Research Articles (Civil Engineering)

Permanent URI for this collectionhttp://hdl.handle.net/2263/1701

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Evolution of bus rapid transit concepts in Sub-Saharan Africa : towards lighter design and incremental deployment
(Elsevier, 2025-08) Chetty, Alison; Venter, Christoffel Jacobus; christo.venter@up.ac.za
While Bus Rapid Transit (BRT) has matured into a standardised set of technologies worldwide, its slow adoption in Sub-Saharan African (SSA) cities has raised questions about its suitability in some contexts. A number of key factors affect BRT adoption in SSA, including poorly developed road networks, constrained demand and affordability limits, and the strength and importance of the legacy informal public transport (PT) ecosystem. In response, some cities have increasingly departed from the conventional infrastructure-heavy BRT design approach towards lighter, more incremental deployment concepts, in an effort to better match local realities and constraints. This paper aims to describe this shift and put it into the context of a continuum of BRT deployment approaches. A literature review presents clarifying terminology and an overview of recent BRT system design in SSA cities. We then describe a phased implementation approach evolving in South African cities that focus on improving existing services gradually towards the final BRT design. Two examples of BRT evolution in large (City of Tshwane) and medium-sized (Rustenburg) cities are described in more detail. The potential implications of design standards are explored and provide insight for cities in developing countries seeking designs best-suited to enhance PT services with limited funding.
Development of appropriate synthetic design storms for small catchments in Gauteng, South Africa
(Computational Hydraulics International, 2025-01) Mouton, Jacobus van Staden; Loots, Ione; Smithers, J.C.
Synthetic design storms are often used as input in dynamic rainfall-runoff simulation models. A number of methods to generate synthetic design storms are described in the literature. However, the selection of an inappropriate synthetic design storm will generate unrealistic simulations. Therefore, the aim of this study was to develop appropriate synthetic design storms for small urban catchments in Gauteng, South Africa. This study evaluated the applicability of the SCS method adapted for South Africa (SCS-SA), the Chicago Design Storm method and the Rectangular Hyetograph method. The performance of each method was evaluated compared to observed rainstorm events. Storm shape and intensity were used for the evaluation. As expected, the Rectangular Hyetograph was the least representative of naturally occurring storm events. The Chicago Design Storm and SCS-SA distribution curves initially performed poorly. Adjustment of the timing of the peak storm intensity to the start of the event resulted in a significant improvement for both methods. A novel approach was used to generate intermediate site-specific SCS-SA rainfall distribution curves anywhere in the study area.
Quantifying informal public transport using GPS data
(Elsevier, 2025-10) De Beer, Lourens Retief; Venter, Christoffel Jacobus; Snyman, Lourens Fourie; lourens.snyman@up.ac.za
Informal public transport modes transport the largest number of passengers in most developing countries. Despite its significance, limited information is available on the extent of its operations, and passenger counts alone do not provide sufficient insight into network coverage or passenger turnover. GPS tracking has emerged as a valuable tool, yet its potential for understanding minibus taxi operations at the road segment level remains underexplored. GPS studies of informal operators have rarely been extrapolated to volume counts per time period, due to statistical problems (non-representative sampling) and small sample sizes. This paper addresses this gap by developing a methodology to determine the minibus taxi vehicle trip count per street segment from GPS data, to map routes, and identify high-traffic corridors, with an illustrative application in the City of Tshwane, South Africa. The methodology includes data inspection, addressing limitations, and counting trips per street segment using a database and QGIS visualisation. Additionally, the paper outlines detailed steps in QGIS for processing GPS data. We show that the method delivers plausible results at the segment level. The methodology can help to address the global South's need for data-driven interventions in its predominant public transport mode.
Coverage versus frequency : exploring service variability among informal public transport operators in South Africa
(Findings Press, 2025-03) Angurini, Manaseh; Venter, Christoffel Jacobus
Informal public transport operators often make routing and dispatch decisions on the fly, as demand and traffic conditions change. This may cause highly variable and unpredictable services for passengers. This paper examines variability in spatial coverage, route alignment, and service frequency over time and space using GPS data in a medium-sized city. We propose using a GINI coefficient to measure the spatial concentration of services. While considerable variation exists, consistent high-frequency routes exist where many services are concentrated throughout the day. In lower demand areas service availability and routes vary much more, causing significant variations in service provision by direction.
