Buildings and constructed assets. Service life planning

A hotel building in Yogyakarta was studied for its service life planning of electronic, electrical, and mechanical components the building. The study aims to assess the service life of hotel buildings including its electronics, mechanical and electrical components, and estimating the service life of each of the components. Data was collected from practitioners working in building maintenance office of several hotels in Yogyakarta. The Data was carefully analysed using descriptive statistics. This paper discusses the approaches and application of the availability of reference service life and service period data can be collected and applied in life cycle costing. The results of service-life data for periodical maintenance and replacement for various electronics, both mechanical and electrical parts are also presented for life cycle planning.

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Prediction analysis of the degradation and the service life building components in artificial method neural network and ISO factor 15686-2

MATEC Web of Conferences ◽

10.1051/matecconf/201925803017 ◽

2019 ◽

Vol 258 ◽

pp. 03017

Author(s):

Siti Aisyah ◽

Akhmad Aminullah ◽

H. Muslikh

Keyword(s):

Neural Network ◽

Service Life ◽

Predictive Analysis ◽

Calculation Formula ◽

Factors Affecting ◽

Life Planning ◽

Close Relationship ◽

Building Components ◽

Input Variables ◽

Building Condition

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.

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