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Predicting sustainability assessment at early facilities design phase
Purpose Integrating the aspects of sustainability into facilities design has become a designers' challenge, and the early design phase is seen as the most important in implementing sustainability into facilities design. Therefore, this paper aims to analyze the factors that influence sustainability assessment of preliminary design of facilities and predicts sustainability assessment depending on those factors. Design/methodology/approach Data were collected by survey questionnaire distributed to project managers using a six-point Likert scale. Obtained data were modeled with general regression neural network (GRNN) using DTREG software. In total, 27 factors were chosen for determining the most accurate predictive model, and their importance was computed. Findings The six most important factors for sustainability assessment of facilities design are: work experience, work on several outline design proposals, resolving issues between stakeholders, prioritization of participants in the design phase, procurement management and defining projects' program and goals. The predictive model that was used for prediction of the sustainability assessment was shown to be highly accurate, with MAPE (mean absolute percentage error) amounting to 2.58 per cent. Practical implications Using the same approach, assessment of every other factor for the preliminary design can be predicted and the factors that are most influential to its sustainability can be obtained. Originality/value The paper supports the sustainability improvement of the preliminary design of future facilities' projects, as well as support during the decision-making process.
Predicting sustainability assessment at early facilities design phase
Purpose Integrating the aspects of sustainability into facilities design has become a designers' challenge, and the early design phase is seen as the most important in implementing sustainability into facilities design. Therefore, this paper aims to analyze the factors that influence sustainability assessment of preliminary design of facilities and predicts sustainability assessment depending on those factors. Design/methodology/approach Data were collected by survey questionnaire distributed to project managers using a six-point Likert scale. Obtained data were modeled with general regression neural network (GRNN) using DTREG software. In total, 27 factors were chosen for determining the most accurate predictive model, and their importance was computed. Findings The six most important factors for sustainability assessment of facilities design are: work experience, work on several outline design proposals, resolving issues between stakeholders, prioritization of participants in the design phase, procurement management and defining projects' program and goals. The predictive model that was used for prediction of the sustainability assessment was shown to be highly accurate, with MAPE (mean absolute percentage error) amounting to 2.58 per cent. Practical implications Using the same approach, assessment of every other factor for the preliminary design can be predicted and the factors that are most influential to its sustainability can be obtained. Originality/value The paper supports the sustainability improvement of the preliminary design of future facilities' projects, as well as support during the decision-making process.
Predicting sustainability assessment at early facilities design phase
Valentina Zileska Pancovska (Autor:in) / Silvana Petrusheva / Aleksandar Petrovski
Facilities ; 35
2017
Aufsatz (Zeitschrift)
Englisch
Scale (ratio) , Predictions , Procurement management , Design , Mathematical models , Decision making , Regression , Errors , Design improvements , Architectural engineering , Architecture , Computation , Sustainability , Proposals , Discipline , Design analysis , Project management , Construction costs , Handbooks , Neural networks , Regression analysis , Environmental impact , Design factors
Lokalklassifikation TIB:
275/6500
Predicting sustainability assessment at early facilities design phase
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