Aggregate mineralogy and chemical properties effects on imaging based morphological shape indices
(Elsevier, 2025-09) Moaveni, Maziar; Lacroix, Brice; Lundstrom, Craig Campbell; Tabaroei, Abdollah; Anochie-Boateng, Joseph K.; joseph.anochieboateng@up.ac.za
Coarse aggregate sources must possess sufficient level of quality to meet both initial design as well as long-term and life-cycle performance requirements for pavement construction. Morphological shape properties, mineralogy, and chemical properties of the aggregate particles can significantly influence their quality and performance in terms of both durability and mechanical properties. As part of this study, a survey was sent out to different highway agencies to collect representative coarse aggregate samples as well as information regarding different practices used by them for morphological, petrographic, and chemical characterizations of aggregate sources. Morphology analysis using machine vision technology was incorporated to identify shape properties of the collected aggregate samples. Additionally, thin section optical petrographic analysis using an Axioscan 7 full slide scanner was utilized to identify mineral composition of the aggregates. Finally, chemical testing and analysis was conducted using Inductively Coupled Plasma Mass Spectrometry (ICP-MS) to detect major element compositions in epoxy impregnated sample of aggregate particles. Statistical analysis including Pearson correlation and multiple linear regression were deployed to investigate the relationship between the parameters representing mineralogy, chemical, and morphological shape properties. The findings of this study indicated 12 minerals and six chemical elements with statistical significance to impact the imaging-based shape indices of aggregates. Subsequently, multiple regression-based prediction models with a relatively satisfactory performance were developed to estimate the aggregate shape indices using mineralogy, chemical properties as well as rock type. The improvements in objectively characterizing aggregate chemical, mineralogical, and shape properties can be used to develop sustainable aggregate production methods and specifications.
Prediction of the fundamental period of infilled reinforced using advanced machine learning methods
(South African Institution of Civil Engineering, 2025-06) Yahiaoui, Asma; Markou, George; Bakas, Nicolaos; Dorbani, Saida
The use of machine learning (ML) to solve civil engineering problems has increased remarkably during the last few decades due to its effectiveness in reliably approximating complex relationships. In this paper, a key parameter of seismic design is estimated using hyperparameter ML algorithms to develop predictive models that compute the fundamental period. Initially, the impact of the train-test split ratio was investigated using three different splits, where the best results were achieved with train-test split ratios equal to 90/10 for all metrics. By predicting the fundamental period with three ML methods, namely XGBoost-HYT-CV, DANN-MPIH-HYT, and RF-HYT, the best fit was acquired by XGBoost-HYT-CV (coefficient of determination R2 = 99.994% and mean absolute error MAE = 0.00428). Although international literature agrees that building height is the primary factor influencing the fundamental period, feature engineering has revealed that the natural logarithm of the percentage of openings is the most significant parameter. This finding underscores the value of feature engineering in generating additional variables and uncovering their impact on output variables. Finally, an equation was derived from POLYREG-HYT that outperformed all existing formulae, deriving a final MAE of 0.0153, approximately three times smaller than the best-performing equations proposed in the international literature.
Quantifying urban land cover imperviousness as input for flood simulation using machine learning : South African case study
(IWA Publishing, 2025-05) Loots, Ione; Smithers, Jeffrey Colin; Kjeldsen, Thomas Rodding; ione.loots@up.ac.za
The imperviousness of urban surfaces is an important parameter in simulating urban hydrological responses, but quantifying imperviousness can be challenging and time-consuming. In response, this study presents a new framework to efficiently estimate the imperviousness of urban surfaces, using satellite images with Red, Green and Blue bands and a land cover dataset with multiple built-up urban classes through remote sensing, machine learning and field verification. The methodology is adaptable to other regions with similar datasets. For a case study in Pretoria, South Africa, major differences in median total impervious area percentages (mTIA%) were identified when compared between land cover groups: residential areas had a lower imperviousness median (mTIA% = 38%) than commercial (mTIA% = 81%) and industrial (mTIA% = 89%) land cover. The mTIA% also varies between 17 and 61% for a range of different formally developed residential classes and between 14 and 43% for a range of different informally developed residential classes. These mTIA% are recommended for any urban area within the South African National Land Cover dataset. These values can be incorporated into hydraulic and hydrological models, which improve the efficiency of parameter estimation for modelling. The methodology successfully quantified temporal imperviousness changes in the study area.
