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Bidding ratios to predict highway project costs (2005)

Williams, Trefor P.

Oxford : Emerald

Engineering, construction and architectural management 12 (2005), S. 38-51

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ISSN: 1365-232X

Source: Emerald Fulltext Archive Database 1994-2005

Topics: Architecture, Civil Engineering, Surveying

Notes: Purpose - Ratios were constructed using bidding data for highway construction projects in Texas to study whether there are useful patterns in project bids that are indicators of the project completion cost. The use of the ratios to improve predictions of completed project cost was studied. Design/methodology/approach - Ratios were calculated relating the second lowest bid, mean bid, and maximum bid to the low bid for the highway construction projects. Regression and neural network models were developed to predict the completed cost of the highway projects using bidding data. Models including the bidding ratios, low bid, second lowest bid, mean bid and maximum bid were developed. Natural log transformations were applied to the data to improve model performance. Findings - Analysis of the bidding ratios indicates some relationship between high values of the bidding ratios and final project costs that deviate significantly from the low bid amount. Addition of the ratios to neural network and regression models to predict the completed project cost were not found to enhance the predictions. The best performing regression model used only the low bid as input. The best performing neural network model used the low bid and second lowest bid as inputs. Originality/value - The nature of bid ratios that can describe the pattern of bids submitted for a project and the relationship of the ratios to project outcomes were studied. The ratio values may be useful indicators of project outcome that can be used by construction managers.

Type of Medium: Electronic Resource

URL: http://dx.doi.org/10.1108/09699980510576880

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Comparison of regression and neural network models for prediction of inspection profiles for aging aircraft (1997)

LUXHØJ, JAMES T. ; WILLIAMS, TREFOR P. ; SHYUR, HUAN-JYH

Springer

IIE transactions 29 (1997), S. 91-101

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ISSN: 1573-9724

Source: Springer Online Journal Archives 1860-2000

Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics

Notes: Abstract Currently under phase 2 development by the Federal Aviation Administration (FAA), the Safety Performance Analysis System (SPAS) contains ‘alert’ indicators of aircraft safety performance that can signal potential problem areas for inspectors. The Service Difficulty Reporting (SDR) system is one component of SPAS and contains data related to the identification of abnormal, potentially unsafe conditions in aircraft and/or aircraft components/equipment. SPAS contains performance indicators to assist safety inspectors in diagnosing an airline's safety ‘profile’ compared with others in the same peer class. This paper details the development of SDR prediction models for the DC-9 aircraft by analyzing sample data from the SDR database that have been merged with aircraft utilization data. Both multiple regression and neural networks are used to create prediction models for the overall number of SDRs and for SDR cracking and corrosion cases. These prediction models establish a range for the number of SDRs outside which safety advisory warnings would be issued. It appears that a data ‘grouping’ strategy to create aircraft ‘profiles’ is very effective at enhancing the predictive accuracy of the models. The results from each competing modeling approach are compared and managerial implications to improve the SDR performance indicator in SPAS are provided.