Seismic actions are usually considered for their inertial effects on the built environment. However, additional effects may be caused by the volumetric-distortional coupling of soil behaviour: the fast cyclic shaking on saturated soils caused by earthquakes generates temporary undrained or quasi-undrained conditions and subsequent pore pressure variations that, if positive, reduce the effective stresses, eventually leading loose granular soils to liquefaction. Whatever the amount of seismically induced pore pressure build up, buildings on shallow foundations suffer settlements and tilts that may be extremely large when soils approach liquefaction, as demonstrated by several recent case histories. The paper proposes an equivalent elastic approach in effective stresses to predict the co-seismic (undrained) component of the seismically induced settlement of shallow foundations, which usually is the most relevant one, by considering the decrease of soil stiffness during the seismic event. The total settlement can be then estimated by adding the post-seismic (drained) component, also evaluated in this paper via a quite simple approach. Even though the equivalent elastic model is stretched into a highly non-linear soil behaviour range, especially when the soil is approaching liquefaction, the model considers the relevant capacity and demand factors and proved effective in simulating some centrifuge tests published in the literature. In the paper, the simplifying assumptions of the approach are clearly indicated, and their relevance discussed. It is argued that notwithstanding some limitations the model is physically based and therefore it allows for understanding and checking the relative relevance of all the parameters related to soil, foundation, and seismic action. Thus, it is a tool of possible interest in the design of shallow foundations in liquefaction-prone seismic areas.

As a new type of granular backfill material, calcareous sand is widely used in the construction of marine transportation infrastructure. And they are subjected to complex irregular long-term dynamic loading such as that from waves, traffic and even earthquakes. In this paper, 22 groups of undrained cyclic shear tests were performed with calcareous sand under various cyclic stress ratios and cyclic stress paths. The influence mechanism of stress path on the cyclic shear behavior of calcareous sand was investigated. The results show that the ultimate residual pore pressure at critical state was not affected by cyclic stress ratios and paths. But the cyclic shear behaviors of calcareous sand including failure pore water pressure and long-term deformation were changed significantly. Axial load plays a dominant role in each stress path. A stress path parameter omega was proposed to characterize the vertical shaking impact of cyclic stress paths with different initial orientation of the sigma 1 axis to vertical alpha sigma 0. And a power function of omega was used to describe the involvement level of soil skeleton in anti-liquefaction. This parameter performs well in representing cyclic stress paths with different orientation to the vertical. A series of formulas were proposed to predict the failure residual pore pressure and the long-term cumulative deformation behavior of calcareous sand. More accurate shakedown discriminant boundaries suitable for almost unbroken calcareous sand were proposed.

The accelerating climate crisis has intensified global efforts to develop renewable energy, with offshore wind power emerging as a key solution due to its vast potential and low environmental impact. However, the stability of offshore wind turbines (OWTs) is increasingly compromised by extreme storm events, such as typhoons, which induce strong winds, large wave loads, and seabed liquefaction. While extensive research has been conducted on monopile foundations, most studies focus either on horizontal loads or seabed responses in isolation, lacking a systematic analysis of the coupled pile-soil interaction in extreme storm conditions. This study develops a pilesoil interaction model incorporating pore pressure response to evaluate the stability of monopile and seabed under extreme storm loads. The model is validated using seabed pore pressure models under wave action and monopile response models under cyclic loading. The model is applied to the stability analysis of monopiles at the Cangnan offshore wind farm, where extreme storm loads are quantified using buoy measurement data and incorporated into the model to calculate the responses of both monopiles and seabeds. The results show that the monopile displacement reaches its maximum at the wave crest, and the displacement and moment of the monopile are positively correlated with wave height and negatively correlated with wave length and period. Although changes in wave parameters do not affect the failure mode of the soil, they influence the magnitude and distribution of pore pressure around the pile. The findings provide critical insights into offshore wind turbine foundation stability, offering a scientific basis for improving design strategies to enhance resilience against extreme weather events.

