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A platform for research: civil engineering, architecture and urbanism
Variability in Geostructural Performance Predictions
https://orcid.org/0000-0002-8415-2487
https://orcid.org/0000-0003-2577-8639
https://orcid.org/0000-0002-3506-5192
Prediction is one of the core activities in geotechnical engineering. Because of our limited knowledge of ground conditions and the imperfection of predictive models, the resulting predictions would deviate from the real behavior. This paper presents a survey of four significant data sources in the literature: (1) load test and case history databases, (2) numerical analyses versus in situ measurements, (3) prediction events and benchmark exercises, and (4) an International Society for Soil Mechanics and Geotechnical Engineering (ISSMGE) survey of international practice. The focus of this paper is to characterize the statistical degree of confidence in predicting various quantities of interest (QoIs) in geotechnical analysis and design (e.g., resistance, load and bending moment, and deformation). Five types of geotechnical structures are involved: (1) foundations (e.g., shallow foundations, spudcans, single piles and pile groups, drilled shafts, and large-diameter open-ended piles), (2) embankments and slopes, (3) reinforced soil walls (e.g., mechanically stabilized earth walls, soil nail walls, and multianchor walls), (4) excavations and retaining structures (e.g., sheet piles, diaphragm walls), and (5) tunnels. Most of the results are presented as the ratio between the measured and the predicted QoI (called a model factor). A better understanding of the prediction variability is provided by the statistics of model factors (e.g., range, central tendency or average, scatter, or dispersion) complemented by a qualitative appreciation of the factors affecting the prediction. Some recommendations are provided on how to improve prediction quality and design practices. This review paper is the first to evaluate the statistics of the model factors from other data sources besides the load test and case history databases.
Variability in Geostructural Performance Predictions
https://orcid.org/0000-0002-8415-2487
https://orcid.org/0000-0003-2577-8639
https://orcid.org/0000-0002-3506-5192
Prediction is one of the core activities in geotechnical engineering. Because of our limited knowledge of ground conditions and the imperfection of predictive models, the resulting predictions would deviate from the real behavior. This paper presents a survey of four significant data sources in the literature: (1) load test and case history databases, (2) numerical analyses versus in situ measurements, (3) prediction events and benchmark exercises, and (4) an International Society for Soil Mechanics and Geotechnical Engineering (ISSMGE) survey of international practice. The focus of this paper is to characterize the statistical degree of confidence in predicting various quantities of interest (QoIs) in geotechnical analysis and design (e.g., resistance, load and bending moment, and deformation). Five types of geotechnical structures are involved: (1) foundations (e.g., shallow foundations, spudcans, single piles and pile groups, drilled shafts, and large-diameter open-ended piles), (2) embankments and slopes, (3) reinforced soil walls (e.g., mechanically stabilized earth walls, soil nail walls, and multianchor walls), (4) excavations and retaining structures (e.g., sheet piles, diaphragm walls), and (5) tunnels. Most of the results are presented as the ratio between the measured and the predicted QoI (called a model factor). A better understanding of the prediction variability is provided by the statistics of model factors (e.g., range, central tendency or average, scatter, or dispersion) complemented by a qualitative appreciation of the factors affecting the prediction. Some recommendations are provided on how to improve prediction quality and design practices. This review paper is the first to evaluate the statistics of the model factors from other data sources besides the load test and case history databases.
Variability in Geostructural Performance Predictions
https://orcid.org/0000-0002-8415-2487
https://orcid.org/0000-0003-2577-8639
https://orcid.org/0000-0002-3506-5192
Prediction is one of the core activities in geotechnical engineering. Because of our limited knowledge of ground conditions and the imperfection of predictive models, the resulting predictions would deviate from the real behavior. This paper presents a survey of four significant data sources in the literature: (1) load test and case history databases, (2) numerical analyses versus in situ measurements, (3) prediction events and benchmark exercises, and (4) an International Society for Soil Mechanics and Geotechnical Engineering (ISSMGE) survey of international practice. The focus of this paper is to characterize the statistical degree of confidence in predicting various quantities of interest (QoIs) in geotechnical analysis and design (e.g., resistance, load and bending moment, and deformation). Five types of geotechnical structures are involved: (1) foundations (e.g., shallow foundations, spudcans, single piles and pile groups, drilled shafts, and large-diameter open-ended piles), (2) embankments and slopes, (3) reinforced soil walls (e.g., mechanically stabilized earth walls, soil nail walls, and multianchor walls), (4) excavations and retaining structures (e.g., sheet piles, diaphragm walls), and (5) tunnels. Most of the results are presented as the ratio between the measured and the predicted QoI (called a model factor). A better understanding of the prediction variability is provided by the statistics of model factors (e.g., range, central tendency or average, scatter, or dispersion) complemented by a qualitative appreciation of the factors affecting the prediction. Some recommendations are provided on how to improve prediction quality and design practices. This review paper is the first to evaluate the statistics of the model factors from other data sources besides the load test and case history databases.
Variability in Geostructural Performance Predictions
ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng.
Tang, Chong (author) / Phoon, Kok-Kwang (author) / Yuan, Jun (author) / Tao, Yuanqin (author) / Sun, Honglei (author)
2025-03-01
Article (Journal)
Electronic Resource
English
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