Journal Articles

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The effects of π-spacer on electronic properties, charge transfer, and chemical reactivity in D-A′-π-A configured molecules: Computational approach
(Journal of Chemical Research, 2026-05-06) Abubakari1, Ismail; Sanama, Michael Kennedy; Tsere, Melkizedeck Hiiti
In this work, a systematic modification of the π-spacer was carried out to evaluate its influence on molecular geometry, electronic structure, charge transport properties, and global chemical reactivity descriptors in a series of D-A′-π-A organic molecules (M1–M4). Density functional theory was employed to analyze key parameters including dihedral angles, highest occupied molecular orbital–lowest unoccupied molecular orbital energies, natural bond orbital interactions, reorganization energies, ionization potential, electron affinity, chemical hardness, chemical potential, electronegativity, and electrophilicity index. The results reveal that M4 demonstrates the most favorable combination of properties, exhibiting the energy gap, reorganization energy, ionization potential, and chemical hardness of 1.4896, 0.4580, 4.8300, and 0.7127 eV, respectively. M4 also shows the highest chemical potential, electronegativity, and electrophilicity index of −4.1174, 4.1174, and 11.8938 eV, respectively. These results demonstrate the critical role of π-spacer engineering in adjusting the electronic behavior of D-A′-π-A systems and suggest that M4 is a promising candidate for high-performance organic semiconducting and optoelectronic uses.
Refining final-year engineering outcomes at Mbeya University, Tanzania
(Open access, 2026) Katambara, Zacharia
Tanzania’s Vision 2025 agenda emphasise rapid industrialisation, necessitating a technically proficient workforce, particularly in mechanical engineering, to sustain growth in manufacturing, mining, and infrastructure. This study evaluates the academic performance of 117 final-year mechanical engineering students at Mbeya University of Science and Technology (MUST), employing Exploratory Analysis and Principal Component Analysis (PCA) to uncover performance patterns and assess curriculum alignment with industrial competencies. Descriptive statistics revealed notable variability in course performance, while correlation analysis identified strong positive associations between GPA and key technical modules, notably ME 8401 (Fluid Power and Control) and ME 8411 (Automation and Robotics). PCA extracted five principal components, explaining 58.2% of the total variance, with Component 1 alone accounting for 30.4%, suggesting a dominant academic structure driven by technical proficiency. High factor loadings for ME 8411, ME 8401, and ME 8415 underscored their central role in student success. At the same time, low-loading courses such as ME 8408 (Industrial Practical Training III) and ME 8413 highlighted potential curricular misalignments. Based on these insights, the study concludes that academic success in mechanical engineering at MUST is tightly linked to performance in core technical subjects. To align educational outcomes with Tanzania’s industrial goals, the study recommends targeted curriculum refinement of underperforming modules, enhancing high-impact courses with structured academic support, routine application of multivariate analytics for curriculum monitoring, faculty training in educational data analysis, and the establishment of formal industry- academic partnerships. These data-driven reforms aim to foster a technically adept graduate pool that meets the evolving demands of Tanzania’s industrial landscape. Keywords Mechanical engineering education, Principal component analysis, Skills gap, Industrialization, Workforce readiness.
