Application of back-propagation artificial neural network to predict maintenance costs and budget for university buildings

Abstract

The study focuses on the operation maintenance phase of buildings on the National Taiwan University campus. Using historical data on maintenance and repair over a 40-year period, lifecycle cost analyses are conducted based on the statistical quantization methods and expert opinions. The study in connection with periodic maintenance; non-periodic repair and demand change, for theses three types of maintenance management. Moreover, multiple regression analysis and back-propagation artificial neural network (BPN) are used to establish a cost model for predicting maintenance costs. The age of the building, number of storeys, and elevator facilities are used as independent variables to estimate maintenance costs. The study helps to set a legitimate standard for arranging repair maintenance costs, and proposes a plan and standard for the repair maintenance strategy of the structures.

Strategies of Public University Building Maintenance—A Literature Survey

“…All of the papers were published in the years 2008-2012, which could lead to the conclusion that the authors abandoned research work on this specific topic. The second noteworthy document entitled "Maintenance cost and budget for university buildings" [28], also affiliated with the National Taiwan University, emerged as a conference paper. It delved into maintenance and repair practices, amalgamating expert opinions with statistical quantification through Life Cycle Cost Analysis (LCCA).…”

“…Out Expanding beyond the limitations of "Set 1" Scopus data reveals that Guo S.J. has authored a total of 42 documents, of which 4 met the specified thresholds [8,[27][28][29]. Among these, three are conference papers and one is an article.…”