Abstracts – Browse Results
Click on the titles below to expand the information about each abstract.
Viewing 9 results ...
Brewer, G, Gajendran, T, Jefferies, M, McGeorge, D, Rowlinson, S and Dainty, A (2013) Value through innovation in long-term service delivery: Facility management in an Australian PPP. Built Environment Project and Asset Management, 3(01), 74-88.
Devkar, G A and Kalidindi, S N (2013) External agencies for supplementing competencies in Indian urban PPP projects. Built Environment Project and Asset Management, 3(01), 58-73.
Devkar, G A and Kalidindi, S N (2013) Modeling and assessment of competencies in urban local bodies for implementing PPP projects. Built Environment Project and Asset Management, 3(01), 42-57.
Eadie, R, Millar, P and Grant, R (2013) PFI/PPP, private sector perspectives of UK transport and healthcare. Built Environment Project and Asset Management, 3(01), 89-104.
Ling, F Y Y and Nguyen, D S A (2013) Strategies for construction waste management in Ho Chi Minh City, Vietnam. Built Environment Project and Asset Management, 3(01), 141-56.
Lingard, H, Cooke, T, Blismas, N and Wakefield, R (2013) Prevention through design: Trade-offs in reducing occupational health and safety risk for the construction and operation of a facility. Built Environment Project and Asset Management, 3(01), 7-23.
Meding, J v, McAllister, K, Oyedele, L and Kelly, K (2013) A framework for stakeholder management and corporate culture. Built Environment Project and Asset Management, 3(01), 24-41.
Silva, N D, Ranasinghe, M and Silva, C R D (2013) Use of ANNs in complex risk analysis applications. Built Environment Project and Asset Management, 3(01), 123-40.
- Type: Journal Article
- Keywords: artificial neural networks; building maintainability; ensemble neural networks; maintenance; maintenance management; neural nets; risk analysis
- ISBN/ISSN: 2044-124X
- URL: https://doi.org/10.1108/BEPAM-07-2012-0043
- Abstract:
Purpose – Artificial neural network (ANN) has been used for risk analysis in various applications such as engineering, financial and facilities management. However, use of a single network has become less accurate when the problem is complex with a large number of variables to be considered. Ensemble neural network (ENN) architecture has proposed to overcome these difficulties of solving a complex problem. ENN consists of many small “expert networks” that learn small parts of the complex problem, which are established by decomposing it into its sub levels. This paper seeks to address these issues. Design/methodology/approach – ENN model was developed to analyse risks in maintainability of buildings which is known as a complex problem with a large number of risk variables. The model comprised four expert networks to represent building components of roof, façade, internal areas and basement. The accuracy of the model was tested using two error terms such as network error and generalization error. Findings – The results showed that ENN performed well in solving complex problems by decomposing the problem into its sub levels. Originality/value – The application of ensemble network would create a new concept of analyzing complex risk analysis problems. The study also provides a useful tool for designers, clients, facilities managers/maintenance managers and users to analyse maintainability risks of buildings at early stages.
Singh, A and Adachi, S (2013) Bathtub curves and pipe prioritization based on failure rate. Built Environment Project and Asset Management, 3(01), 105-22.