Tam, C M and Tong, T K L (2005) Multiple gmdh models for estimating resource requirements. Construction Innovation, 5(2), pp. 115-131. ISSN 1471-4175
Abstract
Accurate estimation or prediction of the resource required for a project is very important for construction. The more accurate the prediction model, the greater the potential for cost savings will be through elimination of any redesign and the minimization of the maintenance expenses. Contractors can also make use of the models for lastminute bid estimation. In the past the estimators perform the task by analogy with similar previous projects. This approach highly relies on their experience and knowledge. Owing to the lack of a scientific and easily apprehensible method in resource estimation, prediction outcomes are mainly based on humans' perception, which is inconsistent and exhibits large variations. This paper proposes the use of multiple Group Method of Data Handling (GMDH) models in developing models for resource estimation. The illustrative example has demonstrated the high accuracy of the approach which is superior to other architectures based on artificial neural networks.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | artificial neural networks; construction; group method of data handling; prediction model; resource estimation |
| Index terms: | accuracy, artificial neural network, prediction model, minimization, estimating, variation, estimator, estimation, cost saving, redesign |
| Subjects: | algorithms, economics, contractual condition, professional development, profession, design methods, modelling and simulation, financial and cost management, prediction and forecasting |
| Topics: | Digital Applications, Design Practice, Contract Administration, Cost Management, Information Management, Research Practice, Roles and Professions |
| Descriptive scope: | 3 PCA |
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