Benjaoran, V; Dawood, N and Scott, D (2004) Bespoke precast productivity estimation with neural network model. In: Khosrowshahi, F (ed.) Proceedings of 20th Annual ARCOM Conference, 1-3 September 2004, Edinburgh, UK.
Abstract
Bespoke precast-concrete components are custom made for construction projects. The variety of product designs results in requiring different manufacturing time. To estimate the productivity of four precast routines, this study identifies twenty influential factors based on the difficulty in product designs and manpower. These influential factors are such as nominal height, length, and width, tiling area, the number of curves, the number of embedded parts, concrete strength, slump, reinforcement weight, and the number of different bar shapes, etc. Productivity estimation models are formulated using two techniques: neural network (NN) and multivariable linear regression (MLR). The estimation performance from both techniques is measured with three statistical values, namely absolute percentage error, mean square error, and correlation coefficient. The experimentation results show that MLR gives insignificantly better performance than NN. However, standardised residuals from the NN are distributed in the narrower range than the ones from the MLR.
| Item Type: | Conference Paper (Paper) |
|---|---|
| Uncontrolled Keywords: | bespoke precast-concrete production; multivariable linear regression; neural network; productivity estimation |
| Index terms: | routine, neural network, estimation, estimate, influential factor, mean square error, reinforcement, experiment, construction project, productivity, product design |
| Subjects: | probability and distributions, innovation and technology management, production management, building materials, artificial intelligence, management, sociology, risk assessment, data collection methods, financial and cost management |
| Topics: | Digital Applications, Construction Materials, Cost Management, Risk Management, Business Strategy, Organizational Design, Project Management, Engineering Principles, Research Practice |
| Descriptive scope: | 4 PCEA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here