College of Engineering and Technology (COETEC)http://localhost/xmlui/handle/123456789/12782024-03-28T13:59:20Z2024-03-28T13:59:20ZDevelopment of an Interdependency Network Model for Analyzing Construction Contractor Payment Risks in Kenya: A Case of Design-Bid-Build Procurement SystemKenyatta, Mark Obegihttp://localhost/xmlui/handle/123456789/62472024-03-27T06:56:00Z2024-03-27T00:00:00ZDevelopment of an Interdependency Network Model for Analyzing Construction Contractor Payment Risks in Kenya: A Case of Design-Bid-Build Procurement System
Kenyatta, Mark Obegi
One of the most important components of systems used to procure construction projects is payment to construction contractors. It is connected to the owner's financial obligations in the design-bid-build D-B-B system. For the owner, paying late, paying insufficiently, or not paying at all portend a favorable risk consequence, as evidenced by incurring a lower realization cost than actual. On the other hand, it portends a risk consequence that is undesirable. To better comprehend how contractor payment risks are initiated and propagated, it is necessary to analyze the interconnectedness nature. However, the connections between various payment risk causes are scarcely accounted for in prior literature. Secondly, little attention has been paid to the influence of contextual determinants on occurrence of payment dispute risks. Thirdly, literature has ignored the connections between application of incompatible procurement practices and occurrence of payment risks. In addressing these gaps, three objectives were tackled. First, the study assessed the influence of contextual factors on the co-occurrence of payment disputes. The second objective determined of compatibility of D-B-B practices and their influence on occurrence of payment risks. The third objective developed an interdependency network model for analyzing contractor payment risks. In tackling the first objective, contextual determinants were gathered from the literature. These were then tested with 29 and 22 payment dispute cases in the private and public sectors, respectively. Using social network analysis (SNA) techniques, such as degree, eigenvector, and Bonacich centralities, and structural hole measures, the results were quantitatively analyzed. In the second objective, incompatible practices were gathered through qualitative synthesis and then rated by 12 subject matter experts. This output was subsequently analyzed using SNA techniques including hierarchical clustering, structural equivalence, and Euclidean distance. In the third objective, incompatible practices were used to generate 12 propositions, which were then evaluated by 12 SME. This output was then used to develop an interdependency network model, which was analyzed using SNA techniques such as one-mode matrix, eigenvector and eigenvalue, and Labda partitioning. A major finding as pertains objective one was that site asset specificity reflected by the practice of separating legal ownership from contractual possession propagates most of the payment dispute cases. As a result, the study recommends an evaluation of the effectiveness of current payment default remedies in addressing the challenge of the inseparability of the site from the final product in order to protect the rights of contractors who have not been paid. A key finding from the second objective was that the linkages between certain D-B-B procurement practices and the owner’s cost saving strategies contributed to most contractor payment risks. As a result, the study recommends adoption equitable risk sharing practices such as social capital. Analysis of the interdependency network model revealed that 20% of the risk practices cause and propagate 80% of payment risks. To effectively reduce the majority of these risks, the study recommends adoption of Social Network Analysis (SNA) methods in identifying and determining the most significant payment risk causes from an interconnected perspective. The study focused on payment risks within the D-B-B procurement system. Therefore, there is need for future studies to explore occurrence of payment risks in other construction procurement systems.
Key words: Contractual practices, design-bid-build procurement system, social network analysis, payment risks, vulnerability assessment
Doctor of Philosophy in Construction Engineering and Management
2024-03-27T00:00:00ZOptimal Shunt Capacitors’ Placement and Sizing in Radial Distribution Systems using Multi-Verse Optimizer, Modified Loss Sensitivity Factors and Matlab Matrix Reduction TechniquesMtonga, Thomson Precious Malumbohttp://localhost/xmlui/handle/123456789/62422024-02-12T09:46:28Z2024-02-12T00:00:00ZOptimal Shunt Capacitors’ Placement and Sizing in Radial Distribution Systems using Multi-Verse Optimizer, Modified Loss Sensitivity Factors and Matlab Matrix Reduction Techniques
Mtonga, Thomson Precious Malumbo
The installation of shunt capacitors in radial distribution systems results in the reduction of branch power flows, currents, power losses and voltage drop. Consequently, this further results in improved voltage profiles and voltage stability margins. However, for efficient attainment of the aforementioned benefits, the installation of shunt capacitors needs to be carried out in an optimal manner, that is, optimally sized shunt capacitors need to be installed at the global optimum buses within a given electrical network. Identification of the global optimum buses at which to install shunt capacitors in radial distribution systems is one critical task that greatly affects the overall cost of total real power losses, shunt capacitors’ purchase, installation, operation, and maintenance (O&M). If an existing approach ably identifies the global optimum buses, then the overall cost of total real power losses, shunt capacitors’ purchase, installation, and O&M would be minimized