In geotechnical engineering, constitutive models are central to capturing soil behavior across diverse drainage conditions, stress paths,and loading histories. While data driven deep learning (DL) approaches have shown promise as alternatives to traditional constitutive formulations, their deployment requires models that are both accurate and capable of quantifying predictive uncertainty. This study introduces a recursive Bayesian neural network (rBNN) framework that unifies temporal sequence learning with generalized Bayesian inference to achieve both predictive accuracy and rigorous uncertainty quantification. A key innovation is the incorporation of a sliding window recursive structure that enables the model to effectively capture path dependent soil responses under monotonic and cyclic loading. By treating network parameters as random variables and inferring their posterior distributions via generalized variational inference, the rBNN produces well calibrated confidence intervals alongside point predictions.The framework is validated against four datasets spanning both simulated and experimental triaxial tests: monotonic loading using a Hardening Soil model simulation and 28 CD tests on Baskarp sand, and cyclic loading using an exponential constitutive simulation of CD CU tests and 37 experimental cyclic CU tests on Ottawa F65 sand. This progression from monotonic to cyclic and from simulated to experimental data demonstrates the adaptability of the proposed approach across varying levels of data fidelity and complexity. Comparative analyses with LSTM, Encoder Decoder,and GRU architectures highlight that rBNN not only achieves competitive predictive accuracy but also provides reliable confidence intervals.

翻译：在岩土工程中，本构模型是捕捉不同排水条件、应力路径和加载历史下土体行为的关键。虽然数据驱动的深度学习方法已显示出作为传统本构公式替代方案的潜力，但其实际应用需要模型兼具准确性与预测不确定性量化能力。本研究提出一种递归贝叶斯神经网络框架，将时序学习与广义贝叶斯推断相统一，以实现预测准确性与严格的不确定性量化。其核心创新在于引入滑动窗口递归结构，使模型能够有效捕捉单调与循环荷载下的路径相关土体响应。通过将网络参数视为随机变量，并借助广义变分推断推求其后验分布，该递归贝叶斯神经网络在输出点预测的同时生成经过良好校准的置信区间。该框架通过涵盖模拟与实验三轴试验的四组数据集得到验证：采用硬化土模型模拟的单调加载试验与28组Baskarp砂的CD试验，以及采用CD-CU试验指数本构模拟与37组Ottawa F65砂实验性循环CU试验的循环加载数据。这种从单调到循环、从模拟到实验数据的递进验证，证明了所提方法在不同数据保真度与复杂程度条件下的适应能力。与LSTM、编码器-解码器及GRU架构的对比分析表明，递归贝叶斯神经网络不仅获得具有竞争力的预测精度，同时能提供可靠的置信区间。