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Dealing with construction cost overruns using data mining

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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 1,600 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.
Content may be subject to copyright.
(Accepted for publication in Taylor and Francis journal, Construction Management
Economics, 9th June 2014)
Authors: Dominic D Ahiaga-Dagbui
School of Engineering,
University of Edinburgh,
(corresponding author)
Simon D Smith
School of Engineering,
University of Edinburgh,
This is an author’s copy of the accepted manuscript.
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.
Keywords: artificial neural networks, bootstrapping, cost overrun, data mining, ensemble
In a construction project, the main obligations of a project team towards their client are
usually reduced to concerns around functional requirements, specific quality, and delivery
within an acceptable budget and time-frame. Usually for most clients, the cost aspect of these
requirement seem to rank highest. Thus, the estimates prepared at the initial stages of a
project can play several important roles: they can form the basis of cost-benefit analysis, for
selection of potential delivery partners, to support a to-build-or-not-to-build decision, and
very often as a benchmark for future performance measure. As suggested by Kirkham and
Brandon (2007), therefore, effective cost estimation must relate the design of the constructed
facilities to their cost, so that while taking full account of quality, risks, likely scope changes,
utility and appearance, the cost of a project is planned to be within the economic limits of
expenditure. This stage in a project life-cycle is particularly crucial as decisions made during
the early stages of the development process carry far-more reaching economic consequences
than the relatively limited decisions which can be made later in the process. Effective cost
estimation is, therefore, so vital, it can seal a project’s financial fate, Nicolas (2004) notes.
However, in spite of the importance of cost estimation, it is undeniably neither simple nor
straightforward because of the lack of information in the early stages of the project, Hegazy
(2002) observes. Many projects consistently fail to meet initially set cost limits due to a
number of causes ranging from the inability to accurately identify and quantify risk (Akintoye
2000), error in estimation (Jennings 2012), design changes and scope creep (Odeck 2004,
Love et al. 2012) and even suspicions of foul-play and corruption (Wachs 1990, Flyvbjerg et
al. 2002).
Developments in the business landscape, however, suggest a growing recognition of
information as a key competitive tool. A vast amount of data is continuously generated by
construction business transactions. As per due diligence or contractual requirements, most
construction firms maintain copious information on each project undertaken. The amount of
data generated by these firms presents both a challenge and opportunity: a challenge to
traditional methods of data analysis since the data are often complex, and ususally,
voluminous. On the other hand, construction firms stand a chance of gaining competitive edge
and performance improvement by making their data work for them using detailed data
mining. Fayyad et al. (1996) noted that the real value of storing data lies in the ability to
exploit useful trends and patterns in the data to meet business or operational goals as well as
for decision support and policy making. Advances in the fields of data warehousing, artificial
intelligence, statistics, visualisation techniques and machine learning now make it possible for
data to be transformed into valuable asset by automating laborious but rewarding knowledge
discovery in databases.
Data mining, simply described here as the analytical process of knowledge discovery in large
databases, has found extensively application in industries such as business (Cf. Apte et al.
2002) and medicine (Cf. Koh and Tan 2005). However, discussions with a number of
construction companies during this research suggest that very few take advantage of the data
available to them to develop business decision-support tools. At best, their analysis is usually
limited to basic sample statistics of averages or standard deviations. Against this backdrop,
we collaborated with a major UK water infrastructure provider to investigate the use of data
mining techniques to develop cost models that can be applied during the early estimation
stages for more reliable cost forecasting. As already pointed out, a lack of information for
reliable estimation has been identified as one of the main causes of cost growth in
construction. It is hoped that data mining might help to convert historical data on projects into
decision-support systems, to partly address the problem of insufficient information for reliable
estimation at early stages of a project. The problem of cost growth and its causes are
examined in the next section of the paper, followed by an overview of data mining and its
applications. The data mining methodology was then applied to the problem of cost
estimation in the construction industry using Artificial Neural Networks (ANN). Some
practical implications of the research have been identified in the conclusions along with some
possible barriers to effective data mining within the construction industry.
Chan and Chan (2004) conducted a critical analysis on existing literature on construction
benchmarking and proposed a framework of both qualitative and quantitative descriptors to
evaluate the success of a construction project. They validated their framework using three
hospital projects and noted that cost performance on a construction project remains one of the
main measures of success even though there were other emerging qualitative measures like
health and safety and environmental performance. We have previously investigated cost
overruns on construction projects as part of a wider research into the potential use of artificial
neural networks for construction cost estimation (Ahiaga-Dagbui and Smith 2012). We
attempted to model final cost using non-traditional cost factors such as project location,
access to site and procurement method. It became obvious that estimating the final cost of
projects can be extremely difficult due to the complex web of cost influencing factors that
need to be considered. For a thorough and reliable estimate of final cost, the estimator has to
be able to take into consideration factors such as the type of project, likely design and scope
changes, risk and uncertainty, effect of policy and regulatory conditions, duration of project,
type of client, ground conditions or tendering method. Trying to work out the influence of
most of these variables at the inception stage of a project can be an exhaustive task, if not all-
together futile. Ignoring most of these factors also creates a recipe for possible cost growth,
disputes, law suits and even project termination in some cases. Jennings (2012) employed a
longitudinal 'process tracking' approach to examine the dynamics between risk, optimism and
uncertainty in construction and how these interact with the phenomenon of cost overruns
using a case study of the 2012 London Olympic games. Jennings found that a high level of
uncertainty surrounds the cost estimation exercise especially in the initial stages of the
project, thus making it difficult to produce reliable cost estimates. What is then resorted to, in
most cases, is the use of some arbitrary percentages, the so-called contingency funds, which
unfortunately has mostly failed to keep construction projects within budget.
