abstract_inverted_index.critical | 21 |
| abstract_inverted_index.greatest | 65 |
| abstract_inverted_index.measures | 97 |
| abstract_inverted_index.methods, | 105 |
| abstract_inverted_index.promptly | 99 |
| abstract_inverted_index.revealed | 75 |
| abstract_inverted_index.conducted | 17 |
| abstract_inverted_index.emergency | 96 |
| abstract_inverted_index.handling, | 58 |
| abstract_inverted_index.hazardous | 88 |
| abstract_inverted_index.presented | 108 |
| abstract_inverted_index.reference | 131 |
| abstract_inverted_index.assessment | 136 |
| abstract_inverted_index.condition. | 89 |
| abstract_inverted_index.conditions | 127 |
| abstract_inverted_index.dependence | 116 |
| abstract_inverted_index.foundation | 12, 39, 72 |
| abstract_inverted_index.heightened | 5 |
| abstract_inverted_index.management | 50 |
| abstract_inverted_index.monitoring | 19, 118 |
| abstract_inverted_index.settlement | 62 |
| abstract_inverted_index.suggesting | 86 |
| abstract_inverted_index.development | 3 |
| abstract_inverted_index.engineering | 126, 139 |
| abstract_inverted_index.indicators, | 119 |
| abstract_inverted_index.integrating | 46 |
| abstract_inverted_index.network’s | 56 |
| abstract_inverted_index.reliability | 35, 77, 93, 104 |
| abstract_inverted_index.significant | 130 |
| abstract_inverted_index.structures. | 42, 140 |
| abstract_inverted_index.time-series | 18 |
| abstract_inverted_index.traditional | 103 |
| abstract_inverted_index.uncertainty | 57 |
| abstract_inverted_index.underground | 1, 138 |
| abstract_inverted_index.copula-based | 28 |
| abstract_inverted_index.multivariate | 48 |
| abstract_inverted_index.comprehensive | 34 |
| abstract_inverted_index.construction. | 14 |
| abstract_inverted_index.safety-influencing | 22 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.69716736 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |