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Bon, R and Luck, R (1999) Outsourcing of property-related management functions in Europe and North America, 1993-1998. Construction Management and Economics, 17(04), 409-12.

Chau, K W (1999) On the issue of plan shape complexity: plan shape indices revisited. Construction Management and Economics, 17(04), 473-82.

Chinyio, E A and Olomolaiye, P O (1999) A needs based methodology for classifying construction clients and selecting contractors-a rejoinder. Construction Management and Economics, 17(04), 413-7.

Cox, I D, Morris, J P, Rogerson, J H and Jared, G E (1999) A quantitative study of post contract award design changes in construction. Construction Management and Economics, 17(04), 427-39.

Kale, S and Arditi, D (1999) Age-dependent business failures in the US construction industry. Construction Management and Economics, 17(04), 493-503.

Kein, A T T, Ofori, G and Briffett, C (1999) ISO 14000: its relevance to the construction industry of Singapore and its potential as the next industry milestone. Construction Management and Economics, 17(04), 449-61.

Love, P E D, Mandal, P and Li, H (1999) Determining the causal structure of rework influences in construction. Construction Management and Economics, 17(04), 505-17.

Pietroforte, R and Tangerini, P (1999) From boom to bust: the case of Italian construction firms. Construction Management and Economics, 17(04), 419-25.

Runeson, G and Loosemore, M (1999) Gate-keepers or judges: peer reviews in construction management. Construction Management and Economics, 17(04), 529-36.

Shapira, A and Schexnayder, C J (1999) Selection of mobile cranes for building construction projects. Construction Management and Economics, 17(04), 519-27.

Shi, J J (1999) A neural network-based system for predicting earthmoving production. Construction Management and Economics, 17(04), 463-71.

  • Type: Journal Article
  • Keywords: artificial neural networks; back propagation; earthmoving; prediction; simulation; site operation
  • ISBN/ISSN: 0144-6193
  • URL: https://doi.org/10.1080/014461999371385
  • Abstract:

    An artificial neural network based system (NN_earth) is developed for construction practitioners as a simple tool for predicting earthmoving operations, which are model led by back propagation neural networks with four expected parameters and seven affecting factors. These networks are then trained using the data patterns obtained from simulation because there are insufficient data available from industrial sources. The trained network is then incorporated as the computation engine of NN_earth. To engender confidence in the results of neural computation, a validation function is implemented in NN_earth to allow the user to apply the engine to historic cases prior to applying it to a new project. An equipment database is also implemented in NN_earth to provide default information, such as internal cost rate, fuel cost, and operator’ s cost. User interfaces are developed to facilitate inputting project information and manipulating the system. The major functions and use of NN_earth are illustrated in a sample application. In practice, NN_earth can assist the user either in selecting a crew to minimize the unit cost of a project or in predicting the performance of a given crew.

Swaffield, L M and Pasquire, C L (1999) Examination of relationships between building form and function, and the cost of mechanical and electrical services. Construction Management and Economics, 17(04), 483-92.

Williams, T (1999) Allocation of contingency in activity duration networks. Construction Management and Economics, 17(04), 441-7.