Sage Journals: Discover world-class research

Abstract

This paper presents an automated cost estimating (ACE) system for low-rise structural steel buildings. ACE is developed and coded using Borland C++ 5.0 in a Windows '95 environment. An efficient user interface allows for cost adjustments and markup allocation to individual cost items. Moreover, a number of project reports can be displayed upon request. ACE integrates artificial intelligence technologies and traditional spreadsheet applications. Neural networks are used in the system development in view of their learning and generalization capabilities. These networks utilize the knowledge obtained from previously constructed building projects to generate conceptual cost estimates in a timely manner. As such, estimates can be generated for different project scopes and characteristics in order to meet the user's needs. ACE uses NeuroShell 2, a commercial software, in the design and training of the neural network models. The capabilities of ACE are demonstrated through an example application. The methodology presented in this paper is not limited to cost estimating. It can easily be adapted to provide decision support for risk management and to assist in developing productivity models in a wide range of industries.

Keywords

cost estimating neural networks productivity models

Get full access to this article

View all access options for this article.

References

Creese

R.C.

, & Li

(1995). Cost estimation of timber bridges using neural networks. Cost Engineering, 37(5) 17–22.

Elazouni

A.M.

, Nosair

I.A.

, Mohieldin

Y.A.

, & Mohamed

A.G.

(1997). Estimating resource requirements at conceptual stage using neural networks. Journal of Comp. in Civil Engineering, 11(4), 217–223.

Flood

, & Kartam

(1994). Neural networks in civil engineering. I: Principles and understanding, and II: Systems and application. Journal of Comp. in Civil Engineering, 8(2), 131–162.

Garret

J.H.

Jr. (1992). Neural networks and their applicability within civil engineering. Proceedings of the Eighth Conference on Comp. Civil Engineering and Geographic Information Systems Symposium, Dallas, TX, 1155–1162.

Hegazy

, Fazio

, & Moselhi

(1994). Developing practical neural network applications using back-propagation. Microcomputers in Civil Engineering, 9, 145–159.

Kartam

, Flood

, & Tongthong

(1993). Artificial neural networks and knowledge-based systems: Complementary AI techniques for civil engineering. Proceedings of the Fifth ASCE International Conference on Comp. in Civil and Building Engineering. Anaheim, CA, pp. 1398–1403.

Moselhi

(1997). Risk assessment and contingency estimating. 1997 AACE International Transactions. Morgantown, WV: AACE International.

Moselhi

, Hegazy

, & Fazio

(1991). Neural networks as tools in construction. Journal of Construction Engineering Management, 117(4), 606–625.

Moselhi

, Hegazy

, & Fazio

(1993). DBID: Analogy-based DSS for bidding in construction. Journal of Construction Engineering Management, 119(3), 466–479.

10.

Moselhi

, Leonard

, & Fazio

(1991). Change orders' impact on construction productivity. Canadian Journal of Civil Engineering, 18(4), 484–492.

11.

Neil

, & Knack

(1984). Predicting productivity. 1984 AACE Transactions. Morgantown, WV: AACE International.

12.

Project Management Institute Standards Committee. (1996). A guide to the project management body of knowledge (PMBOK™ guide). Project Management Institute: Upper Darby, PA.

13.

Rumelhart

D.E.

, Hinton

G.E.

, & Williams

R.J.

(1986). Learning internal representations by error propagation. In Rumelhart

D.E.

, & McClelland

(Eds.), Parallel distributed processing: Explorations in the microstructures of cognition, Vol. 1. Foundations. Cambridge, MA: MIT Press.

14.

Specht

D.F.

(1991). A general regression neural network. IEEE Transactions on Neural Networks, 2(6), 568–576.

Automated Cost Estimating System Using Neural Networks

Abstract

Keywords

Get full access to this article

References