Research on Resource Allocation for IRS-Assisted Energy Harvesting-Cognitive Radio Networks
(2026) KAWALA CHIRU Lilian
(2) To address IRS hardware phase quantization errors, scalability, and low-SNR detection challenges, we establish a unified optimization framework for IRS-assisted Multiple Input Single Output (MISO) EH-CRNs that maximizes SU throughput under practical constraints, including discrete IRS phase shifts, beamforming design, false-alarm control, energy causality, and SU QoS. We develop a quantization-aware alternating optimization algorithm to solve the non-convex problem by decomposing it into interrelated subproblems covering detection probability maximization, false alarm minimization, energy harvesting optimization, and throughput enhancement. The integration of optimized Weighted Energy Detection (WED) coefficients further improves spectrum sensing accuracy in low-SNR regimes, enabling efficient and reliable spectrum access in underlay CRNs. A Nearest Point Search with Penalty (NPSP) method addresses discrete phase shift constraints, while optimization techniques including SDR, SCA, first-order Taylor expansion, Gaussian randomization, and SROCR are employed for convexification and iterative refinement. Simulation results demonstrate significant improvements in spectrum sensing reliability, energy harvesting efficiency, and SU throughput, representing the first comprehensive optimization of the sensing-harvesting-throughput trade-off under realistic deployment conditions and establishing a robust foundation for practical implementation of IRS-assisted MISO EH-CRNs (3) A robust optimization algorithm is proposed to address the challenge of imperfect CSI, which affects both direct PU–SU and cascaded PU–IRS–SU links. An optimization problem is formulated to maximize worst-case SU throughput while guaranteeing reliable spectrum sensing, energy harvesting, SU QoS, and strict PU protection. The design jointly optimizes SU beamforming and IRS phase shifts under bounded channel uncertainties, leveraging worst-case optimization principles. Semi-infinite constraints are reformulated using the S-procedure, SDR, Schur’s complement, and the generalized sign-definiteness principle, with SCA employed to refine non-convex formulations. These techniques ensure tractable problem reformulation and reliable performance under adverse CSI conditions. Simulation results demonstrate that the proposed framework significantly outperforms non-robust benchmarks, achieving strong throughput gains alongside dependable spectrum sensing and energy harvesting.
FUZZY AND CONCEPTUAL-FUZZY MODELLING OF COMPLEX RIVER SYSTEMS WITH SCARCE DATA: CASE OF LETABA RIVER
(University of the Witwatersrand, 2011) Katambara, Z.
The investment in water infrastructure on a number of river systems in South Africa and many other regions of the world so as to meet the ever growing demands for water over the last few decades, has not been matched by the implementation of adequate hydrometric data collection and water-use accountability practices. This has resulted in complex rivers systems with scarce data. A typical example in South Africa is the Letaba River system. The main objective of this research was to investigate the applicability of fuzzy inference based and hybrid fuzzy inference-conceptual modelling approaches to highly developed and complex river systems with scarce data using Letaba River as a case study. For completeness, a standalone conceptual model was included and three models were therefore studied; a fuzzy inference, a hybrid fuzzy inference-conceptual, and a standalone conceptual model
Electrosorptive removal and recovery of phosphorus via flow electrode capacitive deionization using carbon black and activated carbon electrodes
(Elsevier Ltd, 2026) Tserea M.H.; Alphonce F.N.; Lameck A.S.
In this study, activated carbon (AC) and carbon black (CB) were tested as flowable electrode (FE) materials for removing and recovering phosphorus from synthetic wastewater. Experiments used the flow capacitive deionization (FCDI) system under short-circuited closed-cycle (SCC) conditions. Phosphorus removal was evaluated under different operational parameters, including applied voltage, solution flow rate, solution pH, phosphorus concentration, and electrode dosage. Applied voltage and solution pH had the greatest impact on removal efficiency among other parameters. Maximum phosphorus removal efficiencies were 83.74 ± 1.30% with AC and 90.45 ± 1.12% with CB, corresponding to adsorption capacities of 8.76 mg/g and 9.26 mg/g. Physisorption on carbon particles was negligible compared to electrosorption. Electrode regeneration by short-circuiting achieved recovery efficiencies of 68.06 ± 0.46% for AC and 85.46 ± 0.74% for CB. Generally, CB performed better than AC in both removal and recovery, highlighting its promise as an effective FE material. These results exhibit the potential of FCDI as a sustainable technology for phosphorus-rich wastewater treatment and nutrient recovery
Refining final-year engineering outcomes at Mbeya University, Tanzania
(MJRD, 2026-05-20) Katambara, Zacharia
Tanzania’s Vision 2025 agenda emphasise rapid industrialisation, necessitating a technically proficient workforce, particularly in mechanical engineering, to sustain growth in manufacturing, mining, and infrastructure. This study evaluates the academic performance of 117 final-year mechanical engineering students at Mbeya University of Science and Technology (MUST), employing Exploratory Analysis (EA) and Principal Component Analysis (PCA) to uncover performance patterns and assess curriculum alignment with industrial competencies. Descriptive statistics revealed notable variability in course performance, while correlation analysis identified strong positive associations between GPA and key technical modules, notably ME 8401 (Fluid Power and Control) and ME 8411 (Automation and Robotics). PCA extracted five principal components, explaining 58.2% of the total variance, with Component 1 alone accounting for 30.4%, suggesting a dominant academic structure driven by technical proficiency. High factor loadings for ME 8411, ME 8401, and ME 8415 underscored their central role in student success. At the same time, low-loading courses such as ME 8408 (Industrial Practical Training III) and ME 8413 highlighted potential curricular misalignments. Based on these insights, the study concludes that academic success in mechanical engineering at MUST is tightly linked to performance in core technical subjects. To align educational outcomes with Tanzania’s industrial goals, the study recommends targeted curriculum refinement of underperforming modules, enhancing high-impact courses with structured academic support, routine application of multivariate analytics for curriculum monitoring, faculty training in educational data analysis, and the establishment of formal industry-academic partnerships. These data-driven reforms aim to foster a technically adept graduate pool that meets the evolving demands of Tanzania’s industrial landscape.