The Auditor General of Western Australia assessed the management and performance of 20
capital intensive non-residential projects including sports venues, schools and hospitals,
undertaken within the State. The expected cost of all the projects at the time was A$6.157
billion, a staggering $3.275 billion (114%) more than the total original approved budget
estimates. 15 of the 20 projects were expected to exceed their original approved budgets, of
which four were expected to exceed their budgets by more than 200% (Auditor General of
Western Australia 2012).
The 2012 London Olympics bid was awarded at circa £2.4 billion in 2005. This was adjusted
to about £9.3 billion in 2007 after significant scope changes. The project was eventually
completed at £8.9 billion in 2010 (Cf. National Audit Office 2012). The City of Boston’s
Central Artery project (popularly referred to as the Big Dig) was to cost US$2.6 billion but
was completed at US$14.8 billion and 7 years late in 2006 (Gelinas 2007). The UK
Government commissioned report in 1998 on construction industry performance indicated
that over 50% of projects overspent their budget (Egan 1998). A similar report around the
same time in the US suggested that about 77% of projects exceed their budget, sometimes to
the tune of over 200% (General Accounting Office 1997). In more recent years, Flyvbjerg et
al. (2002) sampled 258 infrastructure projects worth US$90 billion from 20 different
countries and found that 90% of the projects experienced budget escalation and that
infrastructure projects in particular have an 86% likelihood of exceeding their initial
estimates. Alex et al. (2010) report up to 60% discrepancy between actual and estimated costs
of over the 800 water and sewer projects examined in their research. Flyvbjerg et al. (2004)
thus concluded that little learning seemed to be taking place within the industry over time.
Sources of overrun
Cost overrun in the construction industry has been attributed to a number of sources including
technical error in design or estimation, managerial incompetence, risk and uncertainty,
suspicions of foul play, deception and delusion, and even corruption. Akintoye and MacLeod
(1997) conducted a questionnaire survey of general contractors and project managers in the
UK construction industry to ascertain their perception of risk and uncertainty as well as their
use of various risk management techniques. They concluded that risk management practice
was largely experience and judgement based and that formal risk management techniques
such as Monte Carlo simulation or stochastic dominance were seldom used due to doubts on
their suitability and lack of knowledge and understanding of these methods. Hitherto, the
industry still seems to struggle to deal with identifying and quantifying the impact of risk
events. This may probably be due to the nature of the industry: it is fragmented, complex,
each project spans several years, is constructed in an environment open to the weather
inclement and has many different parties with varying business interests. Flanagan and
Norman (1993) suggest that the task of risk management in most cases is so poorly
performed, that far too much risk is passively retained, an assertion supported by Jennings'
(2012) recent case study of the possible sources of cost growth on the 2012 London Olympic
Flyvbjerg et al. (2002), as well as Wach (1989, 1990) point to optimism bias and strategic
misrepresentation, or delusion and deception in other words, as possible causes of cost growth
particularly on large publicly funded projects. Flyvbjerg et al (2002) conducted a desk study
analysis of the cost performance of 258 transportation projects worth US$90 billion and
categorised the sources of cost overruns on construction projects into four groups: technical
(error), psychological, economical and political. They concluded that cost escalation could not
be adequately explained by estimation error, but more likely by strategic misrepresentation:
an intentional attempt to mislead. They observed that 9 out 10 of the projects experienced
significant cost escalation over their construction period and that there was evidence of a
systematic bias in the cost estimates as the overruns experienced did not appear to be
randomly distributed. Flyvbjerg et al (2002 pg. 279) controversially concluded that the cost
estimates used to decide whether projects should be given the go-ahead were 'highly and
systematically misleading', strongly suggesting foul play by project promoters.
Further developments of the strategic misrepresentation perspective by Flyvbjerg led to
theories based on optimism bias, after Weinstein (1980). Optimism bias can be explained as
the cognitive disposition to evaluate possible negative future events in a fairer light than
suggested by inference from the base rates. Flyvbjerg (2008) draws on this concept and
suggested that decision-making in policy and infrastructure planning is flawed by the
planning fallacy that we know, or at least are in control of all possible chain of events from
project inception to completion, thereby leading to unjustifiable confidence in the prospects of
the project and unrealistic estimates. While strategic misrepresentation is often intentional,
according to Flyvbjerg (2008), optimism bias is not. Flyvbjerg makes this distinction between
the two concepts with the terms 'deception' and 'delusion' respectively. It is plausible to
reckon how strategic misrepresentation and optimism bias might work in tandem with
business competition embedded in the lowest-bidder culture to often create an unrealistic low
cost target of projects at the pre-construction phase of projects.
