Ajibade A . Aibinu
Ajibade is currently an Associate Professor is Quantity Surveying (Cost Management) and Construction Economics at the Melbourne School of Design, Faculty of Architecture Building and Planning, The University of Melbourne where he was the Assistant Dean, Research Training (2013 – 2017). Prior to joining the University of Melbourne in 2006, he was a research scholar at the National University of Singapore. He has published over 80 research articles, and book and has spoken at numerous conferences. Ajibade ’s research interest cut across the built environment project and asset management from design, construction to operations. He founded the Intelligent Cost Manager (ICM), an AI-based cost management solution that leverages deep learning and predictive modelling to generate cost estimates with greater accuracy using historical data. His work uses design science research methodology grounded in knowledge management theory, innovation adoption theories, and theories in human-computer interaction. In 2017, a paper co-authored and published won the Outstanding Paper Award in the Emerald Literati Network Awards for Excellence. Ajibade is the Lead Guest Editor of, Built Environment Project and Asset Management [BEPAM] journal special issue on Data analytics and big data in construction and asset management. He won the Melbourne School of Design Teaching Excellence Award in 2012. He is currently a Member, Australian Institute of Quantity Surveyors (MAIQS) and Associate, Chartered Institute of Arbitrators (2008-2013).
He holds BSc and MSc in Quantity Surveying from the Obafemi Awolowo University, Ile Ife, Nigeria before he proceeded to Singapore for his PhD in Construction Project Management from the National University of Singapore.
Teaching/Research Aspirations
‘To create a transforming learning experience for students by teaching in a passionate and engaging manner and as much as possible promote interaction and foster students' critical thinking and independent construction of new knowledge rooted in academic research and industry practice so that they are well prepared to become leaders in the construction industry’
Phone: +61 3 8344 6811
Address: Melbourne Schoool of Design
Faculty of Architecture, Building & planning, Room 317
The University of Melbourne,
Victoria 3010
Australia
https://findanexpert.unimelb.edu.au/profile/152800-ajibade-aibinu
He holds BSc and MSc in Quantity Surveying from the Obafemi Awolowo University, Ile Ife, Nigeria before he proceeded to Singapore for his PhD in Construction Project Management from the National University of Singapore.
Teaching/Research Aspirations
‘To create a transforming learning experience for students by teaching in a passionate and engaging manner and as much as possible promote interaction and foster students' critical thinking and independent construction of new knowledge rooted in academic research and industry practice so that they are well prepared to become leaders in the construction industry’
Phone: +61 3 8344 6811
Address: Melbourne Schoool of Design
Faculty of Architecture, Building & planning, Room 317
The University of Melbourne,
Victoria 3010
Australia
https://findanexpert.unimelb.edu.au/profile/152800-ajibade-aibinu
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Books by Ajibade A . Aibinu
Penny Brooker and Suzanne Wilkinson (eds.), Mediation in the Construction Industry: An International Perspectives. CIB/Taylor and Francis (Spoon Press): UK.
ISBN: 978-0-415-47175-6
Papers by Ajibade A . Aibinu
This research investigates the involvement of project’s stakeholders when measuring the level of project scope definition completeness. More specifically, this paper aims to develop a procedure and an evaluation tool to measure the level of project scope definition completeness based on stakeholders’ inputs. The procedure accounts for the differences in stakeholders’ importance to each project scope definition element. The proposed procedure has been validated using two case studies within the context of public building projects.
The proposed procedure could improve project stakeholders’ sense of procedural justice and fairness in project decision-making. It should also improve project scope definition process in practice, which could in turn increase the chance of having a better project outcome, successful project and satisfied stakeholders. The procedure can serve as a first step towards achieving an
an economic stimulus package. In the context of a global financial crisis, the Government called for ‘shovel ready’ projects
requiring state education departments to develop template designs to speed the delivery process. Three years later, new
facilities have been completed in over 1100 government schools in Victoria (DEECD, 2012). This article outlines research by
an interdisciplinary team to track the early occupation of a template design used inVictoria. The design template was unusual: it
enabled schools to continue using traditional classroom teaching or to slide open walls to form larger neighbourhoods suitable
for team teaching. Our research linked different methodological frameworks to undertake post-occupancy evaluation (POE)
of the new spaces. POE strategies are often driven by construction and project management perspectives rather than focus
on organizational issues and user behaviour.
promote or hinder cooperative behaviour in the construction project delivery process. Organizational justice, or at least people’s perceptions of it, influenced 38% of conflict intensity levels, and altered 46% of contractors’ tendencies to dispute. Perceptions about the quantum of claims approved (favourability of the outcome and the perceived fairness of the outcome) influenced the levels of conflict and dispute. However, the way people are treated (quality of treatment) and the way claims are administered (quality of decision-making) have the largest impact on the model developed. Cooperative behaviour can be promoted on projects by managing construction claims in a proactive manner and by proper implementation of the claims mechanism.
rule of thumb for estimating contingency is subjective - based on experience and expert judgment, and are often inadequate. In the research reported in this paper, we propose that learning algorithms trained to use the known characteristic of completed projects could allow quantitative and objective estimation of the inaccuracies in pretender building cost estimates of new projects. The study assumes that the accuracy in the initial estimate (bias) of a completed project is the difference between the actual project completion costs minus the pre-tender cost forecast expressed as a percentage of the actual project completion costs. A three- layer ANN model of feed- forward type with one output node was constructed and trained to generalise nine characteristics of 100 completed projects and the cost data from those projects. The nine input variables of the model are project size (measured by number of storeys and gross floor area), principal structural material, procurement route, project type, location, sector, estimating method, and estimated sum. Estimate accuracy (bias) was used as the output variable. The prediction power stands at 73% correlation coefficient, 3% of Mean Absolute Error and 0.2% Mean Squared Error. It was found that in more than 73% of the test cases the predicted estimate bias did not differ by more than 8.2% from the expected
(Maximum Absolute Error). This means that amount of estimate bias predicted by the ANN are similar to what actually occurred. The trained ANN model can be used as a decision making tool by cost advisors when forecasting building cost at the pretender stage. The model can be queried with the characteristics of a new project in order to quickly predict the error in the estimate of the new project. The predicted error represents the additional
contingency reserve that must be set aside for the project in order to cater for possible cost overruns. The model can also be extended to forecast the likely cost of a project.