to the least value possible. However, if an approach only identifies sub-optimal buses, then the minimization of the aforementioned overall cost would only be partial. There are two general approaches that are used to identify optimal buses on which to install shunt capacitors. These are: approaches for searching of optimal buses from unreduced search spaces and approaches for searching of optimal buses from reduced search spaces. The exhaustive search-based approach, which belongs to the former approach, gives the global optimum buses and consequently the global optimum overall cost of total real power losses, shunt capacitors’ purchase, installation, and O&M. However, its major shortfall is its high computation time. This is mainly so because under this approach, the search for the global optimum buses is carried out in unreduced search spaces. On the other hand, for approaches in which the search for optimal buses is carried out in reduced search spaces, the computation time is also reduced. However, this reduction in the search space, hence the computation time, results in reduced accuracy levels for the attained solutions. Consequently, in an attempt to counter shortfalls of existing approaches, i.e. high solutions’ accuracy at the expense of computation time; and shorter computation time at the expense of solutions accuracy, in this thesis a new optimal shunt capacitors’ placement and sizing approach has been developed, evaluated, and validated. The approach is based on Modified Loss Sensitivity Factors (MLSF), the Multi-Verse Optimizer (MVO) and MATLAB matrix or search space reduction techniques. In the developed approach, the MLSF and MATLAB’s matrix reduction techniques have been used to reduce the search space of optimal buses that require the provision of reactive power through the installation of shunt capacitors. Thereafter, MVO is used to do a concurrent search of the global optimum bus(es) from the reduced search spaces and the corresponding optimum shunt capacitor sizes to be installed. The developed approach was tested on the IEEE 10- and 33-bus radial distribution systems with the aim of minimizing the overall cost of total real power losses, shunt capacitors’ purchase, installation, and O&M while assuming fixed and variable system loading. Despite disregarding some bus combinations, the developed approach was still able to attain the same overall costs, real power losses, reactive power losses and bus voltages as the exhaustive search based algorithm. Additionally, the developed approach was able to attain the least overall cost than those obtained using approaches based on Artificial Bee Colony (ABC), Crow Search Algorithm (CrSA), Cuckoo Search Algorithm (CSA), Differential Evolution (DE), Dragonfly Optimizer (DFO), Genetic Algorithm (GA), Gravitational Search Algorithm (GSA), Grey Wolf Optimizer (GWO), Improved Crow Search Algorithm (ICrSA), Modified Cultural Algorithm (MCA), Moth Flame Optimizer (MFO) and Particle Swarm Optimization (PSO) algorithm. Consequently, because the search space becomes reduced after disregarding some bus combinations, the developed approach attains the global optimum results with relatively shorter computation times than the exhaustive search. In summary, the developed algorithm stands out as a potentially reliable tool for power system planners to adopt and use when solving the radial distribution systems’ optimal shunt capacitors’ placement and sizing problem for either minimization of the overall cost of total real power losses and shunt capacitors’ purchase or minimization of the overall cost of total real power losses, shunt capacitors’ purchase, installation, and O&M. This is so because, unlike available optimal shunt capacitors’ placement and sizing algorithms, the developed algorithm exactly matches the accuracy of the exhaustive search algorithm.
Master of Science in Electrical Engineering
2024-02-12T00:00:00ZShort-Term Vehicle Traffic Flow Forecasting Grey Model for Intelligent Transportation System PerformanceGetanda, Vincent Birunduhttp://localhost/xmlui/handle/123456789/62332024-02-02T11:36:40Z2024-02-02T00:00:00ZShort-Term Vehicle Traffic Flow Forecasting Grey Model for Intelligent Transportation System Performance
Getanda, Vincent Birundu
Vehicular traffic is continuously increasing around the world and the resulting congestion and pollution is a major concern to transportation specialists and decision makers. As population continues to grow it is a challenge to handle traffic demand, traffic jam, CO2 emission, global warming and economic loss. Road capacity is not adequate. Roads and highways are unlikely to expand due to cost and dwindling land supply. To manage these issues it is critical to integrate intelligent transportation system (ITS) in the transport management systems. Short-term vehicle traffic flow forecasting by ITS is vital in proactively monitoring a vehicle traffic system. Unfortunately, effective traffic flow forecasting is a key problem of ITS. Therefore, performance improvement of the predictive models which can enhance the forecasting ability of ITS is very crucial. Hence the objective of this study was to develop a short-term vehicle traffic flow forecasting grey model (GM) for ITS performance. Hence, in this thesis the precision of the original GM is improved by three proposed methods namely data grouping technique (DGT), relative variable smoothing approach (RVSA) and a three-step approach (TSA). To further improve the GM’s precision these new methods were combined with existing methods such as modification of background value (MBV), modification of initial condition (MIC) and Fourier series error correction approach (FSECA). Consequently, hybrid grey models were established. The accuracy improvement on the conventional grey models were measured by employing measures of model performance, namely root mean square error (RMSE), root mean square percentage error (RMSPE), mean absolute error (MAE) and the mean absolute percentage deviation (MAPD). The evaluation results revealed that the hybrid grey models outperformed the conventional GM in vehicle flow modelling and short-term forecasting. For instance in short-term vehicle traffic flow forecasting the improved models (GGM(1,1) and MBVGGM(1,1)) had good accuracy in the range of 80-90% compared to the corresponding conventional GM(1,1) and MBVGM(1,1) which had reasonable accuracy in the range of 50-80%. On the other hand in validating the DGT in improving the fitting accuracy of the conventional GM(1,3) the accuracy was improved from 60.3270% to 96.9706%. This was great improvement in the conventional GM(1,3)’s fitting accuracy. Further, the results of this research show that the proposed new methods i.e. the DGT, the RVSA and the TSA methods have the potential for improving the prediction accuracy of the conventional GMs. Hence the DGT in hybrid grey models can enhance the short-term forecasting ability of the ITS. A case study based on traffic data collected from Nairobi city, Kenya, was presented and analyzed to show the accuracy improvement in both the univariate (GM(1,1)) and multivariate (GM(1,3)) grey models. For instance from this case study computation of the RMSPE had shown that the fitting accuracy of GM(1,3) was improved from 69.7243% to 99.6281% by the TSA method. Thus an improved multivariate grey model can attain high traffic flow forecasting accuracies compared with an improved univariate grey model. Finally, the performance of the grouping technique based GMs on energy consumption and carbon dioxide emissions, outperformed the conventional GMs. From one of the presented empirical cases the grouping technique based multivariate GGM(1,3) attained an accuracy of 96.9706% against 60.3270% of the conventional GM(1,3). Thus the hybrid grey models developed in this thesis are multidisciplinary. However, in comparison with other state of the art improved GM such as the grey model with cosine term (GM(1,1|cos(ωt))), the performance of the proposed models was below that of the GM(1,1|cos(ωt)). In a recent research GM(1,1|cos(ωt)) had a mean absolute percentage error (MAPE) of 0.1% compared to 0.58% of the original GM(1,1). Therefore, there is need to investigate the performance of the proposed models in this research in comparison with the GM(1,1|cos(ωt)), in the future.
Doctor of Philosophy in Electrical Engineering
2024-02-02T00:00:00ZAssessment of Runoff for Design of Farm Ponds for Irrigation in Maragua Watershed, Murang’a County, KenyaMaingi, Susan Mbithehttp://localhost/xmlui/handle/123456789/62322024-02-02T11:02:12Z2024-02-02T00:00:00ZAssessment of Runoff for Design of Farm Ponds for Irrigation in Maragua Watershed, Murang’a County, Kenya
Maingi, Susan Mbithe
This study involved the assessment of runoff and design of three different farm ponds for harvesting runoff to supplement irrigation. The study was carried out in Maragua watershed in Murang'a County where farmers face the challenge of water shortage during the dry season and therefore require water for supplemental irrigation of their horticultural crops. This was achieved by; assessing the water requirement for crops within the watershed, assessment of the runoff from the agricultural fields and design for the storage volume of a farm pond for the generated runoff from the agricultural fields. In this research, CropWAT model was used to determine the crop water requirement, while the Soil Conservation Service – Curve Number Model using ArcGIS 10.1 software was used in the estimation of the runoff depth in mm. The value of runoff depth was converted to runoff volume which was then used to design for the water storage facility. AutoCAD 2019 was used to make technical drawings for the farm pond. The design of the farm pond was done based on different slopes; gentle slopes and steep slopes in accordance to Design manual 2015. The study results showed that ET0 varied from 3.01 to 5.10 mm/day and the effective rainfall varied from 8.0 to 154.4 mm. The ETc values for the garden pea, sweet pepper and tomato were 395.6, 460.1 and 432.7 mm per the growing season respectively. The irrigation requirements were 181.4, 216.6 and 187 mm per season for garden pea, tomato, and sweet pepper respectively. The results indicate increasing ETc throughout the growth stages which is high at the mid-season stage and starts to decrease slightly at the later stages. The results for runoff estimation demonstrated that the SCS-CN method by using satellite imagery data to estimate runoff is convenient and effective. The runoff volume calculated from the area and depth of runoff indicates that the crop land had the highest volume of runoff since it occupied the largest area among the three land use classes which was at 9,368,519.10 m3, 2,858,923.47 m3, 2,511,768.72 m3, for Kambirwa, Gituamba and Maragua ridge micro-watersheds respectively. This runoff come from the entire sub watersheds which will supply water to the farm ponds in the area. The values indicate that there is enough runoff to be harvested to the farm ponds. The design of farm ponds was done for each of the ponds surveyed in the different micro watersheds using the practice manual for small dams. The different capacities for the three farm ponds were; the design criteria was obtained for the three different farm ponds in slopes of 1%, 2% and 3%. the design criteria was able to achieve the water demand, evaporation and seepage losses , dead storage, average storage capacity, spillway channel size and slopes. The study recommends CropWAT model as a suitable decision support tool for policy makers and investors on irrigation and water resources in the region with regard to irrigation water management.
Master of Science in Soil and Water Engineering
2024-02-02T00:00:00Z