Performance of low-cost fiber optic cables as leak detection sensors for water pipelines in unsaturated soil
(Elsevier, 2024) Jacobsz, Schalk Willem; sw.jacobsz@up.ac.za
Large volumes of potable water are lost from leaks in water distribution systems around the world. Such leaks may go undetected for a long time. A passive means of leak detection can be implemented by burying a suitable fiber optic cable in the pipe trench with water distribution pipes when they are installed. Water leaking from pipes into the ground results in a temperature change at the leak location. Leaks into unsaturated soil also cause changes in the bulk density and strength of the soil, resulting in significant soil deformation. Brillouin Frequency Shift (BFS) in optical fibers is sensitive to changes in both temperature and mechanical strain, allowing fiber optic cables to act as efficient leak detection sensors. Purpose-made fiber optic cables may be expensive, but telecommunication grade cables generally have a low cost and are readily available around the world. This paper investigates the performance of five different fiber optic cables, including communication grade fiber optic cables, to act as leak detection sensors in unsaturated ground. It was found that the most efficient leak detection sensors are flexible tight-buffered fiber optic cables.
New predictive models for the computation of reinforced concrete columns shear strength
(MDPI, 2025-01) Ioannou, Anthos I.; Galbraith, David; Bakas, Nikolaos; Markou, George; Bellos, John; u19027436@tuks.co.za
The assessment methods for estimating the behavior of the complex mechanics of reinforced concrete (RC) structural elements were primarily based on experimental investigation, followed by the collective evaluation of experimental databases from the available literature. There is still a lot of uncertainty in relation to the strength and deformability criteria that have been derived from tests due to the differences in the experimental test setups of the individual research studies that are being fed into the databases used to derive predictive models. This research work focuses on structural elements that exhibit pronounced strength degradation with plastic deformation and brittle failure characteristics. The study’s focus is on evaluating existing models that predict the shear strength of RC columns, which take into account important factors including the structural element’s ductility and axial load, as well as the contributions of specific resistance mechanisms like that of concrete, transverse, and longitudinal reinforcement. Significantly improved predictive models are proposed herein through the implementation of machine learning (ML) algorithms on refined datasets. Three ML models, LREGR, POLYREG-HYT, and XGBoost-HYT-CV, were used to develop different predictive models that were able to compute the shear strength of RC columns. According to the numerical findings, POLYREG-HYT- and XGBoost-HYT-CV-derived models outperformed other ML models in predicting the shear strength of rectangular RC columns with the correlation coefficient having a value R greater than 99% and minimal errors. It was also found that the newly proposed predictive model derived a 2-fold improvement in terms of the correlation coefficient compared to the best available equation in international literature.
Extended monitoring of earth pressures behind a 90 m integral bridge
(American Society of Civil Engineers, 2025-04) Morley, Douglas G.; Skorpen, Sarah Anne; Adendorff, Jurie F.; Kearsley, Elsabe P.; Jacobsz, Schalk Willem; Madabhushi, Gopal S.P.; sw.jacobsz@up.ac.za
Despite the popularity of integral bridges, long-term field data are required to better understand the soil strain ratcheting behavior that occurs with thermal cycles. This work presents over 6 years of field data collected from the Van Zylspruit Bridge, a 90-m-long integral bridge in South Africa. Sensors recording temperature, abutment movement, earth pressure, and soil water content were used to understand bridge behavior. Results show only a small increase in earth pressure over time, far less than that expected from physical modeling. One explanation for this may be the smaller-than-expected thermal movements recorded. Further possibilities were investigated through the small-scale modeling of a 1.5-m RC abutment, from which it was found that the starting position of the abutment and concrete drying shrinkage are both unlikely to contribute to the pressure buildup, while soil water content may play a part through the suctions generated. Based on these findings, the strain ratcheting of earth pressures in the field appears to be less severe than modeling efforts would suggest.
