Numerical derivation of CPT-based p-y curves for piles in sand

The formulations for the lateral load-displacement (p-y) springs conventionally used for the analysis of laterally loaded piles have been based largely on the back-analysis of the performance of small-scale instrumented piles subjected to lateral load. Although such formulations have been employed with much success in industry, their applicability to large-diameter piles, such as those used to support offshore wind turbines, is uncertain and has necessitated further research in this area. Moreover, with the growth in popularity of in-situ cone penetration tests (CPTs), there are demands for a theoretically supported direct method that can enable the derivation of p-y curves from the CPT end resistance (q c). In this paper, a numerical derivation of CPT-based p-y curves applicable to both small- and large-diameter laterally loaded single piles in sand is presented. Three-dimensional finite-element analyses are performed using a non-linear elasto-plastic soil model to predict the response of single piles in sand subjected to lateral loads. The corresponding CPT q c profile is derived using the same soil constitutive model by way of the cavity expansion analogue. An extensive series of computations of the lateral pile response and CPT q c values is then employed to formulate a direct method of constructing p-y curves from CPT q c values. The proposed method is shown to be generally consistent with existing empirical correlations and to provide good predictions in relation to the measurements obtained during lateral load tests on instrumented piles in an independent case study

Updated CPT-based p–y formulation for laterally loaded piles in cohesionless soil under static loading

The p–y method is currently the most popular design method to predict the response of piles to lateral load. The authors had previously used numerical methods to develop a cone penetration test (CPT)-based p–y formulation for piles in sand and this has subsequently been shown by independent verification to show considerable promise. This paper addresses some of the uncertainties associated with the original p–y formulation by examining the influence of pile bending stiffness, the presence of a water table, the cross-sectional shape of the pile and soil non-homogeneities. Numerical experiments are presented examining these four effects and lead to an updated proposal for a CPT-based p–y formulation. This formulation, which is consistent with the original proposal, is validated against three-dimensional finite-element calculations and data obtained from a full-scale offshore monopile foundation supporting a wind turbine

Verification of numerically derived CPT based p-y curves for piles in sand

This paper examines the potential of a numerically derived CPT-based formulation for p-y curves in sand (Suryasentana & Lehane 2014) to predict the response observed in lateral load tests conducted on six piles in four different sand deposits. A summary of the methodology employed in the derivation of this formulation is first described before presenting information related to each of the case histories examined. The lateral load displacement data measured in these case histories are shown to compare well with predictions obtained using the p-y formulation. This agreement should encourage further refinement of the formulation and ultimately the direct use of CPT qc profiles for the analysis of laterally loaded piles in sand

Applications of data science in offshore geotechnical engineering : state of practice and future perspectives

Data-driven predictive models are becoming ubiquitous in society with a wide range of applications, including engineering design. For offshore geotechnical engineering, semi-empirical models make up most of the predictive models to date. These models implicitly include knowledge on soil mechanics and foundation behaviour gathered from laboratory testing, scale model testing and field observations. Modern data science techniques enable researchers and practising engineers to leverage state-of-the-art artificial intelligence tools to re-evaluate the creation of these predictive models. This keynote explores basic and advanced applications of artificial intelligence in offshore geotechnical engineering and aims to offer a perspective on the future use of these techniques in solving complex geotechnical problems

Demystifying the connections between Gaussian Process Regression and Kriging

Gaussian Process (GP) regression is a flexible, non-parametric Bayesian approach towards regression prob-lems that has seen increasing adoption for machine learning (ML) applications. Despite its recent popularity within the ML community, GP regression has a long history in geostatistics, where it is better known as kriging and is commonly used for spatial interpolation. The rapid development of GP regression in ML pre-sents significant opportunities for advanced knowledge transfer to the geotechnical engineering community. However, this knowledge transfer has often been inhibited by the different terminology and conventions adopted in both fields. This obscures the underlying science and introduces much potential for confusion. Therefore, this paper aims to reveal the connections between GP regression and kriging theories, with a view of acting as a bridge to increase the uptake of the latest developments in each field

