To study the effect of filling phosphogypsum (PG) on the axial compression behavior of cold-formed thin-walled steel (CFS) walls, four full-scale test specimens were designed and fabricated, in consideration of the filling regions of PG as well as measures with or without wall sheathings. The fabricated specimens were tested under monotonic vertical loads, and the failure processes and failure modes of specimens were elaborated. Each specimen’s axial load-displacement curve, bearing capacity, strain curve, and energy dissipation capacity were investigated in detail. Furthermore, the internal force distributions of wall components and failure mechanisms were revealed. The test results indicated that the failure characteristics of specimens include the buckling of the steel tubes, cracking of wall sheathings, crushing of PG, and distorting of tracks. Compared with the cavity wall specimen, the axial bearing capacity of the specimen filled with PG in the studs only increased by 37.4%, and the bearing capacity of the specimen filled with PG in and between the studs increased by 115.7%. This indicates that filling PG can effectively improve the axial bearing capacity of CFS walls. The bearing capacity of the specimen without wall sheathings is lower than that of the specimen with wall sheathings, indicating that the wall sheathing has a beneficial effect on the bearing capacity of the specimen. In addition, the internal forces of components during the loading process were analyzed. It found that the steel tube and PG made a great contribution to the bearing capacity of the wall. Specifically, the steel tube played a leading role in the early loading stage, while the PG played a leading role in the later loading stage.
The purpose of formal education is to increase students’ abilities, and its content is to impart knowledge through various courses. Thus, it is essential to accurately identify the relationship between knowledge and students’ ability increment to ensure the quality of education and the sustainable development of education. Currently, this relationship is mainly established based on previous educational data and teachers’ experience, which is often imprecise. This paper proposes a framework for knowledge and ability recognition based on the structural characteristics of complex network modules. The proposed framework utilizes a knowledge cognitive-interdependent network model (KCIN) as its object. First, the key knowledge nodes are identified via cognitive convergence flow of knowledge nodes in KCIN. Subsequently, the module structure of the knowledge network is identified by taking the key knowledge nodes as the core. Finally, the relationship between knowledge and ability is established by identifying the similar attributes of nodes in complex network modules. To validate the framework, we use teaching process data on the Data Structure course, which is a fundamental course for Information majors. The results show that the framework can effectively optimize the knowledge–ability relationship acquired from previous data and teacher experience.
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