Inverse Estimation of Local Slamming Loads on a Jacket Structure

Author(s):  
Ying Tu ◽  
Thorvald C. Grindstad ◽  
Michael Muskulus

Slamming loads from plunging breaking waves feature a high impulsive force and a very short duration. It is difficult to measure these loads directly in experiments due to the dynamics of the structures. In this study, inverse approaches are investigated to estimate the local slamming loads on a jacket structure using hammer test and wave test data from a model scale experiment. First, a state-of-the-art approach is considered. It uses two deconvolution techniques to first determine the impulse response functions and then to reconstruct the wave impact forces. Second, an easier applicable approach is proposed. It uses linear regression with the ordinary least square technique for the force estimation. The results calculated with these two approaches are highly identical. The linear regression approach can be extended to account for the loads transferred among different locations. This leads to lower and theoretically more accurate estimation of the loads compared to the previous two approaches. For the investigated case, the total impulse due to the wave is 22% lower. The estimated forces by the extended approach have a resolution at the millisecond level, which provides detailed information on the shape of the forces. The approach is an important tool for statistical investigations into the local slamming forces, and further on for the development of a reliable engineering model of the forces.

Author(s):  
Ying Tu ◽  
Michael Muskulus ◽  
Thorvald C. Grindstad

This article illustrates two inverse methods to estimate the local slamming forces on a jacket structure. The experimental data from the hammer test and the wave test are used as the inputs. One method uses two deconvolution techniques: a conjugate gradient technique to solve the impulse response functions and a weighted eigenvector expansion technique to reconstruct the wave impact forces. The other method uses linear regression with the ordinary least square technique to estimate the wave impact forces. The results calculated with these two different methods are highly identical, which enhances the confidence in the result accuracy. The time series of the reconstructed forces are detailed at a millisecond level, which provides decent information on the shape of the forces. This capability enables the methods to be a very useful tool for the further investigations of the local slamming forces.


2015 ◽  
Vol 5 (2) ◽  
pp. 1
Author(s):  
Miftahol Arifin

The purpose of this research is to analyze the influence of knowledge management on employee performance, analyze the effect of competence on employee performance, analyze the influence of motivation on employee performance). In this study, samples taken are structural employees PT.centris Kingdom Taxi Yogyakarta. The analysis tool in this study using multiple linear regression with Ordinary Least Square method (OLS). The conclusion of this study showed that the variables of knowledge management has a significant influence on employee performance, competence variables have an influence on employee performance, motivation variables have an influence on employee performance, The analysis showed that the variables of knowledge management, competence, motivation on employee performance.Keywords: knowledge management, competence, motivation, employee performance.


2019 ◽  
Vol 16 (1) ◽  
pp. 1-10
Author(s):  
Novegya Ratih Primandari

This research aims to analyze effect of economic growth, inflation and Unemployment on the Rate of Poverty in the Province of South Sumatera. This research used secondary data in the form of time series data from 2001-2017. The method used quantitative approach by applying a linear regression model with OLS estimation Ordinary Least Square (OLS) method. The results of this study indicate that partially and simultaneously Economic Growth, Inflation and Unemployment have a significant effect on the Poverty Rate in the Province of South Sumatera.


2017 ◽  
Vol 10 (1) ◽  
pp. 1
Author(s):  
Zara Liaqat ◽  
Xinya Wang

This paper uses monthly data from 1994 to 2016 in order to analyze the time series properties of the determinants of Canadian softwood lumber exports to the United States. The key findings generally support the hypotheses of previous studies with the exception of the significance of bilateral exchange rate movements. Based on dynamic ordinary least square estimates and several robust cointegraton tests, the paper finds that the estimated coefficients of exchange rate, softwood lumber price ratio and the two softwood lumber trade agreements are highly sensitive to the lag order used in econometric models. On the other hand, the coefficient of housing starts index remains independent of the variation in number of lags included. In addition, we study the long-run response of Canadian exports of lumber to shocks in these determinants by generating impulse response functions.


2021 ◽  
Vol 2 (1) ◽  
pp. 12-20
Author(s):  
Kayode Ayinde, Olusegun O. Alabi ◽  
Ugochinyere Ihuoma Nwosu

Multicollinearity has remained a major problem in regression analysis and should be sustainably addressed. Problems associated with multicollinearity are worse when it occurs at high level among regressors. This review revealed that studies on the subject have focused on developing estimators regardless of effect of differences in levels of multicollinearity among regressors. Studies have considered single-estimator and combined-estimator approaches without sustainable solution to multicollinearity problems. The possible influence of partitioning the regressors according to multicollinearity levels and extracting from each group to develop estimators that will estimate the parameters of a linear regression model when multicollinearity occurs is a new econometrics idea and therefore requires attention. The results of new studies should be compared with existing methods namely principal components estimator, partial least squares estimator, ridge regression estimator and the ordinary least square estimators using wide range of criteria by ranking their performances at each level of multicollinearity parameter and sample size. Based on a recent clue in literature, it is possible to develop innovative estimator that will sustainably solve the problem of multicollinearity through partitioning and extraction of explanatory variables approaches and identify situations where the innovative estimator will produce most efficient result of the model parameters. The new estimator should be applied to real data and popularized for use.


