East African Journal of Engineering

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

Kasongo Anyitike, Aisa Oberlin, PhD — Sat, 04 Oct 2025 09:13:34 +0000

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

Development of Unpaved Road Maintenance Management Strategy for Earth Reinforcement Using Fibre Geotextile and Cohesive Soil for Sandy Subgrade Improvement: A Case Study of Lindi District

Baraka Mbogora; Dickson Gidion — Sat, 04 Oct 2025 09:30:28 +0000

Unpaved rural roads in Lindi District suffer from poor performance due to weak sandy subgrades with low cohesion and high erosion risk, especially in coastal areas. Traditional stabilisation using cohesive soils has proven insufficient for long-term durability. This study aimed to develop a sustainable maintenance strategy by reinforcing sandy subgrades with cohesive soil and fibre geotextile. This study used an experimental research design to develop a cost-effective unpaved road maintenance strategy by stabilising overburden sandy subgrades with fibre geotextiles and cohesive soil in Lindi District, Tanzania. Through field sampling and laboratory testing, including CBR, UCS, Proctor compaction, and Atterberg limits, the study found that blending 30–45% cohesive soil with sand improved strength, moisture retention, and workability. Adding fibre geotextile further enhanced compressive strength, load distribution, and deformation resistance, with CBR values increasing from 3% to 14%. Based on these results, a maintenance strategy was proposed focusing on material selection, application methods, and performance monitoring. The study recommends that TARURA and TANROADS adopt this approach and implement a 250-meter trial section. Additional recommendations include training for engineers, preventive maintenance budgeting, and regular performance assessments. The findings offer a practical, cost-effective solution to improve the strength and durability of unpaved roads in sandy soil regions

Development of a Condition-Based Maintenance (CBM) Model to Enhance Equipment Reliability in Water Treatment Plant: An Analysis of Tanga UWASA, Tanzania

Joseph Samson Mkuki, Mbazingwa Elirehema Mkiramweni, PhD — Mon, 06 Oct 2025 17:29:14 +0000

Tanga Urban Water Supply and Sanitation Authority (Tanga UWASA), Tanzania, is the focus of this study, which attempts to identify important factors, develop, and validate a Condition-Based Maintenance (CBM) model to improve equipment reliability in water treatment plants. It discusses the drawbacks of time-based and reactive maintenance systems and emphasises how condition-based maintenance can improve efficiency, cut down on downtime, and save expenses. Twelve technical factors were assessed using real-time operational data and a structured survey; seven of these were determined to be crucial for the implementation of CBM by Relative Importance Index (RII) analysis. The effect of these factors on equipment reliability was then measured using a multiple regression model, which produced an R2 value of 0.910. After a year of validation, the model's predictive accuracy against real performance data was 97%. According to the results, maintenance plans in water treatment facilities with limited resources can be greatly enhanced by using efficient CBM models that incorporate vibration analysis, pressure monitoring, power quality, and other technical indicators. Through data-driven maintenance planning, this study offers engineers and policymakers in sub-Saharan Africa and around the world empirical support and useful recommendations for sustainable water infrastructure.

Factors for Improving the Reliability of Inter-City Buses in Tanzania: The Case Study of Dar es Salaam

Geriwalda Simon Mushi, Dickson Gidion, PhD — Wed, 08 Oct 2025 15:24:28 +0000

Frequent inter-city bus breakdowns, delays, and safety concerns have become common issues, undermining passenger confidence and the system’s overall efficiency. The study aimed to identify the factors affecting the reliability of inter-city buses and to develop a maintenance management model to improve the reliability of inter-city buses in Tanzania, specifically in the case of Dar es Salaam. The methodology used in this research was a mixed-method approach, combining field surveys, observation, and questionnaires. The data were collected from 96 respondents, including bus owners, maintenance personnel, and drivers, while 36 buses were physically inspected. The data were analysed using statistical software. The results show that the variables linked to mechanical breakdowns, specifically suspension, engine, and transmission systems, were the most failure-prone components. In addition, management and maintenance factors, specifically maintenance planning, the age of buses, maintenance records, the budget for maintenance, and spare parts issues, indicated strong positive results. The maintenance management model and structure were developed and validated using empirical monthly bus data over 11 months. The validation employed a holdout method where the model’s predicted outputs were compared with the actual recorded reliability. The average actual value obtained was 91% and the average predicted reliability was 81%, showing a prediction accuracy gap of 10%. The closest alignment confirms the model’s reliability in reflecting the applicability in the real world. The study recommends adopting the developed maintenance management model to improve the reliability of inter-city buses.

