School of Civil and Architectural Engineering, East China University of Technology, NanChang, China.
School of Civil Engineering, Dalian University, Dalian, China.
PLoS One. 2024 Oct 10;19(10):e0311882. doi: 10.1371/journal.pone.0311882. eCollection 2024.
This paper delves into the engineering applications of lime-stabilized red clay, a highly water-sensitive material, particularly in the context of the climatic conditions prevalent in the Dalian region. We systematically investigate the impact of water content, dry density, and freeze-thaw cycles (with a freezing temperature set at -10°C) on the thermal conductivity of stabilized soil, a crucial parameter for analyzing soil temperature fields that is influenced by numerous factors. By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. Furthermore, we quantitatively analyze the specific influence of water content and other factors on the thermal conductivity of stabilized soil and construct a comprehensive prediction model encompassing BP neural network, gradient boosting decision tree, and linear regression models. Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. Additionally, a secondary validation using experimental data from other researchers confirms the model's good agreement with previous results, demonstrating its robust generalization ability. Our findings provide valuable insights for engineering studies in the Dalian region and red clay areas subjected to extreme climatic conditions.
本文深入探讨了石灰稳定红黏土在工程应用中的问题，石灰稳定红黏土是一种对水非常敏感的材料，特别是在大连地区普遍存在的气候条件下。我们系统地研究了含水量、干密度和冻融循环（冻结温度设定为-10°C）对稳定土导热系数的影响，导热系数是分析受多种因素影响的土壤温度场的关键参数。通过开发和验证经验和机器学习预测模型，我们揭示了导热系数对这些因素的演变：在影响变量范围内，导热系数随含水量和干密度的增加呈指数或线性增加，而随冻融循环的增加呈指数下降。此外，我们定量分析了含水量和其他因素对稳定土导热系数的具体影响，并构建了一个综合预测模型，包括 BP 神经网络、梯度提升决策树和线性回归模型。对比分析表明，所提出的集成模型在预测准确性方面显著优于单个机器学习模型，在冻结和未冻结状态下，均方根误差 (RMSE) 值均低于 0.05，平均绝对百分比误差 (MAPE) 值均低于 2.5%。此外，使用其他研究人员的实验数据进行二次验证，证实了模型与先前结果的良好一致性，表明其具有强大的泛化能力。我们的研究结果为大连地区和处于极端气候条件下的红黏土地区的工程研究提供了有价值的见解。
