Download PDF Engineering Statistics Fifth Edition by Douglas C. Montgomery

Sinopsis

Engineering is about bridging the gaps between problems and solutions, and that process requires an approach called the scientific method.

In 2009 Eileen Huffman, an undergraduate student in civil engineering at Virginia Tech, applied the scientific method to her study of an antique bridge. The Ironto Wayside Footbridge was built in 1878 and is the oldest standing metal bridge in Virginia. Although it has now been restored as a footbridge, in its former life it routinely carried heavy wagonloads, three tons or more, of goods and materials. Ms. Huffman conducted a historical survey of the bridge and found that a load-bearing analysis had never been done. Her problem was to conduct the first known load-bearing analysis of the bridge.

After gathering the available structural data on the bridge, she created a computer model stress analysis based on typical loads that it would have carried. After analyzing her results, she tested them on the bridge itself to verify her model. She set up dial gauges under the center of each truss. She then had a 3.5-ton truck, typical of the load weight the bridge would have carried, drive over the bridge.

The results from this test will be contributed to the Adaptive Bridge Use Project based at the University of Massachusetts Amherst and supported by the National Science Foundation (www.ecs.umass.edu/adaptive_bridge_use/). Her results and conclusions will be helpful in maintaining the bridge and in helping others to restore and study historic bridges. Her adviser Cris Moen points out that her computer model can be used to create structural models to test other bridges.

Ms. Huffman’s study reflects careful use of the scientific method in the context of an engineering project. It is an excellent example of using sample data to verify an engineering model.

Content

1. The Role of Statistics in Engineering
2. Data Summary and Presentation
3. Random Variables and Probability Distributions
4. Decision Making for a Single Sample
5. Decision Making for Two Samples
6. Building Empirical Models
7. Design of Engineering Experiments
8. Statistical Process Control

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