Use of Rayleigh and Love waves in seismic surface wave testing
(South African Institution of Civil Engineering, 2025-03) Islam, Mohammed Shariful; Heymann, Gerhard; gerhard.heymann@up.ac.za
Seismic surface wave tests are widely used in geotechnical engineering due to their non- invasive and cost-effective approach in obtaining important soil parameters such as the small strain shear modulus (G 0) by measuring shear wave velocity (Vs). While conventional tests focus on measuring Rayleigh waves due to their easy generation and detection in the field, Love waves are often overlooked due to challenges in generating and detecting them. This study investigated the utilisation of both Rayleigh and Love waves to obtain shear wave velocity profiles using experimental and synthetic data. Two methods were explored for generating Love waves – using a horizontal harmonic source, and also employing a horizontal impact source. Signal processing code was developed to analyse the surface wave signals and to calculate dispersion data. By conducting discrete and joint inversions with the experimental and synthetic dispersion data, the variation in the shear wave velocity profiles was evaluated. The findings demonstrated that employing both Rayleigh and Love waves in joint inversion reduced the variation in the shear wave velocity profile compared to using Rayleigh waves alone, but only when Love wave signals with low noise levels were available.
New predictive models for the computation of reinforced concrete columns shear strength
(MDPI, 2024-12-24) Ioannou, Anthos I.; Galbraith, David; Bakas, Nikolaos; Markou, George; Bellos, John; u19027436@tuks.co.za
The assessment methods for estimating the behavior of the complex mechanics of reinforced concrete (RC) structural elements were primarily based on experimental investigation, followed by the collective evaluation of experimental databases from the available literature. There is still a lot of uncertainty in relation to the strength and deformability criteria that have been derived from tests due to the differences in the experimental test setups of the individual research studies that are being fed into the databases used to derive predictive models. This research work focuses on structural elements that exhibit pronounced strength degradation with plastic deformation and brittle failure characteristics. The study’s focus is on evaluating existing models that predict the shear strength of RC columns, which take into account important factors including the structural element’s ductility and axial load, as well as the contributions of specific resistance mechanisms like that of concrete, transverse, and longitudinal reinforcement. Significantly improved predictive models are proposed herein through the implementation of machine learning (ML) algorithms on refined datasets. Three ML models, LREGR, POLYREG-HYT, and XGBoost- HYT-CV, were used to develop different predictive models that were able to compute the shear strength of RC columns. According to the numerical findings, POLYREG-HYT- and XGBoost-HYT-CV-derived models outperformed other ML models in predicting the shear strength of rectangular RC columns with the correlation coefficient having a value R greater than 99% and minimal errors. It was also found that the newly proposed predictive model derived a 2-fold improvement in terms of the correlation coefficient compared to the best available equation in international literature.
Short term ageing of asphalt binder in thin asphalt layers
(Elsevier, 2024-03) O'Connell, Johan; Maina, J.W. (James); Steyn, Wynand Jacobus Van der Merwe
The effects of ageing on pavement performance are significant, particularly in terms of fatigue cracking. South Africa has the 10th longest road network in the world, requiring innovative approaches to road construction due to severe budget constraints. Innovative solutions such as thin asphalt concrete layers for surfacing, result in unique ageing rates of the layers, which, in general, have a higher incidence of fatigue cracking than, for example, thicker asphalt concrete layers used in other parts of the world. The objective of this paper is to evaluate how ageing mechanisms affect various asphalt binder properties, and whether they affect them to the same extent or not. Furthermore, the objective of the paper is also to determine the accuracy of the Rolling Thin Film Oven Test (RTFOT) in simulating short-term ageing in the field. The RTFOT provides a relatively good indication of short-term ageing, according to this multi-decade ageing study, and the effect on the asphalt binder properties used as ageing indices depends on the specific property chosen for comparison before and after ageing.
