Geotechnics, Volume 4, Issue 4 (December 2024) – 17 articles

In this paper, the findings presented by Barron (1948) have been corroborated by way of a hydraulic-mechanical coupled finite element analysis. Specifically, the FEM analysis was conducted using a poroelasticity approach in combination with a transient formulation that incorporates Darcy’s law. This study highlights the fact that variations in pore pressure dissipation between the coupled FEM analysis of this study and Barron’s theoretical analysis are minimal. The coupled FEM simulations confirm Barron’s conclusions that, as the well diameter ratio (n) increases, the rate of pore water pressure dissipation decreases. Ultimately, for design purposes, a stress field is also required and consequently, a coupled FEM analysis is necessary. On this basis, results indicate high shear stress concentrations near the upper and lower boundaries, while the mean effective stress decreases from the well bore boundary. Full article

At least 32 case histories have shown that liquefaction can occur in gravelly soils (both natural deposits and manmade reclamations) during severe earthquakes, causing large ground deformations and severe damage to civil infrastructures. Gravelly soils, however, pose major challenges in geotechnical earthquake engineering in terms of assessing their deformation characteristics and potential for liquefaction. In this study, aimed at providing valuable insights into this important topic, a series of isotropically consolidated undrained cyclic triaxial tests were carried out on selected sand–gravel mixtures (SGMs) with varying degrees of gravel content (G_c) and relative density (D_r). The pore water pressure generation and liquefaction resistance were examined and then further scrutinized using an energy-based method (EBM) for liquefaction assessment. It is shown that the rate of pore water pressure development is influenced by the cyclic resistance ratio (CSR), G_c and D_r of SGMs. However, a unique correlation exists between the pore water pressure ratio and cumulative normalized dissipated energy during liquefaction. Furthermore, the cumulative normalized energy is a promising parameter to describe the cyclic resistance ratio (CRR) of gravelly soils at various post-liquefaction axial strain levels, considering the combined effects of G_c and D_r on the liquefaction resistance. Full article

Hurricane Helene triggered 1792 landslides across western North Carolina and has caused damage to 79 bridges to date. Helene hit western North Carolina days after a low-pressure system dropped up to 254 mm of rain in some locations of western North Carolina (e.g., Asheville Regional Airport). The already waterlogged region experienced devastation as significant additional rainfall occurred during Helene, where some areas, like Asheville, North Carolina received an additional 356 mm of rain (National Weather Service, 2024). In this study, machine learning (ML)-generated multi-hazard landslide susceptibility maps are compared to the documented landslides from Helene. The landslide models use the North Carolina landslide database, soil survey, rainfall, USGS digital elevation model (DEM), and distance to rivers to create the landslide variables. From the DEM, aspect factors and slope are computed. Because recent research in western North Carolina suggests fault movement is destabilizing slopes, distance to fault was also incorporated as a predictor variable. Finally, soil types were used as a wildfire predictor variable. In total, 4794 landslides were used for model training. Random Forest and logistic regression machine learning algorithms were used to develop the landslide susceptibility map. Furthermore, landslide susceptibility was also examined with and without consideration of wildfires. Ultimately, this study indicates heavy rainfall and debris-laden floodwaters were critical in triggering both landslides and scour, posing a dual threat to bridge stability. Field investigations from Hurricane Helene revealed that bridge damage was concentrated at bridge abutments, with scour and sediment deposition exacerbating structural vulnerability. We evaluated the assumed flooding potential (AFP) of damaged bridges in the study area, finding that bridges with lower AFP values were particularly vulnerable to scour and submersion during flood events. Differentiating between landslide-induced and scour-induced damage is essential for accurately assessing risks to infrastructure. The findings emphasize the importance of comprehensive hazard mapping to guide infrastructure resilience planning in mountainous regions. Full article

The CERCHAR abrasivity test is a widely used index test in earth and subsurface works, delivering numerical values for abrasion that are critical to the selection of excavation tools, TBM performance or cost and project schedule estimates. The test evaluates the wear of the tip of a standardized metal pin after a scratch test on a rock surface. However, excavatability is not considered in this test. The present study presents an approach to assessing the material removal of a rock specimen due to the scratching action of a steel pin. The concept is tested for a broad range of sedimentary, metamorphic and igneous rocks. The volume of removed rock material is determined by measuring the width of the scratch groove and assuming an idealized trapezoid geometry. The CAI and volumetric removal are used to calculate the CERCHAR abrasivity ratio (CAR), and the results are in good agreement with those from the literature where specialized equipment was used. A classification scheme to estimate the excavatability of rock based on the CAI in combination with the material removal of a rock specimen is introduced. Based on the amount of material removed and the wear on the pin, an estimate can be made as to whether the excavation is likely to be economical in terms of time and material costs. The approach does not require additional testing, but rather makes use of the inherent geometry of the steel pin and the scratch groove on the rock specimen. The approach can be implemented as a complementary analysis to the existing CERCHAR test with little additional effort. Full article

