Multi-factor and multi-level predictive models of building natural period

Wang, Zetao; Shen, Jiaxu

doi:10.1016/j.engstruct.2021.112622

Cited by 10 publications

(3 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Notably, for the case with output correction, the coefficients of determination ( R 2 ) for the first natural period in the X‐ and Y‐directions were 0.96 and 0.97, respectively, which were significantly higher than those of a physics‐based model recently proposed for the design optimization of shear‐wall structures (0.68 and 0.76, respectively) 15 . Additionally, the linear correlation coefficient ( r ) for the largest natural period in both directions (i.e., the fundamental period) was 0.99, which was significantly higher than that of another recently proposed natural period predictive model for shear‐wall structures (0.85) 43 . Hence, the estimation accuracy of the GNN adopted in this study is satisfactory, as shown in Table 3.…”

Section: Numerical Experimentsmentioning

confidence: 55%

“…15 Additionally, the linear correlation coefficient (r) for the largest natural period in both directions (i.e., the fundamental period) was 0.99, which was significantly higher than that of another recently proposed natural period predictive model for shear-wall structures (0.85). 43 Hence, the estimation accuracy of the GNN adopted in this study is satisfactory, as shown in Table 3. MAPE, mean absolute percentage error; TD-S1, task decomposition strategy 1; TD-S2, task decomposition strategy 2; TD-S3, task decomposition strategy 3.…”

Section: Single-step Error Analysismentioning

confidence: 66%

See 1 more Smart Citation

Hybrid surrogate model combining physics and data for seismic drift estimation of shear‐wall structures

Fei,

Liao,

Zhao

et al. 2024

Earthq Engng Struct Dyn

View full text Add to dashboard Cite

To address the issue of costly computational expenditure related to high‐fidelity numerical models, surrogate models have been widely used in various engineering tasks, including design optimization. Despite the successful application of the existing surrogate models, physics‐based models depend largely on simplifications and assumptions, which render parameter calibration challenging; whereas data‐driven models require substantial data to reach their full potential, with their performance often being constrained in tasks when obtaining massive data is difficult. In this study, a hybrid surrogate model is proposed combining physics‐based and data‐driven models to rapidly estimate building seismic responses. The application of this model is exemplified through effective estimation of inter‐story drift ratios (IDRs), being a critical factor in shear‐wall structure design. Initially, a data augmentation technique and a parametric modeling procedure are introduced to significantly enhance the dataset diversity. Subsequently, a task decomposition strategy is proposed to effectively integrate a data‐driven graph neural network (GNN) and a physics‐based flexural‐shear model. Additionally, the output layer and the loss function of the GNN are modified to enhance the estimation accuracy by eliminating fundamental errors. Results of numerical experiments indicate that the proposed hybrid model can complete IDR estimations in an average time of 0.56 s, with a mean absolute percentage error of 12.7%. This performance significantly surpasses that of existing purely data‐driven and physics‐based models. A case study shows that the efficiency of the proposed hybrid model is approximately 100 times greater than that of conventional finite element software. This enables an accurate assessment of the design compliance with code requirements. The results of this study can be applied to the design optimization of seismic‐resistant building structures.

show abstract

Section: Numerical Experimentsmentioning

confidence: 55%

Section: Single-step Error Analysismentioning

confidence: 66%

Hybrid surrogate model combining physics and data for seismic drift estimation of shear‐wall structures

Fei,

Liao,

Zhao

et al. 2024

Earthq Engng Struct Dyn

View full text Add to dashboard Cite

show abstract

“…48 (constructed around 1989 corresponds to Pre‐Code and Low‐Code, respectively), Putuo buildings are classified into 17 categories, with relevant information listed in Table 5. Table 5 also offers dynamic property parameters in which the fundamental periods are estimated based on regression formulas of building height from ambient vibration testing results, 48,49 and are uniformly multiplied by an amplification factor of 1.5 to account for the “true” elastic period before structural yielding. On this basis, the fragility parameters by HAZUS are provided to obtain the structural fragility curves, with fragility curves for two typical buildings displayed in Figure 12.…”

Section: Case Studymentioning

confidence: 99%

Structure‐to‐structure seismic damage correlation model

Xiang,

Shen,

et al. 2024

Earthq Engng Struct Dyn

Self Cite

View full text Add to dashboard Cite

Strong earthquake disasters can cause noticeable damage correlations among regional buildings with similar mechanical properties, termed structure‐to‐structure seismic damage correlation, which has a pronounced impact on the regional seismic risk assessment and therefore needs to be properly quantified. This study first introduces a time‐domain analytical and equivalent frequency‐domain analysis based on random vibration theory for calculating structure‐to‐structure seismic damage correlation coefficients. Subsequently, by employing the spatially consistent white noise excitation and the spatially varying white noise excitation with the Luco–Wong coherence function, the structural filtering effect and the ground motion spatial correlation effect are progressively incorporated, and an analytical interstructural damage correlation model incorporating structural dynamic properties and spatial distance is derived. Comparations with Monte Carlo simulations and existing empirical models demonstrate that the proposed analytical model possesses a clear physical basis and high reliability. Finally, a case study was conducted on a district having 29,461 buildings in Shanghai, China to illustrate the influence of interstructural damage correlation on the regional seismic risk. Results show that disregarding the interstructural seismic damage correlation can lead to underestimation of overall loss uncertainty.

show abstract

Spatiotemporal correlation analysis of the dynamic response of supertall buildings under ambient wind conditions

Yang

Pan

Zhu

et al. 2021

Structural Design Tall Build

View full text Add to dashboard Cite

The structural health monitoring systems of supertall buildings are typical big data systems with abundant and high-dimensional data. Correlation analysis is a vital technique for data mining and analysis. This study aims to reveal the spatial and temporal correlation of 4 years of structural health monitoring data from the 632-m-high Shanghai Tower under normal wind and typhoon conditions. In this paper, the root mean square (RMS) value of the acceleration response is used to characterize the structural vibration intensity. The maximal information coefficient (MIC) and Pearson product-moment correlation coefficient (PPMCC) are adopted to measure the spatial correlations along Shanghai Tower under different wind conditions. The temporal correlation of structural vibration is investigated with kernel density estimation and the density-based spatial clustering of applications with noise (DBSCAN) algorithm.The results show that the structural vibrations of Shanghai Tower in different spatial positions are correlated. The correlation of the structural vibrations of the same floor is at a high level under both normal wind and typhoons, while that of different floors rises with increasing mean wind speed. Furthermore, the MIC is more stable than the PPMCC in measuring this spatial correlation. Based on temporal correlation analysis, a circadian rhythm tendency of the structural vibration intensity of Shanghai Tower on the daily scale is identified, and it is verified that the structural response is temporally correlated. These findings can provide a structural health state estimation index based on the correlation coefficient and provide a basis for the screening of modeling data.

show abstract

Multi-factor and multi-level predictive models of building natural period

Cited by 10 publications

References 38 publications

Hybrid surrogate model combining physics and data for seismic drift estimation of shear‐wall structures

Hybrid surrogate model combining physics and data for seismic drift estimation of shear‐wall structures

Structure‐to‐structure seismic damage correlation model

Spatiotemporal correlation analysis of the dynamic response of supertall buildings under ambient wind conditions

Contact Info

Product

Resources

About