Hamzah M. Beakawi Al-Hashemi

Accurate identification of soil constitutive parameters from pile load tests remains a difficult inverse problem because of non-linearity, limited data and non-unique solutions. Although optimisation-based methods are common, most studies report a single best-fit result without examining convergence reliability, parameter identifiability, or equifinality. This study presents a hybrid particle swarm optimisation–finite-element method (PSO–FEM) framework for inverse geotechnical modelling with explicit assessment of these issues. A multi-run statistical protocol is used to quantify convergence reliability under computational budgets of 60, 90 and 120 forward evaluations, and an identifiability radar metric is introduced to evaluate parameter information content from rank correlation and dispersion. The framework is first tested on a synthetic benchmark with known parameters, showing that calibration reliability improves rapidly with computational budget before reaching diminishing returns, and that physically informed parameter bounds substantially improve both accuracy and stability. Robustness tests indicate that the framework tolerates realistic levels of measurement noise. Application to a field pile load test in layered sandstone achieves high predictive accuracy (R2 = 0.988), but 43 archive-conditioned parameter sets fall within a 5% SSE (sum of squared errors) tolerance, showing that numerical convergence does not imply a unique physical parameter set.

The increasing demand for underground infrastructure in densely populated urban areas has intensified the need for reliable prediction of tunneling-induced ground settlement, particularly under complex geological conditions such as fault zones. This study aims to investigate the effect of fault material properties and heavy rainfall on surface settlement induced by tunnel excavation. A coupled analytical and numerical approach was adopted to evaluate the ground response under faulted conditions. Finite element analysis was conducted to simulate tunnel excavation and ground deformation, while the proposed analytical solution was developed to predict settlement behavior. In addition, a parametric study was performed to investigate the influence of fault material characteristics under rainfall conditions on surface deformation. The obtained results indicate that fault zones significantly affect settlement magnitude and distribution, especially under heavy rainfall due to the reduction in shear strength and changes in hydro-mechanical behavior. The comparison between the analytical solution and numerical simulation demonstrates good agreement, confirming the validity and reliability of the proposed approach. The novelty of this study lies in integrating fault material properties and rainfall effects into the settlement prediction framework, providing a more comprehensive understanding of tunneling performance in faulted ground conditions and offering practical guidance for underground construction in complex urban environments.

Climate change has increased the frequency and intensity of extreme rainfall events, which significantly threatens slope stability by elevating pore-water pressure, reducing effective stress, and weakening soil strength. This study aims to investigate the rainfall-induced hydro-mechanical response of a fill slope containing twin parallel tunnels and to evaluate the effectiveness of reinforcement measures in improving slope stability under such conditions. A combined experimental–numerical approach was adopted. First, a physical model test of twin tunnels excavated within a slope was conducted to provide validation data for the numerical model. Subsequently, a parametric numerical analysis was performed using the finite element method implemented in PLAXIS 3D to simulate the coupled hydraulic–mechanical behavior of the slope–tunnel system under varying rainfall intensities and durations. Reinforcement measures, including piles and plates, were incorporated to assess their stabilizing performance. The results show that rainfall duration has a more significant influence on slope deformation and pore-water pressure than rainfall intensity. Reinforcement measures effectively reduce deformation and enhance slope stability, although they have limited influence on the overall saturation distribution. Reinforced slopes also exhibit increased total discharge due to preferential seepage along soil–structure interfaces. The novelty of this study lies in integrating physical model validation with three-dimensional coupled hydro-mechanical numerical analysis to comprehensively evaluate deformation, pore-water pressure, and seepage behavior in reinforced tunnel–slope systems under rainfall conditions.