Another school of thought on cost overruns, referred to here as the Evolution Theorists,
include Love et al (2012) as well as Gil and Ludrigan (2012). They argue that projects
essentially evolve significantly between conception and completion so that it might be
misleading in most cases to make a direct comparison between the costs at start and end of the
project. Their thesis statement is straightforward: projects change, and when they do, they
often come with increasing costs. Love et al. (2012 pg. 560) provide a counter-perspective to
the delusion and deception perspective on cost overruns, instead suggesting that the industry
'move beyond strategic misrepresentation and optimism bias' to embrace a more holistic
understanding of the phenomenon that includes some level of the process and the social
construct. They introduce the concept of 'pathogens' for example, the many events and actions
that could not be accounted for at the initial stages of the project that eventually add-on to
expected cost as the main drivers of cost growth. They further argue that Flyvbjerg's analyses
are maybe too simplistic and not generalisable to all projects undertaken within the industry.
Their argument would seem sustainable, especially on small, privately funded projects that do
not have strong political or public interest. Besides, it is difficult to draw valid distinctions,
along a continuum of motivation, from reasonable and justifiable optimism, through over-
confidence and delusion, culpable error, to deliberate deceit using just statistical analysis, the
method adopted in Flyvbjerg’s works.
Love et al. (2005) also conducted a questionnaire survey of 161 construction professionals in
the Australian construction industry and found that rework was one of the main contributors
to escalation of cost. The main sources of rework as found in their work are ineffective use of
information technology, staff turnover/allocation to other projects, incomplete design at the
time of tender, insufficient time to prepare contract documentation and poor coordination
between design team members. This conclusion is similar to that reached by Bordat et al
(2004) who found that the "dominant" source of cost overrun was change order due mainly to
"errors and omissions" in design. In a more recent research, Love et al (2014) challenged the
strategic misrepresentation and optimism bias perspective by Flyvbjerg (2008) as lacking in
verifiable causality, and therefore limited in their application.
Ahiaga-Dagbui and Smith (2014) provide a more detailed discussion on other possible causes
of overruns including technical and managerial difficulties and poor estimation, as well as the
dynamics between cost growth and cognitive dispositions such as Prospect Theory
(Kahneman and Tversky 1979) and Kruger-Dunning effects (Kruger and Dunning 1999).
Measuring Overruns
It may be important to note here that much of the current literature and media furore on cost
overrun seem to over-simplify its rather complex causes. As already noted, most construction
projects, especially publicly funded capital-intensive projects tend to go through a long
gestation period after project conception during which many changes to scope and
accompanying costs occur. Sometimes the initial scheme bears little resemblance to the
defined project, as was the case of the New Children Hospital in Western Australia (Auditor
General of Western Australia 2012). The initially approved budget for the hospital was A$207
million. The scope at this stage was to relocate the Princess Margaret Hospital to the Royal
Perth Hospital. However, this scope completely changed during project definition to the
construction of a totally new Medical Center at A$962 million, a cost increase of A$755 if
taken on cursory examination. The Holyrood Project in Edinburgh also experienced a similar
significant scope change, and thereby the astonishing cost growth recorded (see Audit
Scotland 2000, 2004). It seems erroneous, therefore, to make a direct comparison between the
initial estimate A and its final completion cost B: the two schemes are usually very different.
More robust explanations of growth perhaps need to factor-in process and product, as well as
sources of changes to scope. Flyvbjerg's works make a direct comparison between costs A
and B, and wherever B>A, overruns are reported. It might be simplistic though, as pointed out
by Love et al. (2012, 2014), but probably justifiable as estimate A is usually the estimate used
to get project approval when publicly funded projects are being appraised. As it is often
practically difficult to discontinue a project once considerable amount of money has already
been spent to get it started, it may thus be crucial for the industry to find more effective ways
of project approval that better deals with underestimation of true cost and the setting of
unrealistic cost targets.
Going forward: Estimating final cost
Alex et al (2010) reviewed the cost performance on more than 800 construction projects of
the Canada's Drainage and Maintenance Department and observed a discrepancy of up to 60%
between estimated and actual final cost of projects completed between 1999 and 2004. They
partly attributed this problem to the fact that the Department's estimation process was heavily
experienced based, relying largely on professional judgement, just as observed by Akintoye
and MacLeod (1997). A potential downside of experienced-based estimation is the difficulty
in thoroughly evaluating the complex relationships between the many cost influencing
variables already identified in this paper, or its inability to quickly generate different cost
alternatives in a sort of what-if analysis. Furthermore, as noted by Okmen and Öztas (2010) in
their research on cost analysis within an environment of uncertainty, traditional cost
estimation i.e. the estimation of the cost of labour, equipment and materials, and making
allowance for profits and overheads for individual construction items, is deterministic by
nature. It, therefore, largely neglects and poorly deals with uncertainties and their correlation
effects on cost, thereby deemed inadequate in reaching a reliable and realistic final cost. As an
alternative to traditional estimation approaches, data mining, using the learning and
generalisation algorithms within artificial neural networks in combination with statistical
bootstrapping and ensemble modelling is used to develop final cost models in this paper. The
aim here is an attempt at circumventing the problems posed by uncertainty and lack of
information in estimation in the early stages of a project.
