Geotechnical uncertainty, modeling, and decision making
Modeling only constitutes one aspect of decision making. The prevailing limitation of applying modeling to practice is the absence of explicit consideration of uncertainties. This review paper covers uncertainty quantification (soil properties, stratification, and model performance) and uncertainty...
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-10-01
|
| Series: | Soils and Foundations |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S003808062200097X |
| _version_ | 1828306700393775104 |
|---|---|
| author | Kok-Kwang Phoon Zi-Jun Cao Jian Ji Yat Fai Leung Shadi Najjar Takayuki Shuku Chong Tang Zhen-Yu Yin Yoshida Ikumasa Jianye Ching |
| author_facet | Kok-Kwang Phoon Zi-Jun Cao Jian Ji Yat Fai Leung Shadi Najjar Takayuki Shuku Chong Tang Zhen-Yu Yin Yoshida Ikumasa Jianye Ching |
| author_sort | Kok-Kwang Phoon |
| collection | DOAJ |
| description | Modeling only constitutes one aspect of decision making. The prevailing limitation of applying modeling to practice is the absence of explicit consideration of uncertainties. This review paper covers uncertainty quantification (soil properties, stratification, and model performance) and uncertainty calculation with a focus on how it enhances the role of modeling in decision making (reliability analysis, reliability-based design, and inverse analysis). The key output from a reliability analysis is the probability of failure, where “failure” is defined as any condition that does not meet a performance criterion or a set of criteria. In contrast to the global factor of safety, the probability of failure respects both mechanics and statistics, is sensitive to data (thus opening one potential pathway to digital transformation), and it is meaningful for both system and component failures. Resilience engineering requires system level analysis. As such, geotechnical software can provide better decision support by computing the probability of failure/reliability index as one basic output in addition to stresses, strains, forces, and displacements. It is further shown that more critical non-classical failure mechanisms can emerge from spatially variable soils that can escape notice if the engineer were to restrict analysis to conventional homogeneous or layered soil profiles. |
| first_indexed | 2024-04-13T14:46:41Z |
| format | Article |
| id | doaj.art-0ce8d893f138484ca7acbb8a7ab29408 |
| institution | Directory Open Access Journal |
| issn | 2524-1788 |
| language | English |
| last_indexed | 2024-04-13T14:46:41Z |
| publishDate | 2022-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Soils and Foundations |
| spelling | doaj.art-0ce8d893f138484ca7acbb8a7ab294082022-12-22T02:42:43ZengElsevierSoils and Foundations2524-17882022-10-01625101189Geotechnical uncertainty, modeling, and decision makingKok-Kwang Phoon0Zi-Jun Cao1Jian Ji2Yat Fai Leung3Shadi Najjar4Takayuki Shuku5Chong Tang6Zhen-Yu Yin7Yoshida Ikumasa8Jianye Ching9Singapore University of Technology and Design, 8 Somapah Road 487372, Singapore; Corresponding author.Wuhan University, 299 Bayi Road, Wuhan 430072, ChinaGeotechnical Research Institute, Hohai University, Nanjing, ChinaThe Hong Kong Polytechnic University, Hung Hom, Hong KongDepartment of Civil and Environmental Engineering, American University of Beirut, Bliss Street, Beirut, LebanonOkayama University, 3-1-1 Tsushima naka, Kita-ku, Okayama 700-8530, JapanState Key Laboratory of Coastal and Offshore Engineering and Institute of Earthquake Engineering, Faculty of Infrastructure Engineering, Dalian University of Technology, Jingying Rd, Ganjingzi District, Dalian, Liaoning 116024, ChinaDepartment of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong KongTokyo City University, 1-28-1 Tamazutsumi Setagaya-ku, Tokyo 158-8557 JapanDept of Civil Engineering, National Taiwan University, #1 Roosevelt Road Sect. 4, Taipei 10617, TaiwanModeling only constitutes one aspect of decision making. The prevailing limitation of applying modeling to practice is the absence of explicit consideration of uncertainties. This review paper covers uncertainty quantification (soil properties, stratification, and model performance) and uncertainty calculation with a focus on how it enhances the role of modeling in decision making (reliability analysis, reliability-based design, and inverse analysis). The key output from a reliability analysis is the probability of failure, where “failure” is defined as any condition that does not meet a performance criterion or a set of criteria. In contrast to the global factor of safety, the probability of failure respects both mechanics and statistics, is sensitive to data (thus opening one potential pathway to digital transformation), and it is meaningful for both system and component failures. Resilience engineering requires system level analysis. As such, geotechnical software can provide better decision support by computing the probability of failure/reliability index as one basic output in addition to stresses, strains, forces, and displacements. It is further shown that more critical non-classical failure mechanisms can emerge from spatially variable soils that can escape notice if the engineer were to restrict analysis to conventional homogeneous or layered soil profiles.http://www.sciencedirect.com/science/article/pii/S003808062200097XUncertaintyNumerical modelingDecision makingBurland triangleRisk management |
| spellingShingle | Kok-Kwang Phoon Zi-Jun Cao Jian Ji Yat Fai Leung Shadi Najjar Takayuki Shuku Chong Tang Zhen-Yu Yin Yoshida Ikumasa Jianye Ching Geotechnical uncertainty, modeling, and decision making Soils and Foundations Uncertainty Numerical modeling Decision making Burland triangle Risk management |
| title | Geotechnical uncertainty, modeling, and decision making |
| title_full | Geotechnical uncertainty, modeling, and decision making |
| title_fullStr | Geotechnical uncertainty, modeling, and decision making |
| title_full_unstemmed | Geotechnical uncertainty, modeling, and decision making |
| title_short | Geotechnical uncertainty, modeling, and decision making |
| title_sort | geotechnical uncertainty modeling and decision making |
| topic | Uncertainty Numerical modeling Decision making Burland triangle Risk management |
| url | http://www.sciencedirect.com/science/article/pii/S003808062200097X |
| work_keys_str_mv | AT kokkwangphoon geotechnicaluncertaintymodelinganddecisionmaking AT zijuncao geotechnicaluncertaintymodelinganddecisionmaking AT jianji geotechnicaluncertaintymodelinganddecisionmaking AT yatfaileung geotechnicaluncertaintymodelinganddecisionmaking AT shadinajjar geotechnicaluncertaintymodelinganddecisionmaking AT takayukishuku geotechnicaluncertaintymodelinganddecisionmaking AT chongtang geotechnicaluncertaintymodelinganddecisionmaking AT zhenyuyin geotechnicaluncertaintymodelinganddecisionmaking AT yoshidaikumasa geotechnicaluncertaintymodelinganddecisionmaking AT jianyeching geotechnicaluncertaintymodelinganddecisionmaking |