Precast driven piles are extensively used for infrastructure on soft soils, but the buildup of excess pore water pressure associated with pile driving is a challenging issue. The process of soil consolidation could take several months. Measures are sought to shorten the drainage path in the ground, and permeable pipe pile is a concept that involves drainage channels at the peak pore pressure locations around the pile circumference. Centrifuge tests were conducted to understand the responses of permeable pipe pile treated ground, experiencing the whole pile driving, soil consolidating, and axially loading process. Results show that the dissipation rate of pore pressures can be improved, especially at a greater depth or at a shorter distance from the pile, since the local hydraulic gradient was higher. Less significant buildup of pore pressures can be anticipated with the use of permeable pipe pile. For this, the bearing capacity of composite foundation with permeable pipe pile can be increased by over 36.9%, compared to the case with normal pipe pile at a specific time period. All these demonstrate the ability of permeable pipe pile in accelerating the consolidation process, mobilizing the bearing capacity of treated ground at an early stage, and minimizing the set-up effect. (c) 2025 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/ 4.0/).

The study area, located in Martil, northern Morocco, lies in a region with high seismic risk, near a subduction zone. As a result, loose soils, such as sands, lose their shear strength under seismic loads due to an increase in pore water pressure, leading to deformations. The objective of this study is to assess the risk of soil liquefaction at the site where the Lalla Khadija High School will be constructed. The method used to evaluate the liquefaction risk is based on in-situ test results, as proposed by Seed and Idriss (J Soil Mech Found Div 97(9):1249-1273, 1971. https://doi.org/10.1061/JSFEAQ.0000981). Specifically, the liquefaction potential is assessed using data from the cone penetration test (CPT). This methodological approach combines a qualitative evaluation of susceptibility, which identifies the presence of fill materials and Plio-Quaternary sands-potentially liquefiable materials. At this stage, a quantitative evaluation of susceptibility is performed by calculating the safety factor, defined as the ratio between the normalized cyclic resistance ratio of the soil and the normalized cyclic stress ratio induced by the earthquake. The results of the CPT indicate that the normalized penetration resistance (qc1Ncs) consistently exceeds 160, which reflects sufficient soil strength. Consequently, the analysis confirms the absence of liquefaction risk in the sandy layers between depths of 1.8 m and 14 m. Therefore, the studied site has no liquefaction potential. This study has certain limitations. It relies solely on the method of Seed and Idriss (1971) to assess liquefaction risk, thereby restricting comparisons with alternative approaches. Additionally, the analysis focuses exclusively on the Lalla Khadija High School site, preventing extrapolation to the entire Martil plain. Nevertheless, by confirming the absence of liquefaction risk at this site, the study enables optimized foundation design, ensuring the stability of the infrastructure in the event of an earthquake. This contributes to occupant safety and improved seismic risk management in the region.

Seabed instability is one of the important reasons for offshore structure damage. Unlike most previous studies that treated the oscillatory and residual response separately, a coupled model for wave-induced response in non- homogeneous seabeds is proposed in the present study. Effects of spatial derivative terms in seabed parameters are introduced into the accumulation of pore pressure. Model validations are conducted by comparing the present simulation with the previous analytical solutions, wave flume tests, and numerical simulations. The validated model is applied to investigate the effects of grain size, non-homogeneous distribution of seabed parameters, and non-linear wave conditions on the wave-induced seabed dynamic response and liquefaction. It is found that (1) the oscillatory mechanism in pore pressure variation dominates in the coarser seabed, while the residual mechanism becomes obvious with the decreasing grain size, (2) consideration of the non-uniform permeability and Young's modulus would promote and suppress the pore pressure accumulation and liquefaction, respectively, and (3) the simulation error in pore pressure between homogeneous and non-homogeneous seabeds increases with the increase of the wave nonlinearity.

Physical modeling is an efficient method to simulate practical geotechnical problems and to provide insights into soil behavior. This study used geotechnical centrifuge models equipped with motorized pulling systems to pull coupons (thin metal plates) at constant speeds horizontally through clean, saturated sand models that were liquefied by cyclic loading. The model setup was aimed to mimic shearing mechanisms, large shear strains, and large strain rates observed in field-scale flow slides. In-flight cone penetration testing and bender element-based shear wave velocity measurements helped in characterizing soil state at coupon levels before liquefaction. In addition, a miniature pressure transducer was embedded in the coupon along its top horizontal surface to directly measure pore pressure response on the shear surface within the liquefied soil. In total, 11 coupon pulls were completed, with 6 of the 11 tests providing shear-induced pore pressure measurements at the coupon surface. Measured coupon pulling forces and pore pressure responses at shear-surface and free-field were interpreted to identify key behaviors. These key behaviors illustrated that relatively low coupon velocities were required to maintain liquefied conditions at the coupon surface. In addition, pulling force recovery during pore pressure dissipation appeared to be related to coupon velocity (i.e. strain rate).

