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Burchett, J F, Tummala, V M R and Leung, H M (1999) A world-wide survey of current practices in the management of risk within electrical supply projects. Construction Management and Economics, 17(01), 77-90.

Chapman, R J (1999) The likelihood and impact of changes of key project personnel on the design process. Construction Management and Economics, 17(01), 99-106.

Egbu, C O (1999) Skills, knowledge and competencies for managing construction refurbishment works. Construction Management and Economics, 17(01), 29-43.

Green, S D and Simister, S J (1999) Modelling client business processes as an aid to strategic briefing. Construction Management and Economics, 17(01), 63-76.

Laukkanen, T (1999) Construction work and education: occupational health and safety reviewed. Construction Management and Economics, 17(01), 53-62.

Loosemore, M (1999) A grounded theory of construction crisis management. Construction Management and Economics, 17(01), 9-19.

Moatazed-Keivani, R, Ghanbari-Parsa, A R and Kagaya, S (1999) ISO 9000 standards: perceptions and experiences in the UK construction industry. Construction Management and Economics, 17(01), 107-19.

Proverbs, D G, Holt, G D and Olomolaiye, P O (1999) Productivity rates and construction methods for high rise concrete construction: a comparative evaluation of UK, German and French contractors. Construction Management and Economics, 17(01), 45-52.

Raftery, J (1999) Quasi-rational behaviour in the property and construction market. Construction Management and Economics, 17(01), 21-7.

Skitmore, M R and Mills, A (1999) A needs based methodology for classifying construction clients and selecting contractors: comment. Construction Management and Economics, 17(01), 5-7.

Yau, N-J, Yang, J-B and Hsieh, T-Y (1999) Inducing rules for selecting retaining wall systems. Construction Management and Economics, 17(01), 91-8.

  • Type: Journal Article
  • Keywords: artificial intelligence; knowledge discovery; retaining wall selection; rule induction
  • ISBN/ISSN: 0144-6193
  • URL: https://doi.org/10.1080/014461999371853
  • Abstract:

    Rule induction is a paradigm of machine learning that governs how knowledge is acquired from experience. This paradigm not only classifies existing data into logical sets, but also expresses them by ‘if-then’ rules. Rule induction can be applied to the experience-oriented construction industry. A typical example would be to select an appropriate retaining wall system at the project planning stage, in which engineers normally employ certain empirical laws or select from the types for which they have relevant expertise in making appropriate selections. This work presents a novel rule induction approach, capable of inducing from 254 retaining wall cases in engineering design reports into 181 rules, thereby allowing for an appropriate retaining wall system to be selected. A system referred to herein as RULES is also constructed with an illustrative example provided as well. Test results of the system demonstrate that the rule induction approach can effectively resolve retaining wall selection problems at the project planning stage. The approach proposed herein is highly promising for resolving experience-oriented problems in the construction industry.