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Civil-Comp Proceedings
ISSN 1759-3433
CCP: 32
DEVELOPMENTS IN COMPUTATIONAL TECHNIQUES FOR CIVIL ENGINEERING
Edited by: B.H.V. Topping
Paper II.4

An Innovative Decision Support System for Reliability Assessment of Management Structures of Organizations in the Construction Industry

M.S. Morcos and G. Singh

Department of Civil Engineering, University of Leeds, Leeds, UK

Full Bibliographic Reference for this paper
M.S. Morcos, G. Singh, "An Innovative Decision Support System for Reliability Assessment of Management Structures of Organizations in the Construction Industry", in B.H.V. Topping, (Editor), "Developments in Computational Techniques for Civil Engineering", Civil-Comp Press, Edinburgh, UK, pp 33-42, 1995. doi:10.4203/ccp.32.2.4
Abstract
Organizations in the construction industry should be well structured, directed and controlled in order to succeed under the various pressures impinging on them. These pressures can be external, in the form of political, economic, social and technological pressures [PEST] or internal. in the form of physical, human and financial resources. Reliability assessment of the management structures of organizations in the construction industry and the study of the possible causes of their failure as well as proposals of remedies for overcoming them are the main objectives of this paper. This is achieved through the development of a user friendly and interactive Decision Support System (DSS) built on a PC. It is named "ManageRely" (Management Reliability). This innovative DSS uses Knowledge-Based Production Rules [KBPK] together with important qualitative and quantitative reliability techniques like the Failure Mode and Effect Analysis [FMEA], the Function-Component-Parameter analysis [FCP] and the Fault Tree Analysis [FTA]. The Fault Tree Diagram [FTD] is drawn to identify all weak parts and defects in the system. Monte Carlo Simulation [MCS]. as risk analysis and response method. is used to simulate the FTD in order to mimic the behaviour of the system realistically and to help management of the uncertainties in the overall system's performance.

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