In this study, a novel data-driven approach is carried out to predict the pore pressure generation of liquefiable clean sands during cyclic loading. An extensive and comprehensive database of actual stress-controlled cyclic simple shear test results in terms of pore pressure time histories is gathered from a large number of experiments. While the classical machine learning (ML) algorithms help predict the number of liquefaction cycles in a few models, the desired level of accuracy in predicting the actual trend and robustness in pore pressure build-up is only achieved in deep learning (DL) methods. Results indicate that the Long-Short Term Memory (LSTM) working model, employed with Stacked LSTM and the Windowing data processing method, is necessary for making fairly good cyclic pore pressure build-up predictions. This study proposes a model that can ultimately be utilised to predict the pore pressure response of in-situ liquefiable sandy soil layers without resorting to plasticity-based complex theoretical models, which has been the current practice. The robustness achieved in the model reassures the reliability of the study, raising confidence in developing data-driven constitutive models for soils that have the potential to replace conventional plasticity-based theories.

In order to estimate accumulated excess pore pressures in the soil around a cyclically loaded (offshore) foundation structure, cyclic laboratory tests are required. In practice, the cyclic direct simple shear (DSS) test is often used. From numerous undrained tests (or alternatively tests under constant-volume condition) under varying stress conditions, contour diagrams can be derived, which characterize the soil's behavior under arbitrary cyclic loading conditions. Such contour diagrams can then be used as input for finite element models predicting the load-bearing behavior of foundation structures under undrained or partially drained cyclic loading. The paper deals with the general behavior of a poorly graded medium sand in cyclic DSS tests under undrained loading conditions. The main objective of the research was to investigate and parametrize the soil's behavior and to identify possible effects of sample preparation. Numerous tests with varying cyclic stress ratios (CSR) and mean stress ratios (MSR) have been conducted. Also the relative density of the sand was varied. A new set of equations for a relatively easy handable mathematical description of the resulting contour plots was developed and parametrized. In the original tests, the sand was poured into the testing frame and carefully compacted to the desired relative density by tamping. In offshore practice, a preconditioning of a soil sample is usually realised by cyclic preshearing with a certain CSR-value or additionally by preconsolidation under drained conditions. By that, a more realistic initial state of the soil shall be achieved. In order to investigate the effect of such a preconditioning on the resulting contour diagrams, additional tests were conducted in which preshearing and preconsolidation was applied and the results were compared to the test results without any preconditioning. The results clearly show a significant effect of preshearing and an even more pronounced effect of preconsolidation for the considered poorly graded medium sand.

Most of the robust artificial intelligence (AI)-based constitutive models are developed with synthetic datasets generated from traditional constitutive models. Therefore, they fundamentally rely on the traditional constitutive models rather than laboratory test results. Also, their potential use within geotechnical engineering communities is limited due to the unavailability of datasets along with the model code files. In this study, the data-driven constitutive models are developed using only laboratory test databases and deep learning (DL) techniques. The laboratory database was prepared by conducting cyclic direct simple shear (CDSS) tests on reconstituted sand, that is, PDX sand. The stacked long short-term memory (LSTM) network and its variants are considered for developing the predictive models of the shear strain (gamma [%]) and excess pore pressure ratio (ru) time histories. The suitable input parameters (IPs) are selected based on the physics behind the generation of ru and gamma (%) of the liquefiable sands. The predicted responses of gamma (%) and ru agree well in most cases and are used to predict the dynamic soil properties of the PDX sand. The same modeling framework is extended for other sand and compared with existing AI-based constitutive models to verify its practical applicability. In summary, it is observed that though the trained models predicted the time histories of ru and gamma reasonably well; however, they struggled to predict the hysteresis loops at higher cycles. Therefore, more research is needed to verify and enhance the predictability of existing AI-based models in the future before using them in practice for simulating cyclic response.