Multiple Regression Model for the Prediction of Unconfined Compressive Strength of Jet Grout Columns

Akan, Recep; Keskin, Sıddıka Nilay; Uzundurukan, Soner

doi:10.1016/j.proeps.2015.08.072

Cited by 33 publications

(13 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In many areas of knowledge, such as engineering and health, many problems involve investigating the relationship between two or more variables [47,48,49,50]. Multiple linear regression (MLR) is a statistical technique widely used in the literature to verify the relationship between a dependent variable and several independent variables [51]. Therefore, to build the computational model to predict body fat percentage, the concept of multiple linear regression was applied, and MATLAB ® was used to build the model.…”

Section: Methodsmentioning

confidence: 99%

“…The MLR is based on least squares [51], which minimizes the error between the actual results (BFP obtained by the BIA) of the model and the expected results of the training set. The multiple linear regression aims to find an estimate of the real output by means of an equation, according to Equation (1):y=xiβi+…+xnβn+ε where y represents the dependent variable, xi the independent variables, βi indicates the regression coefficients, and ε is the error term.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Development of a Computational Model to Predict Excess Body Fat in Adolescents through Low Cost Variables

Sousa

Santana

Lopes

et al. 2019

IJERPH

View full text Add to dashboard Cite

Background: Excess body fat has been growing alarmingly among adolescents, especially in low income and middle income countries where access to health services is scarce. Currently, the main method for assessing overweight in adolescents is the body mass index, but its use is criticized for its low sensitivity and high specificity, which may lead to a late diagnosis of comorbidities associated with excess body fat, such as cardiovascular diseases. Thus, the aim of this study was to develop a computational model using linear regression to predict obesity in adolescents and compare it with commonly used anthropometric methods. To improve the performance of our model, we estimated the percentage of fat and then classified the nutritional status of these adolescents. Methods: The model was developed using easily measurable socio-demographic and clinical variables from a database of 772 adolescents of both genders, aged 10–19 years. The predictive performance was evaluated by the following metrics: accuracy, sensitivity, specificity, and area under ROC curve. The performance of the method was compared to the anthropometric parameters: body mass index and waist-to-height ratio. Results: Our model showed a high correlation (R = 0.80) with the body fat percentage value obtained through bioimpedance. In addition, regarding discrimination, our model obtained better results compared to BMI and WHtR: AUROC = 0.80, 0.64, and 0.55, respectively. It also presented a high sensitivity of 92% and low false negative rate (6%), while BMI and WHtR showed low sensitivity (27% and 9.9%) and a high false negative rate (65% and 53%), respectively. Conclusions: The computational model of this study obtained a better performance in the evaluation of excess body fat in adolescents, compared to the usual anthropometric indicators presenting itself as a low cost alternative for screening obesity in adolescents living in Brazilian regions where financial resources are scarce.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Development of a Computational Model to Predict Excess Body Fat in Adolescents through Low Cost Variables

Sousa

Santana

Lopes

et al. 2019

IJERPH

View full text Add to dashboard Cite

show abstract

“…The determination of UCS in laboratory is exhausting, time consuming, highly priced and it is difficult to take undisturbed sample for tests [3]. Hence, the prediction of UCS in a direct way is an important concern for engineers and scientists for long years [4].Recently, single variable regression is employed for the relationships between UCS and dimensions of specimens [5,6], UCS and height to diameter ratio [7,8], undrained shear strength and water content [9][10][11] while multiple variable regression is performed for the relationship between undrained shear strength and water content [2,3,12,13]. Furthermore, adaptive neuro-fuzzy interference systems (ANFIS) and artificial neural networks (ANNs) are utilized for UCS of soils [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

The effect of data size of ANFIS and MLR models on prediction of unconfined compression strength of clayey soils

Akan

Keskin

2019

SN Appl. Sci.