Mapping suitability for solar-powered irrigation systems using GIS–AHP in Baringo County, Kenya
(Discover Geoscience, 2026-04-29) Kipkulei H.k., Boitt M., Ahmed A.,Lameck A.S.
Solar-powered irrigation Systems (SPIS) are critical for agricultural production enhancement, food security and climate change adaptation, especially in Arid and Semi-Arid Lands (ASAL). There is increased attention towards shifting to more abundant and cleaner energy potential sources for revitalising irrigation strategies in ASAL areas. This study employed a geospatial approach to identify suitable locations for solar-powered irrigation systems (SPIS) in Baringo County, Kenya. Based on an integrated use of GIS spatial analysis and analytical hierarchy procedure (AHP), suitable locations for solar-powered irrigation were mapped. Precipitation, irrigated areas, proximity to rivers, slope, and solar radiation were analysed and processed to derive spatially explicit SPIS suitability classes ranging from very low to very high suitability. The thematic layers were assigned weights based on Saaty’s AHP method, where weights for each factor were determined from a pairwise comparison matrix, and a Weighted Linear Combination (WLC) approach was used to derive the final suitability classes for the county. The findings reveal that approximately 58% of Baringo County falls within the moderately suitable category for SPIS implementation, while 24% of the area demonstrates high suitability. In contrast, only 0.8% of the county’s land area was classified as either very low or very high suitability. These results offer critical insights for guiding spatially informed planning and investment in suitable, solar-powered agricultural infrastructure within the region. Furthermore, the findings of the study contribute to the ongoing initiatives on the expansion of irrigable land using low-cost and innovative technologies such as SPIS to put marginally arable land under productive use in Kenya.
Entropy Optimization in Transient Hydromagnetics Laminar Flow Through a Vertical Stream With Emission of Thermal Energy and Temperature-Dependent Properties Governed by Newtonian Cooling
(Wiley, 2026-05-02) Kigodi O. J, Shegwando O. B, Matofali A.X, Mzomozi R.F, Berenado R.E
This study incorporates thermal energy, temperature-dependent properties, thermal-dynamic irreversibility in MHD fluid flow embedded in a porous vertical medium driven by Newtonian cooling. The study considers an incompressible, electrically conducting fluid flowing laminarly in a vertical channel bounded by two infinite parallel plates. The governing equations are developed and discretized using Crank-Nicolson finite difference scheme (CN-FDM) where the discretized equations are simulated using MATLAB software. The findings reveal that rising magnetic field by 2% lowers both velocity and temperature up to 0.2% with intensified entropy reduction near the walls. Amplifying permeability by 4% intensifies fluid motion while Bejan number falls by approximately 7%, stabilizing unrecovered fluid friction. Escalating the radioactive parameter by 6% improves the temperature profile while lowering Bejan number. Rising pressure gradient deteriorates the Bejan number by approximately 9% caused by stabilized fluid friction across the channel. Moreover, the temperature profile and Bejan number fall by 12% due to an increase in Prandtl number by 5%. Fluid motion deteriorates by 4% while entropy production at the central region of the channel is reduced by 13% with the increase in Eckert number. Escalating heat generation by 8% amplifies the temperature profile by 16% and reduces fluid resistance by 23%, with intensified entropy production near the walls. Fine-tuning buoyancy forces may lower entropy pro- duction by approximately 30%. Velocity and temperature reach a steady state with buoyancy, while time increases, wall shear and heat transfer before stabilization. The findings reveal that variations of these physical quantities have a great influence to coefficient of skin friction and Nusselt number on both walls. These results inform the MHD and thermal-fluid systems designers to ensure fine-tuning of magnetic field parameter, Darcy number, Eckert number, internal heat generation, Prandtl number, and buoyance effects that enhance effective thermodynamic in these systems.
