PL EN


Preferences help
enabled [disable] Abstract
Number of results
2017 | 132 | 3 | 585-587
Article title

A Neuro-Adaptive Learning (NAL) Approach about Costs of Residential Buildings

Authors
Content
Title variants
Languages of publication
EN
Abstracts
EN
The artificial neural networks and fuzzy logic models are two well-known branches of artificial intelligence and have been broadly and successfully used to simulate input-output systems. Over the last two decades, a different modeling method based on fuzzy logic or neural networks has become popular and has been used by many researchers for a variety of engineering applications. Nowadays, for reducing the amount of experiment costs, modeling methods based on artificial neural networks and fuzzy logic systems have become more popular and have been used by many researchers for many civil engineering management applications. In this study a neuro-adaptive learning approach about costs of residential buildings was designed. As a result, NAL can be an alternative approach for the evaluation of the cost estimations of residential buildings construction.
Year
Volume
132
Issue
3
Pages
585-587
Physical description
Dates
published
2017-09
References
  • [1] Zhe Liu, Zhiliang Ma, Proced. Eng. 123, 291 (2015), doi: 10.1016/j.proeng.2015.10.093
  • [2] R. Sonmez, Expert Syst. Appl. 38, 9913 (2011), doi: 10.1016/j.eswa.2011.02.042
  • [3] Min-Yuan Cheng, Hsing-Chih Tsai, Erick Sudjono, Expert Syst. Appl. 37, 4224 (2010), doi: 10.1016/j.eswa.2009.11.080
  • [4] B. Flyvbjerg, M.K. Holm, S.L. Buhl, J. Am. Plan. Assoc. 68, 279 (2002), doi: 10.1080/01944360208976273
  • [5] A. Niazi, J.S. Dai, S. Balabani, L. Seneviratne, J. Manufact. Sci. Eng. Trans. ASME 128, 563 (2006), doi: 10.1115/1.2137750
  • [6] PMI, A Guide to the Project Management Body of Knowledge, 4th ed., Project Management Institute, 2008
  • [7] J.-S. Chou, Expert Syst. Appl. 36, 2947 (2009), doi: 10.1016/j.eswa.2008.01.025
  • [8] K.A. Artto, J.M. Lehtonen, J. Saranen, Int. J. Proj. Manag. 19, 255 (2001), doi: 10.1016/S0263-7863(99)00082-4
  • [9] J. Berny, P. Townsend, Int. J. Proj. Manag. 11, 201 (1993), doi: 10.1016/0263-7863(93)90036-M
  • [10] J.-S. Chou, M. Peng, K.R. Persad, J.T. O'Connor, Quantity-based approach to preliminary cost estimates for highway projects, Transportation Research Record, p. 22, 2006
  • [11] D. Cooper, D. MacDonald, C. Chapman, Int. J. Project Manag. 3, 141 (1985), doi: 10.1016/0263-7863(85)90065-1
  • [12] A. Franke, Int. J. Project Manag. 5, 29 (1987), doi: 10.1016/0263-7863(87)90007-X
  • [13] A. Laufer, Int. J. Project Manag. 9, 53 (1991), doi: 10.1016/0263-7863(91)90057-3
  • [14] A. Touran, J. Construct. Eng. Manag. 129, 280 (2003), doi: 10.1061/(ASCE)0733-9364(2003)129:3(280)
  • [15] C.H. Wang, Y.C. Huang, Int. J. Project Manag. 18, 131 (2000), doi: 10.1016/S0263-7863(98)00077-5
  • [16] W.C. Wang, Int. J. Project Manag. 22, 99 (2004), doi: 10.1016/S0263-7863(03)00046-2
  • [17] P. Stefanov, A. Savić, G. Dobrić, Acta. Phys. Pol. A 128, B-138 (2015), doi: 10.12693/APhysPolA.128.B-138
  • [18] E. Boutalbia, L. Ait Gougam, F. Mekideche-Chafa, Acta. Phys. Pol. A 128, B-271 (2015), doi: 10.12693/APhysPolA.128.B-271
  • [19] A. Beycioğlu, C. Başyiğit, Acta. Phys. Pol. A 128, B-424 (2015), doi: 10.12693/APhysPolA.128.B-424
  • [20] M. Bilgehan Erdem, Acta. Phys. Pol. A 130, 331 (2016), doi: 10.12693/APhysPolA.130.331
  • [21] N. Ibadov, Acta. Phys. Pol. A 130, 107 (2016), doi: 10.12693/APhysPolA.130.107
  • [22] Y. Özcanli, F. Kosovalı Çavuş, M. Beken, Acta. Phys. Pol. A 130, 444 (2016), doi: 10.12693/APhysPolA.130.444
Document Type
Publication order reference
YADDA identifier
bwmeta1.element.bwnjournal-article-appv132n3p050kz
Identifiers
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.