Development of a Predictive Maintenance Model to Enhance Reliability in Overhead Catenary Systems at Tanzania Railways Corporation: A Case of the Standard Gauge Railway (SGR) from Dar es Salaam to Morogoro

Jamali Hudu Katakweba, Respicius Kiiza — Wed, 08 Oct 2025 15:33:32 +0000

in overhead catenary systems at Tanzania Railways Corporation's Standard Gauge Railway from Dar es Salaam to Morogoro. The research aimed to identify factors affecting OCS reliability, formulate a mathematical predictive model, and validate its performance. A mixed-methods approach involving surveys of 60 professionals, field observations, and multiple regression analysis was employed. Data collection spanned a period of twelve months (2024-2025), encompassing operational parameters, environmental conditions, maintenance records, and system performance metrics, which were analysed using SPSS software. Relative Importance Index analysis identified seven critical factors: wire wear (RII = 0.9100), voltage deviation (RII = 0.9067), low conductivity (RII = 0.8867), support strength (RII = 0.8833), wire tension (RII = 0.8733), excess span length (RII = 0.8733), and high temperature (RII = 0.8700). Five factors showed minimal influence with RII values below 0.31. The multiple regression model achieved R = 0.99 and R² = 0.98, with 98% of the variance in OCS reliability explained by selected factors. ANOVA confirmed statistical significance (F = 13.286, p < 0.05). The predictive equation: Reliability = 0.990 - 0.071X₁ - 0.043X₂ - 0.011X₃ + 0.006X₄ + 0.009X₅ - 0.003X₆ - 0.006X₇. Model validation showed 80.4% prediction accuracy compared to 82% actual reliability, demonstrating effectiveness under both optimal (99.6% vs. 100%) and degraded conditions (44.4% vs. 44%). The model provides a framework for transitioning from reactive to proactive maintenance strategies, enabling Tanzania Railways Corporation to optimise resource allocation and enhance system reliability.

Development of a Maintenance Management Model to Improve the Availability Performance of Water Pumping Systems: A Case of Dodoma Urban Water Supply Authority

Ntambi John Dawa, Fredrick Sanga, PhD — Thu, 09 Oct 2025 19:41:31 +0000

The Dodoma Urban Water Supply and Sanitation Authority (DUWASA) faces critical maintenance challenges in efficiently managing water pumping systems across the region, resulting in reactive maintenance decisions that reduce system availability to 65% compared to the industry standard of 95% (World Bank, 2023). This study developed a comprehensive maintenance management model to address optimal resource distribution and enhance water pumping system availability through evidence-based decision-making frameworks (Ahmad & Kamaruddin, 2012). The research employed a mixed-methods design utilising stratified random sampling of 65 pumping systems from 77 total systems across multiple operational zones. Comprehensive data collection involved questionnaires, structured interviews, data logger monitoring, and detailed system condition assessments focusing on seven critical parameters: ambient temperature variations, voltage fluctuations, equipment age, water mineral content (salinity), availability of spare parts, suspension system failures, and seasonal changes in water levels (Kiliç et al., 2017). Relative Importance Index (RII) analysis revealed ambient temperature variations as the most significant factor (RII = 0.874), followed by voltage fluctuations (RII = 0.846) and water mineral content (RII = 0.837). Multiple regression analysis generated a robust predictive model with strong statistical performance (R² = 0.699), indicating that the seven technical factors explain approximately 70% of system availability performance variance (Sharma & Srivastava, 2018). The resulting regression equation: Water Pumping System Availability = 0.990 - 0.220(Ambient Temperature) - 0.010(Voltage Fluctuations) - 0.410(Equipment Age) - 0.110(Water Mineral Content) + 0.210(Spare Parts Availability) + 0.670(Suspension System Management) + 0.080(Seasonal Water Level Management) provides a quantitative framework for maintenance decision-making. The developed Water Pumping System Maintenance Management Model (WPSMM) represents a computerised application featuring system inventory management, condition monitoring protocols, maintenance team coordination, and performance monitoring capabilities (Poór et al., 2020). System validation across 12 months of operational data demonstrated effective performance prediction with an overall accuracy of 89.48% compared to the actual availability of 90%, confirming the model's practical applicability. This research contributes a context-specific, resource-constraint-aware maintenance framework that enables evidence-based maintenance decisions, facilitating the transition from reactive to proactive maintenance practices while enhancing system availability, extending asset life, and providing a replicable model for similar water utilities in developing countries (Pathirana et al., 2021).