Tracing the Implementation of Pedestrianization Schemes for Enhancing Accessibility and Mobility: A Case of Kariakoo, Dar es Salaam City in Tanzania
(African Conference on Resilient and Sustainable Cities, 2025-03-27) KIMIRO, ABOUBAKARY SAID; BABERE, NELLY JOHN
Pedestrianization schemes were introduced to improve pedestrians' safety and mobility by creating a friendly walking environment, resulting in a sense of belonging for pedestrians within the inner cities. Effective implementation of pedestrianization streets, especially in developed countries, is reported to reduce accidents and ease movement in an urban environment. However, it seems to be different in developing countries where pedestrianization schemes are not well implemented; therefore, the intended goals of their introduction are not attained. This study investigates the implementation of pedestrianization schemes (pedestrian malls and one-way streets) aimed at better accessibility and mobility. Moreover, it examines the challenges facing the implementation of pedestrianization schemes. The study was conducted in five pedestrianized streets (2 pedestrian threes and three one-way streets) in the Kariakoo area. The qualitative approach was employed and methods used in data collection were interviews with officials and 30 pedestrians per street, mapping and observation. Pedestrian malls function as part-time pedestrian streets, which allow vehicular traffic with less than 3 tonnes to the road after 7:00 p.m.; on-street parking is restricted too, but loading and unloading activities are permitted. One-way streets function as shared streets where pedestrians and vehicular traffic share the space, with on-site parking. Hence, pedestrians continue suffering when accessing and moving within pedestrianized streets
Modelling over-reading correction factors for ultrasonic flow meters in wet gas measurement using advanced regression and machine learning techniques
(Elsevier Ltd., 2025-10-08) Shunashu, Ishigita Lucas; Kaunde, Osmund
Accurate wet gas measurement is essential for optimizing production, transmission, and reservoir management in oil and gas operations. Ultrasonic flow meters, though non-intrusive and versatile, often overestimate flow rates due to the presence of liquid phases, leading to significant operational and economic errors. To address this, a data-driven correction model was developed using computational techniques to predict and compensate for over-reading. This study evaluates the performance of several advanced regression and machine learning approaches, including polynomial regression, random forest regression, nonlinear curve fitting, neural networks, multiple linear regression, ridge regression, and lasso regression, using an extensive experimental dataset. Key input variables include liquid volume fraction, Lockhart–Martinelli parameter, Froude number, Weber number, slip ratio, and density ratio. Among the models tested, random forest regression and multiple linear regression achieved the highest accuracy, with average relative absolute errors of 3.02% and 3.20 % respectively. These findings demonstrate the potential of data-driven modeling to enhance the reliability of ultrasonic flow meters in complex wet gas environments.
Integrated Ultrasonic Flow Meter and Microwave Sensing Technology for Wet Gas Measurement: Development and Validation of Over-Reading Correction Models
(John Wiley & Sons, 2025-11-20) Shunashu, Ishigita Lucas; Kaunde, Osmund; Mwakipesile, Duncan
Accurate wet gas flow measurement is essential for production optimisation, custody transfer, and regulatory compliance in theenergy and chemical industries. Conventional ultrasonic flow meters often overestimate gas flow rates due to liquid entrainment,while microwave sensors alone struggle with phase discrimination under dynamic conditions. This study introduces a hybrid metering system, Ultrasonic Flow Meters andMicrowave Sensing Measurement ofWet Gas (USMMW), that integrates transit-time ultrasonic flow measurement with microwave dielectric sensing to correct over-reading errors. Experimental data were collected from a controlled multiphase flow loop using a 2-inch pipeline equipped with an ultrasonic meter and a 2.7 GHz microwave sensor. A data-driven over-reading correction model (OR) was developed using detected liquid volume fraction (LVF) and eight dimensionless parameters derived via the Buckingham Pi theorem.Multiple regression andmachine learning techniques, including multilinear regression (MLR) and random forest regression (RFR), were applied to optimise model performance. Validation results showed that the USMMW system achieved corrected gas flow rates with an average relative absolute error (RAE) of 3.02%, outperforming conventional differential pressure models. The findings demonstrate that USMMW offers a robust, non-intrusive solution for real-time wet gas metering under mist and stratified flow regimes, with potential for scalable industrial deployment.
