1、 本 科 生 毕 业 设 计（论文）外文翻译（ 2009 届）题目：Estimating Future Highway Construction Costs 2009年 2 月 19 日Estimating Future Highway Construction CostsC. G. Wilmot, M.ASCE,1 and G. Cheng, P.E.2Abstract: The objective of this research was to develop a model that estimates future highway construction costs in Louis
2、iana. The model describes overall highway construction cost in terms of a highway construction cost index. The index is a composite measure of the cost of construction labor, materials, and equipment; the characteristics of contracts; and the environment in which contracts are let. Future constructi
3、on costs are described in terms of predicted index values based on forecasts of the price of construction labor, materials, and equipment and the expected contract characteristics and contract environments. The contract characteristics and contract environments that are under the control of highway
4、agency officials, can be manipulated to reflect future cost-cutting policies. Application of the model in forecasting to highway construction costs in Louisiana shows that the model closely replicates past construction costs for the period 19841997. When applied to forecasting future highway constru
5、ction costs, the model predicts that highway construction costs in Louisiana will double between 1998 and 2015. Applying cost-cutting policies and assuming input costs are 20% less than anticipated, the model estimates highway construction costs will increase by 75% between 1998 and 2015.Key words:
6、Highway construction; Costs; Estimation.IntroductionState Departments of Transportation are required to prepare highway construction programs that describe their planned construction activity in the short term. There is usually considerable interest in the program from local authorities, politicians
7、, and interest groups. Draft programs are typically presented to the public and to various agencies at the local, regional, state, and federal level for comment and review. Ultimately, a program will be approved by the state legislature and will become the formal program of construction of the state
8、 Department of Transportation until a new program is developed in the next cycle a few years later.Because individual projects are of considerable importance to politicians and individual interest groups, it is common that progress on a construction program is closely monitored. Any deviation is lik
9、ely to be queried, and the Secretary of the state Department of Transportation or a senior official in the department will often have to defend the situation publicly or in the state legislature. This can lead to perceptions of incompetence and erosion of support from the legislature and the public.
10、To prepare reliable highway construction programs, road authorities must have accurate estimates of future funding and project costs. While future funding is obviously never known witha great deal of certainty, it is often the estimation of project costs that cause upsets in the execution of constru
11、ction programs. Inaccurate cost estimation is one source of error, but another, the escalation in cost of a project over time, is another source disruption to the program that is usually not anticipated and catered for. Typically, when projects are costed, their costs are estimated in terms of the c
12、urrent cost of the project, and this estimate is not adjusted for the year in which the project is scheduled for implementation. These cost increases can be significant and are, of course, cumulative across projects; also, they rise at an increasing rate each year into the future. Estimating future
13、highway construction is the focus of this paper. The model developed in this study was developed with data from the Louisiana Department of Transportation and Development DOTD! and is therefore particular to that state. However, the methodology employed could be employed in other areas.Measuring Pro
14、ject CostsWhen construction in the field lags behind planned construction in the construction program, it is usually because the projects that have been constructed have cost more than anticipated. This is not random variation of actual costs about estimated costs, because, clearly, underestimates w
15、ould cancel out overestimates over time in such a situation. Rather, it is evidence of a consistent underestimateof all projects collectively. The benefit of this is that it can be measured at the overall level, which is much easier to measure than at the individual project level.In the past, change
16、 in overall construction costs has been measured in terms of construction indices. These indices are weighted averages of the cost of a set of representative pay items over time. They have been used to display cost trends in the past. However, there is no reason why cost indices must be restricted t
17、o displaying past trends; they can also portray future overall costs, provided the representative pay items on which the index is based can be forecast. A predictive construction cost index was adopted in this study to describe the change in overall construction costs in the future. The formulation
18、of the index is described later in the paper.Past Increases in Construction CostsWhen the change in overall construction costs in the past is observed（as measured by popular construction cost indices）, it is apparent that they change significantly from year to year and that the changes can sometimes
19、 be quite erratic. The common assumption that construction costs change with the rate of inflation can lead to poor estimates of future construction cost. To illustrate, the Federal Highway Administrations Composite Bid Price Index, an index of overall highway construction costs, is plotted in Fig.