Data mining, otherwise referred to as Knowledge Discovery in Databases (KDD), is an
analytic process for exploring large amounts of data in search of consistent patterns,
correlations and/or systematic relationships between variables, and to then validate the
findings by applying the detected patterns to new subsets of data (StatSoft Inc 2008). Data
mining attempts to scour databases to discover hidden patterns and relationships in order to
find predictive information for business improvement. Questions that traditionally required
extensive hands-on analysis, experts and time, can potentially be quickly answered from a
firm’s existing data.
Goldberg and Senator (1998) report the use of pattern discovery techniques by the Financial
Crimes Enforcement Network (FinCEN) of the United States Department of Treasury since
1993 to detect potential money laundering and fraudulent transactions from the analysis of
about 200,000 large cash transactions per week. Using input factors such as age, housing, and
job title and account balance, Huang et al. (2007) developed a support vector machine credit
scoring model to assess loan applicant's credit worthiness in an attempt to limit a financing
firm's exposure towards default. Hoffman et al. (1997) have also explored the use of data
visualization and mining techniques for DNA sequencing in the area of cell biology. Ngai et
al. (2009) provide a comprehensive review of data mining applications in customer
relationship management area, classifying these applications into four groups of customer
identification, attraction, retention and development. One-to-one marketing and loyalty
programs targeted towards customer retention seem to receive the most attention from
Although data mining is yet to find extensive application in practice within the construction
industry, construction management researchers have been investigating its applicability to
different problem areas. Using some of the concepts of data mining and the theory of
inventive problem solving, Zhang et al. (2009) developed a value engineering knowledge
management system (VE-KMS) that collects, retains and re-uses knowledge from previous
value engineering exercises in an attempt to streamline future exercises, making them more
systematic, organised and problem-focussed. Cheng et al. (2012) also developed EFSIMT, a
hybrid fuzzy logic, support vector machines and genetic algorithm inference model to predict
the compressive strength of high performance concrete using input factors such as the
aggregate ratio, additives and working conditions. This kind of model allows for a more
reliable prediction of the strength of a particular mix for design and quality control purposes
as concrete strength is generally affected by a lot of factors. There is generally a higher rate of
occupational injuries in the construction industry than industries like manufacturing for
example (Cf. Larsson and Field 2002). This might possibly be because of the dynamic and
hazardous environment of a typical construction site. Liao and Perng (2008) thus employ the
use of association rule based data mining to identify the characteristics of occupational
injuries reported between 1999 and 2004 in the Taiwan construction industry. Wet-weather
related injuries and fatalities were particularly significant in their study.
Data Mining Process
Data mining normally follows a generic process of business and data understanding, data
preparation, modelling proper, evaluation of models, and deployment. It starts with the
selection of relevant data from a data warehouse that contains information on organisation
and business transactions of the firm. The selected data set is then pre-processed before actual
data mining commences. The pre-processing stage ensures that the data are structured and
presented to the model in the most suitable way as well as offer the modeller the chance to get
to know the data thoroughly. Pre-processing typically involves steps such as removing of
duplicate entries, sub-sampling, clustering, transformation, de-noising, normalisation or
feature extraction.
The next stage involves the actual modelling, where one or a combination of data mining
techniques is applied to scour down the dataset to extract useful knowledge. This process can
sometimes be an elaborate process involving the use of competitive evaluation of different
models and approaches and deciding on the best model by some sort of bagging system
(StatSoft Inc. 2011). Table 1 provides a framework for selecting a particular data mining
technique. The type of modelling technique adopted depends on a number of factors,
including the aim of the modelling exercise, the predictive performance required and the type
of data available. Each modelling technique can also be evaluated in terms of its
characteristics. For example, regarding 'interpretability', while regression models generate an
equation whose physical properties can be easily interpreted in terms of the variables used in
explaining the phenomenon under study (Hair et al. 1998). Neural networks, on the other
hand, do not produce any equation and have thus been derided as 'black-boxes' by some
researchers including Sarle (1994). However, their power and ability to model complex non-
linear relationships between predictors make them particularly desirable for hard-to-learn
problems and where a priori judgements about variable relationships cannot be justified
(Adeli 2001).
Table 1- Framework for selecting a data mining technique
Data mining
Data mining
Data mining
Pattern discovery
Support Vector
Machine (SVM)
Self-Organising maps
Genetic algorithm, etc
Ease of
The results from the data mining stage are then evaluated and presented into some meaningful
form to aid business decision-making. The knowledge generated is then validated by
deploying the model in a real life situation to test the model’s efficacy.
