Simplified method for the lateral, rotational, and torsional static stiffness of circular footings on a nonhomogeneous elastic half-space based on a work-equivalent framework

Although there are many methods for assessing vertical stiffness of footings on the ground, simplified solutions to evaluate lateral, rotational, and torsional static stiffness are much more limited, particularly for nonhomogeneous profiles of shear modulus with depth. This paper addresses the topic by introducing a novel “work-equivalent” framework to develop new simplified design methods for estimating the stiffnesses of footings under multiple degrees-of-freedom loading for general nonhomogeneous soils. Furthermore, this framework provides a unified basis to analyze two existing design methods that have diverging results. 3D finite element analyses were carried out to investigate the soil–footing interaction for a range of continuously varying and multilayered nonhomogeneous soils, and to validate the new design approach

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

Assessment of Bayesian changepoint detection methods for soil layering identification using cone penetration test data

This paper assesses the effectiveness of different unsupervised Bayesian changepoint detection (BCPD) methods for identifying soil layers, using data from cone penetration tests (CPT). It compares four types of BCPD methods: a previously utilised offline univariate method for detecting clay layers through undrained shear strength data, a newly developed online univariate method, and an offline and an online multivariate method designed to simultaneously analyse multiple data series from CPT. The performance of these BCPD methods was tested using real CPT data from a study area with layers of sandy and clayey soil, and the results were verified against ground-truth data from adjacent borehole investigations. The findings suggest that some BCPD methods are more suitable than others in providing a robust, quick, and automated approach for the unsupervised detection of soil layering, which is critical for geotechnical engineering design

Assessment of numerical procedures for determining shallow foundation failure envelopes

The failure envelope approach is commonly used to assess the capacity of shallow foundations under combined loading, but there is limited published work that compares the performance of various numerical procedures for determining failure envelopes. This paper addresses this issue by carrying out a detailed numerical study to evaluate the accuracy, computational efficiency and resolution of these numerical procedures. The procedures evaluated are the displacement probe test, the load probe test, the swipe test (referred to in this paper as the single swipe test) and a less widely used procedure called the sequential swipe test. Each procedure is used to determine failure envelopes for a circular surface foundation and a circular suction caisson foundation under planar vertical, horizontal and moment (VHM) loading for a linear elastic, perfectly plastic von Mises soil. The calculations use conventional, incremental-iterative finite-element analysis (FEA) except for the load probe tests, which are performed using finite-element limit analysis (FELA). The results demonstrate that the procedures are similarly accurate, except for the single swipe test, which gives a load path that under-predicts the failure envelope in many of the examples considered. For determining a complete VHM failure envelope, the FEA-based sequential swipe test is shown to be more efficient and to provide better resolution than the displacement probe test, while the FELA-based load probe test is found to offer a good balance of efficiency and accuracy

Probabilistic soil strata delineation using DPT data and Bayesian changepoint detection

Soil strata delineation is a fundamental step for any geotechnical engineering design. The dynamic penetration test (DPT) is a fast, low cost in situ test that is commonly used to locate boundaries between strata of differing density and driving resistance. However, DPT data are often noisy and typically require time-consuming, manual interpretation. This paper investigates a probabilistic method that enables delineation of dissimilar soil strata (where each stratum is deemed to belong to different soil groups based on their particle size distribution) by processing DPT data with Bayesian changepoint detection methods. The accuracy of the proposed method is evaluated using DPT data from a real-world case study, which highlights the potential of the proposed method. This study provides a methodology for faster DPT-based soil strata delineation, which paves the way for more cost-effective geotechnical designs

Investigation of local soil resistance on suction caissons at capacity in undrained clay under combined loading

Winkler modelling offers a flexible and computationally efficient framework for estimating suction caisson capacity. However, there is a limited understanding of the local soil resistance acting on caissons at capacity under combined six degrees-of-freedom (6DoF) loading, which is essential for accurately estimating caisson failure envelopes. Furthermore, existing simplified design models for caissons cannot assess capacity under non-planar lateral and moment loading, which is common in offshore wind applications. To address these limitations, this paper presents a comprehensive three-dimensional (3D) finite element analysis (FEA) study, which investigates the local soil resistance acting on the caisson at capacity in undrained clay under combined 6DoF loading. The paper introduces the concept of ‘soil reaction failure envelopes’ to characterise the interactions between soil reactions at capacity. Closed-form formulations are derived to approximate these soil reaction failure envelopes. An elastoplastic Winkler model is then developed, incorporating linear elastic perfectly plastic soil reactions based on these formulations. The results demonstrate that the Winkler model can provide efficient and reasonably accurate estimations of caisson capacity under combined 6DoF loading, even for irregular soil profiles that pose much uncertainty and challenges to existing macro-element models