2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Komang Adi Kurniawan Saputra ◽  
A.A Ketut Jayawarsa ◽  
Putu Budi Anggiriawan

The aim of this research is to examine the factors influencing the original income level of the villages by taking the research location in the villages managing the village fund in Buleleng-Bali Regency. Total number of 129 villages. The number of samples used in this study is equal to the number of population, The sampling technique in this study was total sampling. Meanwhile, to test the hypothesis using multiple linear regression with OLS model (Ordinary Least Square). The results obtained in this study indicate that Local Government Support, Optimalization of Village Asset Utilization and Professionalism of Village Asset Management have a significant effect on original income of the village. The contribution of this theoretical research is to contribute theoretical enrichment that underlies the increase of village original income and its practical contribution, this research can be one of reference for village apparatus, village counselor, and local government in making policy related to asset management or village property


Author(s):  
Triana Kurniwati ◽  
Bagio Mudakir

Semarang city is densely populated that demand of settlement will increase continually, but land in city center is very limited and even it is scarce, therefore the land price which is placed in city center is high. That is why many inhabitant of Semarang city prefer to live in outskirts of the city. The shifting of land demand to the outskirts is also followed by increasing of land price in outskirts, it causes the land price in outskirts is uncontrolled.The research takes location in Banyumanik area. This research area consists of 7 districts, that are Jabungan, Pudak Payung, Banyumanik, Srondol Kulon, Pedalangan, Ngesrep, and Gedawang district. The sample total is one hundred (100). The data is analyzed by using multiple linear regression model with ordinary least square method (OLS).


2020 ◽  
Vol 4 (2) ◽  
pp. 140-148
Author(s):  
Masniya Khairunnisa ◽  
Lilies Setiartiti

This study aims to measure the value of willingness to pay and analyze the factors that affect consumer willingness to pay toward Pertamax fuel. This study's dependent variable is income, number of owned vehicles, the frequency of vehicle use, and product literacy. In this study sample of 100 Pertamax fuel users in Yogyakarta were selected using the Purposive Sampling method. This study using multiple linear regression or Ordinary Least Square (OLS). The results show that the value of consumer willingness to pay toward Pertamax fuel is Rp. 10,545. The factors that influence willingness to pay are income, frequency of vehicle use, and product literacy. These three variables are positive and have a significant influence on consumer willingness to pay toward Pertamaxfuel. While the variable number of the owned vehicle


2021 ◽  
Vol 3 (1) ◽  
pp. 1-8
Author(s):  
Afriamah Afriamah ◽  
Zulkarnain Lubis ◽  
Mitra Musika Lubis

Indonesia is one of the world's largest coffee producers, it can be seen from the amount of exports from Indonesia for coffee export. In the past few years, several companies have carried out massive expansion to get Gayo coffee from Central Aceh Regency and Bener Meriah. The purpose of this study was to analysis what factors influence the volume of Gayo coffee exports from Central Aceh Regency to the United States. The data collection method using the documentary method is the data obtained and viewed by the document in accordance with the variables in the research model in the period 2013-2017. Data collected is secondary data. The analytical method used is multiple linear regression with the method used is the Ordinary Least Square (OLS) Method. From the research using multiple linear regression analysis obtained that variables which have significant effect to the export demand of Gayo Coffee from the United States is Global Coffee Prices. While the production of domestic Gayo coffee, the exchange rate of dollars against the rupiah and the price of foreign Gayo coffee are not significant to the demand for export of Gayo coffee to the United States.


2018 ◽  
Vol 14 (1) ◽  
pp. 1-18
Author(s):  
Krisna Hidajat

This research  is performed  in order  to test the influence  of  the  variable, Capital Adequacy Ratio (CAR), Biaya Operasi terhadap Pendapatan Operasi (BOPO), Loan to Deposit Ratio (LDR), Non Performing Loan (NPL), and Pembentukan Penyisihan Aktiva Produktif (PPAP) toward Return on Asset (ROA).Methodology reseach as the sample used sensus. Sample was accuired 23 banking company listed in JSX over period 2010-2013. Data analysis with multi linear regression of ordinary least square and hypotheses test used t-statistic dan F-statistic at level of significance  5%, a classic assumption  examination  which consist  of data  normality  test,  multicolinierity  test,  heteroskedasticity  test  and autocorrelation test is also being done to test the hypotheses.During research  period show' as variable and data research was normal distributed.  Based  on  multicolinierity  test,  heteroskedasticity  test  and autocorrelation test classic assumption deviation has not founded, tihis indicate that the available data has fulfill  the condition to use multi linear regression model. Empirical evidence show as CAR. BOPO and LDR toward ROA banking listed in JSX over period 2010-2013  at level of significance less than 5% (as 0,01%,0,01% and 0,6% each). While,  two  independent  variable  NPL,  and PPAP  not influence toward R0A at level of significance more than 5% at 88,2% and 72,7%. Where it was proved that together this research is performed in order to test the influence  of the variable  CAR,  BOPO,  LDR,  NPL and PPAP  to have influence toward banking ROA in JSX at level less than 5% (with level of significance 0,05). Prediction capability from these seven variable toward ROA is 35,1% where the balance (64,9%) is affected to other factor which was not to be entered to research model. 


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