Ge Wang

In active learning reliability methods, an approximation of limit state function (LSF) with high precision is the key to accurately calculating the failure probability (P_f). However, existing sampling methods cannot guarantee that candidate samples can approach the LSF actively, which lowers the accuracy and stability of the results and causes excess computational effort. In this paper, a novel candidate samples-generating algorithm was proposed, by which a group of evenly distributed candidate points on the predicted LSF of performance function (either the real one or the surrogate model) could be obtained. In the proposed method, determination of LSF is considered as an optimization problem in which the absolute value of performance function was considered as objective function. After this, a normal search particle swarm optimization (NSPSO) was designed to deal with such problems, which consists of a normal search pattern and a multi-strategy framework that ensures the uniform distribution and diversity of the solution that intends to cover the optimal region. Four explicit performance functions and two engineering cases were employed to verify the effectiveness and accuracy of NSPSO sampling method. Four state-of-the-art multi-modal optimization algorithms were used as competitive methods. Analysis results show that the proposed method outperformed all competitive methods and can provide candidate samples that evenly distributed on the LSF.

The stability of geogrid-reinforced soil structure is closely related to the interface characteristics between geogrid and soil. However, the creep behavior of the soil–geogrid interface is still unrevealed. In this study, using a modified stress-controlled pullout device, influence of the normal pressure, dry density, and water content on creep behavior of interface of compacted loess and high-density polyethylene (HDPE) geogrid is investigated. A three-parameter empirical model and a Merchant element model were established through fitting analysis. Analysis results show that the normal pressure, dry density, and water content have significant effects on the creep shear displacement of the reinforced soil interface. Under the same pullout level, creep displacement of the interface increases with the increase of water content and decreases with the increase of dry density and normal pressure. Both the three-parameter empirical model and Merchant element model can describe the creep characteristics of the reinforced soil interface. The Merchant model is more accurate in the early stage, while the three-parameter empirical model is more suitable for predicting the long-term creep deformation of the interface of compacted loess and geogrid.

Property of geotechnical materials has inherent uncertainty due to the complex formation process and inevitable test error. However, existing long-term deformation prediction methods for geotechnical structure such as a filling embankment are deterministic, which ignores the uncertainty of soil property. In this study, the uncertainty of creep behavior of compacted loess was investigated through repetitive creep tests and statistical analysis. Five different loading levels and two loading modes were considered in the tests. The creep test was repeated 45 times for each condition. Through a statistical analysis for the test results, a modified Merchant creep model was established to improve the accuracy of long-term deformation prediction. An empirical transformation equation between staged loading and separated loading mode of the creep test results was also introduced to improve applicability of the method. On this basis, a non-deterministic predication method for post-construction settlement of loess fill embankment was proposed. Furthermore, the proposed method was applied to the prediction of the post-construction of a 61.5 m loess filling embankment. The measured on-site post-construction settlement value falls within the 95% confidence interval of the predicted range which proves the efficiency and practicability of the proposed non-deterministic predication method. Compared to deterministic methods, the proposed method can describe the predicted deformation in a probabilistic way in the form of contour plot. The proposed method provides a basic approach for the probabilistic design and reliability assessment of filling engineering.