Many experiments and computational techniques have been employed to explain the mechanical properties of frozen soils. Nevertheless, due to the substantial complexity of their responses, modeling the stress–strain characteristics of frozen soils remains challenging. In this study, artificial neural networks (ANNs) were employed for modeling the mechanical behavior of frozen soil, while different testing strategies were carried out. A database covering stress–strain data from frozen sandy soil subjected to varying temperatures and confining pressures, resulting from triaxial tests, was compiled and employed to train the model. Subsequently, different artificial neural networks were trained and developed to estimate the deviatoric stress and volumetric strain, while temperature, axial strain, and confining pressure were considered as the main input variables. Based on the findings, it can be indicated that the models effectively predict the stress–strain behavior of frozen soil with a significant level of accuracy. Full article

Pumiceous deposits, commonly found in volcanic regions such as the Ring of Fire and the Alpide Belt, pose significant engineering challenges due to the presence of highly crushable and compressible grains in their matrix. These deposits exhibit complex geotechnical characteristics and are frequently linked to natural events like landslides and earthquakes. Research in countries such as New Zealand, Japan, Indonesia, Italy, and Central and South America aims to better understand the mechanical behaviour of these materials. Key influencing factors include geological properties, microstructure, shearing characteristics, and the impact of particle breakage. Comparative studies have identified similarities in specific gravity, void ratio, particle size distribution, and shearing mechanisms across regions. However, notable differences appear when compared to hard-grained sands including higher void ratios, variations in relative density due to crushable grains, and increased angularity. Some responses of pumiceous deposits, such as strain softening, liquefaction resistance depending on gradation, and apparent cohesion from grain interlocking, mirror those of hard sands; however, particle crushing plays a crucial role in the behaviour. Accurate numerical modelling, which simulates crushing under different conditions, is essential for characterising pumiceous deposits in situ, providing engineers with a better understanding of these materials across diverse site conditions. Full article

This paper explores the use of machine learning (ML) to analyze borehole data aiming to enhance geotechnical insights, using the Gaza Strip as a case study. The data set consists of 632 boreholes, with features including spatial coordinates, ground level, and soil type per depth. A random forest (RF) classification model was applied to predict soil types, achieving an accuracy of approximately 75%. Notably, the model retained this accuracy even when the data set size was reduced to 30%, suggesting predictable subsurface conditions over large areas. A comparative analysis of common misclassifications revealed that errors mostly occurred between similar soil types, indicating the model’s ability to capture meaningful geological patterns. Unsupervised learning using k-means clustering revealed no clear-cut boundaries between clusters, indicating localized geological anomalies despite large-scale predictability. These findings align with the demonstrated stability of the Gaza Tunnel Network (GTN), a vast network of tunnels which was constructed without comprehensive site investigations. This study demonstrates the potential of ML to improve geotechnical assessments and suggests that fewer boreholes may be needed for large-scale projects, offering cost-saving opportunities. For future research, it is recommended to integrate advanced ML tools, including large language models (LLMs) for analyzing qualitative data from borehole logs, and interpretability methods to enhance model explainability, thus enhancing geological understanding and increasing predictive power. Full article

This study investigates the stability of fine-grained sandy soil slopes under varying rainfall intensities, durations, and geotechnical properties using a parametric analysis within GeoStudio. A total of 4416 unique parameter combinations were analyzed, incorporating variations in unit weight, cohesion, friction angle, slope inclination, slope height, rainfall intensity, and duration. Results reveal that rainfall intensity is the most influential variable on the factor of safety (FS), with higher intensities (e.g., 360 mm/h) on steeper slopes (e.g., 45°) leading to critical FS values below 1, indicating an imminent risk of failure. Under moderate conditions (e.g., 9 mm/h rainfall on slopes of 26.6°), the FS remains above 2. This dataset provides a valuable foundation for training machine learning models to predict slope stability under diverse environmental conditions, contributing to the development of early warning systems for rainfall-induced landslides. Full article

