Browsing by Author "Moghayedi, Alireza"

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    An investigation of the energy and environmental sustainability impact of affordable net-zero energy house in South Africa
    (2025) Hübner, Dylan; Michell, Kathleen; Moghayedi, Alireza
    This dissertation is submitted in fulfilment of the requirements for the degree of Master of Philosophy (MPhil) in Construction Economics and Management at the University of Cape Town. This study aims to explore the energy and environmental sustainability implications linked to affordable net-zero energy housing in South Africa. Affordable housing is intended for individuals who cannot afford market related prices or do not meet the criteria for social housing. Accordingly, this dissertation defines affordability as households spending no more than 30% of their income on gross housing expenses. It seeks to provide insights into the challenges, opportunities, and implications of integrating net-zero energy housing into the affordable housing sector. Given South Africa's shortage of affordable housing, unstable electricity supply, and economic challenges, there is significant opportunity to explore alternative building strategies to address these issues. The research employed an exploratory mixed-method approach rooted in the philosophical foundations of realism. Qualitative data was procured through 4 in-depth semi-structured interviews conducted with 3 sustainability professionals and an affordable housing specialist. Quantitative modelling utilised One Click LCA and the Edge App to estimate the life cycle carbon emissions of an affordable net-zero energy house. The findings indicate that affordable net-zero energy housing can substantially reduce both operational and embodied carbon emissions. By integrating conventional building practices with innovative methods, the life cycle emissions of a house are significantly reduced, surpassing sustainable building regulation requirements. The quantitative analysis of three affordable net-zero energy housing scenarios, incorporating both conventional and innovative building techniques and practices across different levels to mimic South Africa's construction landscape, demonstrates a potential reduction in life cycle carbon emissions ranging from 12% to 94%. Furthermore, South Africa's landscape may not be conducive to net-zero embodied energy houses, suggesting that developers and households should prioritise reducing operational carbon emissions. These findings contribute to knowledge within the professional, affordable, and sustainable housing spaces, thereby facilitating informed decision-making towards a more sustainable and affordable South African residential sector.
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    A Critical Success Factor Framework for Implementing Sustainable Innovative and Affordable Housing: A Systematic Review and Bibliometric Analysis
    (2021-07-23) Moghayedi, Alireza; Awuzie, Bankole; Omotayo, Temitope; Le Jeune, Karen; Massyn, Mark; Ekpo, Christiana Okobi; Braune, Manfred; Byron, Paimaan
    The actualization of affordable housing remains a challenge. This challenge is exacerbated by the increasing societal demand for the incorporation of sustainability principles into such housing types to improve levels of occupant health and well-being whilst avouching the desired levels of affordability. Innovative technologies and practices have been described as beneficial to the effectuation of sustainable affordable housing. However, knowledge concerning the deployment of innovative technologies and practices in sustainable affordable housing (sustainable, innovative, affordable housing—SIAH) delivery remains nascent. Consequently, there is a lack of a common ontology among stakeholders concerning how to realize SIAH. This study aims to contribute toward the development of this body of knowledge through the establishment of the critical success factors (CSFs) for effective SIAH implementation. To achieve this objective, a systematic review and bibliometric analysis focusing on a juxtaposition of sustainable, innovative and affordable housing concepts was carried out based on the relevant literature. This led to the identification and clustering of CSFs for these housing concepts at individual levels and as a collective (SIAH). The findings of the study consisted of the establishment of four distinct yet interrelated facets through which SIAH can be achieved holistically, namely, housing design, house element, housing production method and housing technology. A total of 127 CSFs were found to be aligned to these facets, subsequently clustered, and conclusively used for the development of a SIAH CSF framework. The most frequently occurring CSFs with predominant interconnections were the utilization of energy-efficient systems/fittings, tenure security, a comfortable and healthy indoor environment, affordable housing price in relation to income and using water-efficient systems/fittings CSFs, and establishing the emergent SIAH CSF framework. The framework in this study is useful in the documentation of SIAH features for construction projects and further studies into SIAH CSFs.
