John Hildreth

Highway Construction Data Collection and Treatment in Preparation for Statistical Regression Analysis

Currently, there is not an understanding of the project factors having a statistically significant relationship with highway construction duration. Other industry sectors have successfully used statistical regression analysis to identify and model the project parameters related to construction duration. While the need is seen for such work in highway construction, there are very few studies which attempt to identify duration-influential parameters and their relationship with the highway construction duration. The purpose of this work is to describe the highway construction data needed for such a study, identify a data source, collect early-design project data, and prepare the data for statistical regression analysis. The Virginia Department of Transportation is identified as the optimal data source. The data collected include historical contract and project level parameters. To prepare for statistical regression analysis, the contract duration collected is converted to construction duration by a seasonal adjustment process which removes historically typical nonworking days.

Using the Cumulative Cost Model to Forecast Equipment Repair Costs: Two Different Methodologies

Professionals in the construction industry must be able to accurately forecast costs. Doing so not only helps assure reasonable profits for companies, but it can also help ensure that projects are delivered within budget for clients. Forecasting of equipment repair costs is one element of the larger problem of predicting overall costs. The cumulative cost model can provide construction engineers with a valuable tool for better understanding the nature of repair costs as they relate to production fleets. Data that are being collected (or that could be collected) can assist in the determination of the rate of accumulation of repair costs for a machine for a given period of use or the estimation of fleet repair budgets for a job or period. There are two different methodologies for constructing the repair cost portion of the cumulative cost model: life-to-date (LTD) repair costs and the period-cost-based (PCB) model. This paper will provide the steps and background for each of these two methodologies and compare them using a practical example.

BIDDS: A Bid Item Level Performance Time Database Management System

State highway agencies must strive to set accurate and reasonable contract performance times in order to encourage qualified bidders, produce quality projects, and minimize impacts to the traveling public. The Federal Highway Administration recognizes this and has mandated that all state highway agencies have an approved, written contract time estimating procedure. The Federal Highway Administration also published a number of recommendations to assist individual state highway agencies in meeting this mandate. Among these recommendations is the development of a database of historical production rates and adjustment of these production rates for unique project characteristics. This work describes the development of a bid item level performance time database management system known as BIDDS . Primarily, BIDDS can be used by the scheduler, in conjunction with individual engineering judgment, to establish construction activity production rates. These rates, along with known material quantities, can be used to derive construction activity durations for the development of the pre-advertisement schedule.

Effect of Period Length on Forecasting Maintenance and Repair Costs for Heavy Equipment by the Period Cost Based Methodology

ABSTRACT Substantial economic investments are made by companies in the construction and other processing industries to own and operate heavy equipment fleets. Average total cost minimization models are used by equipment managers to perform a variety of tasks that are largely influenced by the economic performance of the fleet. An important aspect of these models is estimating maintenance and repair costs throughout machine life. The period cost-based (PCB) methodology can be used to model maintenance and repair costs from data collected over a period of time within the life of a machine. The objective of this research was to investigate the effect of the data collection period length on the resulting PCB models and develop recommendations regarding the minimum period length. Data collected from a fleet of excavator-type material handlers were used to construct cost records of varying period lengths, to which the PCB methodology was applied to construct marginal and cumulative cost models. The results show that increasing the period length increases the quality of the marginal cost model and the accuracy of the estimated cumulative costs. However, this is subject to the constraint that the amount of data available for model development does not significantly decrease.

Statistical Considerations and Graphical Presentation of the Residual Value of Heavy Construction Equipment

Hourly equipment cost rates are vital to the long-term profitability of a company. Hourly rates are based on the forecasted owning and operating costs. The residual value is the most elusive element of owning and operating costs. It differs from book value, which is typically calculated for taxation purposes using a depreciation model. The residual value of heavy construction equipment is defined as the monetary value that can be anticipated to be actually realized in an open market transaction. Forecasting it statistically allows for an integrated cost model for heavy construction equipment. A comprehensive study of auction records for various equipment types, makes, and sizes resulted in a statistical model of residual value. Quality of the model was ensured through hypothesis testing and coefficient validation. Residual value grids are intuitive graphical tools used to present the underlying datasets and quickly and accurately forecast residual value with reliability.