Chip seal pavements, consisting of one or more layers of asphalt binder and fine aggregate, can be mechanically characterized as a surface treatment that enhances evenness and trafficability. This paper examines the geotechnical aspects of chip seal applicability compared to traditional hot mix asphalt pavements. An analytical model was employed to design unpaved roads and determine the required thickness of unbound layers. Eight optimization models were developed for hot mix asphalt pavements and four for chip seal pavements, aimed at achieving optimal designs for various input parameters. These outcomes were used to conduct a multi-parametric analysis, incorporating an optimization loop for each combination of design variables. The results indicate that, under low traffic conditions, a chip seal pavement structure can be up to 40% less expensive than an optimal hot mix asphalt pavement structure, particularly when the subgrade has low bearing capacity and is exposed to unfavorable climatic conditions. However, at medium traffic loads, with good subgrade bearing capacity and favorable climate, the chip seal pavement structure incurs costs that are 25% higher than those of the hot asphalt pavement structure. In addition, chip seal pavements should always be designed with integrated geosynthetic reinforcement to minimize construction costs, and chip seal is not as sensitive to frost as hot mix asphalt. Full article

This paper reviews particle packing models and explores their application in geotechnical engineering, specifically for sand-silt mixtures. The review covers key models, including limiting case, linear, and non-linear packing models, focusing on their mathematical structures, physical principles, assumptions, and limitations through the concept of excess free volume. The application of particle packing models in geotechnical engineering is explored in characterizing the properties of sand-silt mixtures, offering insights into maximum, minimum, and critical void ratios and inter-granular void ratio, and the prediction of mechanical properties. Full article

Soil–water characteristics curves (SWCC) have proved useful in estimating parameters used in modeling unsaturated geotechnical properties of soils including permeability and strength. Either saturation, gravimetric, and instantaneous and initial volumetric water content designation can be used to develop SWCCs. Studies have shown that any of the designations will give good estimates for soils that do not undergo volume change with suction change whereas, for soils that undergo substantial volume change, only saturation and instantaneous volumetric water content designation obtained by incorporating shrinkage curves can give correct estimates. Transition oil sands tailings have fines content that cannot be categorized as sandy or fine materials, and research on volume change with suction change in these materials is limited. In this study, HyProps, Tempe cells, and a chilled-mirror water potential meter were used to measure suction and corresponding water contents for samples that were prepared by mixing coarse sand and Fluid Tailing by ratios that mimic transition zone tailings. Shrinkage tests were also performed to observe the extent of volume change with suction increase. Air Entry Values (AEV) estimated from SWCCs based on gravimetric water content were found to be lower than those estimated from saturation-based SWCCs due to substantial volume changes in these materials with suction increase. The use of saturation water content designation is recommended in estimating AEV for transitional oil sands tailings. This is useful information in predicting the long term unsaturated geotechnical behavior of these materials for environmental management and safety purposes. Full article

This paper aims to evaluate the efficiency of various machine learning algorithms integrating with numerical simulations in optimizing oil production for a highly heterogeneous reservoir. An approach leveraging a machine learning workflow for reservoir characterization, history matching, sensitivity analysis, field development and optimization was proposed to accomplish the above goal. A 3D subsurface model representing studied sand-shale sequences was constructed based on geophysical and petrophysical logs, core measurements, and advanced machine learning techniques. After that, a robust sensitivity analysis and history matching process were conducted using a machine learning workflow. The most sensitive control variables were the aquifer properties, permeability heterogeneity in different directions, and water–oil contacts. The history matching results from the constructed geological model showed that the oil rate, water rate, bottom hole pressure, and average reservoir pressure were matched within a 10% deviation from the observed data. Several field development scenarios were generated using the validated model to optimize cumulative oil recovery. Different injection well placement locations, well patterns, and the possibility of converting existing oil-producing wells to water injection wells were investigated. A machine learning-based proxy model was built for the prediction of cumulative oil production and then optimized with hybrid machine learning techniques. The Artificial Neural Network (ANN) algorithm was found to provide higher field cumulative oil production compared with the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) of 3.5% and 26.5%, respectively. Following the detailed proposed machine learning-based workflow, one can effectively decide on the development strategy and apply the findings from this research to their field. Full article

Micropiles were first used to repair the damaged structures of “Scuola Angiulli” in Naples after World War II. They are known as small versions of regular piles, with a diameter of less than 30 cm, and are made of high-strength, steel casing and/or threaded bars, produce minimal noise and vibration during installation, and use lightweight machinery. They are capable to withstand axial loads and moderate lateral loads. They are used for underpinning existing foundations and to restore historical buildings and to support moderate structures. In the literature, several reports can be found dealing with micropiles, yet little has been reported on Micropiled-Raft Foundations (MPR). This technology did not receive the recognition it deserved until the 1970s when its technical and economic benefits were noted. A series of laboratory tests and numerical modeling were developed to examine the parameters governing the performance of MPR, including the relative density of the sand, the micropile spacing, and the rigidity of the raft. The numerical model, after being validated with the present experimental results, was used to generate data for a wide range of governing parameters. The theory developed by Poulos (2001) (PDR) to predict the capacity of pile-raft foundations was adopted for the design of MPR. The PDR method is widely used by geotechnical engineers because of its simplicity. Full article