Development of Maintenance Management Model for Improving Scanner Availability Performance at Tanzania Ports Authority: A Case of Dar es Salaam Port

Abdulrahman Ally Makwita, Fredrick Sanga, PhD — Fri, 10 Oct 2025 05:04:37 +0000

This study aimed to identify the most critical factors affecting scanner availability and develop a robust model to enhance operational performance. Data were systematically collected from 50 scanner maintenance and operation records at the Tanzania Ports Authority. SPSS software was used to analyse the data through multiple regression and ranking techniques to ensure accuracy and reliability. A detailed Relative Importance Index (RII) analysis revealed that Preventive Maintenance Compliance was the most influential factor (RII = 0.932; 93.2%), followed closely by Environmental Factors (dust, humidity, and temperature) and Software/Control System Reliability (both RII = 0.920; 92.0%). Other highly ranked factors included Detector Module Degradation (RII = 0.904), Modulator Performance/High Voltage Stability, Spare Parts Availability and Quality (both RII = 0.900), and Conveyor System/Mechanical Component Wear (RII = 0.896). Conversely, factors such as X-Ray Head Component Failures, Water Cabin Cooling System Effectiveness, and Sensor Calibration Drift were found to have lower impacts, with RII values below 35%. The developed regression model demonstrated excellent statistical performance, with an R-squared of 0.980 indicating a strong positive correlation between the identified factors and scanner availability. The model explains 96% of the variance in scanner availability (R² = 0.960) and remains robust when adjusted for the number of predictors (Adjusted R² = 0.954). A low standard error (2.847) and a high F-statistic (159.42) further confirm the model’s reliability and significance. In conclusion, this research provides a data-driven maintenance management model tailored to the operational context of the Tanzania Ports Authority. The Authority can significantly reduce scanner downtime and enhance equipment availability by prioritising critical factors such as preventive maintenance compliance and environmental control. These findings provide practical guidance for maintenance managers and policymakers seeking to implement targeted strategies that ensure continuous and reliable scanning operations.

Effects of Co-sensitising Chlorophyll and Betanin dyes on Optical Absorption properties and Photovoltaic performance of titanium dioxide-based Dye-S

Zachariah Moronge, Duke Oeba, Charles Muga — Mon, 13 Oct 2025 00:00:00 +0000

To address the increasing energy demands, renewable energy technologies, such as Dye-Sensitised Solar Cells (DSSCs), are considered due to their low fabrication costs and environmental friendliness. However, to date, reduced optical absorption and low carrier collection are the primary reasons for the low Power Conversion Efficiency (PCE) of DSSCs. Previous studies have shown that natural dyes are potential sensitisers and co-sensitisers that enhance the performance of these solar cells. The present work focused on the application of natural dyes, betanin (Beta vulgaris) and chlorophyll (Spinacea oleracea), in DSSCs synthesised using titanium dioxide (TiO2) mesoporous films. The pigments were blended in a 1:1 volume ratio. The measurements of optical characteristics were performed using a UV-Vis spectrometer (400-800 nm), while an FT-IR spectrometer was used to determine functional groups. Chlorophyll had a broad absorption peak at 427 and 673 nm, whereas betanin showed a peak at 527 nm. The spectral interactions were confirmed by the shift of chlorophyll peaks to 440 and 671 nm, whereas betanin exhibited a bathochromic shift of 22 nm in the composite dye, respectively. FT-IR analysis revealed the presence of various functional groups, including O-H, C=O, C-H, and C-O, which are essential for light absorption and binding to TiO2. The co-sensitised DSSCs showed enhanced photovoltaic performance compared to the single-dye cells with a current density (JSC) of 1.22 mA/cm2, open-circuit voltage (VOC) of 0.63 V, and PCE of 0.56 %. These results demonstrate that natural dye co-sensitisation can be a practical approach for enhancing light trapping and efficiency in DSSCs, offering a sustainable alternative for solar energy applications

Development of an Internal Corrosion Maintenance Management System to Enhance the Operational Availability of Natural Gas Midstream Pipeline Systems: A Case Study of Gasco's Mtwara to Dar Es Salaam Pipeline System

