This research intends to investigate a new hybrid artificial intelligence (AI) technique compared to some common CPT methods in estimating axial ultimate pile bearing capacity (UPBC) using cone penetration test (CPT) data in geotechnical engineering applications. A data series of 108 samples was collected in order to develop a new hybrid structure of an adaptive neuro-fuzzy inference system (ANFIS) network, and the group method of the data handling (GMDH) type neural network was optimized by applying the particle swarm optimization (PSO) algorithm over the hybrid ANFIS-GMDH topology, which leads to a new hybrid AI model called as ANFIS-GMDH-PSO. The derived database provides information related to pile load tests,in situfield CPT data, and soil–pile information for introducing the proposed hybrid neural system. The cross-section of the pile toe, average cone tip resistance along embedded pile length, and sleeve frictional resistance along the shaft had been considered as input parameters for the proposed network. The results of this research indicated that the proposed ANFIS-GMDH-PSO model predicted the UPBC with an acceptable precision compared to various CPT methods, including Schmertmann, De Kuiter & Bringen, and LPC/LPCT methods. Moreover, ANFIS-GMDH-PSO network model performance was compared to CPT-based models in terms of statistical criteria in order to achieve a best fitted model. From the statistical results, it was found that the developed ANFIS-GMDH-PSO model has achieved a higher accuracy level in terms of statistical indices compared to CPT-based empirical methods, such as Schmertmann model, De Kuiter & Beringen model, and Bustamante & Gianeselli for predicting driven pile ultimate bearing capacity.

中文翻译：

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开发一种新的混合软计算技术，利用锥入试验数据预测桩的极限承载力

本研究旨在研究一种新的混合人工智能 (AI) 技术，与一些常见的 CPT 方法相比，在岩土工程应用中使用锥形穿透测试 (CPT) 数据估计轴向极限桩承载力 (UPBC)。为了开发自适应神经模糊推理系统（ANFIS）网络的新混合结构，收集了108个样本的数据系列，并通过应用粒子群优化了数据处理（GMDH）型神经网络的组方法基于混合 ANFIS-GMDH 拓扑的优化 (PSO) 算法，这导致了一种新的混合 AI 模型，称为 ANFIS-GMDH-PSO。派生数据库提供与桩载试验相关的信息，原位现场 CPT 数据，以及用于引入所提出的混合神经系统的土壤桩信息。桩趾的横截面、沿嵌入桩长度的平均锥尖阻力和沿轴的套筒摩擦阻力已被视为所提出网络的输入参数。这项研究的结果表明，与包括 Schmertmann、De Kuiter & Bringen 和 LPC/LPCT 方法在内的各种 CPT 方法相比，所提出的 ANFIS-GMDH-PSO 模型以可接受的精度预测了 UPBC。此外，ANFIS-GMDH-PSO 网络模型性能与基于 CPT 的模型在统计标准方面进行了比较，以获得最佳拟合模型。从统计结果来看，