[雙語(yǔ)翻譯]建筑施工成本外文翻譯--建筑施工成本的規(guī)模收益（英文）

1、 Procedia Engineering 125 ( 2015 ) 18 – 24 1877-7058 © 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/

2、4.0/).Peer-review under responsibility of organizing committee of The 5th International Conference of Euro Asia Civil Engineering Forum (EACEF-5) doi: 10.1016/j.proeng.2015.11.004 ScienceDirectAvailable online at www.sc

3、iencedirect.comThe 5th International Conference of Euro Asia Civil Engineering Forum (EACEF-5) Returns to scale in buildings construction costs: Indonesian cases Andreas Wibowoa,b,* a RIHS Ministry of Public Works Indone

4、sia, Jalan Panyawungan Cileunyi Wetan Kabupaten Bandung, 40393, Indonesia b Graduate Program in Civil Engineering Parahyangan Catholic University, Jalan Merdeka 30 Bandung, 40117, Indonesia Abstract This paper examines

5、returns to scale in building constructions in Indonesia based on large sample sizes of different project types (i.e., hotel or apartment, hospital, office, campus, and plant). The analysis demonstrates that costs tended

6、 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 returns to scale. This finding affirms those of previous studies th

7、at 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 simple unit-cost approach remains a reliable method for early cos

8、t estimates. This paper also identifies some potential issues associated with constant returns-to-scale applications. © 2015 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of organizing

9、committee of The 5th International Conference of Euro Asia Civil Engineering Forum (EACEF-5). Keywords: cost-capacity factor; constant returns to scale; regression analysis; building construction; Indonesia. 1. Introdu

10、ction The relationship between costs and sizes in any production systems is rationally modeled as monotonic increasing functions. This argument should also apply to construction industries: larger project sizes would te

11、chnically require more efforts and resources to complete, which lead to higher costs to incur. While this fact is self evident, a more compelling issue that merits attention of cost engineers is how the rates of increa

12、se in construction costs compare with those in sizes or capacities ? returns to scale. Cost-size functions may exhibit increasing, constant, decreasing returns to scale or a combination of different modes of returns to

13、 scale for different cost or size ranges. In process industries (e.g., chemical, pharmaceutical, petroleum, water treatment industries), the use of increasing returns-to-scale assumption has been quite common for early

14、cost estimates. Under this assumption, one would expect * Corresponding author. E-mail address: andreaswibowo1@yahoo.de © 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-N

15、C-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of organizing committee of The 5th International Conference of Euro Asia Civil Engineering Forum (EACEF-5)20 Andreas W

16、ibowo / Procedia Engineering 125 ( 2015 ) 18 – 24 3. Data acquisition and analysis This research benefited from the rich construction cost database system of Research Institute for Human Settlements (RIHS) unde

17、r Indonesia’s Ministry of Public Works and Public Housing. The fields set up in the database system include location, size, area, function, budget source (public, private), hotel class (for hotels only), number of stori

18、es (including basements), and some information on technical specifications (e.g., construction materials, types of foundations, sources of electricity power, elevators). At the time of writing this paper, the updating

19、of database (2014) is still on progress, so this research used the complete data of 2013 that contained about 1,050 project data points across the country with a wide variety of building types, ranging from airport ter

20、minal to laboratory buildings. However, not entire data points and fields were usable for this research and a substantial amount of data had to be removed, primarily due to blank entries of the required fields. It is w

21、orth noting here that the cost data stored in the system is disclosed contractual values, including profits and overheads and not the true costs of projects which remain unknown and confidential for public. Sizes or ca

22、pacities of projects were proxied by gross floor 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. D

23、ata normalization Because cost data may stem from 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, als

24、o known as data normalization [10], involves a cost 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 loca

25、tion) [9,11]. However, given that no detailed information 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 co

26、nvert costs from one city to another city, the following formula was used: cost indexcost indexjj iiC C ?(3) where Cj = cost at city j, Ci = cost at city i. Jakarta was selected as the reference city. Consumer price ind

27、ex (CPI) published by Statistics Indonesia (Badan Pusat Statistik, BPS) is not appropriate to use as cost index as it measures price changes of consumer goods and services not essentially relevant to construction activ

28、ities and a misuse of this index could lead to misleading conclusions. Construction cost index (CCI; Indeks Kemahalan Konstruksi), also published by BPS, was deemed more suitable in the sense that it more reflects pric

29、e changes in construction and was therefore used for this research; this index is a composite of 33 construction material prices, 6 heavy equipment rental costs, and 8 construction labor costs. Furthermore, unlike the

30、BPS construction Wholesale Price Index (WPI; Indeks Harga Perdagangan Besar) that tracks price changes from year to year for the same location––a periodical index, CCI is of a spatial index that compares prices for dif

31、ferent locations but same year. 3.2. Descriptive statistics of unit costs Cost data normalized using Eq. (3) presented a considerably high dispersion in terms of costs and GFA. Provided that the existence of outliers c

32、an distort the regression results, it is thus essential to remove them from datasets. The first step to detect outliers was to calculate unit costs for each building type by dividing the normalized cost data points wit

33、h their respective GFA. As had been anticipated, this process unveiled a great deal of nonsense data which were either extremely low or high. To secure more reliable data sets, this research applied a very restrictive c

34、riterion for every data point to be incorporated into regression analysis: it must fall within the range of the first quartile (Q1) and the third quartile (Q3) under the Tukey’s hinges definition. This objective of rel