Dynamic reliability analysis of Aerial Building Machine under extreme wind loads using improved QBDC-based active learning

Abstract

Aerial building machine (ABM) is a new comprehensive construction equipment used for the construction of high-rise buildings, and the research of its wind-induced response is essential for structural safety. However, the traditional reliability estimation for complex structures is generally computationally expensive. This study proposes a novel and time-saving active learning (AL) approach to predict the structural time-variant response and calculate the reliability under extreme wind loads based on improved Query-by-Dropout-Committee (QBDC), Deep Learning (DL) and Probability Density Evolution Method (PDEM). To establish an effective training set, the load sample pool from the turbulent wind field is constructed by the number theory method (NTM). Then a DL surrogate model, aimed to simulate the excitation-response relationship of the structure, is well-trained from data calculated by the Finite Element Method (FEM) under the workflow of the improved QBDC-based AL approach. Lastly, the structural responses predicted by the surrogate model are used to solve the Generalized Density Evolution Equation (GDEE) to obtain the Probability Density Function (PDF) of the structural response. The dynamic reliability can be gained using absorbing boundary condition. To demonstrate the feasibility of the proposed approach, an ABM in China is taken as a case study. It is found that (1) The external trusses of the platform are weak positions under the horizontal wind loads. It is suggested to append the longitudinal (windward direction) trusses to reduce the local stress and deformation. (2) Under turbulent wind loads with the average speed of 40 m/s, the maximum structural response of the ABM will exceed the threshold value and the reliability is 0.932. (3) The reliability calculation error derived by the proposed approach and the traditional PDEM is within 0.5 % in this study, while the computational efficiency of the AL approach is 77.08 % higher than that of the traditional PDEM. The novelty lies in the proposed active learning approach that can easily obtain the stochastic response and dynamic reliability of the ABM, where the analysis workflow not only has a low computational cost but also maintains a high calculation accuracy.