Jumanne Kongoka, Esebi Nyari — Mon, 13 Oct 2025 00:00:00 +0000

Internal corrosion significantly threatens midstream natural gas pipelines' reliability, safety, and efficiency, notably Tanzania's GASCO-operated 542 km Mtwara-Dar es Salaam pipeline transporting gas from Mnazi Bay since 2015. This study identifies key corrosion drivers, develops a predictive model for operational availability, and proposes a validated Internal Corrosion Management System (ICMS) to enhance performance. Using a mixed-methods approach involving surveys from 46 of 64 professionals at GASCO, TPDC, and EWURA, chemical analyses of 2,615 SCADA records, and machine learning modeling via XGBoost, Random Forest, and Support Vector Machine algorithms, the research highlighted major factors through Relative Importance Index analysis: solid/liquid contaminants (RII=0.932), moisture (RII=0.924), infrequent pigging (RII=0.852), design/material issues (RII=0.800), microbiological activity (RII=0.752), operational conditions (RII=0.728), and inhibitors (RII=0.708). The XGBoost model achieved superior performance with 87.43% accuracy, 87.21% precision, 87.32% recall, and 87.27% F1-score, outperforming Random Forest and SVM. Feature importance analysis identified the Corrosive Index (0.23), Fe₂O₃ percent (0.19), and Cl percent (0.15) as the most influential predictors. Model validation demonstrated robust performance with 91.67% severe case detection and 86.9% temporal F1-score. The developed ICMS, integrated with SCADA systems, provides real-time monitoring, automated severity classification, and maintenance prioritisation, supporting data-driven decisions to lower costs and ensure sustainable gas supply under EWURA regulations aligned with NACE SP0110, ASME B31.8S, and API 1160 standards

Assessment of Energy and Environmental Impacts of Transition from Road to Rail Passenger Transportation: A Case Study of Tanzania's SGR Electric Train

Florence Edward, Mashauri Adam Kusekwa, Oscar Zongo — Tue, 14 Oct 2025 00:00:00 +0000

The study assesses the energy and environmental impacts of transitioning from road to rail passenger transportation in Tanzania, with a focus on the Dar es Salaam–Dodoma corridor, which is served by the newly implemented Standard Gauge Railway (SGR) electric train. Tanzania's heavy reliance on diesel-powered road transport contributes to high fuel consumption and significant greenhouse gas emissions. This research evaluates the SGR as a sustainable alternative, powered primarily by hydropower. Using a mixed-methods approach, the study collected and analysed data from the Tanzania Railway Corporation, transport authorities, and utility providers to compare energy consumption and emissions between the SGR electric trains and conventional diesel buses operating on the same route. Findings reveal that the SGR transports 4,231 passengers daily across five train services, equivalent to 89 conventional buses. Electric trains consume approximately 9,065.21 kWh daily, while buses require 160,378.00 kWh to transport the same passenger volume, representing a daily energy saving equivalent to 14,275 diesel litres. Environmentally, SGR operations generate only 2.982 tCO₂e per day, compared to 40.548 tCO₂e from buses, resulting in a substantial daily reduction of 37.566 tCO₂e in emissions. The analysis demonstrates that electric rail transport consumes 4.15–4.74 kWh per kilometre and 1.92–2.85 kWh per passenger, which is significantly lower than buses, at 54.06–94.61 kWh/km and 36.99–38.27 kWh/passenger. Similarly, emissions per passenger-kilometre are markedly lower for rail transport across all metrics. These results confirm that transitioning from road to electric rail passenger transport offers substantial energy efficiency gains and environmental benefits, supporting Tanzania's sustainable development objectives and climate commitments. The study provides empirical evidence for policymakers to prioritise rail infrastructure expansion, reducing the transport sector's carbon footprint while meeting growing mobility demands in an economically and environmentally sustainable manner.