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    Evaluating the impact of innovative cyber technologies on the delivery process of South African sustainable affordable housing
    (2025) Mahachi, Jeffrey; Michell, Kathleen; Moghayedi, Alireza
    The present study aimed to evaluate the impact of innovative technologies on the delivery and affordability of low-income housing in South Africa. Given the rising demand for affordable housing due to a growing population and urbanisation, the study aimed to investigate the role of new technologies in enhancing housing delivery. The study explored the use of various innovative technologies such as Unmanned Aerial Vehicles (UAVs), Building Information Modelling (BIM), Geographic Information Systems (GIS), 3D Printing, Virtual Reality (VR), Augmented Reality (AR), Artificial Intelligence (AI), Smart Sensors, Modular and Prefabrication, Object-Oriented Programming (OOP), and Project Portfolio Management (PPM) as potential solutions. The research adopted a mixed-method approach, combining qualitative and quantitative methods. The target population included home developers, experts, policymakers, and academics involved in affordable housing development. Qualitative data were gathered through expert interviews until saturation was achieved, while quantitative data was obtained from 100 survey questions filled out by experts involved with affordable housing in South Africa. The data analysis method consisted of thematic analysis and descriptive and inferential statistics. The results of the study indicated that these innovative technologies have the potential to not only accelerate the delivery of affordable housing but also make it more cost-efficient. The analysis showed that 3D printing, modular and prefabrication were three technologies that could significantly increase housing delivery, while BIM, GIS, VR, OOP, and PPM could efficiently aid in the planning of affordable housing, reducing design conflicts, improving project schedules, and cutting development costs. Smart Sensors, AR, and UAVs could indirectly enhance housing delivery by monitoring construction, ensuring the site is built on schedule and correctly, and monitoring construction worker productivity. However, the study also identified high costs and limited social acceptance as major challenges. To address these issues, the study emphasised the need for the government to promote the adoption and implementation of these technologies through financial incentives and subsidies for companies that adopt them, as well as investment in research and development. The study also stressed the importance of promoting the use of these technologies in high-end housing developments in addition to affordable housing projects. In conclusion, the results of this study highlight the significance of considering innovative technologies in the delivery of affordable housing in South Africa. The findings suggest that the government has a critical role to play in promoting the adoption and implementation of these technologies through financial incentives and investment in research and development, helping to overcome current challenges and making affordable housing a reality for all.
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    Modelling uncertainty of cost and time in infrastructure projects
    (2019) Moghayedi, Alireza; Windapo, Abimbola Olukemi
    The construction of infrastructure projects is characterised by cost overruns and time delays. Scholars view that the estimation approach and inappropriate tools and techniques used to forecast possible uncertainty in the construction processes are a primary cause of cost overruns and time delays on construction projects. Uncertainties encountered in the construction process are underestimated and these impact on the final cost and time of construction projects through a combination of individual construction activities. The study, therefore, examines the initial and final cost of construction activities, towards developing a hybrid tool that captures and models’ different sources of uncertainty in infrastructure projects and their effect on cost and time underestimation. The study adopted a sequential exploratory mixed method research approach that went beyond the basic mixed method approaches, employing a combination of sequential and concurrent aspects of mixed methods. Data was gathered through a series of expert panel estimation sessions, technical brainstorming of experienced professionals (with 30 years’ experience and more) in the construction of infrastructure projects, and a structured self-administered questionnaire survey distributed to project managers of South African highway projects. The developed hybrid tool models the main structures from the activity level to the entire highway project. Consequently, three identified uncertainties in the construction process of infrastructures, namely variability in the construction process, correlations between the costs, times and cost-time of construction activities and disruptive events, are modelled jointly at the construction activity level. Data obtained from both qualitative and quantitative approaches were analysed using various techniques. The probability distribution function of cost and time were modelled using the lognormal and triangular probability distributions; while Monte Carlo Simulation (MCS), Copula analysis technique, the Markov processes, and Adaptive Neuro-Fuzzy Inference System (ANFIS) analytical technique were used in modelling the variability of the cost and time activity, correlation between costs, time and cost-time activities, and to model the occurrence of disruptive events, so that the impact size of disruptive events on the cost and time of activities respectively, can be intelligently assessed. The developed uncertainty model was validated against the final cost and time of a project case study, as well as against historical data of construction cost overruns and time delays in infrastructure projects. The study found that the different uncertainties had a distinct influence on construction cost and time of different project structures. Furthermore, the comparison of the deterministic estimates with the uncertainty estimates shows that the accumulated impact of the three uncertainty sources significantly increases the construction cost and time of infrastructure projects. Based on these findings, the research concludes that the disruptive event is the main cause of cost overruns and time delays in infrastructure projects. In the scale of activity, the correlation between the costs of different activities in the same structure causes the largest increase in the cost of activity, while the correlation between the times of repeated activity in the same structure causes the largest increase in time of the activity. Furthermore, the study concludes that the improvement in the accuracy of cost and time estimation of infrastructure projects depends on a combination of probability analysis and intelligent machine learning. The contributions of the study to construction management knowledge include a clear definition of uncertainty and the sources of uncertainties in the construction of infrastructure projects; an in-depth understanding of the construction process of linear infrastructure projects; and an improvement in the quality of data used (combination of experts’ estimation and historical data) for research in the area of project performance. The developed uncertainty model based on three sources of uncertainty at the activity level provides infrastructure project planners with a hybrid dynamic tool to accurately model and predict the construction cost and time of infrastructure projects at any stage of the project. Also, the uncertainty model has three other purposes: it is the preparatory point for allocation of budget, it facilitates the update of the impact of uncertainties and evaluates the effectiveness of countermeasures to mitigate against the threat of uncertainties.