Spatial assessment of water quality in a hierarchically structured river system using stream order classification and multivariate statistical techniques: A case study from Tunduma, Tanzania
(ASSSCIENCE PUBLISHING, 2025-10-20) William, Matungwa; Katambara , Zacharia; Shegwando,Omari
Water quality assessment is essential for understanding pollutant dynamics, supporting evidence-based watershed management, and protecting public health. While numerous studies have utilized statistical and modeling approaches, limited attention has been paid to how stream order influences water quality variability, particularly in urban catchments of sub-Saharan Africa. This study investigates spatial patterns of water quality in a hierarchically structured stream network in Tunduma, Tanzania, by integrating Strahler stream order classification with multivariate statistical techniques, based on monthly monitoring of six surface water points over 12 months (n = 72) during both wet and dry seasons to analyze physicochemical, nutrient, and microbial parameters. Hierarchical cluster analysis, combined with Pearson correlation matrices and significance testing, was employed to assess pollutant similarity and accumulation patterns across different stream orders. Results revealed that phosphate (PO4 3−) concentrations ranged from 0.42 to 1.49 mg/L and nitrate (NO3 − ) levels ranged from 4.3 to 13.2 mg/L. Strong positive correlations (r > 0.95) were observed among ion-derived parameters, such as electrical conductivity, total dissolved solids, and the concentrations of calcium (Ca2+) and magnesium (Mg2+). Third-order stream segments exhibited elevated concentrations of total suspended solids (0.990), biochemical oxygen demand (0.982), and microbial indicators, with fecal coliforms of 0–5 CFU/100 mL and total coliforms of 0–18 CFU/100 mL, reflecting cumulative pollutant loading in downstream reaches. The integration of Strahler stream ordering and cluster-based analytics enabled the identification of pollution hotspots and revealed the critical role of hydrological connectivity in shaping water quality trends. This research contributes a novel spatial–statistical framework for stream-based water quality assessment in East African urban contexts, offering practical insights for catchment-scale pollution control and resource management.
Enhanced Liquid Detection in Wet Gas Metering Via Microwave Sensing and Random Forest Regression
(A S M E, 2026-03) Ishigita Lucas Shunashu; Osmund Kaunde; Duncan Mwakipesile
Modelling Over-Reading Correction Factors for Ultrasonic Flow Meters in Wet Gas Measurement Using Advanced Regression and Machine Learning Techniques
(Elsevier, 2025-10-08) Shunashu, Ishigita Lucas; Kaunde, Osmund
Accurate wet gas measurement is essential for optimizing production, transmission, and reservoir management in oil and gas operations. Ultrasonic flow meters, though non-intrusive and versatile, often overestimate flow rates due to the presence of liquid phases, leading to significant operational and economic errors. To address this, a data-driven correction model was developed using computational techniques to predict and compensate for overreading. This study evaluates the performance of several advanced regression and machine learning approaches, including polynomial regression, random forest regression, nonlinear curve fitting, neural networks, multiple linear regression, ridge regression, and lasso regression, using an extensive experimental dataset. Key input variables include liquid volume fraction, Lockhart–Martinelli parameter, Froude number, Weber number, slip ratio, and density ratio. Among the models tested, random forest regression and multiple linear regression achieved the highest accuracy, with average relative absolute errors of 3.02% and 3.20 % respectively. These findings demonstrate the potential of data-driven modeling to enhance the reliability of ultrasonic flow meters in complex wet gas environments.
