## 7 Research Gaps and Future Recommendations  
Although numerous studies have analyzed the cost components of pipeline rehabilitation methods, a significant gap persists in the comprehensive application of LCCA for comparing the CIPPRT trenchless method and OCCM. Existing research often isolates construction, environmental, or social costs without fully integrating them into a unified LCCA framework. This fragmented approach limits the understanding of the holistic  
\begin{table}
\begin{tabular}{c|p{113.8 pt}|p{113.8 pt}|p{113.8 pt}|p{113.8 pt}|p{113.8 pt}} \hline \multirow{2}{*}{**Citation**} & \multicolumn{1}{p{113.8 pt}|}{**Scope of the Paper/ Application**} & \multicolumn{1}{p{113.8 pt}|}{**All Tool Used**} & \multicolumn{1}{p{113.8 pt}|}{**Selection Criteria of ML Technique**} & \multicolumn{1}{p{113.8 pt}|}{**Dataset Collection/ Methodology**} & \multicolumn{1}{p{113.8 pt}|}{**Research Gaps/ Challenges**} \\ \hline \multirow{9}{*}{\ \begin{tabular}{c} [PERSON] et al. \\ [9] \\ \end{tabular}  } & \multirow{9}{*}{Risk-based asset management framework that uses machine learning and digital technologies to estimate the direct and indirect costs of trenching operations on roads by identifying pavement defects.} & \multirow{9}{*}{ \begin{tabular}{c} ML techniques like image processing \\ (YOLO-v8 algorithm) \\ \end{tabular}  } & \multicolumn{1}{p{113.8 pt}|}{A dataset generated from 665 annotated images was employed to train the model. This digital framework integrates real-time and factors (e.g., lysosomes, utilizing IoT sensors and other vehicles).} & \multirow{9}{*}{- Limited datasets. Environmental factors (e.g., radios, buildings and surface conditions).} \\  &  
economic and sustainability-based impacts of these methods over the entire associated service life. Moreover, while CIPPRT has been widely recognized for its lower environmental footprint and reduced social disruption compared to OCCM, inconsistencies in methodologies and datasets have prevented a robust, standardized comparison. Environmental costs, such as emissions and resource use during material manufacturing and installation, are frequently underreported for CIPPRT. Similarly, OCCM's significant social costs, including traffic delays, road damage, and business disruptions, are often inadequately quantified, particularly in urban settings with high levels of utility congestion. Another gap lies in the evaluation of CIPPRT and OCCM across varying project conditions, such as pipe diameters and depths, and geographic contexts. Most studies generalize findings, failing to address the variabilities in costs due to regional regulations, material availability, and subsurface characteristics. Additionally, the integration of machine learning tools to predict life-cycle costs, which could enhance the precision of cost estimation models, remains underexplored for these specific methods. Addressing these gaps requires a comprehensive LCCA framework that systematically incorporates construction, environmental, and social costs. Empirical validation of this framework under diverse real-world scenarios and leveraging ML for cost prediction can enable a more accurate and sustainable comparison of CIPPRT and OCCM, facilitating informed decision-making in pipeline rehabilitation projects. The authors declare no conflict of interest.