Interval Fields for Geotechnical Engineering Uncertainty Analysis under Limited Data

verfasst von: Chengxin Feng, Matteo Broggi, Yue Hu, Matthias G.R. Faes, Michael Beer
Abstract: Spatial uncertainty is a critical challenge in many engineering fields. To date, probabilistic methods have been applied to describe the uncertainty of engineering parameters with considerable achievements. However, they rely heavily on the availability of large quantities of informative data, but in practice, acquiring enough informative data is impossible. This paper proposes an interval field-based framework to analyze the influence of parameter uncertainty on their safety performance under sparse test data, in which the locations of test values are taken into account. It also considers uncertainties in stratigraphy and spatial properties in the geotechnical engineering case, allowing the research framework to utilize more available test information compared with previous studies. First, the interval field samples based on B-spline basis functions are generated, allowing for flexibility in accounting for realistic situations and integrating measured data from in situ exploration. Then, the finite-element strength-reduction method is used to estimate the safety factor (fs) of geotechnical engineering. Subsequently, a Bayesian global optimization is used to efficiently evaluate the upper and lower bounds of the fs interval. Finally, three geotechnical engineering cases are presented to illustrate the validity of the proposed framework. This framework provides new insights into engineering uncertainty analysis even with sparse data, highlighting its potential for practical applications in geotechnical engineering projects.
Organisationseinheit(en): Institut für Risiko und Zuverlässigkeit
Externe Organisation(en): Technische Universität Dortmund
The University of Liverpool
Tongji University
Typ: Artikel
Journal: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Band: 11
ISSN: 2376-7642
Publikationsdatum: 20.01.2025
Publikationsstatus: Elektronisch veröffentlicht (E-Pub)
Peer-reviewed: Ja
ASJC Scopus Sachgebiete: Tief- und Ingenieurbau, Bauwesen, Sicherheit, Risiko, Zuverlässigkeit und Qualität
Elektronische Version(en): https://doi.org/10.1061/AJRUA6.RUENG-1467 (Zugang: Geschlossen)

BibTeX

@article{11de43cf72024c858c4a69e46d202672,
title = "Interval Fields for Geotechnical Engineering Uncertainty Analysis under Limited Data",
abstract = "Spatial uncertainty is a critical challenge in many engineering fields. To date, probabilistic methods have been applied to describe the uncertainty of engineering parameters with considerable achievements. However, they rely heavily on the availability of large quantities of informative data, but in practice, acquiring enough informative data is impossible. This paper proposes an interval field-based framework to analyze the influence of parameter uncertainty on their safety performance under sparse test data, in which the locations of test values are taken into account. It also considers uncertainties in stratigraphy and spatial properties in the geotechnical engineering case, allowing the research framework to utilize more available test information compared with previous studies. First, the interval field samples based on B-spline basis functions are generated, allowing for flexibility in accounting for realistic situations and integrating measured data from in situ exploration. Then, the finite-element strength-reduction method is used to estimate the safety factor (fs) of geotechnical engineering. Subsequently, a Bayesian global optimization is used to efficiently evaluate the upper and lower bounds of the fs interval. Finally, three geotechnical engineering cases are presented to illustrate the validity of the proposed framework. This framework provides new insights into engineering uncertainty analysis even with sparse data, highlighting its potential for practical applications in geotechnical engineering projects.",
author = "Chengxin Feng and Matteo Broggi and Yue Hu and Faes, {Matthias G.R.} and Michael Beer",
note = "Publisher Copyright: {\textcopyright} 2025 American Society of Civil Engineers.",
year = "2025",
month = jan,
day = "20",
doi = "10.1061/AJRUA6.RUENG-1467",
language = "English",
volume = "11",
journal = "ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering",
issn = "2376-7642",
publisher = "American Society of Civil Engineers (ASCE)",
number = "2",
}