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Bearing Capacity Enhancement of Hexagonal Skirted Footings: Numerical, Regression, and ANN-Based Prediction
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This paper presents a comprehensive numerical analysis of the improvement in bearing capacity and settlement performance of hexagonal shallow footings with inclined skirts. Various numerical analyses were conducted using PLAXIS 3D to investigate the influence of skirt length-to-footing width (L/B) ratios and skirt inclination angles (θ) on hexagonal footings in loose sand. The models showed very good agreement with experimental data reported in previous studies, with an R² value of 0.996 and a maximum error of less than 4.31%. It was concluded that the inclusion of inclined skirts has a positive effect on bearing capacity, increasing it by up to approximately 2.97 times compared to non-inclined configurations, while significantly reducing settlement. In addition to numerical simulations, an empirical formula for bearing capacity and settlement was developed using multiple regression based on geometric and inclination parameters. The model demonstrated a good fit (R² = 0.993). Furthermore, an Artificial Neural Network (ANN) model with a 4-10-10-1 architecture was proposed to predict bearing capacity using normalized input parameters, including skirt depth, inclination angle, stress, and settlement ratio. During training, validation, and testing, R² values greater than 0.998 were achieved, indicating a high level of accuracy with low prediction error. These findings highlight the importance of skirt inclination in enhancing foundation design, providing an efficient and cost-effective approach to increase the safety factor of foundations constructed on weak soils without the need for additional structural elements such as panels or strips.

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Publication Date
Fri Mar 28 2008
Journal Name
Ama, Agricultural Mechanization In Asia, Africa & Latin America
The Effect of Two Type of Plows With Four Speeds on the Field Capacity and Bulk Density
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Publication Date
Fri Jan 01 2021
Journal Name
Journal Of Economics And Administrative Sciences
Comparison Between Maximum Likelihood and Bayesian Methods For Estimating The Gamma Regression With Practical Application
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In this paper, we will illustrate a gamma regression model assuming that the dependent variable (Y) is a gamma distribution and that it's mean ( ) is related through a linear predictor with link function which is identity link function g(μ) = μ. It also contains the shape parameter which is not constant and depends on the linear predictor and with link function which is the log link and we will estimate the parameters of gamma regression by using two estimation methods which are The Maximum Likelihood and the Bayesian and a comparison between these methods by using the standard comparison of average squares of error (MSE), where the two methods were applied to real da

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Publication Date
Fri Sep 06 2024
Journal Name
Journal Of Economics And Administrative Sciences
Comparison of Some Wavelet Transformations to Estimate Nonparametric Regression Function
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The purpose of this article is to improve and minimize noise from the signal by studying wavelet transforms and showing how to use the most effective ones for processing and analysis. As both the Discrete Wavelet Transformation method was used, we will outline some transformation techniques along with the methodology for applying them to remove noise from the signal. Proceeds based on the threshold value and the threshold functions Lifting Transformation, Wavelet Transformation, and Packet Discrete Wavelet Transformation. Using AMSE, A comparison was made between them , and the best was selected. When the aforementioned techniques were applied to actual data that was represented by each of the prices, it became evident that the lift

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Publication Date
Tue Jun 01 2021
Journal Name
International Journal Of Nonlinear Analysis And Applications
Bayes estimators of a multivariate generalized hyperbolic partial regression model
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Publication Date
Thu Dec 19 2024
Journal Name
Baghdad Science Journal
Solution of Wave Equation by Linear Regression Artificial Neural Network
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تعتبر المعادلات التفاضلية الموجية من اهم المواضيع التي تمثل على سبيل المثال الحركة الموجية للاهتزازات الأرضية . ومن هنا فان ايجاد  حلول تقريبيه لمثل هذه المعادلات بدقة وسرعه عالية وبشكل اسرع من الحلول التحليلية والمعقدة , اصبح ممكنا من خلال استخدام الذكاء الاصطناعي واساليب  التعلم  الالي. في هذا البحث هناك ثلاثة أهداف الأول هو تحويل مشكلة القيمة الأولية للمعادلة الموجية إلى شكلها القانوني وإيجاد حلها ا

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Publication Date
Sat Sep 01 2012
Journal Name
Journal Of Economics And Administrative Sciences
A comparison Of Some Semiparametric Estimators For consumption function Regression
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    This article aims to explore the importance of estimating the a semiparametric regression function ,where we suggest a new estimator beside the other combined estimators and then we make a comparison among them by using simulation technique . Through the simulation results we find  that the suggest estimator is the best with the first and second models ,wherealse for the third model we find Burman and Chaudhuri (B&C) is best.

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Publication Date
Wed Jul 01 2020
Journal Name
Iop Conference Series: Materials Science And Engineering
Experimental and Numerical Comparison of Prestressed Perforated Concrete Rafters of Different Configurations
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Abstract<p>This paper demonstrates an experimental and numerical study aimed to compare the influence of openings of different configurations on the flexural behavior of prestressed concrete rafters. The experimental program consisted of testing six simply supported prestressed concrete rafters; 5 rafters are perforated, and the other one is solid as a reference. All rafters were tested under monotonic midpoint load. The variable which has been investigated in this work was the opening’s configuration (quadrilateral or circular) with the same upper and lower chords depths. The results indicate improvement in the beam flexural behavior using the circular openings compared to the quadrilateral o</p> ... Show More
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Publication Date
Wed Feb 01 2023
Journal Name
Periodicals Of Engineering And Natural Sciences (pen)
Bitcoin Prediction with a hybrid model
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In recent years, Bitcoin has become the most widely used blockchain platform in business and finance. The goal of this work is to find a viable prediction model that incorporates and perhaps improves on a combination of available models. Among the techniques utilized in this paper are exponential smoothing, ARIMA, artificial neural networks (ANNs) models, and prediction combination models. The study's most obvious discovery is that artificial intelligence models improve the results of compound prediction models. The second key discovery was that a strong combination forecasting model that responds to the multiple fluctuations that occur in the bitcoin time series and Error improvement should be used. Based on the results, the prediction acc

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Publication Date
Tue Feb 28 2023
Journal Name
Periodicals Of Engineering And Natural Sciences (pen)
Bitcoin Prediction with a hybrid model
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. In recent years, Bitcoin has become the most widely used blockchain platform in business and finance. The goal of this work is to find a viable prediction model that incorporates and perhaps improves on a combination of available models. Among the techniques utilized in this paper are exponential smoothing, ARIMA, artificial neural networks (ANNs) models, and prediction combination models. The study's most obvious discovery is that artificial intelligence models improve the results of compound prediction models. The second key discovery was that a strong combination forecasting model that responds to the multiple fluctuations that occur in the bitcoin time series and Error improvement should be used. Based on the results, the prediction a

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Publication Date
Thu Sep 01 2022
Journal Name
Computers And Electrical Engineering
Automatic illness prediction system through speech
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