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Ahmed, S M, Sang, L P and Torbica, & M (2003) Use of Quality Function Deployment in Civil Engineering Capital Project Planning. Journal of Construction Engineering and Management, 129(04), 358–68.

Allouche, E N, Ariaratnam, S T and MacLeod, C W (2003) Software for Planning and Cost Control in Directional Drilling Projects. Journal of Construction Engineering and Management, 129(04), 446–53.

Attalla, M and Hegazy, T (2003) Predicting Cost Deviation in Reconstruction Projects: Artificial Neural Networks versus Regression. Journal of Construction Engineering and Management, 129(04), 405–11.

Chan, E H W and Tse, R Y C (2003) Cultural Considerations in International Construction Contracts. Journal of Construction Engineering and Management, 129(04), 375–81.

Chang, A S and Tsai, Y (2003) Engineering Information Classification System. Journal of Construction Engineering and Management, 129(04), 454–60.

Cheng, M and Ko, C (2003) Object-Oriented Evolutionary Fuzzy Neural Inference System for Construction Management. Journal of Construction Engineering and Management, 129(04), 461–9.

  • Type: Journal Article
  • Keywords: Fuzzy sets; Neural networks; Algorithms; Construction management; fuzzy neural nets; construction industry; inference mechanisms; decision support systems; project management; civil engineering;
  • ISBN/ISSN: 0733-9364
  • URL: https://doi.org/10.1061/(ASCE)0733-9364(2003)129:4(461)
  • Abstract:
    Problems in construction management are complex, full of uncertainty, and vary with environment. Fuzzy logic, neural networks, and genetic algorithms (GAs) have been successfully applied in construction management to solve various kinds of problems. Considering the characteristics and merits of each method, this paper combines the above three techniques to develop an Evolutionary Fuzzy Neural Inference Model (EFNIM). Integrating these three methods, the EFNIM uses GAs to simultaneously search for the fittest membership functions with the minimum fuzzy neural network (FNN) structure and optimum parameters of FNN. Thus, the best adaptation mode is automatically identified. Furthermore, this research work integrates the EFNIM with an object-oriented (OO) computer technique to develop an OO Evolutionary Fuzzy Neural Inference System for solving construction management problems. Simulations are conducted to demonstrate the application potential of the EFNIS. This system could be used as a multifarious intelligent decision support system for decision-making to solve manifold construction management problems.

Fu, W K, Drew, D S and Lo, H P (2003) Competitiveness of Inexperienced and Experienced Contractors in Bidding. Journal of Construction Engineering and Management, 129(04), 388–95.

Hassim, S, Kadir, M R A, Lew, Y and Sim, Y (2003) Estimation of Minimum Working Capital for Construction Projects in Malaysia. Journal of Construction Engineering and Management, 129(04), 369–74.

Hegazy, T and Petzold, K (2003) Genetic Optimization for Dynamic Project Control. Journal of Construction Engineering and Management, 129(04), 396–404.

Ibbs, C W, Kwak, Y H, Ng, T and Odabasi, A M (2003) Project Delivery Systems and Project Change: Quantitative Analysis. Journal of Construction Engineering and Management, 129(04), 382–7.

Lee, S and Halpin, D W (2003) Predictive Tool for Estimating Accident Risk. Journal of Construction Engineering and Management, 129(04), 431–6.

Lu, M and Li, H (2003) Resource-Activity Critical-Path Method for Construction Planning. Journal of Construction Engineering and Management, 129(04), 412–20.

Navon, R and Goldschmidt, E (2003) Can Labor Inputs be Measured and Controlled Automatically?. Journal of Construction Engineering and Management, 129(04), 437–45.

Warszawski, A (2003) Analysis of Costs and Benefits of Tall Buildings. Journal of Construction Engineering and Management, 129(04), 421–30.