The accurate determination of the ratio between design capacity and the measured capacity of piles is crucial for designing safe and cost-effective foundations. However, conventional methods for pile design rely on empirical equations which are unable to consider the complexity of soil-pile interactions due to lack of information about pile conditions and loading history. To overcome this issue, this study proposes a novel hybrid machine learning model named Tree-structured Parzen Estimator based Extreme Gradient Boosting (TPE-XGB) to estimate the effect of various pile and soil-related parameters including pile type, diameter, tip depth allowable pile on the ratio between design capacity from soil investigation and measured capacity from testing. For this purpose, 69 full-scale pile load tests were conducted, and GIS-based mapping was conducted to analyze spatial data. The TPE-XGB model was trained on the experimental data, and the results demonstrated the high efficacy of TPE-XGB model in predicting the output with 95% accuracy and minimal error (RMSE = 0.027). In addition, to interpret the findings of black-box TPE-XGB algorithm, Shapely Additive Analysis (SHAP) and Individual Conditional Expectation (ICE) analysis were used to identify the most important features and to explore the nonlinear relationships between input features and model output respectively. Finally, to facilitate the practical implementation of this study’s findings, a graphical user interface (GUI) was developed, allowing engineers to easily input site-specific parameters and obtain explainable model predictions. This data-driven approach offers a reliable tool for engineers to optimize pile design while ensuring transparency and reliability in decision-making.

Over the past five decades, the Tallet Alsauda district of Aleppo (Syria) has experienced multiple catastrophic collapses, attributed to a network of subsurface chalk cavities formed through historic quarrying and possible natural karstification. Yet, no comprehensive investigation has previously been conducted to characterise the cavities or clarify the governing failure mechanisms. Such assessments are particularly difficult in historic urban environments, where void geometries are irregular, subsurface data scarce, and underground access limited. This study addresses these challenges through an integrated programme of fourteen boreholes, laboratory testing, and inverse-distance interpolation to reconstruct subsurface geometry and overburden thickness. These data-informed three-dimensional finite element simulations are designed to test the hypothesis that chalk deterioration, driven by both natural and anthropogenic processes, controls the instability of cavity roofs. Rock mass parameters, particularly the Geological Strength Index (GSI), were progressively reduced and evaluated against the site’s documented collapse history. The simulations revealed that a modest decline in GSI from ~53 to 47 precipitated abrupt displacements (>300 mm) and upward-propagating plastic zones, consistent with field evidence of past collapses. These results confirm that instability is governed by threshold reductions in material strength, with sewer leakage identified as a principal trigger accelerating chalk softening and roof destabilisation.

Findings from a four-year monitoring study of the Tensar mechanically stabilised earth (MSE) walls and adjacent roads along the Fujairah Freeway in the United Arab Emirates are presented in this article. The study area covers the mountainous terrain of the ten-lane freeway that connects Dubai on the west to Fujairah on the east coast. Using Synthetic Aperture Radar (SAR) images, the interferometric stacking technique with Persistent Scatterers (PS) was applied to detect and measure the surface displacements of the Tensar walls and adjacent roads. Three specific areas of interest were studied in detail, revealing varying degrees of displacement over the four-year monitoring period, with the largest movements observed in the embankment 550m east of Wall 20. These findings provide valuable insights into the performance and stability of the Tensar MSE walls and adjacent roads, which can inform future design and maintenance decisions for similar infrastructure projects.

The grain size analysis plays a significant role in any geotechnical study. The grain size analysis, by means of sieving, is usually used for coarse material of particle size > 75 μm. For the fine material; the sedimentation methods are frequently adopted (e.g., hydrometers). Other methods also exist such as electron microscopy, digital image analysis and laser diffraction. The fine geomaterials commonly undergo agglomeration which makes the recognition of individual grain size using digital image analysis or electron microscopy challenging. To facilitate and enhance the grain-size analysis, this study was conducted using the Laser Diffraction System (LDS). Seven samples with different nature (composition and texture) and sources were analyzed by hydrometer and LDS. For LDS, various factors were studied such as air pressure, sonication, dilution, refractive index, and distribution method (volume or number). The results were compared qualitatively and quantitatively based on soil classification systems, fractal dimensions, and other parameters. Furthermore, this study provided a novel criterion to determine which LDS distribution method (volume or number) is to be used depending on the Liquid Limit. A combined sieve-LDS system is recommended to obtain the entire grain size distribution. It is concluded that the LDS is a viable technique that can replace the time-consuming hydrometer method to assess the grain-size distribution.