A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Risk analysis in maintainability of high-rise buildings under tropical conditions using ensemble neural network
Purpose – The aim of this research study is to develop a risk-based framework that can quantify maintainability to forecast future maintainability of a building at early stages as a decision tool to minimize increase of maintenance cost. Design/methodology/approach – A survey-based approach was used to explore the risk factors in the domain of maintainability risks under tropical environmental conditions. The research derived ten risk factors based on 58 identified causes related to maintainability issues as common to high-rise buildings in tropical conditions. Impact of these risk factors was evaluated using an indicator referred to as the “maintenance score (MS)” which was derived from the “whole-life maintenance cost” involved in maintaining the expected “performance” level of the building. Further, an ensemble neural network (ENN) model was developed to model the MS for evaluating maintainability risks in high-rise buildings. Findings – Results showed that predictions from the model were highly compatible and in the same order when compared with calculations based on actual past data. It further showed that, maintainability of buildings could be improved if the building was designed, constructed and managed properly by controlling their maintainability risks. Originality/value – The ENN model was used to analyze maintainability of a high-rise building. Thus, it provides a useful tool for designers, clients, facilities managers/maintenance managers and users to analyze maintainability risks of buildings at early stages.
Risk analysis in maintainability of high-rise buildings under tropical conditions using ensemble neural network
Purpose – The aim of this research study is to develop a risk-based framework that can quantify maintainability to forecast future maintainability of a building at early stages as a decision tool to minimize increase of maintenance cost. Design/methodology/approach – A survey-based approach was used to explore the risk factors in the domain of maintainability risks under tropical environmental conditions. The research derived ten risk factors based on 58 identified causes related to maintainability issues as common to high-rise buildings in tropical conditions. Impact of these risk factors was evaluated using an indicator referred to as the “maintenance score (MS)” which was derived from the “whole-life maintenance cost” involved in maintaining the expected “performance” level of the building. Further, an ensemble neural network (ENN) model was developed to model the MS for evaluating maintainability risks in high-rise buildings. Findings – Results showed that predictions from the model were highly compatible and in the same order when compared with calculations based on actual past data. It further showed that, maintainability of buildings could be improved if the building was designed, constructed and managed properly by controlling their maintainability risks. Originality/value – The ENN model was used to analyze maintainability of a high-rise building. Thus, it provides a useful tool for designers, clients, facilities managers/maintenance managers and users to analyze maintainability risks of buildings at early stages.
Risk analysis in maintainability of high-rise buildings under tropical conditions using ensemble neural network
Facilities ; 34
2016
Article (Journal)
English
Maintenance costs , Data collection , Pattern recognition , Architectural engineering , Economics , Engineers , Architecture , High rise buildings , Civil engineering , Risk factors , Construction costs , Research , Building & construction , Neural networks , Property management & built environment , Building construction
Local classification TIB:
275/6500
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