Building condition assessment using artificial neural network and structural equations

Document Type


Publication Date

Fall 8-15-2021


Building facilities condition assessment is considered a fundamental aspect of an effective decision-making maintenance management plan to fulfill service requirements. A noticeable dearth of studies is believed to have delivered condition assessment approaches for existing buildings; however, these approaches are still deemed premature, with some limitations in demand enhancement. This paper presents a novel physical condition assessment framework for existing educational buildings that contribute to the body of knowledge by offering a state-of-the-art approach incorporating an Artificial Neural Network (ANN) predictive model and a Structural Equation Model (SEM). The ANN predictive model aims to forecast the future condition-rating states for each facility component in various building spaces. Simultaneously, the SEM determines the proportionate weights of building facilities components. The primary objectives of this paper are to prioritize building components for maintenance purposes and record the potential effects of several parameters influencing the condition state of building components. These objectives can be achieved via four sequential modules: 1) scan to BIM module; 2) condition assessment prediction module; 3) proportionate weight determination module; and 4) entire space rating value module. Condition-monitoring data on six different buildings' internal components are analyzed to anticipate their future condition. The components carried out are: 1) wooden flooring tiles; 2) gypsum board ceiling tiles; 3) wooden doors; 4) wooden windows, 5) split air conditioner units; and 6) desktop computers. The overall coefficient of determination (R2) of the developed ANN models for the predicted six components conditions are 0.99, 0.99, 0.927, 0.88, 0.97, and 0.972, respectively.