Reliability assessment and forecasting of moment ratio/factor of safety for sheet pile walls utilizing hybrid ANFIS enhanced by optimization techniques

Furquan Ahmad, Pijush Samui, K K Keshav
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Abstract

Addressing the inherent uncertainties in geotechnical engineering, particularly concerning natural materials, this study focuses on the crucial aspect of reliability analysis in geotechnical structures. The research delves into the Factor of Safety, specifically examining the Moment Ratio, for cantilever sheet pile walls in cohesionless soil. The study employs both the First-Order Reliability Method (FORM) and Second-Order Reliability Method (SORM), integrating various optimization techniques such as Genetic Algorithm, Particle Swarm Optimization, Firefly Algorithm, and Biogeography-Based Optimization. The research systematically evaluates the performance of a model, utilizing the widely-adopted Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict pile moment ratios based on soil properties such as the angle of shearing resistance and unit weight. Rigorous criteria are applied to assess the model's efficacy, revealing the superior predictive capabilities of the hybrid ANFIS and Particle Swarm Optimization (ANFIS-PSO) model. This abstract encapsulates a comprehensive methodology for evaluating and quantifying risk in civil engineering projects related to cantilever sheet pile walls, providing valuable insights for the development and implementation of robust structures in the field.

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利用优化技术增强的混合 ANFIS 对钢板桩墙的弯矩比/安全系数进行可靠性评估和预测
针对岩土工程(尤其是天然材料)中固有的不确定性,本研究侧重于岩土结构可靠性分析的关键方面。研究深入探讨了无粘性土中悬臂板桩墙的安全系数,特别是动量比。研究采用了一阶可靠度法(FORM)和二阶可靠度法(SORM),并整合了各种优化技术,如遗传算法、粒子群优化、萤火虫算法和基于生物地理学的优化。研究系统地评估了模型的性能,利用广泛采用的自适应神经模糊推理系统(ANFIS),根据土壤特性(如抗剪角和单位重量)预测桩矩比。应用严格的标准来评估模型的有效性,揭示了混合 ANFIS 和粒子群优化(ANFIS-PSO)模型的卓越预测能力。本摘要囊括了评估和量化与悬臂板桩墙相关的土木工程项目风险的综合方法,为在现场开发和实施稳固结构提供了宝贵的见解。
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