Architecture and Built Environment

Purpose To determine how well the facilities are performing in order to support the organizational goals and user requirement it is very vital to conduct regular building evaluations which necessitates the important usage of BPE where it provides the current status quo of building. Presently, there are various BPE approaches developed around the world, and as evidenced there are no in-depth studies on BPE in tropical countries to evaluate the BP. However, adapting these approaches might not necessarily be applicable in the context of tropical countries due to geographical, climatic, cultural and other differences. This has been identified as the gap in this research and aimed to formulate a holistic TBPE scoring framework, for the assessment of performance of buildings. Design/methodology/approach The study was quantitative in nature. Firstly, a comprehensive literature survey was carried out. This was followed by an expert survey to sieve out the most significant BP factors identif...

With the global energy consumption reaching unsustainable levels, the need for regulating energy consumptions has been emphasised. Hence a variety of methods are followed in different countries to minimise the impacts of embodied energy (EE) and operational energy (OE) in buildings. Considering either EE or OE in its individuality is not a pragmatic approach and it is important to consider means of reducing both EE and OE in parallel. The design stage was identified as the most suitable stage for integrating energy efficiency measures, since most crucial project decisions are taken at this stage. Although a multitude of research has been conducted on EE and OE individually, there seems a lack of research that focuses on both these aspects together. The extensive literature review was followed by 5 preliminary interviews with subject matter experts and then semi structured interviews with 12 experts were conducted. It was revealed that determining strategies for achieving simultaneou...

Most construction projects overrun their budgets. Among the myriad of explanations giving for construction cost overruns is the lack of required information upon which to base accurate estimation. Much of the financial decisions made at the time of decision to build is thus made in an environment of uncertainty and oftentimes, guess work. In this paper, data mining is presented as a key business tool to transform existing data into key decision support systems to increase estimate reliability and accuracy within the construction industry. Using 1600 water infrastructure projects completed between 2004 and 2012 within the UK, cost predictive models were developed using a combination of data mining techniques such as factor analysis, optimal binning and scree tests. These were combined with the learning and generalising capabilities of artificial neural network to develop the final cost models. The best model achieved an average absolute percentage error of 3.67% with 87% of the validation predictions falling within an error range of ±5%. The models are now being deployed for use within the operations of the industry partner to provide real feedback for model improvement.

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 quick cost predictions and the generation of various alternative solutions for a construction project in a sort of what-if analysis for the purposes of comparison. The use of the model could also greatly reduce the
time and resources spent on estimation.

Originality/value – A thorough discussion on the dynamics between cognitive dispositions, learning and cost estimation has been presented. It also presents a conceptual model for understanding two often conflated issues of cost overrun and under-estimation.

Producing reasonably accurate cost estimates at the planning stage of a project important for the subsequent success of the project. The estimator has to be able to make judgement on the cost influence of a number of factors including site conditions, procurement, risks, price changes, likely scope changes or type of contract. This can shroud the estimation process in uncertainty, which has often resulted in project cost overruns. The knowledge acquisition, generalisation and forecasting capabilities of Artificial Neural Networks (ANN) are explored in this pilot study to build final cost estimation models that incorporate the cost effect of some of the factors mentioned above. Data was collected on ninety-eight water-related construction projects completed in Scotland between 2007-2011. Separate cost models were developed for normalised target cost and log of target costs. Variable transformation and weight decay regularisation were then explored to improve the final model’s performance. As a prototype of a wider research, the final model’s performance was very satisfactory, demonstrating ANN ability to capture the interactions between the predictor variables and final cost. Ten input variables, all readily available or measurable at the planning stages for the project, were used within a Multilayer Perceptron Architecture and a Quasi-Newton training algorithm.

Successfully managing large construction projects within defined budget and time constraints has always been a major challenge largely because crucial decisions about the project's ultimate fate have to be made within an environment of significant uncertainty at the beginning of the project. It is not surprising that cost and time overruns are commonplace on construction projects. Existing literature often suggests economical, technical, political or managerial roots to this phenomenon. A less explored possible cause within construction management framework is the escalation of commitment to a course of action. This theory, grounded in social psychology and organisation behaviour, suggests the tendency of people and organisations to become 'locked-in' and 'entrapped' in a particular course of action and thereby 'throw good money after bad' to make the venture succeed. This defies conventional rationality behind subjective expected utility theory. Through a critical analysis of the literature, we identify different frequently cited enablers of escalation of commitment.  Using a hindsight constructivist approach, we then demonstrate references to some of these enablers on the Scottish Parliament project. We found strong evidence in support of possible strategic misrepresentation, confirmation bias, self-justification and optimism bias. We highlight the importance of setting realistic time and budget constraints to circumvent escalation and make several recommendations to attenuate unwarranted escalation of commitment, including the use of  an objective outsider to evaluate responses to disconfirming information and the structuring of incentive systems that do not punish for inconsistency in order to curb the effects of  self-justification and reputation management.