A convex modular modelling (CMM) framework for developing thermodynamically consistent constitutive models

This paper presents a theoretical framework termed the convex modular modelling (CMM) framework, which provides a convenient and expedient approach for constructing thermodynamically consistent constitutive models. This paper demonstrates how the CMM framework can be used to build increasingly complex constitutive models by mixing and matching re-usable components from a library of convex base functions in a systematic manner. It also describes the use of the modified LogSumExp (MLSE) function as a general and smooth approximation to the pointwise maximum function for any yield function (e.g. the Mohr-Coulomb/Tresca yield function). The MLSE function is then used to develop several new yield functions such as a convex and smooth approximation of the Matsuoka-Nakai yield function, a generalised polygonal yield function and a 'Reuleaux triangle'-shaped yield function. As CMM is simple to use, it potentially offers a more accessible path for constitutive modellers to take advantage of the hyperplasticity framework to develop robust constitutive models

Practical approach for data-efficient metamodeling and real-time modeling of monopiles using physics-informed multifidelity data fusion

This paper proposes a practical approach for data-efficient metamodeling and real-time modeling of laterally loaded monopiles using physics-informed multifidelity data fusion. The proposed approach fuses information from one-dimensional (1D) beam-column model analysis, three-dimensional (3D) finite element analysis, and field measurements (in order of increasing fidelity) for enhanced accuracy. It uses an interpretable scale factor–based data fusion architecture within a deep learning framework and incorporates physics-based constraints for robust predictions with limited data. The proposed approach is demonstrated for modeling monopile lateral load–displacement behavior using data from a real-world case study. Results show that the approach provides significantly more accurate predictions compared to a single-fidelity metamodel and a widely used multifidelity data fusion model. The model’s interpretability and data efficiency make it suitable for practical applications

Machine learning to inform tunnelling operations : recent advances and future trends

The proliferation of data collected by modern tunnel-boring machines (TBMs) presents a substantial opportunity for the application of machine learning (ML) to support the decision-making process on-site with timely and meaningful information. The observational method is now well established in geotechnical engineering and has a proven potential to save time and money relative to conventional design. ML advances the traditional observational method by employing data analysis and pattern recognition techniques, predicated on the assumption of the presence of enough data to describe the physics of the modelled system. This paper presents a comprehensive review of recent advances and applications of ML to inform tunnelling construction operations with a view to increasing their potential for uptake by industry practitioners. This review has identified four main applications of ML to inform tunnelling – namely, TBM performance prediction, tunnelling-induced settlement prediction, geological forecasting and cutterhead design optimisation. The paper concludes by summarising research trends and suggesting directions for future research for ML in the tunnelling space

Prediction of pipe-jacking forces using a Bayesian updating approach

An accurate estimation of the jacking forces likely to be experienced during microtunnelling is a key design concern for the structural capacity of pipe segments, the location of intermediate jacking stations, and the efficacy of the pipe-jacking project itself. This paper presents a Bayesian updating approach for the prediction of jacking forces during microtunnelling. The proposed framework was applied to two pipe-jacking case histories completed in the United Kingdom: a 275-m drive in silt and silty sand, and a 1,237-m drive in mudstone. To benchmark the Bayesian predictions, a classical optimization technique, namely genetic algorithms, is also considered. The results show that predictions of pipe-jacking forces using the prior best estimate of model input parameters provided a significant overprediction of the monitored jacking forces for both drives. This highlights the difficulty of capturing the complex geotechnical conditions during tunnelling within prescriptive design approaches and the importance of robust back-analysis techniques. Bayesian updating was shown to be a very effective option, in which significant improvements in the mean predictions and associated variance of the total jacking force are obtained as more data are acquired from the drive