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All Journal Civil Engineering Journal Acta Pedagogia Asiana
Bing Han
North China University of Technology
Civil Engineering, Building, Construction & Architecture Education Other

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Hierarchical Learning-Based System Decomposition for Time-Dependent Structural System Reliability Assessment Yan, Bingchuan; Han, Bing; Xie, Huibing; Yu, Jiaping
Civil Engineering Journal Vol. 12 No. 2 (2026): February
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2026-012-02-05

Abstract

Time-dependent reliability assessment of structural systems is challenging when degradation and multiple interacting failure modes govern failure. Under these conditions, the system limit state function (LSF) may be highly nonlinear, non-smooth, and available only implicitly through high-fidelity analysis. This paper proposes a system decomposition and hierarchical learning (DHL) framework to construct an evaluable surrogate system LSF for degradation-driven, time-variant reliability analysis. The structural system is decomposed into dominant failure modes and their connectivity. Artificial neural networks are trained hierarchically to learn the decomposed relationships. Mode-level surrogates approximate the LSF of each failure mode. A system-level surrogate then integrates the mode-level performance quantities and time to capture mode interaction and mechanism switching. The resulting surrogate is combined with Monte Carlo simulation and the probability density evolution method to compute time-dependent failure probabilities and, when required, the evolution of the system performance probability density. Two benchmark problems—a highly nonlinear parallel system and a rigid–plastic portal frame with correlated collapse mechanisms under degrading capacities—are used to evaluate the approach. DHL improves system-level surrogate fidelity relative to direct system-level ANN learning, with mean reliability prediction errors below 3.1% and 1.23% in the two benchmarks, respectively, while remaining compatible with both sampling-based and density-evolution propagation schemes.
Using Asynchronous Discussion Forums to Enhance Engagement of Students in Online Teaching Chu, Guanying; Wang, Yu; Bu, Qinglei; Han, Bing; Xue, Fei; Lim, Enggee
Acta Pedagogia Asiana Volume 5 - Issue SI - 2026
Publisher : Tecno Scientifica Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/apga.v5iSI.1049

Abstract

The shift towards HyFlex learning in the post-pandemic era has introduced new challenges for higher education, particularly in maintaining student engagement and motivation in online learning environments. This paper examines the potential of anonymous asynchronous online discussion forums (AODFs) to enhance participation and engagement in large online classes. We propose a new model of forum management that integrates question-answering and peer-to-peer interaction, allowing students to post questions anonymously while responses remain non-anonymous. The study investigates the evolving roles of teachers and students in promoting and participating in forum activities, adopting a “students as partners” perspective. Data from the implemented AODF indicate increased student participation and motivation, with a substantial portion of non-academic questions addressed through peer discussion. Challenges such as lurking behavior and the limitations of relying solely on technology are also highlighted. The study underscores the critical role of instructors in evaluating and adapting emerging technologies to meet student needs and foster a sense of community in online learning environments.
Co-Authors Bu, Qinglei Chu, Guanying Lim, Enggee Xie, Huibing Xue, Fei Yan, Bingchuan Yu Wang Yu, Jiaping