[雙語翻譯]建筑施工成本外文翻譯--建筑施工成本的規(guī)模收益

1、2700 英文單詞， 英文單詞，1.5 萬英文字符，中文 萬英文字符，中文 4600 字文獻(xiàn)出處： 文獻(xiàn)出處：Wibowo, Andreas. Returns to Scale in Buildings Construction Costs: Indonesian Cases[J]. Procedia Engineering, 2015, 125:18-24.Returns to scale in buildings construct

2、ion costs: Indonesian casesAndreas WibowoAbstractThis paper examines returns to scale in building constructions in Indonesia based on large sample sizes of different project types (i.e., hotel or apartment, hospital, off

3、ice, campus, and plant). The analysis demonstrates that costs tended to vary with sizes at a constant rate, as shown by cost capacity factors close to unity, with the exception of campus cases that supported decreasing r

4、eturns to scale. This finding affirms those of previous studies that non-constant returns to scale in cost-size relationships appear to weakly exist for building constructions. At the very least, it also implies that a s

5、imple unit-cost approach remains a reliable method for early cost estimates. This paper also identifies some potential issues associated with constant returns-to-scale applications.Keywords: cost-capacity factor; constan

6、t returns to scale; regression analysis; building construction; Indonesia.1. IntroductionThe relationship between costs and sizes in any production systems is rationally modeled as monotonic increasing functions. This ar

7、gument should also apply to construction industries: larger project sizes would technically require more efforts and resources to complete, which lead to higher costs to incur. While this fact is self evident, a more com

8、pelling issue that merits attention of cost engineers is how the rates of increase in construction costs compare with those in sizes or capacities ? returns to scale. Cost-size functions may exhibit increasing, constant,

9、 decreasing returns to scale or a combination of different modes of returns to scale for different cost or size ranges.In process industries (e.g., chemical, pharmaceutical, petroleum, water treatment industries), the us

10、e of increasing returns-to-scale assumption has been quite common for early cost estimates. Under this assumption, one would expect the estimated costs of building new larger facilities to run at lower rates than the inc

11、rease in capacities; thus, unit costs would tend to further decline over larger sizes. The application of the wellknown six-tenths rule, for instance, suggests that doubling the capacity would increase the cost by only a

12、bout 50%. However, a study by Remer and Wong [1] for airport terminal constructions shows the contrary that doubling the size needs more than double the implementation cost, thereby representing decreasing returns to sca

13、le.This paper investigates the returns-to-scale behavior for building construction projects in Indonesia. Over the years, abundant articles have been published focusing on parametric cost modeling for buildings but littl

14、e effort, let alone those using Indonesia’s building cases, was devoted to exploring the relationships between costs and sizes. Understanding these relationships is of particular useful for preparing preliminary cost est

15、imates at early project stages during which the levels of project definition are not sufficient and no better information is available for more accurate estimations. From the academic standpoint, the objective of the pre

16、sent paper is threefolds ? it attempts to enrich body of literature in this area, updates and extends similar previous studies [2,3] using a larger dataset, and fuels scholarly debates on the interpretation of research f

17、indings for future studies.contractual values, including profits and overheads and not the true costs of projects which remain unknown and confidential for public. Sizes or capacities of projects were proxied by gross fl

18、oor areas (GFA) in this research. Cost data were grouped by the type of building and those with a very limited number of samples were omitted for subsequent analysis.3.1. Data normalizationBecause cost data may stem from

19、 different locations and years, they must be adjusted for location and time to ensure every observation is based on the same reference point. This adjustment process, also known as data normalization [10], involves a cos

20、t index, a dimensionless number which relates the cost of an item at a specific time (or location) to the corresponding cost at some arbitrarily specified time (or location) [9,11]. However, given that no detailed inform

21、ation on the start dates of projects were available in the system and the compiled data were from the same year, 2013, adjustments were only made for locations. To convert costs from one city to another city, the followi

22、ng formula was used:where Cj = cost at city j, Ci = cost at city i. Jakarta was selected as the reference city. Consumer price index (CPI) published by Statistics Indonesia (Badan Pusat Statistik, BPS) is not appropriate

23、 to use as cost index as it measures price changes of consumer goods and services not essentially relevant to construction activities and a misuse of this index could lead to misleading conclusions. Construction cost ind

24、ex (CCI; Indeks Kemahalan Konstruksi), also published by BPS, was deemed more suitable in the sense that it more reflects price changes in construction and was therefore used for this research; this index is a composite

25、of 33 construction material prices, 6 heavy equipment rental costs, and 8 construction labor costs. Furthermore, unlike the BPS construction Wholesale Price Index (WPI; Indeks Harga Perdagangan Besar) that tracks price c

26、hanges from year to year for the same location––a periodical index, CCI is of a spatial index that compares prices for different locations but same year.Fig. 1. (a) Histogram of normalized costs for hotel/apartment; (b)

27、Histogram of areas for hotel/apartment.3.2. Descriptive statistics of unit costsCost data normalized using Eq. (3) presented a considerably high dispersion in terms of costs and GFA. Provided that the existence of outlie