数据存储量的扩大和计算能力的提升为基于观测数据推断时间序列的因果关系开辟了新途径。在时间序列因果推断的基本性质和研究现状的基础上，系统梳理了5种基于观测数据的时间序列因果推断方法，即Granger因果分析方法、基于信息论的方法、因果网络结构学习算法、基于结构因果模型的方法和基于非线性状态空间模型的方法。然后，根据不同应用场景的数据特点，结合方法的功能和适配性，对基于观测数据的时间序列因果推断方法在经济金融、医疗和生物学、地球系统科学和其他工程领域的典型应用进行了简要介绍。最后，结合时间序列因果推断的重难点问题，比较5种方法的优缺点，分析下一步研究重点，展望未来的研究方向。

<p class=\"MsoNormal\" style=\"text-align: justify; line-height: 200%;\">Axial bearing capacity (ABC) of piles is usually determined by static load test (SLT). However, conducting SLT is costly and time-consuming. High strain dynamic pile testing (HSDPT) which is provided by pile driving analyzer (PDA) is a more recent approach for predicting the ABC of piles. In comparison to SLT, PDA test is quick and economical. Implementing feed forward back-propagation artificial neural network (ANN) for solving geotechnical problems has recently gained attention mainly due to its ability in finding complex nonlinear relationships among different parameters. In this study, an ANN-based predictive model for estimating ABC of piles and its distribution is proposed. For network construction purpose, 36 PDA tests were performed on various concrete piles in different project sites. The PDA results, pile geometrical characteristics as well as soil investigation data were used for training the ANN models. Findings indicate the feasibility of ANN in predicting ultimate, shaft and tip bearing resistances of piles. The coefficients of determination, R², equal to 0.941, 0.936, and 0.951 for testing data reveal that the shaft, tip and ultimate bearing capacities of piles predicted by ANN-based model are in close agreement with those of HSDPT. By using sensitivity analysis, it was found that the length and area of the piles are dominant factors in the proposed predictive model.</p><p class=\"MsoNormal\" style=\"text-align: justify; line-height: 200%;\"> </p><p class=\"MsoNormal\" style=\"text-align: justify; line-height: 200%;\"><strong>Resumen</strong></p><p class=\"MsoNormal\" style=\"text-align: justify; line-height: 200%;\">La Capacidad Axial de Soporte (ABC, en inglés) de un pilote de construcción se determina usualmente a través de una Prueba de Carga Estática (SLT, inglés). Sin embargo, estas pruebas son costosas y demandan tiempo. La evaluación de las Dinámicas de Alto Esfuerzo de Pilotes (HSDPT, inglés), que la provee el programa de Análisis de Excavación (PDA, inglés), es una forma de aproximación más reciente para preveer la Capacidad Axial de Soporte. En comparación con la Prueba de Cargas Estática, la evaluación PDA es rápida y económica. La implementación de Redes Neuronales Arficiales (ANN, en inglés) que permita resolver problemas geotécnicos ha ganado atención recientemente debido a su posibilidad de hallar relaciones no lineales entre los diferentes parámetros. En este estudio se propone un modelo predictivo ANN para estimar la Capacidad Axial de Soporte de pilotes y su distribución. Para fines de una red de construcción se realizaron 36 pruebas PDA en pilotes de diferentes proyectos. Los resultados de los Análisis de Excavación, las características geométricas de los pilotes, al igual que los datos de investigación del suelo se utilizaron para probar los modelos ANN. Los resultados indican la viabilidad del modelo ANN en predecir la resistencia de los pilotes. Los coeficientes de correlación, R², que alcanzaron 0.941, 09.36 y 0.951 para la evaluación de los datos, revelan que la capacidad del pilotaje en el último rodamiento, en el cojinete del eje y en la punta que se predijeron con el modelo ANN concuerda con las establecidas a través del HSDPT. A través del análisis de respuesta se determinó que la longitud y el área de los pilotes son factores dominantes en el modelo predictivo propuesto.</p>

为了解决传统经验公式模型对钢筋混凝土锈胀开裂预测方法存在的公式不统一、精度有限等问题的局限性，本文提出一种基于Stacking集成模型融合的钢筋混凝土锈胀开裂预测方法。首先，通过文献收集的223组钢筋混凝土锈胀开裂试验数据进行数据预处理；其次，基于支持向量回归(SVR)、随机森林(RF)和极端梯度提升树(XGBoost)算法分别进行贝叶斯优化超参数、模型训练及评估，采用决定系数R2、平均绝对误差(MAE)和均方根误差(RMSE)对比分析3种机器学习模型的预测性能，并在此基础上搭建基于Stacking融合多种算法的预测模型；最后，对所提出的机器学习模型和传统经验公式模型进行泛化性能验证，并探讨基于XGBoost模型的可解释性分析。结果表明：与其他机器学习模型相比，基于Stacking集成模型的预测精度和泛化性能较好，且可解释性分析结果说明模型预测逻辑与实际工程经验较为吻合。研究结果有助于提高钢筋混凝土锈胀开裂预测精度，可为决策者在实际工程提供科学的理论指导。

In predicting corrosion-induced cracking of reinforced concrete, traditional empirical formulas used are varied with limited precision of prediction. To address these limitations, this paper presents a method based on the stacking of models to predict the cracking of reinforced concrete due to corrosion induced expansion. Firstly, 223 sets of test data on the cracking of reinforced concrete due to corrosion induced expansion were collected from published articles and processed in advance. Next, Bayesian optimization of hyperparameters, model training, and evaluation were conducted separately based on Support Vector Regression (SVR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) algorithms. Determination coefficient (R²), mean absolute error (MAE), and root mean square error (RMSE) were utilized for a comparative analysis of the prediction performances of three machine learning models. On this basis, a prediction model integrating multiple algorithms with the Stacking method was proposed. Finally, the generalization performances of the proposed prediction model and traditional empirical formula models were verified, and the XGBoost model was employed to analyze the interpretability of the proposed model. As revealed in the results, the proposed model has better prediction accuracy and generalization performance than other machine learning models. The interpretability analysis result demonstrates that the prediction of the proposed model logic matches the practical engineering experience. This finding is conducive to improve the prediction accuracy of thecorrosion-induced cracking of reinforced concrete, and can provide scientific theoretical guidance for decision-makers in practical engineering.