The data mining process described in the previous section of this paper is now applied to cost
estimation within a partnering major water infrastructure client in the UK. The aim here is in
two folds: to develop decision-support systems from existing data to complement the existing
estimation process within our collaborating organisation and also to investigate ways of
circumventing the problem of lack of information for reliable estimation at the early stages of
a project. Many crucial business decisions have to be made at this stage including tender
evaluations, contract award, project feasibility or securing loans to finance the project. Our
collaborating organisation typically has three stage of estimation before inviting bids from
contractors. The third stage estimate, Gate Three, is usually based on about 50-60%
completed scope design and is used for evaluation of tenders after which detailed design is
carried out by the selected contractor in a sort of design and build contract framework. The
estimates produced by the models developed in this paper thus allows the organisation to
forecast its total likely commitment before tendering and before definitive estimates are
The data collection process involved an initial shadowing of the tendering and estimation
procedure within the organisation. We were thus allowed to be quasi members of the
tendering team of the company on some of its projects to observe how the estimates were
produced. It was also an opportunity to gain a first-hand understanding of how the data to be
used of the modelling was generated and what different variables meant. The initial dataset
contained over 5000 projects completed between 2000 and 2012. The scope of these projects
varied from construction of major water treatment plants to minor repairs and upgrade.
Project values ranged from a mere £1000 to £30 million and durations from 3 months to 5
years. The initial analysis involved drilling down into the database to find what might be
useful in modelling final cost. To ensure some level of homogeneity in the data, K-means
cluster analysis was used to create clusters of project cases based on duration and cost. V-fold
cross-validation with Mahalanobis distance was used to search for optimum number of
clusters between 2 and 10 clusters. This distance measure was preferred to the popular square
Euclidean distance because it helps account for the variance of each variable as well as the
covariance between cost and duration of the project cases. The cases to be used in the
modelling also had to be without significant missing data and somewhat representative of the
entire dataset. One of the clusters containing about 1600 projects completed between 2004
and 2012 was used for the models reported in this paper. One hundred of these project cases
were selected using stratified random sampling with cost as the strata variable to be used for
independent second stage validation of the final models. Stratified random sampling was used
because this would hopefully allow for the selection of cases that are representative of the
entire range of possible cases within the dataset. The remaining data was then split in a
70:15:15% ratio for training, testing and first stage validation respectively. Further details on
the dataset used for the modelling is found in Table 2.
Table 2- Overview of data used for model development
Types of project
Type of
Water mains,
manholes, combined
sewer overflows,
repairs, upgrades
1 month to
5 years
2004 to
Data Pre-processing
The pre-processing stage ensures that the data are structured and presented to the model in the
most suitable way as well as offer the modeller the chance to get to understand the data
thoroughly. Cost values were normalised to a 2012 baseline using the infrastructure resources
cost indices by the Building Cost Information Services with a base year 2000. This allowed
for cost values to be somewhat comparable across different years. Numerical predictors were
further standardized to zScores using
  
Equation 1
Where: zScore is the standardized value of a numerical input, xi
µ is the mean of the numerical predictor
σ is the standard deviation of the numerical predictor
Since neural networks was to be used for the actual modelling exercise, standardizing either
input or target variable into a smaller range of variability would potentially aid the effective
learning of the neural net whiles improving the numerical condition of the optimization
problem (StatSoft Inc 2008). If one input has a range of 0 to 1, while another has a range of 0
to 30 million, as was the case in the data that were used in this analysis, the net will expend
most of its effort learning the second input to the possible exclusion of the first. All
categorical variables were coded using a binary coding system.
The next stage involved deciding which predictors to use in the modelling exercise. It was
easy to remove predictors such as project manager, project ID or year of completion from the
set of predictors on precursory examination as they were likely not to be good predictors
when the model is used in practice. Table 3 contains details on the set of initial predictors
used at the beginning of the modelling.
Table 3 - Initial list of variables for model development
Type of data
Project Information
Tendering Strategy
Open competitive
Selective competitive
Procurement Option
Design and build
Management types
Site Information
Ground Condition
Type of Soil
Other Information
Delivery Partner*
Scope of Project
Purpose of Project
Operating Region
1. Other factors include project duration (months) and awarded target cost (£). Model output
was final cost at completion (£).
2. *indicated as X, Y and Z for confidentiality reason
Data visualisation using scatter and mean plots in the earlier stages of the modelling
suggested non-linear relationships between most of the variables and final cost. Also, most of
the predictors were categorical, rather than the usual numeric type. It was thus decided to use
Artificial Neural Networks (ANN) for the actual modelling because of their ability to cope
with non-linear relationships and categorical variables (Cf. Anderson 1995). ANN is an
abstraction of the human brain with abilities to learn from experience and generalise based on
acquired knowledge (Moselhi et al. 1991). It is also able to cope with multicollinearity, a
statistical condition where two or more variables are highly correlated or dependent on each
thereby resulting in spurious predictions when both of those variables are included in the
model (Marsh et al. 2004). Neural networks has previously been applied to forecasting tender
price (Elhag and Boussabaine 1998, Emsley et al. 2002) and for quantification of risk in
construction by McKim (1993). See Moselhi et al. (1991) for a review of neural network
application in construction management research.
Standard Models
The cost models were developed using an iterative process of fine-tuning the network
parameters and inputs until acceptable error levels were achieved or when the model showed
no further improvement. The model training began with a search for optimal model
parameters. This was done in a trial and error manner to begin with, training several networks
and examining them for possible performance improvement using the input factors in Table 3
and Cost at Completion as model output. Two different network architectures, the Multilayer
Perceptron (MLP) and the Radial Basis Function (RBF), were experimented at this stage.
