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This is a Mixed-Methods study (also classified as: Quantitative Study).

September 30, 2025East African Journal of Engineering0 citationsOpen Access

Development of Maintenance Management Model to Enhance Performance for Rural Roads: A Case of Karatu District - Tarura

MNMsetu NaidyeDar es Salaam Institute of Technology DGDickson K. GidionDar es Salaam Institute of Technology

Key Points

A predictive model was developed to enhance rural road performance, identifying critical maintenance factors.
The study surveyed 50 infrastructure professionals, highlighting material compaction quality as the most significant factor.
Multiple regression analysis produced a robust model with an R² of 0.852, explaining over 85% of performance variance.
Validation of the model across road segments confirmed its ability to guide maintenance efforts effectively.

Abstract

Rural roads constitute the backbone of Tanzania's transportation infrastructure, playing a critical role in socio-economic development, poverty alleviation, and accessibility to essential services, particularly in agricultural and tourism-dependent districts such as Karatu. Despite their significance, these roads face severe maintenance challenges characterised by poor serviceability and sustainability. This has resulted in limited accessibility, increased transportation costs, and reduced mobility for rural populations in Tanzania, specifically in Karatu District. This research identifies ten critical factors influencing rural road maintenance management in Tanzania, including material compaction quality, traffic volume and loading, erosion control measures, maintenance timing and frequency, surface roughness, drainage system functionality, climate conditions, terrain characteristics, design compliance, and structural integrity. A mixed-methods approach combining quantitative data from structured questionnaires with qualitative insights from interviews and field observations was employed to develop a model using multiple regression analysis. The study surveyed 50 infrastructure professionals using a 5-point Likert scale to assess factor significance. Material compaction quality emerged as the most critical factor (RII = 0.932), followed by traffic volume and loading (RII = 0.896), and erosion control measures (RII = 0.884). The multiple regression analysis produced a robust predictive model with an R² of 0.852, indicating that the identified factors explain 85.2% of rural road performance variance. The developed model demonstrates strong predictive capability and practical applicability across various road scenarios. Model validation across five representative road segments showed performance indices ranging from 13% (Very Severe) to 81% (Very High Performance), confirming the model's ability to distinguish performance levels and provide actionable maintenance guidance. This research contributes to the knowledge base on rural road maintenance by presenting a comprehensive, evidence-based model that supports decision-making and resource allocation among rural road authorities.

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