## 3 Results and Discussion
### Applicability  
Unlike numerical simulations, predictive maintenance requires preventive action based on observed data before failures occur. In future prediction, mistakes are unavoidable, which may lead to an underestimation of failure indicators. To address this issue, we developed a scoring model that uses raw data to identify consistently high-risk behaviors before failures occur.  
To determine the indicator weights, we first constructed a prototype. We used ChatGPT for this. ChatGPT is a probabilistic AI model. Then, we evaluated whether elevated total scores could anticipate failures. Traditional approaches rely on expert-driven sensitivity analysis. Our method estimates weights from ChatGPT results. This enables rapid model development. Though this approach is potentially arbitrary, it allows for efficient adaptation to the target system.  
Predictive maintenance models are inherently system-specific and not universally applicable. Nevertheless, non-generalizable models can provide practical insights to stakeholders, such as landfill operators. This study highlights the utility of AI-weighted scoring  
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline  & & & \multicolumn{3}{c}{**Factors Affecting High**} & \multicolumn{3}{c}{**Time From the Last Alert**} \\ \hline
**RBC No.** & **Date** & **Score** & **(a)** & **(b)** & **(c)** & **(d)** & **(e)** & **Periods Giving High Score** & **(High Score) to the Failure** \\ \hline
1 & 25 February 2025 & 8 points & 3 & & & 5 & 7–21 December 2009 & 66 days after \\ \cline{2-7}  & 16 January 2012 & 7 points & 2 & & & 5 & 25 July 2011, 17 October 2011, 15 December 2011 & 32 days after \\ \cline{2-7}  & 18 September 2012 & 7 points & 2 & & & 5 & 25 June 2012 & 85 days after \\ \cline{2-7}  & 10 July 2017 & 7 points & 2 & & & 5 & 6 April–19 June 2017 & 21 days after \\ \hline
2 & 27 August 2012 & 8 points & 3 & 2 & 3 & 26 July 2010–23 August 2012 & 4 days after \\ \cline{2-7}  & 21 January 2016 & 8 points & 3 & & 2 & 3 & 17 February 2014–4 January 2016 & 17 days after \\ \cline{2-7}  & 3 August 2020 & 7 points & 2 & & & 5 & 10 August 2017–24 February 2020 & 161 days after \\ \cline{2-7}  & 17 January 2022 & 7 points & 2 & & & 5 & 12 October 2020–24 June 2021 & 207 days after \\  & 8 points & 3 & 2 & & 3 & 18 October–11 November 2021 & 67 days after \\  & 10 points & 3 & 2 & & & 5 & 15 November 2021–13 January 2022 & 4 days after \\ \hline \hline \end{tabular}
\end{table}
Table 5: Breakdown of high-risk scores and contributing factors for failure prediction in rotating biological contactors. This table summarizes cases where elevated scores were assigned based on the scoring model. Each score is decomposed into six contributing indicators: (a) frequency of abnormal noise, (b) peak current level, (c) current fluctuation range, (d) unusually low current, (e) duration of the abnormal period, and (f) presence of a sudden rise in current. The table also shows the corresponding monitoring period and the number of days from the high score to the actual failure event.  
as a viable alternative to expert-based methods. The value lies in the replicable methodology, which does not require expert knowledge.  
Reliable failure prediction is essential for long-lived infrastructure. Like roads and tunnels, landfills require over 50 years of maintenance, making predictive models essential. Based on this study, current fluctuations, particularly abrupt increases, emerged as key failure indicators. Although this insight is qualitative, it is transferable across sites and supports proactive maintenance strategies. Data accumulation was also found to be essential for improving prediction reliability.  
Recent advances in Internet of Things (IoT) technology allow analog current meters to be replaced with sensors for finer resolution. Furthermore, introducing vibration and acoustic sensors makes it easier to detect anomalies, which enhances maintenance data collection and site management.