Deterministic Wind Load Dynamic Analysis of High Rise Steel Buildings Including P-Delta Effects

Dheeb, Ahmed Sada; Abbas, Rafaa M.

doi:10.33261/jaaru.2019.26.1.017

Cited by 1 publication

(2 citation statements)

References 4 publications

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“…However, a sensitivity analysis for different damping values is performed comparing correlation coefficients for each value to quantify its impact. Table 3 shows peak absolute errors for each event (#1-6), direction (x,y) and storey (1)(2)(3), where error estimating displacements from experimental accelerometer and other measurements is 0.5-1.0 mm depending on magnitude for the method used here [41,46]. Figure 7 shows the 3 rd storey x and y displacement responses, measured and simulated, for all 6 events during periods of strong motion.…”

Section: Analyses and Evaluationmentioning

confidence: 99%

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Automated structural dynamic modelling using model-free health monitoring results

Tondut

Chase

Zhou

2020

BNZSEE

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Structural health monitoring (SHM) methods provide damage metrics and localisation, but not a means of answering subsequent questions concerning immediate or long-term damage mitigation, risk, or safety in re-occupancy. Models based on the SHM results would provide a means to test these issues, but typically require extensive human input, which is not available immediately after an event to enhance and optimise immediate decision-making. This work presents a simple, readily automated modelling approach to translate SHM results from the proven hysteresis loop analysis (HLA) method into foundation models for immediate use. Experimental data from a 3-storey structure tested at the E-Defense facility in Japan are used to assess model performance. The model’s ability to capture the essential dynamics is assessed by comparing peak dynamic displacement and cross correlation coefficient (Rcoeff). For all 6 events, 3 storeys, and 2 directions, median (5-95% Range) of peak displacement error was 0.82 (0.17, 4.09) mm, and average Rcoeff = 0.82, all of which were significantly improved if the worst event was excluded. Overall, accurate nonlinear, time-varying baseline models were created using data from SHM damage identification and localisation methods using relatively quite simple model structures. The method is readily automated via algorithm, and the models were suitable for initial investigation and analysis on safety, damage mitigation, and thus re-occupancy. Such models could take SHM from being a tool for damage identification and extend it into further decision-making, creating far greater utility for engineers and owners, which could further spur impetus for investment in monitoring.

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Section: Analyses and Evaluationmentioning

confidence: 99%

“…Increasing urbanisation has magnified seismic risk in seismic zones [1], and the resulting structural damage poses a major risk with significant social and economic impacts. Structural health monitoring (SHM) provides methods to detect, localise, and quantify damage after major events.…”

Section: Introductionmentioning

confidence: 99%