Chih-Ping Lin

Broadband complex permittivity spectroscopy enables characterization of the frequency‐dependent electrical behavior of wet particulate media. Conventional field methods, including microwave‐based techniques and time‐domain reflectometry (TDR), rely on apparent permittivity over limited or implicit frequency ranges, restricting their ability to resolve key polarization mechanisms, particularly in the kHz–MHz range. This study presents an integrated broadband complex permittivity spectroscopy system that combines a low‐frequency inductance‐capacitance‐resistance (LCR) meter with a high‐frequency TDR instrument, enabling continuous measurement across a wide frequency spectrum for both laboratory and field applications. The system was tested on near‐saturated samples of sand, kaolinite, bentonite, and sand‐bentonite mixtures. In the low‐frequency range, the system effectively captures electrode and spatial polarization effects, which show strong correlations with specific surface area and bulk electrical conductivity. At higher frequencies, electrical response is dominated by orientational polarization and is closely related to volumetric moisture content. Conductivity values obtained through TDR waveform inversion align well with those derived from spectral fitting of impedance measurements. The proposed framework extends the accessible frequency range for soil permittivity characterization and provides a cost‐effective, rapid, and scalable tool for assessing soil physical and hydraulic properties. Its potential for long‐term, in situ monitoring applications is particularly promising.

An efficient semi‐analytical approach based on the Fourier–Bessel series (FBS) system of vector functions and the stiffness matrix method is proposed to study the dynamic response of axially loaded piles embedded in transversely isotropic (TI) layered soil. The method utilizes a robust free‐field matrix formulation of layered soil that integrates with the pile matrix, enabling efficient computation of expansion coefficients, termed as Love numbers. These coefficients need to be computed only once for multilayered soil. They are then stored and reused to quickly obtain responses at multiple field points using FBS synthesis, avoiding the computational complexity of the Hankel integral transform. The proposed formulation is validated against various existing solutions, demonstrating excellent agreement. Furthermore, the effects of compressibility, slenderness, pile spacing, soil anisotropy, layering, and frequency‐dependent loading on the pile–soil interaction are investigated, offering practical insights for improved and reliable pile design.

This study presents an integrated framework combining scientometric analysis, machine learning (ML), and explainable artificial intelligence (XAI) to predict the unconfined compressive strength (UCS) of lime and cement-stabilised soils. A century-spanning scientometric review of lime- and cement-based soil stabilisation literature (1912–2026), complemented by a focused scientometric assessment of machine-learning-based UCS prediction studies (2011–2025), revealed a mature yet evolving research domain with strong thematic shifts toward sustainability, binder innovation, and data-driven modelling. A curated dataset of 194 cleaned UCS instances was developed from literature, incorporating soil chemistry, texture, and stabiliser dosage. Four ML models, namely artificial neural network (ANN), random forest (RF), adaboost (AdB), and extreme gradient boosting ( XGB), were developed, with XGB demonstrating the highest predictive accuracy and lowest error metrics. XAI tools, including SHapley Additive exPlanations (SHAP) summary, decision, force, and local interpretable model-agnostic explanations (LIME) plots, and individual conditional expectation (ICE) plots and partial dependence plots (PDP), were applied to interpret model behaviour. Cement and lime emerged as the dominant global predictors, while organic content and pH exerted strong moderating effects; soil gradation variables contributed minimally. The XAI outputs aligned with established stabilisation mechanisms, confirming the physical plausibility of the ML predictions. Overall, this study provides a transparent and mechanistically interpretable ML-based predictive framework that enhances scientific understanding and supports reliable engineering decision-making in soil stabilisation practice.

SUMMARY: This study proposes a dispersion-spectrum inversion method for improved estimation of shear-velocity (VS) profiles in marine sediments using underwater multichannel analysis of surface waves (UMASW). The method leverages an efficient forward modelling algorithm combined with a Markov chain Monte Carlo (McMC) global search to address the nonlinearity inherent in the inversion process. Comparisons with field and synthetic data demonstrate that the VS profiles inverted using the full dispersion spectrum (in terms of the frequency-phase velocity spectrum, FVS) exhibit greater stability and reliability than those obtained through traditional fundamental-mode (FM) inversion. Pseudo 2-D VS profiles are constructed by interpolating 1-D profiles obtained from the FVS-McMC inversion, showing more continuous subsurface interfaces that align with borehole data. Parametric studies further highlight the influence of Poisson’s ratio, water layer thickness and VS contrast on the dispersion behaviour, underscoring the robustness of the proposed approach for offshore site characterization.

This study optimizes in-hole electrical resistivity tomography (ERT) by integrating surface electrodes to assess directional resistivity variations through numerical simulations and field applications. The proposed methodology involves rotating surface electrodes around a borehole at various azimuths while keeping the in-hole electrodes stationary. Four in-hole imaging arrays—A-BMN, A-MNB, AB-MN, and AM-NB—are evaluated for their directional performance using synthetic azimuthal apparent resistivity datasets. The arrays are tested under two scenarios: the in-panel scenario, where subsurface anomalies align with the surface electrodes, and the off-panel scenario, where surface electrodes are positioned opposite the anomaly. The analysis considers array measurement sensitivities, modeling accuracy, anomaly detection resolution in the in-panel scenario, and the impact of symmetric sensitivity effects in the off-panel scenario. The results reveal that the A-BMN and A-MNB arrays exhibit high measurement sensitivity and moderate modeling accuracy, but symmetric effects significantly constrain their directional performance. In contrast, the AB-MN array shows low sensitivity, poor model accuracy, a pronounced symmetric effect, and limited directional response. The AM-NB array, however, demonstrates high measurement sensitivity, improved model accuracy, minimal symmetric effects, and robust directional capabilities. Field validation involved rotating surface electrodes to eight predefined azimuths around monitoring wells while keeping in-hole electrodes stationary. Following chemical injection at a nearby injection well, measurements revealed substantial resistivity and conductivity variations at azimuths corresponding to the injection well’s orientation. Rose diagrams of resistivity data identified dominant flow paths and primary contaminant dispersion azimuths. This optimized, directionally sensitive in-hole ERT approach significantly improves multi-directional detection capabilities, enabling effective characterization of anisotropic subsurface conditions. By incorporating rotating surface electrodes, the study establishes in-hole ERT as a reliable method for identifying fluid flow pathways and elongated subsurface anomalies, advancing its application in hydrogeological, environmental, and geotechnical investigations.