建筑专业毕业设计外文翻译1

本 科 生 毕 业 设 计（论文）外文翻译题目：Estimating Future Highway Construction Costs Estimating Future Highway Construction Costs,1 and G. Cheng, P.E.2Abstract: The objective of this research was to develop a model that estimates future highway construction costs in Louisiana. 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 construction 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 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 construction 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: 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, 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 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 likely 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.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 construction 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 current 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 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 Project 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 would 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 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 to 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 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 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. 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 highway 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 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 been 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 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 collapsed 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 reliance 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 records 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 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 information 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 against 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 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 estimating 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 projects 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. Similarly, 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 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 competition among contractors.Model StructureThe 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 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 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 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 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 is 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 is 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 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 19841997 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 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 immediately following this event (i.e., 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 of projects.ConclusionsThis study has shown that the literature indicates that a comprehensive set of factors contributes to the cost of highway construction. In this study, the most influential factors were found to be the cost of the material, labor, and equipment used in constructing the facility. However, characteristics of individual contracts and the contracting environment in which contracts are let also affect construction costs. In particular, contract size, duration, location, and the quarter in which the contract is let were found to have a significant impact on contract cost. Bid volume, bid volume variance, number of plan changes, and changes in construction practice, standards, or specifications also make a significant impact on contract costs. Other factors are expected to have an impact on construction costs but were not included in this analysis because no data on their values were available.The model developed in this study reproduces past overall construction costs reasonably accurately at the aggregate level. Predicted overall construction costs are not significantly different from observed costs at the 99% level of significance. This accuracy is largely the result of the aggregate level at which construction costs are measured in this study; at the individual contract level, the submodels capture only between 42 and 72% of the variation in the data. It is suspected that much of this variation is due to unobserved, essentially subjective factors that influence the bid prices in individual contracts. However, some of these idiosyncratic variations at the individual contract level average out in the aggregation process.This model can be used by highway officials in Louisiana to test alternative contract management strategies. Increasing contract sizes, reducing the duration of contracts, reducing bid volume and bid volume variance, reducing the number of plan changes, and reducing the proportion of contracts let in the fourth quarter all serve to reduce overall construction costs. Highway officials can assess the impact of strategies they believe are achievable by applying the model. Most importantly, though, the model can assist in estimating future construction costs and providing the means to produce more reliable construction programs.ReferenceAssociate Professor, Louisiana Transportation Research Center and Dept. of Civil and Environmental Engineering, Louisiana State Univ., Baton Rouge, LA 70803-6405.Civil Engineer, GEC, Inc., 9357 Interline Ave., Baton Rouge, LA 70809. Estimating Future Highway Construction CostsJOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT ASCE / MAY/JUNE 2003：272279Huyn P.N., Geneserth M.R. and Letsinger R. (1993). Automated concurrent engineering in design. World Computing, Vol. 26 (1), pp 7476. ISO (1994). ISO 10303-1 Part 1: Overview and fundamental principles, International Organization for Standardization, Geneva, Switzerland. Kalay Y.E., Khemluni L. and Choi J.W. (1998). An integrated model to support distributed collaborative design of buildings. Automation in Construction, Vol. 7 (23), pp 177188. Lee H.K., Lee Y.S., Kim K.H. and Kim J.J. (2007). A cost-based information model for an interior design in a large-scale housing project, ICCIT 07, 2007 International Conference on Convergence Information Technology, Poster Sessions: Session 4.Luiten G.T.B. and Tolman F.P. (1997). Automating communication, in civil engineering. Journal of Construction Engineering and Management, Vol. 123 (2), pp 113-120. 公路建设造价的未来,1 and G. Cheng, P.E.21联合教授，路易斯安娜运输研究中心和国家环境工程局，路易斯安娜国立大学2注册工程师摘要；本文的目标是建立一个用来估算路易斯安娜未来公路建设所需的工程费用的模型。根据公路建设造价索引，该模型介绍了中所有公路建设的工程费用。这索引是一份综合定额，包括建造的人工费，材料费和机械费，项目特征和项目周边的环境情况等等。未来工程的费用是用预计值来进行指定的，预计值是建立在建造人工费，材料费，机械费和预见项目特征和项目环境基础上的预测价格。在公路管理机构控制下的项目特征和项目环境能可根据未来的成本削减政策来操作处理。公路造价预算模型在路易斯安娜的运用表现出这模型几乎是19841997年时建设造价模型的重现。未来在运用公路造价预算时，该模型预测路易斯安娜的公路造价将在1998年到2015年间的增加两倍。在应用削减成本政策和假设投入的资金比预算减少20%的情况下，模型预测公路造价将在1998年到2015年间提高75%。关键词：公路建设，造价，预算引言国家交通部被要求筹备能反映他们短期建设计划的公路建设纲要。通常地方当局，政治家和利益集团都会对此项目投以极大的关注。草案一般会向社会、地方、省、国家各部门和标准联合会征求意见和建议。最后，项目将由国家立法机关批准，在一段时间后的下一轮新项目立项中成为国家交通部的正式建设项目。因为特殊项目对于政治家和个别利益集团是非常重要的，通常要对工程过程进行监理。任何偏差都有可能被质疑，国家交通部长和资深官员将不得不公开或找国家立法机构对情况进行辩护。这会导致使人不相信你的能力，并使来自立法机构和公众的支持受到冲击。筹备可靠的公路建设项目，公路管理机构必须对预留资金和工程造价有精确的评估。然而很明显，预留资金永远不能确保，通常是对在建设项目实施中引起混乱的项目造价的评估。不准确的造价意见是一个错误来源，但另一方面，不断过时的工程造价是另一个使项目失败的原因，这些通常没有预料和准备，一般，工程造价是根据一系列现在的工程造价判断的，如果不符，完工时进行调整。造价增加是有效的，当然是渐增的，而且，他们每年按一定比例增加。本文的核心是估计未来公路建设。本研究中模型建立的资料来自路易斯安娜交通和发展部（DOTD），是特别针对这个州的。然后，这种方法可在其他领域使用。项目造价估量当某领域建设落后于建设计划中的进度，通常是因为已经建造的项目费超过了预期费用。实际费用相对于预算不是随意变动的，因为很明显在这种情况下过低估计与过高估计会相互抵消。而且，这是所有项目全部过低评价的证据。这样的好处是能在一般水平估量，这比在特别的项目水平估量容易得多。过去，在一般建设造价中的变化已经测量在一系列建设索引中。这些索引不断加入一些典型费用项目的平均造价。他们已经习惯于根据过去造价。但是无法解释造价索引必须严格