Optimisation Framework for Enhancing Construction Productivity of Medium-Level Construction Projects (Kenyan Counties Perspective)

Tue, 14 Oct 2025 14:40:08 +0000

Extensive studies have been conducted on optimisation frameworks for construction projects to determine project productivity, albeit biased towards the BIM Principle, Architectural, Engineering, and Construction (AEC) Models, and those that focus on large and complex projects. The objective of this study was, therefore, to develop an integrated (Preconstruction & construction factors) optimisation framework for medium-level projects. Key parameters considered as independent variables were the cost of construction, Project management techniques, Pre-project planning and Lean technology. Project completion time, quality, and costs were listed as aspects of the dependent variable (Project productivity). Structured questionnaires were administered to six professional subgroups and one subgroup of key stakeholders as the study participants. These comprised landlords/homeowners, architects, structural engineers, quantity surveyors, building contractors, construction project managers and Ministry of Housing and Infrastructure officials. Descriptive and diagnostic research methods were used to analyse data. The results on the performance of the framework based on multivariate regression analysis show that there is a strong relationship between performance (Y) and variable factors X1(Cost of construction), X2(Project management techniques), X3(Pre-project planning), and X4(Lean technology). On the importance of the independent variables, Project management techniques took the lead, followed by cost, pre-project planning & lean technology. The results on the performance of the framework based on case studies show the capacity of the framework to optimise by as much as 23.3% & 24.9% on Case studies 1 &2, respectively; however, the percentages should be unique for each specific project. The model was overwhelmingly endorsed by key stakeholders through a validation process over its Key Performance Indicators, adequacy in measuring &scoring indicators and its ability to determine project productivity with a mean rating of 8.63± 0.890 on a scale out of 10. Further research is recommended on the models’ applicability to small-level construction & civil engineering projects & new/unique projects.

Modelling the Factors Affecting NSSF Building Maintenance Management Practices

Benard Geofrey, Joseph Mkilania, PhD — Wed, 15 Oct 2025 20:04:27 +0000

This study focuses on modelling the factors affecting NSSF building maintenance management practices in order to facilitate asset performance and financial sustainability. Through RII and multiple regression analysis, the study pinpointed the key determinants as being the choice of materials, water and waste management, monitoring of structural integrity, design for maintenance, energy system efficiency, quality of contractor, and age of building. The model that was built showed a strong correlation between these aspects and Return on Investment (ROI) with a high coefficient of determination (R² = 0.923). The results of validation indicated that the actual ROI (2.77%) and the predicted ROI (3.01%) were very close, and a very low Mean Absolute Percentage Error (MAPE = 0.17%) was given as evidence of great accuracy and reliability by the predictive framework. The results suggest that the commitment made to preventive and condition-based maintenance rather than the execution of only reactive strategies will lead to the enhancement of the building's durability, efficiency of operation, and financial returns over time. The suggested model is an excellent NSSF decision-support tool for maintenance planning based on evidence, resource allocation at the most efficient level, and maintenance of property value in the investment portfolio.

Investigation on the Influence of Opening Velocity for Vacuum Arc Control under Different Axial Magnetic Field Contact

Kurwa Mabojano Mangara, Felix Exavery Tebo, Qiang ma, Liu Tianlu — Thu, 16 Oct 2025 20:50:36 +0000

The world campaign toward green energy in the electrical engineering field highlighted replacing traditional SF6 circuit breakers with environmentally friendly vacuum circuit breakers (VCBs) in transmission-level power systems as an effective method to reduce SF6 greenhouse gas emissions. However, the challenge of interrupting high currents and minimising arc re-ignition caused the anode phenomena effect, which significantly led to the focus on optimising the vacuum circuit beaker's contact opening velocity for a succession of arc control and improving interruption ability at high voltage levels shortly after arc initiation. Increased focus on the influence of opening velocity in vacuum interrupters for arc control, driven by the need to improve VCBs performance, safety, reliability, and energy efficiency in modern electrical systems, advances in arc physics, material science, and the integration of smart grid technologies. By optimising opening velocity in vacuum arcs under different axial magnetic field contact designs, this paper aims to enhance the operation of vacuum circuit breakers, ensuring their continued success in fault protection. The research was conducted by simulation using the finite element analysis (FEA) method using Ansys software. The experiment used three kinds of AMF contacts: 1/3, 2/3, and Cup type contacts, with 100mm diameter, subjected to a peak current of 9.8kA, 12.1kA, 14.7kA, and 17.3kA. The contact opening velocities applied were 1.8m/s, 2.4m/s, and 3.0m/s, representing low opening velocity (LOV), medium opening velocity (MOV), and high opening velocity (HOV), respectively. The results on axial magnetic field distribution, arc voltage, and arc morphology behaviour have been studied and analysed

