This paper presents a comprehensive on-site decision-making framework for assessing the structural integrity of a jacket-type offshore platform in the Gulf of Mexico, installed at a water depth of 50 m. Six critical analyses—(i) static operation and storm, (ii) dynamic storm, (iii) strength-level seismic, (iv) seismic ductility (pushover), (v) maximum wave resistance (pushover), and (vi) spectral fatigue—are performed using SACS V16 software to capture both linear and nonlinear interactions among the soil, piles, and superstructure. The environmental conditions include multi-directional wind, waves, currents, and seismic loads. In the static linear analyses (i, ii, and iii), the overall results confirm that the unity checks (UCs) for structural members, tubular joints, and piles remain below allowable thresholds (UC < 1.0), thus meeting API RP 2A-WSD, AISC, IMCA, and Pemex P.2.0130.01-2015 standards for different load demands. However, these three analyses also show hydrostatic collapse due to water pressure on submerged elements, which is mitigated by installing stiffening rings in the tubular components. The dynamic analyses (ii and iii) reveal how generalized mass and mass participation factors influence structural behavior by generating various vibration modes with different periods. They also include a load comparison under different damping values, selecting the most unfavorable scenario. The nonlinear analyses (iv and v) provide collapse factors (Cr = 8.53 and RSR = 2.68) that exceed the minimum requirements; these analyses pinpoint the onset of plasticization in specific elements, identify their collapse mechanism, and illustrate corresponding load–displacement curves. Finally, spectral fatigue assessments indicate that most tubular joints meet or exceed their design life, except for one joint (node 370). This joint’s service life extends from 9.3 years to 27.0 years by applying a burr grinding weld-profiling technique, making it compliant with the fatigue criteria. By systematically combining linear, nonlinear, and fatigue-based analyses, the proposed framework enables robust multi-hazard verification of marine platforms. It provides operators and engineers with clear strategies for reinforcing existing structures and guiding future developments to ensure safe long-term performance.

This paper presents a comprehensive on-site decision-making framework for assessing the structural integrity of a jacket-type offshore platform in the Gulf of Mexico, installed at a water depth of 50 m. Six critical analyses—(i) static operation and storm, (ii) dynamic storm, (iii) strength-level seismic, (iv) maximum wave resistance (pushover), (v) seismic ductility (pushover), and (vi) spectral fatigue—are performed using SACS software to capture both linear and nonlinear interactions among the soil, piles, and superstructure. The environmental conditions include multi-directional wind, waves, currents, and seismic loads. In the static-linear analyses (i, ii, iii), the overall results confirm that the unity checks (UC) for structural members, tubular joints, and piles remain below allowable thresholds (UC < 1.0), thus meeting API RP 2A-WSD, AISC, IMCA, and Pemex P.2.0130.01-2015 standards for different load demands. However, these three analyses also show hydrostatic collapse due to water pressure on submerged elements, which is mitigated by installing stiffening rings in the tubular components. The dynamic analyses (ii, iii) reveal how generalized mass and mass participation factors influence structural behavior by generating various vibration modes with different periods. They also include a load comparison under different damping values, selecting the most unfavorable scenario. The nonlinear analyses (iv, v) provide collapse factors (Cr = 8.53 and RSR = 2.68) that exceed the minimum requirements; these analyses pinpoint the onset of plasticization in specific elements, identify their collapse mechanism, and illustrate corresponding load–displacement curves. Finally, spectral fatigue assessments indicate that most tubular joints meet or exceed their design life, except for one joint (node 370). This joint’s service life extends from 9.3 years to 27.0 years by applying a burr grinding weld-profiling technique, making it compliant with the fatigue criteria. By systematically combining linear, nonlinear, and fatigue-based analyses, the proposed framework enables a robust, multi-hazard verification of marine platforms. It provides operators and engineers with clear strategies for reinforcing existing structures and guiding future developments to ensure safe, long-term performance.

Herbicide-contaminated wastewater poses a critical environmental challenge, largely due to the difficulty of accurately predicting Chemical Oxygen Demand (COD) removal efficiency. This study introduces a systematic Artificial Neural Network (ANN) approach to model COD dynamics during the advanced oxidation (sonophotocatalytic) degradation of two commercial herbicides (Alazine and Gesaprim). Multiple backpropagation algorithms—including Gradient Descent, Conjugate Gradient, Quasi-Newton, and Levenberg-Marquardt—were evaluated to identify the most effective training strategy for COD prediction. The optimal ANN architecture (6-10-1) consisted of six input neurons, ten hidden neurons, and one output neuron, using a hyperbolic tangent sigmoid transfer function in the hidden layer and a linear transfer function in the output layer. Trained on a dataset of 500 samples (70% for training; 30% for validation), this model achieved near-perfect correlation between measured and predicted COD values (R² = 0.9999, RMSE = 0.0140). Key findings include: • Parameter Characterization: Six operational variables (reaction time, pH, TiO₂ concentration, UV intensity, ultrasound frequency, and herbicide concentration) were assessed for their impact on COD removal. • Dominant Variables: Reaction time emerged as the most influential parameter (59.91%), followed by herbicide concentration (8.3%) and TiO₂ concentration (7.5%). • Algorithm Performance: Levenberg-Marquardt provided superior predictive performance (p < 0.01) among the tested backpropagation algorithms. • Robust Validation: Friedman tests confirmed the reliability of the ANN models for COD prediction. Uniqueness of the Present Work While prior studies have underscored the growing importance of ANNs in wastewater treatment and herbicide degradation, none directly match the objectives and depth of this research. Beyond comparing multiple backpropagation algorithms and integrating sensitivity analysis, a novel “Neural Networks for Statistical Modeling” methodology was developed, complete with a comprehensive Graphical User Interface (GUI) and advanced statistical tests. This unique combination of algorithmic evaluation, in-depth statistical analysis, and user-friendly implementation offers new perspectives on optimizing herbicide degradation processes, with potential to reduce operational costs and environmental risks in agricultural and industrial applications. Future studies will focus on practical implementation strategies and scaling up to full-scale operations, further solidifying the model’s efficacy for real-time monitoring and process optimization in advanced oxidation processes.