Development of Maintenance Management Model to Enhance Availability Performance of Railway Tunnels: A Case Study of Tazara

Kasongo Anyitike; Aisa Oberlin

doi:10.37284/eaje.8.2.3758

Kasongo Anyitike Dar es Salaam Institute of Technology
Aisa Oberlin, PhD Dar es Salaam Institute of Technology

DOI: https://doi.org/10.37284/eaje.8.2.3758

Keywords: Railway Tunnels, Maintenance Management, Predictive Modelling, Infrastructure Availability, TAZARA, Developing Countries

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Abstract

Railway tunnel maintenance management in developing countries faces significant challenges due to ageing infrastructure, resource constraints, and reactive maintenance approaches. This study developed a maintenance management model to enhance the availability performance of railway tunnels in the Tanzania-Zambia Railway Authority (TAZARA) system. A mixed-methods research design was employed, combining quantitative analysis with qualitative insights from 21 maintenance professionals across the TAZARA network. The Relative Importance Index (RII) methodology was used to rank ten technical factors affecting tunnel maintenance performance. Multiple linear regression analysis was applied to develop a predictive model incorporating seven significant factors: Rock Mass Quality Index, Safety Incident Rate, Inspection Frequency, Tunnel Age, Access Time, Maintenance Budget, and Equipment Downtime. RII analysis revealed that Rock Mass Quality Index (RII = 0.791), Safety Incident Rate (RII = 0.773), and Inspection Frequency (RII = 0.755) were the most significant factors influencing maintenance performance. The developed regression model demonstrated exceptional predictive capability with a correlation coefficient (R) of 0.943 and a coefficient of determination (R²) of 0.889, explaining 88.9% of the variance in tunnel performance. The predictive equation: Tunnel Performance = 0.22 + 0.025(RMQI) - 0.019(SIR) + 0.032(IF) - 0.06(TA) + 0.18(AT) + 0.03(MB) - 0.05(ED), with Inspection Frequency emerging as the dominant predictor contributing 70.1% to performance enhancement. Model validation using twelve months of operational data showed perfect prediction accuracy during normal operations (100% correlation between predicted and actual availability), with average tunnel availability of 98%. The validated maintenance management model provides TAZARA with an evidence-based tool for proactive maintenance planning and resource optimisation. The findings emphasise the critical importance of systematic inspection programs, safety management integration, and equipment reliability enhancement for maximising tunnel availability performance in resource-constrained environments

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