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Neural networks in the advisory system for repairs of industrial concrete floor

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Języki publikacji
EN
Abstrakty
EN
An advisory system for repairs of industrial concrete floors is a supporting tool for making material and technological decisions in the sphere of problems of recurrent character. The presented advisory system has the character of a hybrid system. Various elements of tools from the artificial intelligence group have been used in it. Artificial neural networks are of particular importance for functioning of the system. They act as an inference engine. The article presents, inter alia, an approach in the sphere of teaching artificial neural networks on the basis of an expert’s knowledge, as well as utilization of fuzzy sets for data transformation and for increasing the size of the case set. The conclusions indicate the profits resulting from utilization of artificial neural networks like speed of operation or absence of the need to possess complete knowledge.
Rocznik
Strony
255--263
Opis fizyczny
Bibliogr. 13 poz., rys., tab., wykr.
Twórcy
autor
Bibliografia
  • [1] H. Adeli, A. Karim. Scheduling/cost optimization and neural dynamics model for construction. Journal of Construction Management and Engineering, ASCE 123(4): 450–458, 1997.
  • [2] R. Apanaviciene. Construction projects management effectiveness modeling with neural networks. Journal of Civil Engineering and Management, 9(1): 59–67, 2003.
  • [3] C. Bishop. Neural Networks for Pattern Recognition. Oxford University Press, 1995.
  • [4] P. Dębski. Budget forecasting for buildings repairs with the use of artificial neural networks. Review of construction [in Polish]: Przeglad budowlany, 10: 34–36, 2001.
  • [5] I. Flood, P. Christophilos. Modeling construction process using artificial neural networks. Automation in Construction, 44: 1996/1, 307–320, 1996.
  • [6] M. Gajzler. Hybrid advisory system for repairs of concrete industrial floors (in Polish). Ph.D. Thesis, Faculty of Civil and Environmental Engineering, Poznan University of Technology 2008.
  • [7] M. Gajzler. Text and data mining techniques in aspect of knowledge acquisition for decision support system in construction industry. Technological and Economic Development of Economy, Vilnius: Technika, 162: 219–232, 2010.
  • [8] T. Hegazy. Neural network model for parametric cost estimation of highway projects. Journal of Construction Engineering and Management, 124(4): 210–218, 1998.
  • [9] B. Hoła, K. Schabowicz. Mathematical-neural model for assessing productivity of earthmoving machinery. Journal of Civil Engineering and Management, 13(1): 47–54, 2007.
  • [10] A. Leśniak. The method for calculating indirect costs of construction works using artificial neural networks (in Polish). Ph.D. Thesis, Cracow University of Technology, 2004.
  • [11] C. Tam, T. Tong, S. Tse. Artificial neural networks model for predicting excavator productivity. Construction and Architectural Management, No. 5/6, 446–452, 2002.
  • [12] P. Urbański. The use of artificial neural networks for the estimation of degree of technical wear [in Polish], Statsoft Polska, 105–119, 2004.
  • [13] Z. Waszczyszyn, M. Słoński. Selected problems of artificial neural networks development. In Waszczyszyn Z. [Ed.], Advances of soft computing in engineering, 237–316. Springer, 2009.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPB2-0070-0009
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