Open-ended pipe piles (OEPPs) are widely used in offshore foundations, yet accurately predicting their driving responses remains challenging due to soil plug complexities. Existing pile driving analysis models inadequately characterize the effects of soil plug, potentially leading to driving problems such as hammer refusal, pile running, and structural damage. This paper proposes an effective soil plug (ESP) model for OEPP driving analysis. The ESP model considers the effective range of soil plug, which exerts internal resistance that increases exponentially with depth while the beyond of effective range contributes only mass inertia. It also accounts for the relative slippage at the pile-soil plug interface. A differential iterative method is developed to solve the ESP model. Subsequently, investigations including the model validation and parameter analysis are conducted. Model validations against existing models and field measurements confirms the reliability of the ESP model. Parameters sensitivity analysis reveals the importance of soil plug length and distribution type of internal resistance on the pile dynamic responses. In addition, if soil plug slippage occurs, the displacement peak of soil plug increases with depth rather than one-dimensional wave attenuation. Furthermore, contrary to previous assumptions of continuous slippage, the soil plug experiences a discontinuous jump-sliding mode under long-duration impact loading. These findings provide theoretical basis for OEPP driving simulation and interpretations of high-strain dynamic test.

Two types of grounding systems are recommended for use in the international standard IEC 62305-3, Part 3: Physical damage to structures and life hazard. One of these is a radial-based grounding system (type-A), which is used in soil resistivities of up to 3000 Omega m and is considered in this paper. It is a well-known fact that during lightning strikes, only a part of the grounding wire contributes to dissipating the lightning current into the surrounding soil. This effective part of the grounding system depends on several features, such as soil resistivity, burial depth, and rise time of the dissipated lightning current. The effect of all of these features on the effective length of the type-A grounding system is explored in this paper. A suitable supervised machine learning regression model is developed, which will enable readers to accurately approximate the effective length of the type-A grounding system for realistic values of input features. The trained model in the paper yielded an R2 value of 0.99998 on the test set. In addition, two simple mathematical formulas are also provided, which produce similar but less accurate results (R2 values of 0.989883 and 0.998557, respectively).