Automated modal identification using principal component and cluster analysis: Application to a long‐span cable‐stayed bridge

2019 ◽  
Vol 26 (10) ◽  
Author(s):  
Jian‐Xiao Mao ◽  
Hao Wang ◽  
Yu‐Guang Fu ◽  
Billie F. Spencer
Keyword(s):  
Cluster Analysis ◽  
Principal Component ◽  
Modal Identification ◽  
Cable Stayed Bridge ◽  
Long Span ◽  
Analysis Application ◽  
Automated Modal Identification ◽  
And Cluster Analysis

STUDY ON PLANKTON OF DIFFERENT CATEGORIES OF LAKES IN SUMMER BY MEANS OF PRINCIPAL COMPONENT ANALYSIS, FACTOR ANALYSIS AND CLUSTER ANALYSIS

2010 ◽  
Vol 36 (1) ◽  
pp. 43-50
Author(s):  
Luo-Jun GONG ◽  
Shi-Ping ZHANG ◽  
Bang-Xi XIONG ◽  
Ding-Zhu LIU ◽  
Jin-Zhong LI ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Factor Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
And Cluster Analysis

Dynamic Property Evaluation of a Long-Span Cable-Stayed Bridge (Sutong Bridge) by a Bayesian Method

2018 ◽  
Vol 19 (01) ◽  
pp. 1940010 ◽  
Author(s):  
Yan-Chun Ni ◽  
Qi-Wei Zhang ◽  
Jian-Feng Liu
Keyword(s):  
Natural Frequency ◽  
Dynamic Characteristics ◽  
Structural Damage ◽  
Dynamic Properties ◽  
Modal Identification ◽  
Modal Parameters ◽  
Modal Parameter ◽  
Cable Stayed Bridge ◽  
Long Span ◽  
Sutong Bridge

Modal identification aims at identifying the dynamic properties including natural frequency, damping ratio, and mode shape, which is an important step in further structural damage detection, finite element model updating, and condition assessment. This paper presents the work on the investigation of the dynamic characteristics of a long-span cable-stayed bridge-Sutong Bridge by a Bayesian modal identification method. Sutong Bridge is the second longest cable-stayed bridge in the world, situated on the Yangtze River in Jiangsu Province, China, with a total length of 2 088[Formula: see text]m. A short-term nondestructive on-site vibration test was conducted to collect the structural response and determine the actual dynamic characteristics of the bridge before it was opened to traffic. Due to the limited number of sensors, multiple setups were designed to complete the whole measurement. Based on the data collected in the field tests, modal parameters were identified by a fast Bayesian FFT method. The first three modes in both vertical and transverse directions were identified and studied. In order to obtain modal parameter variation with temperature and vibration levels, long-term tests have also been performed in different seasons. The variation of natural frequency and damping ratios with temperature and vibration level were investigated. The future distribution of the modal parameters was also predicted using these data.

scholarly journals PRINCIPAL COMPONENT ANALYSIS AND CLUSTER ANALYSIS IN MULTIVARIATE ASSESSMENT OF WATER QUALITY

2017 ◽  
Vol 18 (2) ◽  
pp. 92-96 ◽  
Author(s):  
Elzbieta Radzka ◽  
Jolanta Jankowska ◽  
Katarzyna Rymuza
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
And Cluster Analysis

scholarly journals Use of Highly Discriminating Morphological Characters and Isozymes in the Study of Spanish Chestnut Cultivars

2006 ◽  
Vol 131 (6) ◽  
pp. 770-779 ◽  
Author(s):  
Santiago Pereira-Lorenzo ◽  
María Belén Díaz-Hernández ◽  
Ana María Ramos-Cabrer
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Multivariate Analysis ◽  
Castanea Sativa ◽  
Principal Component ◽  
Market Value ◽  
Morphological Characters ◽  
Minor Importance ◽  
Breeding Programs ◽  
And Cluster Analysis

Morphological characters (six traits) and isozymes (four systems, five loci) were used to discriminate between Spanish chestnut cultivars (Castanea sativa Mill.) from the Iberian Peninsula. A total of 701 accessions (representing 168 local cultivars) were analyzed from collections made between 1989 and 2003 in the main chestnut growing areas: 31 were from Andalucía (12 cultivars), 293 from Asturias (65 cultivars), 25 from Castilla-León (nine cultivars), four from Extremadura (two cultivars) and 348 from Galicia (80 cultivars). Data were synthesized using multivariate analysis, principal component analysis, and cluster analysis. A total of 152 Spanish cultivars were verified: 58 cultivars of major importance and 94 of minor importance, of which 18 had high intracultivar variation. Thirty-seven cultivars were clustered into 14 synonymous groups. Six of these were from Galicia, one from Castilla-León (El Bierzo), four from Asturias, one from Asturias and Castilla-León (El Bierzo), and two from Asturias, Castilla-León (El Bierzo), and Galicia. The chestnut cultivars from Galicia and Asturias were undifferentiated in genetic terms, indicating that they are not genetically isolated. Overall, chestnut cultivars from southern Spain showed the least variation. Many (58%) of Spanish cultivars produced more than 100 nuts/kg; removing this low market-value character will be a high priority. The data obtained will be of use in chestnut breeding programs in Spain and elsewhere.

scholarly journals Assessment of Physicochemical Properties of Tequila Brands: Authentication and Quality

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Alejandra Carreon-Alvarez ◽  
Amaury Suárez-Gómez ◽  
Florentina Zurita ◽  
Sergio Gómez-Salazar ◽  
J. Felix Armando Soltero ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Refractive Index ◽  
Physicochemical Properties ◽  
Analysis Of Variance ◽  
Principal Component ◽  
Component Analysis ◽  
Total Variability ◽  
And Cluster Analysis ◽  
Physicochemical Data

Several physicochemical properties were measured in commercial tequila brands: conductivity, density, pH, sound velocity, viscosity, and refractive index. Physicochemical data were analyzed by Principal Component Analysis (PCA), cluster analysis, and the one-way analysis of variance to identify the quality and authenticity of tequila brands. According to the Principal Component Analysis, the existence of 3 main components was identified, explaining the 87.76% of the total variability of physicochemical measurements. In general, all tequila brands appeared together in the plane of the first two principal components. In the cluster analysis, four groups showing similar characteristics were identified. In particular, one of the clusters contains some tequila brands that are not identified by the Regulatory Council of Tequila and do not meet the quality requirements established in the Mexican Official Standard 006. These tequila brands are characterized by having higher conductivity and density and lower viscosity and refractive index, determined by one-way analysis of variance. Therefore, these economical measurements, PCA, and cluster analysis can be used to determinate the authenticity of a tequila brand.

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