摘要(英) |
Taiwan is in the Pacific seismic zone and experiences an average of 18,500 earthquakes per year, including several disastrous ones, such as the 1999 Chi-Chi earthquake, the 2016 Meinong earthquake, and the 2022 Chishang-Guanshan earthquake. Many reinforced concrete (RC) buildings, especially those with RC frames and masonry-infill walls, have suffered significant damage or even collapse in these earthquakes. According to the
Ministry of the Interior, Taiwan′s average age of buildings is 33 years. As the building age increases, the seismic risk for structures with RC frames and masonry-infill walls also increases. Therefore, effectively analyzing and assessing such structures′ seismic performance and collapse risk is an important issue. This study focuses on the seismic risk of older buildings in Taiwan, particularly the RC moment frames with masonry-infill walls.Existing numerical simulations of masonry-infill walls often use empirical formulas derived from experimental data, requiring multiple corrections and making the process cumbersome. This often leads to the neglect of masonry-infill walls in practical analysis. Recent developments
in probabilistic seismic risk assessment, which consider uncertainties in structural dynamic response and ground motion, provide more understandable results in terms of collapse probability and economic loss compared to traditional performance assessments. However, understanding
the structural response under different earthquake intensities requires extensive nonlinear dynamic time-history analysis, and establishing models for RC frames with masonry-infill walls is a current challenge for academics and practitioners.
In light of this, the study involves the extensive collection of specimens of RC frames with masonry-infill walls from both domestic and international sources. It integrates the open-source seismic engineering analysis software OpenSees with Python to achieve automated modeling of RC frames with masonry-infill walls. Particular emphasis is placed on the Pinching4 model and validating the hysteresis loops obtained from
experiments. The results show that the proposed automated program can more accurately simulate the hysteresis behavior of structures with masonry-infill walls. This study also incorporates probabilistic seismic risk assessment to assess the collapse risk of RC frames with masonry-infill walls, effectively improving the efficiency of nonlinear time-history analysis. The findings indicate that masonry-infill walls, which differ in lateral stiffness from pure frame structures, also impact the probability of collapse. |
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