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 in this experimental research. This helped to extract information embedded in data on completed construction projects, in an attempt to address the problem of dearth of information in the early stages of a project.  92% of the 100 validation predictions were within ±10% of the actual final cost of the project whiles 77% were within ±5% of actual final cost. This indicates the model's ability to generalise 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.

Construction projects routinely overrun their cost estimates. A plethora of studies have thus been dedicated to investigating the root causes, sizes, distribution and nature of overruns. The causes range from a poor understanding of the impact of systemicity and complexity projects, unrealistic cost targets and misguided trade-offs between project scope, time and cost to suspicions of foul play and even corruption. In spite of the vast attention dedicated to the problem of cost overrun, there has been limited evidence to support the claim that the size or occurrence of cost overruns is reducing in practice. A review of the literature reveals that it may not be an exaggeration to claim that the bulk of our current cost overrun research may largely be inadequate and deficient to deal with the complexity posed by construction projects. This paper provides a critique of current cost overrun research and suggests that the adoption of systems thinking is required to better understand the nature of cost overruns. We explore some of the embedded methodological weaknesses in the approaches adopted in a majority of cost overrun research, particularly the lack of systems thinking and demonstrable causality. We reach the following conclusion - cost overrun research has largely stagnated in the refinement and advancement of the knowledge area. It has largely been superficial and replicative. A significant paradigm and methodological shift may be required to address this perennial and complex problem faced in construction project delivery.

Understanding the cause of cost overruns in transportation infrastructure projects has been a topic that has received considerable attention from academics and the popular press. Despite studies providing the essential building blocks and frameworks for cost overrun mitigation and containment, the problem still remains a pervasive issue for Governments worldwide. The interdependency that exists between 'causes' that lead to cost overruns materialising have largely been ignored when considering the likelihood and impact of their occurrence. The vast majority of the cost overrun literature has tended to adopt a deterministic approach in examining the occurrence of the phenomenon; in this paper a shift towards the adoption of pluralistic probabilistic approach to cost overrun causation is proposed. The establishment of probabilistic theory incorporates the ability to consider the interdependencies of causes so to provide Governments with a holistic understanding of the uncertainties and risks that may derail the delivery and increase the cost of transportation infrastructure projects. This will further assist in the design of effective mitigation and containment strategies that will ensure future transportation infrastructure projects meet their expected costs as well as the need of taxpayers.

The construction of mass housing has become a very important policy in the government of Ghana’s efforts at reducing the country’s housing deficit. Unlike traditional one-off building projects, Mass Housing Projects (MHPs) typically present a plethora of asynchronous and synchronous communication among project team members. The efficiency and effectiveness of these communications can be very critical to the successful delivery of the project within cost and time limits. The uniqueness and overall importance of MHPs also presents project managers with communication management challenges which are a clear departure from those associated with traditional one-off construction projects. In this paper, project team communication challenges associated with MHPs in Ghana are critically explored. This would form the basis for recommending practical measures and avenues to improve quality of project team communication on MHPs. By adopting a survey approach, data was collected via structured q...

The methodology, analysis, and the unfounded conclusions presented in the paper “Underestimating costs in public works projects: error or lie?” by Flyvbjerg, Holm, and Buhl (2002), published in the Journal of the American Planning Association are critically questioned. Flyvbjerg, Holm, and Buhl attribute the cause of cost underestimation in transport infrastructure projects to delusion (optimism bias) and deception (strategic misrepresentation). The bifurcation of the cost underestimation problem into error or lie presents a false dichotomy - an either/or choice that is invalid when juxtaposed with the real-world nature of procuring large infrastructure assets. Put simply, the conclusions presented by Flyvbjerg, Holm, and Buhl are akin to being fake news. Unfortunately, the persistent reverberation of these convenient narratives and factoids in both academia and media has led to these explanations becoming an accepted norm. In this paper, the claims made by Flyvbjerg, Holm, and Buhl are debunked. A call is made for policy-makers to embrace and utilize evidence-based research so that informed decisions about capital cost estimates and potential risks can be better ascertained at the front-end of major transport infrastructure projects.

The expected final budgeted costs of infrastructure assets are often exceeded during project delivery. Being able to determine the likelihood of changes to the final budget can enable clients to implement strategies to manage and control costs during construction. To understand the changing nature of costs, the cost profiles of 1,093 water infrastructure projects that were delivered by a water utility company are examined. Cost overruns were experienced in 656 projects. Only 1 project was delivered on budget with the remaining 436 being completed under the ‘Final Budget Approval’. A mean cost overrun and underrun of +19.97% and -32%, were found, respectively. The ‘best fit’ distribution for cost overruns and underruns for determining their probability of occurrence were calculated. The research moves beyond examining the cost performance of heterogeneous datasets that have dominated previous studies to the use of a homogeneous sample, which enables more reliable contingency forecasts to be determined.