Enhanced Liquid Detection in Wet Gas Metering Via Microwave Sensing and Random Forest Regression
(ASME, 2026-03-01) Shunashu, Ishigita Lucas; Kaunde,Osmund; Mwakipesile,Duncan
This study explored the integration of machine learning regression models with a microwave transmission line sensor for estimating liquid volume fraction and liquid flowrate in wet gas flows. Under low liquid loading conditions (gas volume fraction 95–99.9%), four models: Bruggeman, support vector regression, Gaussian process regression, and random forest regression were evaluated. Random forest regression delivered the best tradeoff between accuracy, robustness, and computational efficiency, achieving a relative absolute error of 2.23% for liquid volume fraction and approximately 5% for liquid flowrate, with a Durbin–Watson statistic of 2.02 indicating minimal residual autocorrelation. Feature importance analysis identified the mixture dielectric constant as the dominant predictor (approximately 97% contribution), while other dimensionless parameters had a limited impact. Support vector regression failed to generalize, and although Gaussian process regression showed slightly higher accuracy, its computational cost limited real-time applicability. Overall, random forest regression combined with microwave sensing offers a scalable, nonintrusive solution for wet gas metering, with future validation needed under industrial hydrocarbon–water conditions and liquid loading flow regimes
A FRAMEWORK OF STRATEGIES TO REDUCE ROAD CONSTRUCTION PROJECTS’ DELAY IN TANZANIA: A CASE OF TARURA ROAD PROJECTS
(Mbeya University of Science and Technology, 2025-08-30) GABRIEL SEPERATUS
The construction industry is globally recognised as one of the fastest-growing sectors, contributing directly and indirectly to the development of several other sectors of the economy. Despite its significant importance, and based on persistent reasons, the industry has often been overwhelmed with various challenges, including the inability to finish the road construction projects within a given schedule. This study aimed to examine the stakeholder’s perception of prevailing best practice measures to reduce construction project delays in Tanzania. The study adopted the questionnaire tool and the survey interview to collect the respondent’s opinion from 208 respondents having experience of more than five years obtained through purposive sampling. The mean scores and the relative importance index (RII) of the data were computed using the SPSS 24 tool to obtain the descriptive information and inferential statistics. The findings have revealed ten potential factors for construction project delays and thirteen best practices that, whenever implemented, can assist in minimising delays. Moreover, the identified best practice measures were categorised in clusters to indicate the project participant who plays the significant role in minimising the delays. Furthermore, the findings acknowledged strategies were categorised in six clusters, namely effective project management, procurement and supply, resource adequacy (monetary or financial), design or technical, information and communication, and external strategies. The current study proposes future research to focus on identifying the relationship between the strategic cluster categories in recognising which cluster category correlates highly towards minimising the construction project delays
A Review of the Impact of Co-Digestion Substrates on the Methane Yield
(iRASD, 2025-06-22) Matwani , J.; Iddphonce, R.
This review highlights the impact of anaerobic co-digestion (ACD) on improving energy recovery from biogas production systems. Various factors from selected papers were reviewed to figure out their influence on ACD performance. Such factors include Carbon/Nitrogen (C/N) ratio, biodegradability of feedstock, microbial diversity, activity, buffering capacity, and trace element concentrations. Findings show ACD significantly enhances process stability and increases methane yield by 20% to 65% compared to mono-digestion. The process shares more insights on mechanisms for addressing environmental pollution challenges as it offers alternative approaches for reducing greenhouse gas emissions. Despite promising achievements in ACD systems, several limitations of the process still exist, requiring the attention of future studies to explore the full potential of technology. Specific areas include optimizing the mixing ratio of substrates to prevent acidification and ammonia toxicity risks that may occur during the process, hence affecting the system efficiency. Research should focus on process design and proper feedstock selection, considering innovative approaches such as bioaugmentation, supplementation with carbon compounds and nanoparticles, to improve microbial activity, process efficiency, and stability. Also, there is a need to develop predictive models that will accurately incorporate C/N ratio effects on digestion kinetics and nutrient transformation. Current models are complex, which hinders their scalability; thus, the use of machine learning could enhance model accuracy.
A Systematic Review of Value Engineering Practices in Construction Projects in Tanzania.