Is first mile behaviour similar to last mile behaviour? A case study on a rapid rail system in South Africa
(Routledge, 2025) Watts, Daniel; Venter, Christoffel Jacobus; Hayes, Gary; christo.venter@up.ac.za
First and last mile behaviours to and from public transport are rarely studied together, limiting insights into preference differences between access and egress trips. This paper addresses this gap through a case study of an urban rapid rail system in South Africa. Data are from an online stated preference survey conducted amongst train passengers, in which mode choices for the access and egress trips during the morning peak are captured. Nested logit choice models for access and egress trips differ both in nesting structure and the relative size of coefficients. Values of travel and walk time are three times larger for the egress than for the access trip, suggesting that time-saving strategies are more important on the last mile than the first mile part of a commute trip. We explore the impacts of these differences by modelling hypothetical improvement scenarios to access and egress conditions.
Technology foresight for the South African road transport sector by 2035
(AOSIS, 2024-08-30) Rust, Frederik C.; Sampson, Leslie R.; Cachia, Adriana A.; Verhaeghe, Benoit M.J.A.; Fourie, Helena S.; Smit, Michelle A.; Hoffman, Alwyn; Steyn, Wynand Jacobus Van der Merwe; Venter, Karien; Lefophane, Samuel
BACKGROUND : Foresight can be used to define futuristic orientated research and development (R&D) that is required to position the road transport sector for a challenging future. OBJECTIVES : To develop a set of futuristic R&D projects that could be added to a balanced SANRAL R&D portfolio to position SANRAL and the transport sector for the future on a 15-year horizon. METHOD : Inputs into and ranking of the drivers, trends and technologies that will impact the transport sector were obtained from interviews with eminent thinkers, participants in workshops and a survey leading to five potential future scenarios. Qualitative and quantitative data analysis yielded several key solutions (KSs) and key interventions (KIs) to position the sector. This was complemented with the novel use of technology trees to analyse the linkages between new and existing knowledge and to identify gaps in knowledge and subsequently the identification of key R&D opportunities. RESULTS : Through backcasting from the desired future scenario as well as using 412 stakeholder inputs, 12 KSs and 61 KIs were defined and ranked. The top 30, most futuristic KIs were analysed using 18 hierarchical technology trees to define R&D opportunities. CONCLUSION : The analysis emphasised the importance of new technologies such as data science, machine learning, smart transport and advanced materials to position the sector. CONTRIBUTION : The use of a novel, structured technology foresight approach that utilises scenario development combined with hierarchical technology trees was demonstrated. To position the road transport sector for a challenging future, 12 new thematic KSs and 61 KIs were developed.
Application of laterite-based geopolymer mortar for masonry bedding
(Trans Tech, 2024-12) Nuru, Zeyneb Kemal; Kearsley, Elsabe P.; Elsaigh, Walied A.; elsabe.kearsley@up.ac.za
This paper explores the production and properties of geopolymer cement mortar using laterite soils. The aim was to evaluate the laterite-based geopolymer mortars for masonry bedding applications. The testing programme encompassed three series of mixes tested to determine setting times, flowability, flexural strength and compressive strength. Two types of sands were used including standard sand and natural sand. The effect of water-to-laterite ratios, activating agent concentration, and cement-to-sand ratio were established. The properties of standard cement paste, and mortar were used as a reference. The study found that geopolymer mortar made from laterite meets the requirements for masonry bedding.
Life cycle assessment of an avocado : grown in South Africa-enjoyed in Europe
(Springer, 2024-11) Blaauw, Sheldon Alfred; Broekman, Andre; Maina, J.W. (James); Steyn, Wynand Jacobus Van der Merwe; Haddad, William A.
Please read abstract in article.
