Abstract

Knowledge of seabed properties away from investigated locations is often required, for example, when geotechnical surveys are sparse or when the field layout changes between investigation and construction phases. In such cases, design lines that appropriately incorporate the uncertainty of the seabed properties must be defined to ensure reliable (yet not overly costly) design. This paper explores how two different approaches, traditional engineering judgement and advanced statistical methods, fare at quantifying the uncertainty of a real offshore site. This is achieved by ‘hiding’ different data and ‘scoring’ the predictive performance of the methods against a range of criteria. The work reveals that complex geological sites are significantly more challenging to represent than the stationary random fields often examined in research and suggests that more advanced approaches incorporating broader data sets are required to reduce uncertainty.

Abstract

Soil boundary delineation is an important task in geotechnical site characterization. It can be achieved by either extracting borehole samples, conducting laboratory tests, and classifying them according to a soil classification system such as the Unified Soil Classification System (USCS) or utilizing multiple cone penetration test (CPT) soundings, and identifying soil boundaries at the soundings from the Ic (soil behavior type index) profiles. However, most soil-layer delineation methods can only take a single type of test result as the input. For instance, the well-known Markov random field (MRF) method can only take soil-type data such as sand, silt, or clay at boreholes as the input. Recognizing that soil classifications and soil properties are correlated, this paper proposes a novel coupled MRF-Bayesian framework to infer the spatial variation of USCS classifications (e.g., sand, silt, and clay) as well as soil properties by integrating both CPT and borehole data. This integrated approach leverages both CPT and borehole data to address some main challenges e.g., uncertainties and multivariate soil data input in underground stratification problems by simultaneous sampling of soil properties and soil types. The new unified framework can accommodate multivariate data, hence the new framework is compatible with the geotechnical engineering practice. The uncertainties for the spatial variation of USCS classification at sounding locations are quantified through a “layer-specific” Bayesian updating i.e., updating posterior cross-correlation behaviors for different layers (such as sand, silt, and clay), independently. In this Bayesian updating, soil-type data can provide some information about the soil properties according to the unified soil classification system. Further, the soil boundaries can be identified across the entire domain by the realization of conditional random fields of soil properties once the spatial variation of USCS classification is inferred at sounding locations, followed by a 3-dimensional Markov random field process.

Abstract

Soil boundary delineation is an important task in geotechnical site characterization. It can be achieved by either extracting borehole samples, conducting laboratory tests, and classifying them according to a soil classification system such as the Unified Soil Classification System (USCS) or utilizing multiple cone penetration test (CPT) soundings, and identifying soil boundaries at the soundings from the Ic (soil behavior type index) profiles. However, most soil-layer delineation methods can only take a single type of test result as the input. For instance, the well-known Markov random field (MRF) method can only take soil-type data such as sand, silt, or clay at boreholes as the input. Recognizing that soil classifications and soil properties are correlated, this paper proposes a novel coupled MRF-Bayesian framework to infer the spatial variation of USCS classifications (e.g., sand, silt, and clay) as well as soil properties by integrating both CPT and borehole data. This integrated approach leverages both CPT and borehole data to address some main challenges e.g., uncertainties and multivariate soil data input in underground stratification problems by simultaneous sampling of soil properties and soil types. The new unified framework can accommodate multivariate data, hence the new framework is compatible with the geotechnical engineering practice. The uncertainties for the spatial variation of USCS classification at sounding locations are quantified through a “layer-specific” Bayesian updating i.e., updating posterior cross-correlation behaviors for different layers (such as sand, silt, and clay), independently. In this Bayesian updating, soil-type data can provide some information about the soil properties according to the unified soil classification system. Further, the soil boundaries can be identified across the entire domain by the realization of conditional random fields of soil properties once the spatial variation of USCS classification is inferred at sounding locations, followed by a 3-dimensional Markov random field process.

Abstract

The construction time and cost of a rock tunnel project are highly dependent on the rock mass quality and encountered ground behaviour. In most rock tunnel projects, the knowledge about the ground conditions along the tunnel is limited, making it difficult to predict accurately the construction time and cost. The KTH model takes a probabilistic approach to address this problem; however, it does not account for the spatial variability of the ground conditions. This paper investigates an alternative probabilistic ground model to be used within the KTH model that enables accounting for the spatial variability through Markov random field theory. The new ground model employs a parametric approach to describe the properties of the Markov field, hence, enabling the simulation of the ground conditions with limited data, but does not consider the epistemic uncertainty in the model parameters. This will be the addressed in future research.

Abstract

The presence of karstification processes in the terrain poses a threat to the safety of the foundations of structures, which in the case of gypsum materials becomes more dangerous due to the rate at which they can progress. This work describes the case of a Spanish high-speed railway bridge whose route runs over a Tertiary gypsum formation belonging to the lower Miocene unit of the Madrid Basin. These are very firm materials in a healthy state, that allow the direct support of shallow foundations, which stand out for their high heterogeneity, with the presence of massive gypsum banks as well as alternating levels of gypsum and clay. However, these materials can be affected by karst processes of dissolution, weathering, and replacement by clays, in which case there is a marked degradation of their mechanical properties, and the eventual local appearance of cavities. In these circumstances, it is necessary to carry out soil improvement treatments, such as compaction grouting, or resort to deep foundations with piles that transmit the loads of the structure to a lower substrate in a non-disturbed state. By its nature, the detection of this type of processes by drilling rotary boreholes is not trivial, and there is often uncertainty associated with its spatial variability. This communication presents the results obtained after the execution of a geotechnical research campaign, consisting of the realization of seismic tomography borehole-profiles under each support of the viaduct, supported by the execution of conventional rotary boreholes. The results of this exploration have made it possible to understand and define the spatial extent of the materials degraded by karstification and the selection of robust foundation typologies that favour the sustainable and resilient nature of the infrastructure.