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The purpose of this paper is to understand the prevailing conflict management styles in Sri Lankan commercial building industry from the main stakeholders’ perspective. The dual concern theory is applied to this study as a theory foundation.

The Sri Lankan commercial building industry is currently experiencing a construction boom after 30 years of civil war. Creating a dispute free environment through well conflict management is one of the main ways to keep the continuous demand and development of construction. However, the Sri Lankan construction sector is arranged in such a way that they directly approach the dispute resolution rather than avoiding the dispute initially through proper conflict management. Four cases are selected for this study with the intention of conducting meetings with three main stakeholders of each case who represented client, consultants and contractors, respectively.

The research findings reveal that compromising is the most common conflict management style used by the industry. Forcing style obtained the second place in the ranking whereas obliging and avoiding received third and fourth places.

This study is conducted to provide a full picture of conflicts faced by the Sri Lankan commercial building industry and their proper management so that the future projects will use this information to diminish the destructive effects of conflict situations and provide a real value for money.

Developing a reliable cost estimate at the early stage of construction projects is challenging due to inadequate project information. Most of the information during this stage is qualitative, posing additional challenges to achieving accurate cost estimates. Additionally, there is a lack of tools that use qualitative project information and forecast the budgets required for project completion. This research, therefore, aims to develop a model for setting project budgets (excluding land) during the pre-conceptual stage of residential buildings, where project information is mainly qualitative.

Due to the qualitative nature of project information at the pre-conception stage, a natural language processing model, DistilBERT (Distilled Bidirectional Encoder Representations from Transformers), was trained to predict the cost range of residential buildings at the pre-conception stage. The training and evaluation data included 63,899 building permit activity records (2021–2022) from the Victorian State Building Authority, Australia. The input data comprised the project description of each record, which included project location and basic material types (floor, frame, roofing, and external wall).

This research designed a novel tool for predicting the project budget based on preliminary project information. The model achieved 79% accuracy in classifying residential buildings into three cost_classes ($100,000-$300,000, $300,000-$500,000, $500,000-$1,200,000) and F1-scores of 0.85, 0.73, and 0.74, respectively. Additionally, the results show that the model learnt the contextual relationship between qualitative data like project location and cost.

The current model was developed using data from Victoria state in Australia; hence, it would not return relevant outcomes for other contexts. However, future studies can adopt the methods to develop similar models for their context.

This research is the first to leverage a deep learning model, DistilBERT, for cost estimation at the pre-conception stage using basic project information like location and material types. Therefore, the model would contribute to overcoming data limitations for cost estimation at the pre-conception stage. Residential building stakeholders, like clients, designers, and estimators, can use the model to forecast the project budget at the pre-conception stage to facilitate decision-making.

Project-based learning is one of the most effective methods of transferring academic knowledge and skills to real-world situations in higher education. However, its effectiveness is not much investigated focusing on the students' narrative. This study aims at evaluating the students' experience and perspective on adopting project-based learning in master by research and doctoral programmes for proactive skills development.

This study evaluates the self-reflection of 10 postgraduate students and their supervisor who have participated in developing a software tool for solar photovoltaics (PV) integrated building envelope design, management and the related education.

Findings reveal that the students have effectively improved their knowledge on the subject via collaborating with the industry, self-learning/observation, peer learning, problem-solving and teamwork. Dividing the project into student-led tasks has improved the decision-making and leadership skills, risks identification, planning and time management skills. The overall experience has (1) built up confidence in students, (2) enhanced their creativity and critical thinking and (3) improved their proactive skills and context knowledge.

A clear research gap can be seen in exploring the effectiveness of project-based learning for master by research and doctoral programmes, which mainly focus on extensive research. These programmes do not necessarily focus on developing students' proactive skills, which is the main requirement if they intend to work in the construction industry. This paper addresses the above research gap by demonstrating the effectiveness of project-based learning for developing the proactive skills in a research-intensive learning environment.

The complicated nature of megaprojects requires appropriate analysis of multiple stakeholders to achieve project objectives and to accommodate stakeholder interests. During the last two decades, many stakeholder theories and empirical studies have sprouted. Although previous studies have contributed to the development of stakeholder theory, it seems that these theoretical advances have not been fully adopted and acknowledged in practices, especially in megaprojects. The purpose of this paper is to explore the evolution of stakeholder analysis and engagement practices adopted in the Australian megaprojects over the last two decades.

Four mega construction projects are described and analysed in this study. Secondary data were first assembled in order to get general knowledge of each case. Interviews were conducted with the project directors. Project documents were collected from the project teams and reviewed. Wherever the project information was unclear, e-mails were sent to the directors and the team members to confirm the details.

Project teams have started to apply snowball rolling and stakeholder attribute assessment methods to analyse stakeholders. However, there is still a way to adopt the “network” analysis perspective because the project teams are reluctant to use complicated tools which need specialists’ assistance. The stakeholder engagement practices have evolved to an extent where the project teams monitor the dynamics of stakeholders’ requirements. Projects teams have identified the importance of continuity to manage stakeholders in these massive projects. However, a structured method selection mechanism for stakeholder engagement has not been developed.

This study will help academics to understand the adoption progress and status of stakeholder management methods.