Big data generation and comparative analysis of machine learning models in predicting the fundamental period of steel structures considering soil-structure interaction
(World Scientific Publishing, 2024-11) Van der Westhuizen, Ashley Megan; Bakas, Nikolaos P.; Markou, George; george.markou@up.ac.za
The computing of the fundamental period of structures during seismic design is well documented in design codes but is mainly dependent on the height of the structure, which is considered to be the most influential parameter. It is, however, important to consider a phenomenon called the soil–structure interaction (SSI), as this has been found to have a detrimental effect, especially for buildings founded on soft soils. A pilot research project foresaw the use of machine learning (ML) algorithms trained on relatively limited datasets for the development of a more accurate and objective fundamental period formula. Therefore, a dataset that consists of 98,308 fundamental period data points was created through the use of a High-Performance Computer (HPC), which is the largest dataset of its kind. The HPC results were then used to train, test, and validate different ML algorithms. It was found that XGBoost-HYT-CV with hyperparameter tuning performed the best with a correlation of 99.99% and a mean average percentage error (MAPE) of 0.5%. Furthermore, the XGBoost-HYT-CV model outperformed all under-study ML models when using an additional dataset that consisted of out-of-sample building geometries and soil properties, with a resulting MAPE of 9%. Finally, irregular buildings were also used to test the performance of the proposed predictive models.
Using machine learning algorithms to develop a predictive model for computing the maximum deflection of horizontally curved steel I-beams
(MDPI, 2024-08) Ababu, Elvis; Markou, George; Skorpen, Sarah Anne; george.markou@up.ac.za
Horizontally curved steel I-beams exhibit a complicated mechanical response as they experience a combination of bending, shear, and torsion, which varies based on the geometry of the beam at hand. The behaviour of these beams is therefore quite difficult to predict, as they can fail due to either flexure, shear, torsion, lateral torsional buckling, or a combination of these types of failure. This therefore necessitates the usage of complicated nonlinear analyses in order to accurately model their behaviour. Currently, little guidance is provided by international design standards in consideration of the serviceability limit states of horizontally curved steel I-beams. In this research, an experimentally validated dataset was created and was used to train numerous machine learning (ML) algorithms for predicting the midspan deflection at failure as well as the failure load of numerous horizontally curved steel I-beams. According to the experimental and numerical investigation, the deep artificial neural network model was found to be the most accurate when used to predict the validation dataset, where a mean absolute error of 6.4 mm (16.20%) was observed. This accuracy far surpassed that of Castigliano’s second theorem, where the mean absolute error was found to be equal to 49.84 mm (126%). The deep artificial neural network was also capable of estimating the failure load with a mean absolute error of 30.43 kN (22.42%). This predictive model, which is the first of its kind in the international literature, can be used by professional engineers for the design of curved steel I-beams since it is currently the most accurate model ever developed.
Studying transfers in informal transport networks using volunteered GPS data
(Elsevier, 2025-04) Ankunda, G.; Venter, Christoffel Jacobus; u21742040@tuks.co.za
Multimodal integration is an important issue in public transport systems due to its influence on both passenger experience and overall network efficiency. In most countries in the global South, achieving integration is particularly problematic because of the informal nature of most public transport. Decentralised service planning and demand responsiveness lead to often uncoordinated, highly variable service patterns, which are not optimised from a passenger perspective. Efforts to promote integration are also hampered by a lack of planning data on routes, service frequencies, and transfer locations. This research asks whether GPS data supplied by passengers as they move through the network can be used to help form a better understanding of the extent and quality of the transfer experience. The data was collected in the City of Tshwane, South Africa, among informal minibus-taxi passengers. Post-processing involved the use of a machine learning algorithm to identify in-vehicle, wait and walk segments, which were used to identify transfers between one vehicle and another. The results showed that many transfers are spatially efficient with short walk and wait times, but that a minority of transferring passengers may experience very long transfers. Transfers encompass a diverse range of behaviours including pacing, shopping and browsing, and typically involve much more walking than waiting. Transfers also occur across a wide range of locations, but tend to be concentrated in certain nodes and along street segments. Strategies to improve transfer facilities as well as general walkability might be targeted at such locations. The study demonstrated that volunteered GPS data is a promising source of information to help planners understand the transfer experience in multimodal networks in data-poor environments.

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