Development of Life Cycle Maintenance Prediction Model for Cost Efficiency in Low Volume Paved Roads: A Case Study of Namtumbo District in Tanzania

Fabian Bazil Lugalaba; Philip Mzava; Joseph Mkilania

doi:10.37284/eaje.8.1.3702

Fabian Bazil Lugalaba Dar es Salaam Institute of Technology
Philip Mzava, PhD Dar es Salaam Institute of Technology
Joseph Mkilania, PhD Dar es Salaam Institute of Technology

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Résumé

This study aimed to develop and validate a life cycle maintenance prediction model for cost efficiency in low-volume paved roads (LVPRs) in Namtumbo District, Tanzania, to enhance cost-effective infrastructure management by identifying key factors influencing maintenance costs and providing a predictive tool for resource allocation. To accomplish this objective, a quantitative approach was employed, involving a survey of 60 respondents, including TARURA engineers, contractors and stakeholders. The data were collected concerning five factors, namely, traffic patterns, resource availability, age of road, weather and climate, and maintenance techniques, and were analyzed using descriptive statistics, RII and MLR, with the help of SPSS, so as to identify cost drivers and develop the model, which was validated through R-square, ANOVA, MAE, and prediction of cost efficiency metrics. The results indicated that weather, climate, and road age were the most significant cost drivers, with high rainfall and temperature fluctuations accelerating deterioration, and older roads requiring intensive repairs. Moreover, resource availability and traffic patterns had notable impacts, while maintenance techniques were less influential due to limited adoption. The results further indicated that the developed and validated regression model explained 71.7% of cost variance, with a predicted cost efficiency of 91.75%. This model provides a tool for TARURA to forecast maintenance costs, prioritise interventions, and optimise resource use in Namtumbo road maintenance projects. The findings underscore the need for climate-resilient designs, lifecycle-based maintenance, and improved resource access to reduce costs

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