Purpose: Assessing the failure probability of engineering structures is still a challenging task in the presence of various uncertainties due to the involvement of expensive-to-evaluate computational models. The traditional simulation-based approaches require tremendous computational effort, especially when the failure probability is small. Thus, the use of more efficient surrogate modeling techniques to emulate the true performance function has gained increasingly more attention and application in recent years. In this paper, an active learning method based on a Kriging model is proposed to estimate the failure probability with high efficiency and accuracy.Design/methodology/approach: To effectively identify informative samples for the enrichment of the design of experiments, a set of new learning functions is proposed. These learning functions are successfully incorporated into a sampling scheme, where the candidate samples for the enrichment are uniformly distributed in the n-dimensional hypersphere with an iteratively updated radius. To further improve the computational efficiency, a parallelization strategy that enables the proposed algorithm to select multiple sample points in each iteration is presented by introducing the K-means clustering algorithm. Hence, the proposed method is referred to as the adaptive Kriging method based on K-means clustering and sampling in n-Ball (AK-KBⁿ).Findings: The performance of AK-KBⁿ is evaluated through several numerical examples. According to the generated results, all the proposed learning functions are capable of guiding the search toward sample points close to the LSS in the critical region and result in a converged Kriging model that perfectly matches the true one in the regions of interest. The AK-KBⁿ method is demonstrated to be well suited for structural reliability analysis and a very good performance is observed in the investigated examples.Originality/value: In this study, the statistical information of Kriging prediction, the relative contribution of the sample points to the failure probability and the distances between the candidate samples and the existing ones are all integrated into the proposed learning functions, which enables effective selection of informative samples for updating the Kriging model. Moreover, the number of required iterations is reduced by introducing the parallel computing strategy, which can dramatically alleviate the computation cost when time demanding numerical models are involved in the analysis.

Numerical investigations on the performance of ultra-high performance concrete (UHPC) subjected to projectile impacts have attracted extensive attention, but there are still deficiencies in the accuracy and computational efficiency of related simulation methods. To make up for these deficiencies, a mesoscale equivalent model for UHPC is developed to simulate the response of UHPC under projectile impacts. In this model, an equivalent treatment is conducted on steel fibers to reduce their quantity under the premise that the interfacial shearing force between the fibers and the matrix remains equal. Based on the mesoscale equivalent model, numerical simulations of uniaxial compressive tests and projectile penetration tests on UHPC specimens are performed in LS-DYNA, and the numerical results are compared with the corresponding experimental results to verify the developed model. It is found that the mesoscale equivalent model could accurately reproduce the failure mode and stress-strain curve of UHPC specimens when the amplification factor of steel fibers is lower than 5. When the amplification factor is 5, the computational efficiency of the numerical models for penetration tests is significantly improved, and the maximum relative error between the numerical results of the crater diameter and penetration depth and experimental results is 11.7%. The successful application of the mesoscale equivalent model provides a more precise and in-depth perspective in simulating the response of UHPC with steel fibers subjected to projectile impact. Then, the influence of projectile striking velocities, UHPC compressive strengths, and volume percentages of steel fibers on the depth of penetration (DOP) are further numerically assessed. Based on the simulated data, modifications of the Young equation for predicting the DOP are conducted, and the maximum relative error of the modified equation is 13.9%. This demonstrates that the modified Young equation can accurately predict the DOP of UHPC subjected to projectile impacts.

The precast ballastless track slab has been widely applied in the Chinese high-speed railway system. However, the current slabs, made of normal concrete (NC) materials and designed in a “plane” structural configuration, were found vulnerable to the train loads and temperature effects. This paper proposed an innovative slab type, which was made of an alternative reactive powder concrete (RPC) material and designed in a “frame” structural type. Four frame slabs with different cross section profiles were parametrically investigated, and the best configuration was proposed. The results demonstrated the proposed RPC slabs can well meet the requirements of the stiffness and strength. Compared with the NC plane slabs, the warping deformation of the RPC frame slabs were decreased as much as by 41.7% and 37.0%, respectively, under the −10°C temperature gradients and the combination of train and temperature effects. The simulated tensile stresses were within the corresponding RPC strength capacity. The crac...

Reinforced concrete (RC) slabs are the main load-bearing member of engineering structures, which may be threatened by blast loading. Predicting and analyzing the damage condition and failure mode of RC slab is a necessary means to ensure structural safety and reduce the potential hazards. In this study, two machine learning (ML) models are proposed using data from the published literature and complementary numerical simulations. By comparing six algorithms, it is determined that Extreme Gradient Boosting (XGBoost) is the optimal structural damage model and Categorical Boosting (CatBoost) is the optimal failure mode classification model. In addition, the Shapley additive explanations (SHAP) method was used to analyze the importance and correlation of features. The results show that the TNT charge mass, explosion distance, and compressive strength are the key features. On this basis, when the TNT charge mass is more than 2.5 kg, the sensitivity of the explosion distance increases, and when the compressive strength is more than 50 MPa, the impact on the structural damage is not significant. The research results can predict the structural damage and failure modes of RC slab under blast loading quickly and accurately, and provide guidance for the explosion-proof design of RC slabs.

To investigate the seismic performance of prefabricated circular hollow piers with socket and slot connection, eight 1/3.5-scale specimens constructed with polyvinyl alcohol (PVA) fiber at the pier body were tested. The main test variables included the axial compression ratio, grade of pier concrete, shear-span ratio, and stirrup ratio. The seismic performance of prefabricated circular hollow piers was studied and analyzed from the aspects of the failure phenomenon, hysteresis curve, bearing capacity, ductility index, and energy dissipation capacity. The test and analysis results showed that all specimens suffered from flexural shear failure, and the increase in axial compression ratio and stirrup ratio would lead to more significant spalling of the concrete at the bottom of the specimen, but the existence of PVA fiber would improve this phenomenon. In a certain range, the increase in axial compression ratio, stirrup ratio, and the decrease in shear span ratio can improve the bearing capacity of the specimens. However, an excessive axial compression ratio would easily lead to a decrease in the ductility of the specimens. The increase in the stirrup ratio and shear-span ratio caused by the change in height can improve the energy dissipation characteristics of the specimen. On this basis, an effective shear-bearing capacity model of the plastic hinge area of prefabricated circular hollow piers was proposed, and the prediction effects of specific shear capacity models on test specimens were compared.