Self Cite

View full text Add to dashboard Cite

The shear strength (S u) of soils is one of the most widely used parameters for designing structures safely, where Su is found with the unconfined compression test (UCS). Although UCS can be acquired by performing uniaxial compression test it would be extremely helpful to predict the UCS without performing any compression test, namely, using computational methods considering different parameters of soils such as consistency limits, fine grain ratio, liquidity index, and void ratio. The goal of present work is predicting UCS taking into account these soil parameters with the aid of developed Adaptive Neuro-Fuzzy Inference System (ANFIS) model and the Multiple Linear Regression (MLR) analyses. On the other hand, the effect of the size of the training set of designed models on the results is examined, also. For this aim, four different models composed of different training and test set ratios have been constructed and analyzed using ANFIS and MLR. It is concluded that UCS can be predicted using MLR analysis and ANFIS model with best 0.76 and 0.91 values of determination coefficient (R 2) around the x = y line respectively, and the effect of the size of the training set of models on ANFIS is more pronounced than MLR models.

show abstract

“…This analysis is a linear statistical technique that is beneficial for predicting the best relationship between dependent variable and independent variables. The model can be formulated as the following equation (Agha et al, 2012;Anas et al, 2013;Akan et al, 2015).…”

Section: Methodsmentioning

confidence: 99%

Süt sığırcılığı işletmelerinde süt üretim maliyetini etkileyen faktörler

Özsayın¹

2017

Anadolu Journal of Agricultural Sciences

View full text Add to dashboard Cite

The aim of this study was to determine factors affecting milk production cost in dairy cattle farms. The main material of the study was formed by using the data obtained from the surveys which were conducted at a total of 175 dairy cattle farms chosen by the stratified random sampling method in Biga district of Çanakkale province from January to December of 2015. The data were analyzed by using the multiple regression method. The results of this study indicated that 41.7% of the farms had the lowest number of cattle (5-10 head), the prominent age group of farmers' ranged from 36-46 years (45.1%) in the farms and 12.5% of the farmers had the highest income (≥ 24000 TL). According to the results of the multiple regression model, it was determined that some factors such as the time spent in dairy cattle farm, farmers' dairy farming experience, farmers' educational level, farmers' feed procurement, livestock diseases and maize silage production in the farms had significant effects on milk production costs. In conclusion, these factors were explained to have important impacts on decreasing farmers' milk production costs.Süt sığırcılığı işletmelerinde süt üretim maliyetini etkileyen faktörler Keywords: Çanakkale Dairy cattle Milk production Regression model ÖZET Bu çalışmanın amacı, süt sığırcılığı işletmelerinde süt üretim maliyetini etkileyen faktörlerin belirlenmesidir. Çalışmanın ana materyalini, 2015 Ocak ve 2015 Aralık dönemleri arasında Çanakkale'nin Biga ilçesindeki süt sırğırcılığı işletmelerinden tesadüfi tabakalı örnekleme yöntemiyle seçilen 175 işletmeden elde edilen veriler oluşturmaktadır. Verilerin analizinde, çoklu regresyon metodu kullanılmıştır. Araştırma sonuçları, işletmelerin %41.7'sinin en düşük süt sığırı sayısına (5-10 baş) sahip olduğunu, işletmelerdeki en göze çarpan yaş aralığının 36-46 yaş (%45.1) olduğunu ve çiftçilerin %12.5'nin en yüksek gelire sahip olduğunu göstermektedir. Çoklu regresyon model sonuçlarına göre, süt sığırcılığı işletmelerinde harcanan zamanın, işletmecilerin süt sığırcılık deneyimlerinin, çiftçilerin eğitim düzeyinin, çiftçilerin yem temininin, hayvan hastalıklarının ve işletmelerde slajlık mısır üretimi gibi faktörlerin süt üretim maliyetleri üzerindeki etkilerinin önemli olduğu belirlenmiştir. Sonuç olarak, bu faktörlerin süt üretim maliyetlerinin azaltılmasında önemli etkilerinin olduğu açıklanmıştır.Anahtar Sözcükler: Çanakkale Süt sığırı Süt üretimi Regresyon modeli

show abstract

Multiple Regression Model for the Prediction of Unconfined Compressive Strength of Jet Grout Columns

Cited by 33 publications

References 11 publications

Development of a Computational Model to Predict Excess Body Fat in Adolescents through Low Cost Variables

Development of a Computational Model to Predict Excess Body Fat in Adolescents through Low Cost Variables

The effect of data size of ANFIS and MLR models on prediction of unconfined compression strength of clayey soils

Süt sığırcılığı işletmelerinde süt üretim maliyetini etkileyen faktörler

Contact Info

Product

Resources

About