Improving Reliability in Predicting the Degradation of Building Assets
Creators: name: Wickramasinghe, Gangodawila Appuhamillage
Publisher: name: RMIT University
Year: 2025
Subjects: classificationCode: 400508
subject: Infrastructure engineering and asset management

description: Managing ageing assets within available budgets is a major challenge at present. In the absence of an effective asset maintenance system in place to ensure that all building components are in working order, the relevant institutions may incur significant losses due to unscheduled repairs and downtime. Asset management systems are representative of maintenance strategies. Predictive maintenance is a cutting-edge maintenance strategy that employs predictive modelling of building degradation to achieve optimal maintenance schedules and budgets. Generic condition ratings of building components based on subjective visual inspection are typically collected and analysed over time to determine age-related deterioration trends and life-cycle costs. This study suggests two alternative approaches: (i) Deficiency-based condition ratings of building components from seven Sri Lankan local councils were analysed to develop component-level reliability-based Markov models (engineering-based approach), and (ii) nominal replacement costs and times for building components assigned by estimators from the City of Melbourne were used to arrive at degradation rates for component groups using a novel concept of cumulative lost value ratio (CLVR), defined as the ratio of the cumulative loss of initial values of constituent components of a component group to their total initial value (monetary-based approach).

In the main study of the engineering-based approach, snapshot data for 12 selected components were collected using both deficiency-based and generic deterioration-based condition scales, and the validity and accuracy of the predictions were evaluated using Markov Chain Monte Carlo (MCMC) and generalised reduced gradient non-linear optimisation (GRG NLO) techniques. The MCMC produced marginally more reliable results, and predictions based on deficiency-based ratings yielded better overall results than generic deterioration-based ratings. Following these evaluations, the most reliable predictions were used to facilitate maintenance service bundling, where degradation patterns of components were studied and categorised into three groups. The engineering-based approach was also used to investigate (i) the correlation between deficiencies and age-based deterioration, (ii) a methodology for determining an upper age for which data is accepted in order to eliminate the effects of maintenance inputs, (iii) the impact of weather, exposure, and the level of utilisation on degradation, and (iv) the development of a cost-based condition index based on the rectification costs.

A follow-up study investigated the impact of factors other than age on three building components: coastal exposure on concrete beams, level of utilisation on rendered cement floors, and rainfall on timber windows. In the first stage, the difference between randomly split data and categorised data based on high and low levels of influencing factors was used to determine if the factors had a perceptible influence on degradation. Second-stage entailed the development of models employing GRG NLO, multiple linear regression, simple neural network, deep neural network, and Random Forest techniques. Age was the dominant factor in degradation. Coastal exposure had the most perceptible influence on concrete beams, while rainfall had the least perceptible influence on timber windows. This is likely owing to the competing effects of rainfall-produced moisture and solar radiation in the absence of rainfall. Random Forest performed best in a model comparative study with two input features, while LIME explanation enabled better comprehension of the findings. The latter showed that the factors' influence was most apparent during the components' mid-life.

A lost value ratio (LVR) index was introduced to characterise the condition of each component by using the loss of initial value as a ratio, which is the ratio of the rectification cost to the initial cost at a given condition. Since the conditions are defined by specific deficiency definitions, rectification costs and LVRs for each physical condition for the selected components were determined using a standard Building Schedule of Rates. After converting physical conditions to LVR bands, maintenance managers can immediately assess degradation costs. Five LVR bands were determined using a novel ranking and clustering process. 83% of the LVR bands assigned to physical conditions agreed with industry professionals or differed by one band only.

In contrast to the engineering-based approach, the second approach is monetary-based and introduces the concept of a CLVR for component groups to track their degradation over time. This approach investigated a new component group level index and the validity of using Markov models at the component group level, where the stochasticity is primarily based on the mix of components in a component group rather than on differences in physical degradation processes. CLVRs are determined based on the anticipated nominal replacement costs and timeframes for the group of building components. Markov degradation models were fitted to the proportions of buildings with a particular component group in each of the five CLVR bands (C1 to C5) for each year. According to data from the City of Melbourne, eight categories of building components accounted for 87% of the portfolio's total building value. Comparing their degradation patterns, they were categorised into four groups. This approach removes the need for time-consuming and typically inconsistent physical condition surveys and enables the modelling of a much larger proportion of building assets than would otherwise be possible.

In summary, this research was primarily focused on improving the reliability of predictive modelling of building degradation. A deficiency-based approach was established, outperforming the generic deterioration-based approach and enhancing predictability. Hence, in terms of the impact of the research findings, the use of a deficiency-based approach will directly improve the reliability aspects of predictive maintenance. Also, the techniques introduced to estimate the relatively maintenance-free ages for components will be useful for data screening. Longer durations and relatively low maintenance can be expected with the deficiency-based approach in the long run. Deficiency-based ratings can be directly converted to cost-based condition ratings employing the LVR-based condition index. The GRG NLO categorisation technique was employed as a new technique to identify the impact of the influencing factors. Also, Random Forest is recommended for predictive modelling with multiple input features due to its better overall performance compared to other techniques used. Furthermore, using a monetary-based approach, a paradigm for forecasting degradation based on nominal cost data was developed to replace the conventional approach that uses physical condition data.