(Arid Zone Journal of Engineering, Technology & Environment, 2025-11-27) Kindole, A.; Lingwanda M. I,; Tekka, R. S
Value engineering (VE) has become a vital component of construction management, improving project outcomes in terms of cost, time, quality, safety, environmental performance, stakeholder satisfaction and social value. This study systematically reviews VE practices to assess their applications, benefits, barriers, and future research directions, with emphasis on Tanzania’s construction industry. A structured search of Google Scholar and Semantic Scholar retrieved peer reviewed publications from 2005 to 2025, yielding about 70 sources, of which 32 highly relevant studies were analyzed in depth. The review shows that VE delivers quantitative benefits such as cost and time reduction, alongside qualitative gains including enhanced construction quality and better management through multidisciplinary teamwork. However, its adoption in Tanzania remains limited due to inadequate awareness, cost-driven procurement systems, and a shortage of trained VE professionals. This study further identifies critical success factors (CSFs) across VE phases, including effective project information preparation, cost-based comparison of design alternatives, systematic planning and implementation, and strong stakeholders support. It concludes by recommending the development of localized VE guidelines and increased awareness among clients and top management to enhance decision-making and promote wider VE adoption in Tanzania.
Assessment of Scouring Effect of Msingi Masonry Arch Bridge in Mkalama, Singida, Tanzania
(ABUAD Journal of Engineering Research and Development (AJERD), 2025-08-26) Barthazar, Dickson; Katambara, Zakaria; Kifanyi, Gislar
This study presents an integrated geotechnical and hydraulic assessment of the Msingi Masonry Arch Bridge in Mkalama District, Singida, Tanzania, to evaluate scour vulnerability, subsurface strength, and structural load capacity. Field investigations included Dynamic Probing Light (DPL) testing, core sampling, and particle size analysis at six test pits (DS1–DS6), alongside laboratory tests adhering to BS 1377:1990 standards. Results revealed significant spatial variability in soil gradation and compaction, with deeper layers demonstrating high bearing capacities (up to 1555.8 kN/m²), while surface strata exhibited loose conditions and higher susceptibility to erosion—particularly in zones with elevated fines content. Hydraulic modelling, using site-specific parameters such as hydraulic radius (1.88 m), channel slope (0.0082), and Manning’s coefficient (0.017), predicted a scour depth of 2.6 m, compared to the observed 2.0 m. Structural analysis using the MEXE method yielded an allowable axle load of 28.05 tonnes, translating to a foundation pressure of 98.6 kN/m², which is within safe soil capacity limits. Despite current structural stability, the narrow scour margin and near-threshold loading conditions indicate elevated long-term vulnerability. The study recommends immediate installation of scour countermeasures, selective foundation deepening in weak zones, and routine monitoring to enhance the resilience and longevity of the bridge.
Stakeholders’ Awareness and Perceptions on the Use of Force Account Method in Public Building Construction Projects in Tanzania.
(DASJR, 2025-09-29) Magania, Faraji M.; Tekka, Ramadhani Said; Chengula, Duwa Hamisi
The Force Account Method (FAM) is increasingly utilized as a procurement approach for public building construction projects in Tanzania, primarily due to its potential for cost savings, flexibility, and enhanced accountability. This study investigates stakeholders’ awareness of FAM selection criteria and their perceptions of its practical benefits. A descriptive survey design was employed, collecting data from 128 participants representing implementing agencies, contractors, consultants, and regulatory bodies through structured questionnaires. The results indicate high awareness of key selection criteria, especially the necessity for sufficient technical staff and the importance of minimizing disruption to ongoing operations. Stakeholders identified limited funding and uncertainty in disbursements as significant justifications for FAM, though opinions varied regarding remoteness and the clarity of work quantity definitions. Most participants agreed that FAM improves cost efficiency, adaptability to unforeseen changes, and public confidence in transparency. Nevertheless, concerns were raised about project completion timelines and the consistency of quality outcomes, with regulatory bodies and implementing agencies expressing differing perspectives. These findings underscore FAM’s advantages in affordability and governance, while also revealing deficiencies in project efficiency and technical oversight. The study concludes that FAM substantially contributes to value for money in Tanzania’s public construction sector. However, enhancements in institutional capacity, standardized guidelines, and monitoring mechanisms are necessary to address persistent challenges related to quality and timeliness. The findings offer actionable insights for policymakers, regulatory authorities, and practitioner aiming to improve the effectiveness of FAM in achieving sustainable infrastructure development.

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