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Fox depth correction factor 1

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embedment 1

knowledge based variable structure decoupling 1

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Multivariable regression model for Fox depth correction factor

Ravi Kant MITTAL, Sanket RAWAT, Piyush BANSAL

Frontiers of Structural and Civil Engineering 2019, Volume 13, Issue 1,   Pages 103-109 doi: 10.1007/s11709-018-0474-6

Abstract: This paper presents a simple and efficient equation for calculating the Fox depth correction factor used in computation of settlement reduction due to foundation embedment. Classical solution of Boussinesq theory was used originally to develop the Fox depth correction factor equations which were rather complex in nature. The equations were later simplified in the form of graphs and tables and referred in various international code of practices and standard texts for an unsophisticated and quick analysis. However, these tables and graphs provide the factor only for limited values of the input variables and hence again complicates the process of automation of analysis. Therefore, this paper presents a non-linear regression model for the analysis of effect of embedment developed using “IBM Statistical Package for the Social Sciences” software. Through multiple iterations, the value of coefficient of determination is found to reach 0.987. The equation is straightforward, competent and easy to use for both manual and automated calculation of the Fox depth correction factor for wide range of input values. Using the developed equation, parametric study is also conducted in the later part of the paper to analyse the extent of effect of a particular variable on the Fox depth factor.

Keywords： settlement     embedment     Fox depth correction factor     regression     multivariable    

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Knowledge-Based Variable Structure Decoupling Control of a Nonlinear Multivariable System

Tu Chengyuan,Zeng Yanjun

Strategic Study of CAE 2001, Volume 3, Issue 10,   Pages 48-52

Abstract: knowledge-based variable-structure decoupling is developed, and be utilized to control a nonlinear multivariable

Keywords： nonlinear multivariable system     decoupling control     knowledge based variable structure decoupling    

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Assessment of temporal and spatial variations in water quality using multivariate statistical methods: a case study of the Xin'anjiang River, China

Xue LI,Pengjing LI,Dong WANG,Yuqiu WANG

Frontiers of Environmental Science & Engineering 2014, Volume 8, Issue 6,   Pages 895-904 doi: 10.1007/s11783-014-0736-z

Abstract: This study evaluated the temporal and spatial variations of water quality data sets for the Xin'anjiang River through the use of multivariate statistical techniques, including cluster analysis (CA), discriminant analysis (DA), correlation analysis, and principal component analysis (PCA). The water samples, measured by ten parameters, were collected every month for three years (2008–2010) from eight sampling stations located along the river. The hierarchical CA classified the 12 months into three periods (First, Second and Third Period) and the eight sampling sites into three groups (Groups 1, 2 and 3) based on seasonal differences and various pollution levels caused by physicochemical properties and anthropogenic activities. DA identified three significant parameters (temperature, pH and ) to distinguish temporal groups with close to 76% correct assignment. The DA also discovered five parameters (temperature, electricity conductivity, total nitrogen, chemical oxygen demand and total phosphorus) for spatial variation analysis, with 80.56% correct assignment. The non–parametric correlation coefficient (Spearman R) explained the relationship between the water quality parameters and the basin characteristics, and the GIS made the results visual and direct. The PCA identified four PCs for Groups 1 and 2, and three PCs for Group 3. These PCs captured 68.94%, 67.48% and 70.35% of the total variance of Groups 1, 2 and 3, respectively. Although natural pollution affects the Xin'anjiang River, the main sources of pollution included agricultural activities, industrial waste, and domestic wastewater.

Keywords： Xin'anjiang River     multivariable statistical analysis     temporal variation     spatial variation     water quality    

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Ventricular Doppler Echocardiographic Measurements for Physiological Variances Using a Novel Optimized Multivariable Article

Guihua Yao, Xiangyun Chen, Wenjing Yang, Qing Zhang, Jing Liu, Huan Liang, Hui Sun, Yao Xu, Li Wang, Jinfeng Xu, Cheng Zhang, Fengrong Sun, Mei Zhang, Xueying Zeng, Yun Zhang

Engineering 2022, Volume 16, Issue 9,   Pages 115-122 doi: 10.1016/j.eng.2021.05.007

Abstract: An optimized multivariable allometric model (OMAM) and scaling equations were developed in 70% of the

Keywords： Doppler echocardiography     Physiological variance     Allometric model     Normal reference values    

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Title Author Date Type Operation

Multivariable regression model for Fox depth correction factor

Ravi Kant MITTAL, Sanket RAWAT, Piyush BANSAL

Journal Article

Knowledge-Based Variable Structure Decoupling Control of a Nonlinear Multivariable System

Tu Chengyuan,Zeng Yanjun

Journal Article

Assessment of temporal and spatial variations in water quality using multivariate statistical methods: a case study of the Xin'anjiang River, China

Xue LI,Pengjing LI,Dong WANG,Yuqiu WANG

Journal Article

Ventricular Doppler Echocardiographic Measurements for Physiological Variances Using a Novel Optimized Multivariable

Guihua Yao, Xiangyun Chen, Wenjing Yang, Qing Zhang, Jing Liu, Huan Liang, Hui Sun, Yao Xu, Li Wang, Jinfeng Xu, Cheng Zhang, Fengrong Sun, Mei Zhang, Xueying Zeng, Yun Zhang

Journal Article