An assessment of anomaly detection methods applied to microtunnelling

The proliferation of data collected by modern tunnel boring machines presents a substantial opportunity for the application of data-driven anomaly detection (AD) techniques that can adapt dynamically to site specific conditions. Based on jacking forces measured during microtunneling, this paper explores the potential for AD methods to provide a more accurate and robust detection of incipient faults. A selection of the most popular AD methods proposed in the literature, comprising both clustering- and regression-based techniques, are considered for this purpose. The relative merits of each approach is assessed through comparisons to three microtunneling case histories in which anomalous jacking force behavior was encountered. The results highlight an exciting potential for the use of anomaly detection techniques to reduce unplanned downtimes and operation costs

Automated procedure to derive convex failure envelope formulations for circular surface foundations under six degrees of freedom loading

Failure envelope formulations are typically employed to assess the ultimate capacity of foundations under combined loading and for incorporation in macro-element models. However, the complex interaction between each load component, especially for six degree of freedom (6DoF) loading, means that determining satisfactory formulations is often a complex process. Previous researchers have identified this difficulty as an obstacle to the adoption of the failure envelope approach in foundation engineering applications. To address this issue, the paper describes a systematic procedure for deriving globally convex failure envelope formulations; the procedure is applied to a circular surface foundation, bearing on undrained clay, in 6DoF load space. The formulations are shown to closely represent the failure load combinations determined from finite element analyses for a variety of loading conditions, including non-planar horizontal-moment loading. An example macro-element model based on the proposed formulation is described; the macro-element model provides a close representation of the foundation behaviour determined from a separate finite element analysis. A key aspect of the paper is that it demonstrates an automated process to determine well-behaved failure envelope formulations. The automated nature of the process has considerable advantages over the manual procedures that have previously been employed to determine failure envelope formulations

Artificial Intelligence (AI) driven 3D point scanner for monitoring soil plug hazards during the installation of suction caisson foundations

Soil plug hazards pose a significant risk to the successful installation of suction caisson foundations but are currently inadequately monitored using only a single beam echosounder. To address this issue, a new artifi-cial intelligence (AI) driven three-dimensional (3D) point scanner is proposed for monitoring soil plug haz-ards. The proposed scanner is controlled using a Bayesian Optimisation (BO) algorithm, which automatical-ly adapts its data acquisition path in real-time based on previously acquired measurements. Preliminary la-boratory tests were conducted to assess the effectiveness of the proposed scanner. The results showed that the proposed scanner can accurately estimate 3D surfaces with fewer measurement points than a comparable scanner using the conventional scanning method, typically used in existing 3D point scanners. As the pro-posed scanner can estimate the state of the entire surface in much shorter time than existing sensors, it poten-tially offers a more effective method to monitor soil plug hazards

A systematic framework for formulating convex failure envelopes in multiple loading dimensions

The failure envelope approach is widely used to assess the ultimate capacity of shallow foundations for combined loading, and to develop foundation macro-element models. Failure envelopes are typically determined by fitting appropriate functions to a set of discrete failure load data, determined either experimentally or numerically. However, current procedures to formulate failure envelopes tend to be ad hoc, and the resulting failure envelopes may not have the desirable features of being convex and well-behaved for the entire domain of interest. This paper describes a new systematic framework to determine failure envelopes – based on the use of sum of squares convex polynomials – that are guaranteed to be convex and well-behaved. The framework is demonstrated by applying it to three data sets for failure load combinations (vertical load, horizontal load and moment) for shallow foundations on clay. An example foundation macro-element model based on the proposed framework is also described

Small-strain, non-linear elastic Winkler models for uniaxial loading of suction caisson foundations

Soils exhibit non-linear stress–strain behaviour, even at relatively low strain levels. Existing Winkler models for suction caisson foundations cannot capture this small-strain, non-linear soil behaviour. To address this issue, this paper describes a new non-linear elastic Winkler model for the uniaxial loading of suction caissons. The soil reaction curves employed in the model are formulated as scaled versions of the soil response as observed in standard laboratory tests (e.g. triaxial or simple shear tests). The scaling relationships needed to map the observed soil-element behaviour onto the soil reaction curves employed in the Winkler model are determined from an extensive numerical study employing three-dimensional finite-element analysis. Key features of the proposed Winkler model include: computational efficiency, wide applicability (it can be used for caisson design in clay, silt or sand) and design convenience (the required soil reaction curves can be determined straightforwardly from standard laboratory test results). The proposed model is suitable for small and intermediate caisson displacements (corresponding to fatigue and serviceability limit state conditions) but it is not applicable to ultimate limit state analyses