RBF models the relationship between inputs and targets in a 2 phases: it first performs a
probability distribution of the inputs before the searching for relationships between the input
and output space in the next stage (StatSoft Inc 2008). MLPs on the other hand model using
just the second stage of the RBF. The MLP models were superior to the RBF networks and so
the rest of the modelling was carried out using just MLPs. It was found that the best trial
results were achieved with MLPs with a single hidden layer having between 3-10 nodes.
Consequently, using a custom range of 3-10 hidden nodes in 1 hidden layer, a dataset size
split of 70% for training, 15% for testing and another 15% for first stage validation, 1000
networks were trained, retaining the best 10 performing networks for further examination.
These 10 networks were selected based on their overall performance, measured using the
correlation coefficient between predicted and output values as well as the Mean Squared
Errors (MSE). MSE is defined here as:
  
  
 Equation 2
Where: Oi is the predicted final cost of the ith data case (Output); Ti is the actual final cost of
the ith data case (Target) and n is the sample size.
The higher the MSE value, the poorer the network at generalisation, whereas the higher the
correlation coefficient, the better the network. The p-values of the correlation coefficients
were also computed to measure their statistical significance. The higher the p-value, the less
reliable the observed correlations. The best 10 retained networks were then further validated
using the 100 independent validation cases that were selected using the stratified sampling at
the beginning of the modelling exercise.
Five different activation functions, i.e. identity, logistic, tanh, exponential and the sine
functions were iterated in both hidden and output layers, using gradient descent, conjugate
descent and the Broyden-Fletcher-Goldfarb-Shanno (BFGS) training algorithms. See Fausett
(1994) and Gurney (1997) for the fundamentals for neural network architectures, algorithms,
or Skapura (1996) for a practical guide to developing neural network models. Early stopping,
the process of halting training when the model error stops decreasing, was used to prevent
memorising or over-fitting the dataset in order to improve generalization. Over-fitted models
perform very well on training and testing data, but fail to generalise satisfactorily when new
‘unseen’ cases are used to validate their performance.
Redundant predictors, those that do not add new information to the model because they
basically contain the same information at another level with other variables, were detected
using spearman correlations, bi-variate histograms or cross-tabulation. These were tendering
strategy, procurement option and type of soil. This is likely due to the invariant nature of
these predictors as most of the project were procured through design and build contracts with
a mix of open-competitive and negotiated tendering strategies. Type of soil was found to be
linearly dependent on ground condition, thereby not making any additional contribution to the
model's output.
All the best 10 models identified at this stage had 12 input nodes from five input factors.
These five significant input factors are purpose of project (wastewater, water or general),
scope of project (new-build, upgrade or replacement), ground condition (contaminated or
non-contaminated ground), delivery partner (anonymised as X,Y, Z) and estimated project
duration. The models also had between 3 and 7 nodes in a single hidden layer with one
output, i.e. final cost. They were trained with a tanh or logistic activation function between
their input and hidden layers, and an identity transfer function in the output layer.
Bootstrapping is a general technique, attributed to Efron (1992), for estimating sampling
distributions that allow for treating the observed data as though it were the entire (discrete)
statistical population (StatSoft Inc. 2011). It provides an avenue for using subsamples from a
sample in a manner that addresses the variability and uncertainty in statistical inferences.
Traditional approaches to statistical inference are based on the assumption of normality in the
data distribution. This is reasonable and largely accepted but where this assumption is wrong,
Efron (1992) warns that the corresponding sampling distribution of the statistic may be
seriously questionable. In contrast, non-parametric bootstrapping provides a way to estimate a
statistic of population without explicitly deriving the sample distribution. During the
development of the models presented so far, the dataset was divided into three subsets for
training, testing and validation. On a closer examination, this might be regrettable, as not all
the data gets used for training, testing or validation, and thus some level of information within
the entire dataset is lost in the learning process. If bootstrapping is employed, a different split
of data is used each time for training or testing so as to glean as much information as possible
from the entire dataset.
Statisticians disagree though on the number of bootstrap samples (BS) necessary to produce
reliable results. Most textbooks suggest choosing a sufficiently large enough bootstrap sample
size without specific guidance on an optimum size. Efron and Tibshirani (1993), as well as
Pattengale et al. (2010), however, suggest that an minimum of 100 or a maximum of 500 BS
is generally sufficient in most cases. Bootstrapping was thus applied to the dataset in this
manner: 600 different training, validation, testing BS sample sets were generated by
perturbing the entire dataset for each model using sampling with replacement over a uniform
probability distribution. This should ensure that as many data cases as possible get used in the
training, validation or testing samples sets. With the same inputs, neural network
architectures, activation functions, hidden layers and nodes used in the case of the standard
sample models developed in the previous section, 1000 neural network models were then
trained and tested, retaining the best 10 performing models just as before. The 10 retained
models were then further validated using the 100 separate validation cases just as was done
Figure 1 shows the performance of the best 10 models from both the standard and
bootstrapped models validated with the 100 validation cases. It is obvious that bootstrapped
models far outperform the standard models. Whiles the bootstrapped models overestimated
actual final cost by about 4% on average, the standard models overestimated by 8.35% on
average. Furthermore, the bootstrapped models underestimated actual final cost with an
average error of about -6%, whereas the standard models averaged about -10%. This
performance improvement is likely due to the fact that by using the 600 bootstrapped sample
sets, the models were afforded a wider learning space than the standard models. The
bootstrapped models were then carried forward for further analysis discussed below.
