Modeling Deep Neural Networks to Learn Maintenance and Repair Costs of Educational Facilities

Abstract

Educational facilities hold a higher degree of uncertainty in predicting maintenance and repair costs than other types of facilities. Moreover, achieving accurate and reliable maintenance and repair costs is essential, yet very little is known about a holistic approach to learning them by incorporating multi-contextual factors that affect maintenance and repair costs. This study fills this knowledge gap by modeling and validating deep neural networks to efficiently and accurately learn maintenance and repair costs, drawing on 1213 high-confidence data points. The developed model learns and generalizes claim payout records on the maintenance and repair costs from sets of facility asset information, geographic profiles, natural hazard records, and other causes of financial losses. The robustness of the developed model was tested and validated by measuring the root mean square error and mean absolute error values. This study attempted to propose an analytical modeling framework that can accurately learn various factors, significantly affecting the maintenance and repair costs of educational facilities. The proposed approach can contribute to the existing body of knowledge, serving as a reference for the facilities management of other functional types of facilities.

A deep neural network algorithm-based approach for predicting recovery period of accidents according to construction scale

“…Consequently, the results and framework of this study are considered to be highly significant. The reason is that the DNN models can better reveal the non-linearities of accidents and various influencing indicators at the construction site than the MRA model [ 47 , 48 , 54 ]. When comparing the two DNN models, the small- and medium-sized construction site model showed a 36.2 % higher prediction error in MAE and 1.4 % in RMSE.…”

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