20、1 together with the Consumer Price Index （CPI）, a common expression of general inflation. The FHWA CBPI for the entire nation and for Louisiana alone is plotted in the diagram. All indices have been normalized to a value of 100 in 1987 for comparison purposes. From the diagram, it is clear that high
21、way construction costs change erratically and even display different short and long-term trends from to those of the CPI. It is also apparent that construction cost changes are different in Louisiana from those in the nation as a whole. While not shown here, review of the FHWA CBPI from other states
22、 shows that many of them show a deviation from national values.Past Methods of Forecasting Highway Construction CostForecasting future highway construction costs has been achieved in basically three ways in the past. First, unit rates of construction such as dollars per mile by highway type have bee
23、n used to estimate construction costs in the short term. However, this method has generally been found to be unreliable, because site conditions such as topography, in situ soil, land prices, environment, and traffic loads vary sufficiently from location to location to make average prices inaccurate
24、 estimates of the price of individual projects or even of all projects in a particular year. Second, extrapolation of past trends, or time-series analysis, has been used to forecast future overall construction costs （Koppula 1981; Hartgen et al. 1997）. Typically, construction costs have been collaps
25、ed in these analyses to a single overall expression of constructioncost such as the FHWA CBPI or the Engineering News Records Building Construction Index ENR BCI! or Construction Cost Index ENR CCI!. However, these types of models are usually only used for short-term forecasting due to their relianc
26、e on the notion that past conditions are maintained in the future. Third, models have been established that describe construction costs as a function of factors believed to influence construction costs. The relationship between construction costs and these factors have been established from past rec
27、ords of construction costs. Typically, the models established in this manner have been used to estimate the cost of individual contracts. These models, with their relational structure, are the only models expected to provide reliable long-term estimates. The model developed in this study is of this
28、type.Proposed Construction Cost ModelIt is clear that there are numerous factors that affect construction costs. However, it is striking that most construction cost models developed in the past have used only a few of the many influential factors identified above. One reason for this is that informa
29、tion is generally not available on many factors in data sets used to estimate models. Another reason is that information on the qualitative conditions surrounding each contract is difficult to obtain. These are problems that prevail in most circumstances and are difficult to overcome.To mitigate aga
30、inst the effect of an incomplete set of factors, two strategies can be employed. First, it may be possible to represent some of the absent factors by surrogate variables that are in the data set. For example, as mentioned earlier, annual bid volume has been used in the past as an inverse measure of
31、the level of competition prevailing in the construction industry at that time （Herbsman 1986）. Similarly, the number of plan changes each year can serve as a measure of design quality. Second, if the modeling of construction cost is changed from estimating the cost of individual projects to estimati
32、ng overall construction costs each year, the modeling task is simplified. This is because it is no longer necessary to try to model individual projects in which conditions inflate the price in one case and deflate it in another, since such conditions would tend to cancel themselves out among project
33、s in the same year. For example, firms that reduce their bid prices in an effort to win a particular contract could be balanced out within the same fiscal year by those that increase their prices because they already have enough work and are not particularly interested in winning the contract. Simil
34、arly, those firms with expertise in the type of construction required will be balanced out by those with low levels of expertise in that area. Thus, it is generally more tolerable to operate with fewer relevant factors when modeling at the aggregate or overall level than when modeling at the disaggr
35、egate level.The objective of this study is to establish a model, estimated on historical quantitative data, that incorporates as many relevant variables as possible and is capable of estimating the future overall cost of highway construction on an annual basis. The model is intended to assess the im
36、pact of alternative future conditions on highway construction costs and assist officials of the Louisiana DOTD to identify management policies that will help limit the increase in highway construction costs in the state.It was also the perception of those interviewed that contracts let in the fourth
37、 quarter of the fiscal year tended to result in higher bid prices. This was because there was a tendency for projects to accumulate in the fourth quarter due to various delays, and the increased volume of projects resulted in decreased competition among contractors.Model StructureThe model developed
38、 to predict overall highway construction costs in this study is based on five submodels of price estimation. Each submodel estimates the price of a pay item representative of cost model a dominant construction area. Dominant construction areas were identified from past expenditure in different areas
39、 of highway construction. From the Louisiana DOTD data for the period19841997, it was found that more than 50% of all highway construction expenditure occurred in the areas of asphalt concrete surfaces, Portland cement concrete surfaces, excavation and embankment, structural steel, structural concre
40、te, and reinforcing steel. Interestingly, these construction areas are identical to those used to estimate the FHWA CBPI. The structural steel construction area was not included in the model developed in this study, because more than 98% of expenditure in this construction area was bid as a lump sum
41、 in each contract with no record of the amount of steel included in the bid. This made comparison of the cost of structural steel among contracts impossible. The other five construction areas included in the model were all represented by pay items whose prices were expressed in terms of rates, which
42、 permitted comparison among contracts.A schematic representation of the overall model with its five submodels is shown in Fig. 2. Each submodel estimates the price of a representative pay item from each of the five dominant construction areas. The contribution of each submodel to the overall model i
43、s accomplished by combining the prices of the representative pay items in an index similar to that of the FHWA CBPI. In this case, because the formulation is slightly different from the FHWA CBPI and is constructed specifically to reflect past and future overall construction costs in Louisiana, it i
44、s named the Louisiana Highway Construction Index and is defined asValidationModel performance is ideally validated using data not used in the estimation of the model. In this case no such data was available. Dividing the existing data set into two portions to estimate the model on one portion and us
45、e the other for validation was not practical, given the limited sample size in some of the submodels. For example, the concrete pavement submodel has a total of only 212 observations, and estimating the submodel on the highly variable data on fewer observations would reduce the accuracy of the estim
46、ates. Thus, the performance of the model was assessed by observing how well it reproduced observed construction costs.Using the same data as that on which the model was calibrated, the estimated and observed LHCI values for the period 19841997 are shown in Fig. 3. The 95% confidence limit of the obs
47、erved LHCI is also shown in the figure to illustrate that the estimated LHCI values are, for the most part, contained within the 95% confidence limit of the observed LHCI values. The chisquared test of the similarity of the estimated and observed LHCI values indicates that a significant difference c
48、ould not be observed at the 99% level of significance.Investigating the behavior of the construction cost index in Fig. 3 reveals interesting reasons behind the observed behavior. Reviewing the data and observing its impact on the forecasts through the model allows an analyst to determine the primar
49、y causes of change in construction costs during certain periods in the past. For example, the main cause of the decrease in construction costs observed in the period 19841986 can be traced back to a decline in labor and petroleum costs during that period. The rapid increase in construction costs from 1995 to 1996 was primarily due to a combination of rising petroleum costs and an increased proportion of smaller contracts. The drop in construction costs observed
