Engineered cementitious composite (ECC) is a unique material, which can significantly contribute to self-healing based on ongoing hydration. However, it is difficult to model and predict the self-healing performance of ECC. Although different machine learning (ML) algorithms have been utilized to predict several properties of concrete, the application of ML on self-healing prediction is considerably rare. This paper aims to provide a comparative analysis on the performance of various machine learning models in predicting the self-healing capability of ECC. These models include four individual methods, linear regression (LR), back-propagation neural network (BPNN), classification and regression tree (CART), and support vector regression (SVR). To improve prediction accuracy, three ensemble methods, namely bagging, AdaBoost, and stacking, were also studied. A series of experimental works on the self-healing performance of ECC samples was conducted, and the results were used to develop and compare the accuracy among the ML models. The comparison results showed that the Stack_LR model had the best predictive performance, showing the highest coefficient of determination (R2), the lowest root-mean-squared error (RMSE), and the smallest prediction error (MAE). Among all individual models studies, the BPNN model performed the best in terms of the RMSE and R2, while SVR performed the best in terms of the MAE. Furthermore, SVR had the smallest prediction error (MAE) for crack widths less than 60 μm or greater than 100 μm, while CART had the smallest prediction error (MAE) for crack widths between 60 μm and 100 μm. The study concluded that the individual and ensemble methods can be used to predict the self-healing of ECC. Ensemble models were able to improve the accuracy of prediction compared to the individual model used as their base learner, i.e., a 2.3% to 4.9% reduction in MAE. However, selecting an appropriate individual and ensemble method is critical. To improve the performance accuracy, researchers should employ different ensemble methods to compare their effectiveness with different ML models.
The development and utilization of underground space is an effective way to make intensive use of resources, solve "big city disease" and achieve high-quality development. The expansion and renovation of underground space in a central urban area is likely to cause serious damage to surrounding structures. In this study, a deep foundation excavation for the reconstruction of an urban subway station in the Greater Bay Area was chosen for analysis using the finite element method. Different from common excavation engineering, the interaction between the three coupling factors of train dynamic load, foundation excavation, and viaduct pile foundation were analyzed. Six different cases were calculated considering different working conditions of excavation depth and train dynamic load. Soil was evaluated using modified Cam-Clay model. The physical parameters of the soil were determined through on-site and laboratory tests. The results were compared with monitoring data, and the accuracy of the finite element model was verified. The settlement and influence range of the soil, and displacement and internal forces of viaduct piles were analyzed. The maximum settlement of the soil occurred in the direction of the short side of the foundation pit. The maximum value was approximately 0.53 times the excavation depth. The settlement increased by approximately 49% when applying the train load. The dynamic load had an aggravating influence on the horizontal displacement of the top of the pile, with a maximum increase of 51%. Moreover, the dynamic load increased the negative bending moment of the viaduct piles. This study provides a reference for the design and construction of geotechnical engineering projects.
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