The development of a suitable set of input ground motions is crucial for dynamic time history analyses. The US Nuclear Regulatory Commission (NRC) requires that these motions generate response spectra closely matching the plant’s design spectrum. Additionally, the NRC recommends verifying the motions’ power spectral densities (PSDs) against a target function to ensure sufficient energy across all frequencies. Current NRC guidelines in Standard Review Plan (SRP) provide a general method for creating target PSDs for any design spectrum. However, this method does not explicitly consider the influence of strong motion duration on the relationship between PSD and response spectrum. This article proposes an improved approach that incorporates the expected strong motion duration into the target PSD generation process. The method first constructs a Fourier amplitude spectrum (FAS) compatible with both the design spectrum and the expected strong motion duration. Subsequently, a large set of synthetic motions based on this FAS is used to construct the target PSD function. It is shown that current target PSD functions tabulated in SRP 3.7.1 implicitly infer an expected strong motion duration of approximately 9 s. The proposed method can be used to construct target PSDs tailored to different strong motion durations. Full article

The amount of energy dissipated in the soil during cyclic loading controls the amount of pore pressure generated under that loading. Because of this, the normalized dissipated energy per unit volume is the basis for both pore pressure generation models and energy-based liquefaction analyses. The pattern of energy dissipation in the soil in load-controlled cyclic triaxial and load-controlled cyclic direct simple shear tests and displacement-controlled cyclic triaxial and displacement-controlled cyclic direct simple shear tests is quite different. As a result, the pattern of pore pressure generation associated with load-controlled tests is markedly different from that in displacement-controlled tests. Pore pressure generation patterns for each of the four test types were proposed based upon the manner in which the load was applied during the test and the soil’s response to that loading. The results of four tests, two load controlled and two displacement controlled, were then used to verify these patterns. Pore pressure generation rates in load-controlled and displacement-controlled tests are different when plotted against their cycle ratios. Conversely, the tests produce nearly identical patterns when plotted against energy dissipation ratio. This occurs because of the relationship between energy dissipation ratio and pore pressure generation is independent of the loading pattern. Full article

A soil–foundation–structure system (SFSS) often exhibits different responses compared to a fixed-base structure when subjected to earthquake ground motion. Both kinematic and inertial soil–foundation–structure interactions can significantly influence the structural performance of buildings. Numerous parameters within an SFSS affect its overall response, introducing inherent uncertainty into the solution. Performing time history analyses, even for a linear elastic coupled SFSS, requires considerable computational effort. To reduce the computational cost without compromising accuracy, the use of the Latin Hypercube Sampling (LHS) technique is proposed herein. Sampling techniques are rarely employed in soil–foundation–structure interaction analyses, yet they are highly beneficial. These methodologies allow analyses determined by sampling to be conducted using commercial codes designed for deterministic analyses, without requiring any modifications. The advantage is that the number of analyses determined by the sampling size is significantly reduced as compared to considering all combinations of input parameters. After identifying the important samples, one can evaluate the seismic demand of selected soil–foundation–bridge pier systems using finite element numerical software. This paper indicates that LHS reduces computational effort by 60%, whereas structural response components (translation, rocking) show distinct trends for different systems. Full article

Open-ended steel piles are commonly used as the foundation for offshore structures. Numerous model and field tests have demonstrated a time-dependent increase in the resistance of these piles, a phenomenon referred to as pile ageing or pile setup. Additionally, for open-ended steel piles with comparably small diameters, soil plugging enhances the resistance against axial compressive loads. Realistically predicting these effects is necessary for their reliable incorporation into design practice. This contribution presents static compression and tension pile load testing conducted in an experimental pit filled with wet, uniformly graded silica sand. In total, twelve piles ($L=$ 5.5 m, $D_o=$ 325 mm) were driven into homogeneously compacted sand using a pneumatic impact hammer. Firstly, static compression pile load testing was executed at various times after installation. Subsequently, static tension pile load tests were carried out. The results of the static compression pile load tests indicate that the compressive resistance doubles over an ageing period of 64 weeks. The experimental investigations of the effect of soil plugging showed marginal soil plugging during pile installation, but a significant influence of the soil plug on the compressive resistance. Full article