Figure 1 - Validation Results (Standard Models vs Bootstrapping)
Ensemble Network
All modelling techniques are prone to two main types of error, bias and variance, largely
because models essentially try to reduce complicated problems into simple forms and then
attempt to solve the ‘reduced’ problem using an imperfect finite dataset. Bias is the average
error any particular model will make across different datasets whereas variance reflects the
sensitivity of the model to a particular choice of dataset (StatSoft Inc. 2011). The use of
ensembles can improve the results that are produced from individual models by combining
them in a way that achieves some sort of compromise between variance and bias. Also known
as Committee Methods (Cf Oza 2006), ensembles attempt to leverage the power of multiple
models to achieve better prediction accuracy than any of the individual models could on their
own. It is perhaps a way of consulting a 'committee of several experts', the 10 different
bootstrapped models in this case, before reaching a final decision either by averaging, voting
or by 'winner-takes-all', whichever is most appropriate (see Jordan and Jacobs 1994, Breiman
1996). The result, at least in theory, is a model (the ensemble) that is more consistent in its
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Model 9
Model 10
Average Percentage Error
Validation Model
Validation Results
Std. Average % Underestimation
Std. Average % Overestimation
Bootstrap Average % Underestimation
Bootstrap Average % Overestimation
predictions and on average, at least as good as the individual networks from which it was
built. A weighted average algorithm was thus applied to combine the 10 best bootstrapped
models to trade off bias and variance to improve performance.
Table 4 compares the performance of the ensemble model with the bootstrapped models and
the standard models. It is obvious that significant improvement has been achieved by applying
the ensemble technique to the 10 bootstrapped models.
Table 4- Summary of results (Standard, Bootstrapped & Ensemble Models)
Average percentage error
Standard models
Bootstrapped models
Ensemble model
In Table 5, details of a sample of 20 results out of the 100 validation cases used to test the
ensemble model are highlighted. It shows a comparison between the ensemble final cost
prediction and the actual final cost of the project, with a measure of the actual monetary error
Table 5- Sample results from ensemble model validation
Actual final cost
Ensemble prediction
Ensemble error
Ensemble absolute
% error
Table 6 shows a summary the performance of the ensemble model for all the 100 validation
cases. 92% of the 100 validation predictions were within ±10% of the actual final cost of the
project with 77% within a ±5% of actual final cost. Only 8 out of the 100 validation had
predictions beyond ±10% of the final cost of the project case.
Table 6- Summary of validation performance of ensemble model
Percentage Error
Number of cases
Percentage of total validation set
Within ±5%
±5% < x > ±10%
Beyond ± 10%
A lot of project and cost information is usually generated on any one particular construction
project. If this is done in a meaningful and retrieval manner for a number of projects over
time, a vast database of potentially valuable asset results. This can be converted into valuable
decision-support systems using data mining methodologies. The possibilities are that these
decision-support systems could help construction practitioners in making better informed and
reliable decisions as well as reduce the time and resources spent in reaching these decisions.
Cost growth, attributed to a number of causes including the unavailability and uncertainty of
necessary information for reliable estimation at the early stages of a project, remains one the
major problems in the construction industry. We make a case for using data mining in modern
construction management as a key business tool to help transform information embedded in
construction data into decision-support systems that can complement traditional estimation
methods for more reliable final cost forecasting. Using a combination of non-parametric
bootstrapping and ensemble modelling in artificial neural networks, cost models were
developed to estimate the final construction cost of water infrastructure projects. 92% of the
100 validation predictions were within ±10% of the actual final cost of the project with 77%
within ±5% of actual final cost. We are now exploring avenues of transforming the models
into standalone desktop applications for deployment within the operations of the industry
partner that collaborated in this research.
The models developed in this paper will be particularly useful at the pre-contract stage of the
partnering construction firm in this research as it will provide a benchmark for evaluating
submitted tenders. They could further allow the quick generation of various alternative
solutions for a construction project using what if analysis for the purposes of comparison. The
method and approach adopted to develop the models can be extended to even more detailed
estimation so long as relevant data can be acquired. It must be pointed out that reliable cost
planning and estimation forms only one aspect of dealing with cost growth in construction. A
more holistic approach must include effective project governance and client leadership,
accountability and measures of cost control. Also, an effective data mining exercise does
depend heavily on both quantity and quality of data. Companies that want to employ data
mining techniques thus have to be intentional in how they collect and store their data, making
sure it contains relevant business and operational data to solve the problem at hand.
Adeli, H (2001) Neural networks in civil engineering: 1989-2000. Computer-Aided Civil and
Infrastructure Engineering, 16(2), 126-42.
