Dominic Doe Ahiaga-Dagbui

Rethinking construction cost overruns: cognition, learning and estimation

Purpose – Drawing on mainstream arguments in the literature, the paper presents a coherent and holistic view on the causes of cost overruns, and the dynamics between cognitive dispositions, learning and estimation. A cost prediction model has also been developed using data mining for estimating final cost of projects. The paper aims to discuss these issues. Design/methodology/approach – A mixed-method approach was adopted: a qualitative exploration of the causes of cost overrun followed by an empirical development of a final cost model using artificial neural networks. Findings – A conceptual model to distinguish between the often conflated causes of underestimation and cost overruns on large publicly funded projects. The empirical model developed in this paper achieved an average absolute percentage error of 3.67 percent with 87 percent of the model predictions within a range of ±5 percent of the actual final cost. Practical implications – The model developed can be converted to a desktop package for qui...

Dealing with construction cost overruns using data mining

One of the main aims of any construction client is to procure a project within the limits of a predefined budget. However, most construction projects routinely overrun their cost estimates. Existing theories on construction cost overrun suggest a number of causes ranging from technical difficulties, optimism bias, managerial incompetence and strategic misrepresentation. However, much of the budgetary decision-making process in the early stages of a project is carried out in an environment of high uncertainty with little available information for accurate estimation. Using non-parametric bootstrapping and ensemble modelling in artificial neural networks, final project cost-forecasting models were developed with 1600 completed projects. This helped to extract information embedded in data on completed construction projects, in an attempt to address the problem of the dearth of information in the early stages of a project. It was found that 92% of the 100 validation predictions were within ±10% of the actual final cost of the project while 77% were within ±5% of actual final cost. This indicates the model’s ability to generalize satisfactorily when validated with new data. The models are being deployed within the operations of the industry partner involved in this research to help increase the reliability and accuracy of initial cost estimates.

Toward a Systemic View to Cost Overrun Causation in Infrastructure Projects: A Review and Implications for Research

Infrastructure cost overruns receive a significant amount of attention in the academic literature as well as the popular press. The methodological weaknesses in the dominant approaches adopted to explain cost overrun causation on infrastructure projects are explored in this article. A considerable amount of cost overrun research is superficial, replicative, and thus has stagnated the development of a robust theory to mitigate and contain the problem. Future research should move from single-cause identification and the traditional net-effect correlational analysis to a search for causal recipes through systems thinking and retrospective sensemaking to address the high-level interactions between multiple factors.

Costing and technological challenges of offshore oil and gas decommissioning in the U.K. North Sea

AbstractA significant number of offshore oil and gas installations in the U.K. North Sea have either exceeded or are approaching the end of their designed economic life. Operators and contractors a...

Planning for production in construction: controlling costs in major capital projects

Abstract There has been limited research that has examined how the public sector can guarantee their major capital projects are delivered within budget. A Strategic Asset Management Framework (SAMF) developed by the Western Australian State Government, was implemented that ensured their major capital projects were delivered within 5% of their budget. Interviews were conducted with stakeholders who had participated in the delivery of capital projects using the SAMF to understand how it had been used to successfully deliver projects. The interviews highlighted the importance of the SAMF in addressing optimism bias and strategic misrepresentation with the use of independent auditors. The research provides invaluable insights from practice that have been used to control and manage the capital expenditure of assets. Such knowledge is pivotal for ensuring strides are made forward to addressing the cost growth phenomenon that continues to plague major capital projects.

Mapping Relational Efficiency in Neuro-Fuzzy Hybrid Cost Models

Significant improvements are achievable in the accuracy of cost estimates if cost models adequately incorporate issues of flexibility and uncertainty. This study evaluates the relational efficiencies of the fuzzy composition operators “ the max-min and max-product, in establishing the final cost of water infrastructure projects. Cost and project data was collected on 1600 water infrastructure projects completed in Scotland between 2000 and 2011. Neural network is first used to develop relative weightings of relevant cost predictors. These were then standardized into fuzzy sets to establish a consistent effect of each variable on the overall target cost. The strength and degree of relationship of the normalized cost predictor weightings and the fuzzified project attributes were combined using the max-min and max-product composition operators to obtain project cost predictions. The predictions from the two composition operators are compared with the actual cost figures. Results show comparable performance in the efficiency of the composition operators. Based on statistical correlations, the max-product composition operator achieved on average a deviation of 1.71% while the max-min composition had an average deviation of 1.86%. Improvements in the relational efficiency of neuro-fuzzy hybrid cost models could assist in developing a robust framework for realistic cost targets on construction projects.