Emerging Technologies and Advanced Analyses for Non-Invasive Near-Surface Site Characterization

This dissertation introduces novel techniques for estimating the soil small-strain shear modulus (Gmax) and damping ratio (D), crucial for modeling soil behavior in various geotechnical engineering problems. For Gmax estimation, a machine learning approach is proposed, capable of generating two-dimensional (2D) images of the subsurface shear wave velocity, which is directly related to Gmax. The dissertation also presents a method for estimating frequency dependent attenuation coefficients from ambient vibrations collected using 2D arrays of seismic sensors deployed across the ground surface. These attenuation coefficients can then be used in an inversion process to estimate D. The developed techniques for Gmax and D estimation have undergone rigorous validation and testing through synthetic simulations and field experiments, demonstrating their effectiveness. Furthermore, the dissertation presents a comprehensive dataset collected using cutting-edge seismic sensing technologies, including distributed acoustic sensing, three-component seismometers, and a large mobile shaker truck. This dataset has been archived and made publicly available, aiding researchers worldwide in developing and testing new non-invasive imaging techniques. Finally, the dissertation concludes with a review and comparison of recent advancements in non-invasive subsurface imaging techniques and their application at the same site

Identifying Fibre Orientations for Fracture Process Zone Characterization in Scaled Centre-Notched Quasi-Isotropic Carbon/Epoxy Laminates with a Convolutional Neural Network

This paper presents a novel X-ray Computed Tomography (CT) image analysis method to characterize the Fracture Process Zone (FPZ) in scaled centre-notched quasi-isotropic carbon/epoxy laminates. A total of 61 CT images of a small specimen were used to fine-tune a pre-trained Convolutional Neural Network (CNN) (i.e., VGG16) to classify fibre orientations. The proposed CNN model achieves a 100% accuracy when tested on the CT images of the same scale as the training set. However, the accuracy drops to a maximum of 84% when tested on unlabelled images of the specimens having larger scales potentially due to their lower resolutions. Another code was developed to automatically measure the size of the FPZ based on the CNN identified 0°plies in the largest specimen which agrees well with the manual measurement (on average within 3.3%). The whole classification and measurement process can be automated without human intervention

A Frequency-Domain Beamforming Procedure for Extracting Rayleigh Wave Attenuation Coefficients and Small-Strain Damping Ratio from 2D Ambient Noise Array Measurements

The small-strain damping ratio plays a crucial role in assessing the response of soil deposits to earthquake-induced ground motions and general dynamic loading. The damping ratio can theoretically be inverted for after extracting frequency-dependent Rayleigh wave attenuation coefficients from wavefields collected during surface wave testing. However, determining reliable estimates of in-situ attenuation coefficients is much more challenging than achieving robust phase velocity dispersion data, which are commonly measured using both active-source and ambient-wavefield surface wave methods. This paper introduces a new methodology for estimating frequency-dependent attenuation coefficients through the analysis of ambient noise wavefield data recorded by two-dimensional (2D) arrays of surface seismic sensors for the subsequent evaluation of the small-strain damping ratio. The approach relies on the application of an attenuation-specific wavefield conversion and frequency-domain beamforming. Numerical simulations are employed to verify the proposed approach and inform best practices for its application. Finally, the practical efficacy of the proposed approach is showcased through its application to field data collected at a deep, soft soil site in Logan, Utah, USA, where phase velocity and attenuation coefficients are extracted from surface wave data and then simultaneously inverted to develop deep shear wave velocity and damping ratio profiles.Comment: 42 pages, 12 figure

Evaluation of Downdrag Loads on Bridge Pile Foundations in Inundated Collapsible Soils

69A3551947137This report and the accompanying computer code (Software, Drag_Pile) describe the mobilized response analysis of piles under axial loads with the consideration of downdrag forces caused by the settlement of inundated collapsible soil layer(s). Pile foundations in collapsible soils may experience a sudden increase in the axial load (i.e., negative skin friction) due to the inundation of the surrounding soils, which may lead to significant reduction in the pile capacity and excessive pile settlement. A soil-pile model is developed to determine the downdrag force (negative skin friction) acting on the pile and pile settlement due to the inundation of the collapsible soil layer(s) in the vicinity of the pile foundations

Axially loaded piles in inundated collapsible soils under compression and tension forces

The paper studies the behavior of axially loaded piles driven into inundated collapsible soils under compression and tension forces. Collapsible soils exhibit a considerable drop in the void ratio (i.e., significant changes in dry unit weight, friction angle, and shear strength) with the increase of water content. Several sandy and silty specimens of collapsible soils with different initial conditions are utilized to develop a correlation that evaluates the post-inundation soil properties and stress–strain relationship (as obtained from the conventional triaxial test). The developed model is integrated into a pile–soil model to predict the axial response of piles loaded after the inundation of the collapsible soils (i.e., no negative skin friction). The developed technique allows the assessment of the axial load transfer (t-z) and pile-head load–displacement curves. Comparisons between predicted and measured stress–strain curves of inundated collapsible soils and responses of axially loaded piles in such soils are presented for validation.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Identifying fibre orientations for fracture process zone characterisation in scaled centre-notched quasi-isotropic carbon/epoxy laminates with a convolutional neural network

This paper presents a novel X-ray Computed Tomography (CT) image analysis method to characterise the Fracture Process Zone (FPZ) in scaled centre-notched quasi-isotropic carbon/epoxy laminates. A total of 61 CT images of a small specimen were used to fine-tune a pre-trained Convolutional Neural Network (CNN) (i.e., VGG16) to classify fibre orientations. The proposed CNN model achieves a 100% accuracy when tested on the CT images of the same scale as the training set. However, the accuracy drops to a maximum of 84% when tested on unlabelled images of the specimens having larger scales potentially due to their lower resolutions. Another code was developed to automatically measure the size of the FPZ based on the CNN identified 0 degree plies in the largest specimen which agrees well with the manual measurement (on average within 3.3%). The whole classification and measurement process can be automated without human intervention