Use of Statistical Modelling Techniques for Simulation

1993 ◽  
Author(s):  
Ebrahim Mazharsolook ◽  
David C. Robinson ◽  
Jonathan D. Casey
Keyword(s):  
Quality Control ◽  
Statistical Modelling ◽  
Test Results ◽  
Final Test ◽  
Product Model ◽  
Design Changes ◽  
Discrete Manufacturing ◽  
Similar Product ◽  
Modelling Techniques

Abstract Statistical methods are explored for the use in modelling of discrete manufacturing. The developed methodologies based on Design of Experiments (DOE) and stepwise regression to obtain the product model are described. This model is then embedded within a software system which is used for simulation of design changes, process changes and disturbances. The software is used to predict final test results in respect of up-stream parameter changes. A case study is presented o show the implementation of this method of modelling in Quality Control of manufacture. This case study has successfully been implemented. The system is currently assisting the company in design of similar product. Feasibility of applying Artificial Intelligen (AI) techniques to Model-Based Quality Control (MBQC) is investigated. An outline of the future development of Hybrid MBQC is then presented.

Pitting corpus-based classification models against each other: a case study for predicting constructional choice in written Estonian

2017 ◽  
Vol 0 (0) ◽  
Author(s):  
Jane Klavan
Keyword(s):  
Statistical Modelling ◽  
Model Fit ◽  
Discriminative Learning ◽  
Classification Models ◽  
Statistical Classification ◽  
Multivariate Statistical ◽  
Lexical Choice ◽  
Modelling Techniques ◽  
The Moment

AbstractIn the context of constructional alternatives, we may assume that speakers’ choice between alternative forms is influenced by a multitude of factors. At the moment, multivariate statistical classification modelling seems to be the best tool available to capture this knowledge quantitatively. There is a vast array of techniques available. In this paper, two distinct modelling techniques are applied – logistic regression and naïve discriminative learning – to predict the choice between two constructional alternatives in written Estonian. One of the central questions in statistical modelling concerns the evaluation of model fit. It is proposed that for linguistic analysis, the performance of alternative corpus-based models can be evaluated by, first, pitting them against each other and second, pitting them against experimental data. Previous work on modelling constructional and lexical choice has focused on one of the two aspects. The present paper takes this line of analysis further by combining the two approaches.

PENGKAJIAN KEKUATAN BETON STRUKTUR JEMBATAN PASCA KEBAKARAN

2013 ◽  
Vol 12 (3) ◽  
Author(s):  
Sudarmadi Sudarmadi
Keyword(s):  
Compressive Strength ◽  
Concrete Strength ◽  
Ultrasonic Pulse Velocity ◽  
Ultrasonic Pulse ◽  
Pulse Velocity ◽  
Test Results ◽  
Concrete Compressive Strength ◽  
Strength Assessment ◽  
Concrete Testing

In this paper a case study about concrete strength assessment of bridge structure experiencing fire is discussed. Assessment methods include activities of visual inspection, concrete testing by Hammer Test, Ultrasonic Pulse Velocity Test, and Core Test. Then, test results are compared with the requirement of RSNI T-12-2004. Test results show that surface concrete at the location of fire deteriorates so that its quality is decreased into the category of Very Poor with ultrasonic pulse velocity ranges between 1,14 – 1,74 km/s. From test results also it can be known that concrete compressive strength of inner part of bridge pier ranges about 267 – 274 kg/cm2 and concrete compressive strength of beam and plate experiencing fire directly is about 173 kg/cm2 and 159 kg/cm2. It can be concluded that surface concrete strength at the location of fire does not meet the requirement of RSNI T-12-2004. So, repair on surface concrete of pier, beam, and plate at the location of fire is required.

Sign in / Sign up

Export Citation Format

Share Document