Technical Performance of Organic Rankine Cycle (ORC) for Low-Enthalpy Geothermal Power Generation

Thu, 16 Oct 2025 21:07:39 +0000

The present study evaluates the performance of low-enthalpy geothermal brine as a heat source for power generation using Organic Rankine Cycle (ORC) technology at the Kiejo-Mbaka hot springs. The Kiejo-Mbaka pilot well brine temperature, pressure, and mass flow rate were measured as 348 K, 1.3 bar, and 1 kg/s, respectively. Besides, five environmentally friendly working fluids with low boiling points and low global warming potential (R600, R123, R245ca, R245fa, and R141b) were selected for this analysis. A thermodynamic investigation of the ORC system was conducted considering the effects of expander inlet temperature, expander inlet pressure, mass flow rate, and condensation temperature. The system performance was assessed using net power output (Pnet) and thermal efficiency (ηth) as key indicators. The results show that the dry fluids R141b and R600 achieved the highest performance, with thermal efficiency reaching approximately 39.0% and net power output of 177.48 kW. Among the tested fluids, R245ca and R141b demonstrated strong performance in terms of thermal efficiency, while R600 and R141b excelled in net power output under the given operating conditions. Overall, R141b emerged as the most promising working fluid for the Kiejo-Mbaka geothermal source, by achieving a thermal efficiency of 42.20% along with consistently high-power output across different conditions.

Quantitative Assessment of Factors Influencing Masonry Arch Bridge Performance

Petro Simon, Aisa Oberlin, PhD — Fri, 24 Oct 2025 13:23:10 +0000

The performance and longevity of masonry arch bridges depend on several interrelated factors, with ageing, design, monitoring, and environmental conditions being the most significant. Ageing alone contributes to nearly 40% of structural deterioration, as long-term fatigue leads to cracks, misalignment, and loss of structural capacity. Design and construction issues account for approximately 25% of observed failures, often due to poor load distribution or inadequate arch geometry. Environmental impacts, including moisture, chemical weathering, and freeze–thaw cycles, represent around 20% of degradation, weakening both the masonry materials and bonding agents. Traffic loads further add strain, increasing stress levels by up to 15% across the bridge’s lifespan, especially under heavy vehicles. Despite these risks, monitoring and inspection practices remain insufficient in many cases. Traditional inspections may miss hidden defects, while advanced tools such as laser scanning, ultrasound, and drone-based monitoring can detect over 90% of internal or concealed damage compared to conventional methods. By applying quantitative assessment methods to evaluate these factors, engineers can prioritise critical vulnerabilities, design effective maintenance strategies, and enhance overall resilience. This systematic evaluation not only supports evidence-based preservation but also ensures that limited resources are directed toward the most impactful interventions, extending the service life of masonry arch bridges and safeguarding their structural and cultural value for future generations

Development of Maintenance Management Model for Sustainable Infrastructure Performance of Bus Rapid Transit (BRT) Stations for Dar Es Salaam Region

Wahabu Yahaya Nyamzungu, Joseph Mkilania — Mon, 27 Oct 2025 18:29:09 +0000

In response to escalating transportation challenges driven by rapid urbanisation in Dar es Salaam, Tanzania’s largest city and economic hub, the Government of Tanzania launched the Bus Rapid Transit (BRT) system in 2016 to alleviate traffic congestion, enhance urban mobility, and promote sustainable public transportation infrastructure. However, the long-term sustainability of the BRT system hinges on effective maintenance to ensure operational reliability. This study aimed to develop a maintenance management model to enhance the sustainability of BRT station infrastructure in Dar es Salaam. Employing a quantitative approach, data from 32 BRT stations were analysed using the Relative Importance Index (RII) to rank 12 maintenance factors. 7 high-impact maintenance factors were identified, with Platform Surface Condition Management (RII = 0.963) and Drainage Water Management (RII = 0.956) as top priorities. A multiple regression model was developed to predict the Sustainable Infrastructure Performance Index (SIPI), achieving strong explanatory power (R² = 0.792). Validation across six stations with diverse maintenance scenarios confirmed the model’s sensitivity and accuracy, with SIPI scores ranging from 20% to 88%. The findings provide a robust, data-driven framework for optimising BRT maintenance, enabling DART operators to prioritise high-impact factors, predict infrastructure performance, and allocate resources efficiently, which in turn, contribute to sustainable urban transportation solutions in Dar es Salaam.