建筑专业毕业设计外文翻译

建筑专业毕业设计外文翻译内容摘要：

it is generally more tolerable to operate with fewer relevant factors when modeling at the aggregate or overall level than when modeling at the disaggregate 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 impact 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 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 petition among contractors. Model Structure The model developed 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 of highway construction. From the Louisiana DOTD data for the period1984–1997, 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 concrete, 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 in each contract with no record of the amount of steel included in the bid. This made parison 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 permitted parison 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 is acplished by bining 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 is named the Louisiana Highway Construction Index and is defined as Validation Model 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 use 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 estimates. 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 1984–1997 are shown in Fig. 3. The 95% confidence limit of the observed 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 could 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 primary 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 1984–1986 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 bination of rising petroleum costs and an increased proportion of smaller contracts. The drop in construction costs observed immediately following this event (., in 1997) was mainly the consequence of an increase in the average size of projects from those let in 1996, very few projects being let in the fourth quarter, and a decrease in the average duration。