Dissertations for Construction Project Management (CPM)

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Browsing Dissertations for Construction Project Management (CPM) by Subject "construction projects"

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    Examining The Utilization of Scrum by Engineering Consultants to Manage Construction Projects from Initiation until Closing
    (The British University in Dubai (BUiD), 2020-02) Mazin Ali Almomani, Rakan
    The construction industry is considered a key contributing factor to almost every countries’ economy. Taking that into account, having strong, stable and successfully completed projects from A to Z is a vital component in determining the further development of a countries infrastructure and its position within the market and overall GDP. As of decades past, construction projects have been managed by applying the traditional project management methodologies or what is known as waterfall methodology, which is sequential in nature where construction activities flow in a sequence (a finish to start relationship). This sequential pattern also applies to other project phases, such as pre and post construction phase (execution phase). Many projects around the world have been successfully completed, in terms of time and cost, when managed by the traditional methodology, but at the same time there have been many that have not reached completion in particular complex projects or mega projects where multiple parties are involved. Construction projects go through many phases before it remotely reaches the construction phase (Execution phase), which is usually executed and completed by a contractor after the project has been designed and specified by a consultant. The role and involvement of a Client in construction projects is usually shallow and their main communication is with the project consultant as it is the responsibility of the project consultant to manage proper execution and timeline. The consultant’s role exceeds beyond the design phase into supervision as the project commences and begins to be erected from the ground up as well as work as the client’s representative in monitoring the project’s duration, cost and even selecting materials on behalf of the client. Clients usually do not get deeply involved in their projects due to their limited experience and knowledge in this type of works and so they go on to hire consultants on their behalf, which in many cases creates conflict and may cause delays and cost overrun due to rework and losses of materials. Throughout decades, many professionals in construction and managing construction projects tried to apply different methodologies and solutions striving for the completion of successful projects that were on time and within budget and most importantly matched the client’s desires and expectations. One of the latest project management methodologies is Scrum, which is considered an agile methodology. This methodology has many features that can be utilized to enhance managing construction projects by the traditional methodology. This research will discuss the utilization of Scrum in managing construction projects from a consultant’s point of view, since it can be applied in many phases apart from the construction phase, which is usually handled by contractors and in general wider than client’s scope in managing construction projects. Scrum may not be a comprehensive methodology to manage construction projects, but it has proved to be sufficient and effective when applied in certain phases, or in managing certain activities within certain phases. This research will describe Scrum in depth, show results from applying Scrum as the only methodology to manage certain phases, how it can be combined with the traditional methodology to accomplish the desired results, and how Scrum can positively affect time, cost, and overall client satisfaction.
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    Investigation of Forming a Framework to shortlist contractors in the tendering phase
    (The British University in Dubai (BUiD), 2022-10) DABASH, MOHANNAD SALAH
    The aim of this research is to create a framework that can predict the best contractor to be awarded a construction contract by a consultant/client using a different set of variables known as “Decision factors.” This research was conducted to improve the traditional tendering process, the model was used to predict the “Success Rate” for the project by assessing each contractor’s possibility of completing the project successfully using their compatibility with the project. The model creation was divided into multiple phases which started with finding the decision factors through an extensive literature review, and then determining the weights of each decision factor by conducting a survey that professional experts took. After obtaining the weights of the decision factors, a model using Machine Learning algorithm on Google Colab was written using the Python language. The model to shortlist contractors in the tendering phase was created using machine learning to enable more contractors to submit for a project without having to waste time and money on the tendering process; if they are compatible with the project, then they have a high chance of getting it by being short-listed for the project, which they can then submit their tender package for; this will also ensure that the best company gets the job for the client which will act as a great step towards improving the tendering in construction projects. For the consultant, it will decrease the load of going through numerous tender packages and ensuring that the best companies will tender for the project. This research has generated a base model that can be altered depending on the project requirements which can assist all parties involved within the tendering process to save time and money and improve the success rate of projects. The limitation of this research is that to use the framework to its full extent, it needs a huge database that includes data from numerous previous projects to be able to accurately predict the success rate of the upcoming project; however, if it could be regulated through governmental institutes then the database can be quickly collected within a relatively short period of time.