Prediction of the maintenance performance cost in dwellings and building sites located in Spain using multilayer perceptrons

Abstract

interest to the building engineering sector. The determination and

programming of maintenance tasks is essential to allow finance

entities to establish market order according to a given budget.

The process works slowly, and some optimization is generally

required. In this paper, two multilayer perceptrons (MLPs) are

developed to determine the economic cost of maintenance works

in the two types of real estate asset of more interest to building

sector: building sites and dwellings. After training using 76 case

studies for building sites and 317 for dwellings, the optimal MLP

configurations are shown to have 6 and 12 nodes respectively, and

the input variables that most influenced their behavior are also

determined. Furthermore, the MLPs showed more optimal behavior

than models using multiple linear regression. Finally, the MLPs

were tested for 15 new case studies for each model, predicting

the budgeted costs of the associated maintenance works with

deviations of less than 11% compared with the actual value in

most cases.