Reference no: EM132503496

KB7044 Engineering Management Data Analysis - Northumbria University

Learning Outcome 1. Know how to perform correlation and regression analyses on a set of given data and interpret the results.
Learning Outcome 2. Perform straightforward statistical inferences.
Learning Outcome 3. Practice the principle of risk-based approach to data analysis through a mathematically case study with analytical and numerical approaches.
Learning Outcome 4. Use the probabilistic-based method so derived to support decision making under uncertainty.
Learning Outcome 5. Be able to carry out your own literature research prior to solving engineering decision making problems (in the form of an independent learning project) and present the result with an in depth discussion.

Analytical Approach: Case 1

Tasks

Your first Problem is to investigate if there is a reasonable degree of correlation between uncertainty and actual stress in the section. If there are reasons to believe that correlation exists between certain factors then a regression analysis needs to be performed. A sample consisting of 22 data items, which is shown in Table 1, is then collected.

Table 1: Data for Correlation & Regression Analysis

Problem 1 Organise/sort the data to see if patterns can be observed. Perform correlation and regression analysis on this set of data. Explain and interpret the results as clearly as possible.

Problem 2 Your second Problem is to look into variability in the yield stress of the material used. According to the supplier of the material, the yield stress of the material is 130 MPa. A sample consisting of 10 specimens have been prepared and tested. The results are shown in Table 2. Analyse the data by plotting histogram and/or x-y plot.
Question: Is there sufficient evidence to accept the manufacturer's claim that the mean yield stress of the material is 130 MPa? What would you recommend? Do you have reason to suspect "noise" from the data set?

Table 2: Yield Test Result from the Sample

Problem 3 On the basis of this analytical framework, set up a spreadsheet to calculate the numerical values of the probability that the excess capacity <= 0 (i.e. risk of structure yielding) over a range of excess capacity (recommended range: 10 kN - 100 kN). A sample spreadsheet is shown in Table 3.

You may also want to plot the sensitivity of the problem (over variation of one or more parameters), the sensitivity of the decision problem over a range of yield stress's standard deviation is shown in Figure 2.

Problem 4 The risk-based model can then be used to determine an acceptable level of failure probability, and hence the optimum excess capacity (or margin) as contrast to the safety factor approach. The main consideration is the trade-off between additional material cost to provide a given level of excess capacity and the penalty cost incurred by failed components.

Problem 5 Comment on the appropriateness of current safety factor, using data from Table 1. Recommend revised level of safety factor. Note: you can assume the current safety factor(s) to be constant(s).

Problem 6 Tasks 1 -4 relates to reliability (and quality) issues. Extend your investigation to include failure of materials (i.e. beyond yielding to complete failure by compressive stress). What would be acceptable failure probability? Given that the Tensile/Compressive Stress of the material is 360 MPa with a standard deviation of 10 MPa.

Numerical Approach: Case 2

Tasks

For the worked example you are to formulate a deterministic and a probabilistic solution procedure for the example problem.

Problem 7 Your first Problem is to solve the problem deterministically, employing equations (11) - (13), using Excel spreadsheets or MathCAD. The members of staff of the company are familiar with the deterministic solution procedure and this should be used as a starting point for the worked example before introducing the probabilistic solution procedure of simulation.

What is the safety factor/margin against tensile failure?

Problem 8 Your second Problem is to model the variables associated with the problem.

• Eccentricity, e, can be modelled using a Beta distribution having: α = 4, β = 2 , A(min) = 60mm, B(max) = 90 mm, as shown in Figure 6..

• Length of the section, l, can be modelled by a normal distribution having a mean of 350 mm and a standard deviation of 20 mm.
• Width of the section, w, can be modelled by a normal distribution having a mean of 250 mm and a standard deviation of 12 mm.
• To make "reasonable adjustments" in view of the Covid-19, you have a choice of modelling the load, P, using only ONE of the following:
(a) The load, P, can be modelled by a triangle distribution having a mode value, M, of 1000 kN, a low value, L, of 700 kN, and a high value, H, of 1200 kN, as shown in Figure 7. The triangle distribution is used when one is unable to model the parameter confidently with a precise distribution. In this case, the designer can only give a low, high and mode values of the parameters.

(b) Alternatively, P can be modelled as a Beta distribution with α = 4, β = 2 , A(min) = 700 KN, ??(max) = 1000 KN

You need only to model load based on (a) or (b). (Note - load P, original assignment - triangle distribution, adjusted current assignment released 2 April - modified, either triangle distribution or Beta distribution).

• The value of c can be taken as L/2.

You are to model the parameters with the given information. Random sampling of the parameters for the simulation process is to be done on the basis of above distributions.

Problem 9 Perform the Monte Carlo simulation to obtain the distributions of the following output parameters:

The axial stress, σ a .
The flexural (bending) stress, σ f .
The combined compressive stress, σ f + σ a .
The combined tensile stress, σ f - σ a .
The distance, a, of axis of zero stress.

The simulation should be performed with no less than 1,000 random sampled data points for all the parameters.

The statistics and distributions of the output parameters are to be presented in your report, together with samples of all intermediate computations.

An example spreadsheet for the above Problem is shown in Figure 8.

Problem 10 Your last Problem is to explore the effect of eccentricity has on the tensile failure, and hence determine an optimum quality (as measured in terms of variability) eccentricity on the basis of cost. A batch size of 10,000 is to be assumed for the cost calculations.

The low safety margin against tensile failure is a reason for concern as the concrete struts had already been fabricated. However, by using different setup processes the characteristics of eccentricity, e, can be varied. Essentially e can be modelled by Beta distributions between 60 and 90 mm but with different α and β values (i.e. different skewness).

The additional setting up costs to achieve different characteristics of eccentricity is shown in Table 4.

Attachment:- Engineering Management Data Analysis.rar