The research study reported in this paper aims to combine the Artificial Neural Networks with ISO 15686 Buildings and constructed assets - Service life planning, a framework-based approach to offering a more reliable deterioration forecasting more reliable for building. This paper discusses the existing data and develop a close relationship definition between factors affecting the condition of the service life of the building, the value of building condition and determine the level of degradation of the building component, also predicted the age of the building components in accordance with a specific time variables. Data examination conducted in this research is building condition data of student dormitory at the Universitas Gadjah Mada, the data will be used to calibrate the model of damage to consider a number of factors that influence. To help demonstrate the concept, factors affecting the decline is considered in the analysis of the design level, the level of implementation of the work, the indoor environment, the external environment, the level of care and conditions of use. Predictive analysis with artificial methods of neural network (ANN) with ISO factor input variables and factors age of the building components and the severity level of degradation of the building components (Sw) as output, this will generate a calculation formula that shows the effect of each variable input to output. Predictive analysis carried out with the reverse approach in which after calculation formula obtained by ANN method, then the next step is to find the value of the variable age of the building components according to the value of degradation that has been determined.
Service life planning for electronics, mechanical and electrical components of a hotel building