Ahiaga-Dagbui, D D and Smith, S D (2012) Neural networks for modelling the final target cost of
water projects. In: Procs 28th Annual ARCOM Conference, Smith, S D, Ed., Edinburgh, UK:
Association of Researchers in Construction Management, 307-16.
Ahiaga-Dagbui, D D and Smith, S D (2014) Rethinking construction cost overruns: Cognition,
learning and estimation. Journal of Financial Management of Property and Construction,
19(1), 38-54.
Akintoye, A (2000) Analysis of factors influencing project cost estimating practice. Construction
Management & Economics, 18(1), 77-89.
Akintoye, A S and MacLeod, M J (1997) Risk analysis and management in construction. International
Journal of Project Management, 15(1), 31-8.
Alex, D P, Al Hussein, M, Bouferguene, A and Siri Fernando, P (2010) Artificial neural network
model for cost estimation: City of Edmonton’s water and sewer installation services. Journal
of Construction Engineering and Management, 136, 745-56.
Anderson, J A (1995) An Introduction to neural networks. Cambridge, Massachusetts: MIT Press.
Apte, C, Liu, B, Pednault, E P D and Smyth, P (2002) Business applications of data mining.
Communications of the ACM, 45(8), 49-53.
Audit Scotland (2000) The new Scottish Parliament building, an examination of the management of
the Holyrood project, Edinburgh, UK: Audit Scotland.
Audit Scotland (2004) 'Management of Holyrood Building Project' (Audit Report prepared for the
Auditor General of Scotland), Edinburgh, UK: Audit Scotland.
Auditor General of Western Australia (2012) Managing Capital Projects, Perth, Australia: Office of
the Auditor General of Western Australia, (last accessed in May
Bordat, C, McCullouch, B G, Sinha, K C and Labi, S (2004) An Analysis of Cost Overruns and Time
Delays of INDOT Projects., Joint Transportation Research Program, Indiana Department of
Transportation and Purdue University, West Lafayette, Indiana, Publication FHWA/IN/JTRP-
Breiman, L (1996) Bagging predictors. Machine learning, 24(2), 123-40.
Chan, A P and Chan, A P (2004) Key performance indicators for measuring construction success.
Benchmarking: An International Journal, 11(2), 203-21.
Cheng, M-Y, Chou, J-S, Roy, A F V and Wu, Y-W (2012) High-performance concrete compressive
strength prediction using time-weighted evolutionary fuzzy support vector machines inference
model. Automation in Construction, 28(0), 106-15.
Efron, B (1992) Bootstrap methods: Another look at the jackknife. In: Kotz, S and Johnson, N L
(Eds.), Breakthroughs in Statistics, pp. 569-93: Springer.
Efron, B and Tibshirani, R (1993) An introduction to the bootstrap. Vol. 57, New York: Chapman and
Egan, J (1998) Rethinking construction: the report of the Construction Task Force to the Deputy
Prime Minister, John Prescott, on the scope for improving the quality and efficiency of UK
construction, London: Department of the Environment, Transport and the Regions
Construction Task Force.
Elhag, T M S and Boussabaine, A H (1998) An artificial neural system for cost estimation of
construction projects. In: 14th Annual ARCOM Conference, Hughes, W, Ed., University of
Reading: Association of Researchers in Construction Management, 219-26.
Emsley, M W, Lowe, D J, Duff, A, Harding, A and Hickson, A (2002) Data modelling and the
application of a neural netowrk approach to the prediction of total construction costs.
Construction Management and Economics, 20, 465-72.
Fausett, L V (1994) Fundamentals of neural networks: architectures, algorithms, and applications.
Prentice-Hall Englewood Cliffs, NJ.
Fayyad, U, Piatetsky-Shapiro, G and Smyth, P (1996) The KDD process for extracting useful
knowledge from volumes of data. Communications of the ACM, 39(11), 27-34.
Flanagan, R and Norman, G (1993) Risk Management and Construction. Oxford: Blackwell Science
Flyvbjerg, B (2008) Curbing optimism bias and strategic misrepresentation in planning: Reference
class forecasting in practice. European Planning Studies, 16(1), 3-21.
Flyvbjerg, B, Holm, M K S and Buhl, S L (2002) Understanding costs in public works projects: Error
or lie? Journal of the American Planning Association, 68(279-295).
Flyvbjerg, B, Holm, M K S and Buhl, S L (2004) What causes cost overrun in transport infrastructure
projects? Transport Reviews, 24(1), 3-18.
Gelinas, N (2007) Lessons of Boston’s Big Dig. City Journal, Autumn 2007, Accessed on 8th May
General Accounting Office (1997) Transportation infrastructure- managing the costs of large-dollar
highway projects, Washington DC: United States General Accounting Office (GAO).
Gil, N and Lundrigan, C (2012) The leadership and governance of megaprojects. In: CID Technical
Report No. 3/2012: Centre for Infrastructure Development (CID), Manchester Business
School, The University of Manchester, 18.
Goldberg, E G and Senator, T E (1998) The FinCEN AI System: Finding financial crimes in a large
database of cash transactions. In: Jennings, N R and Woodridge, M J (Eds.), Agent
Technology: Foundations, Applications and Markets, pp. 283-302 Berlin: Springer.
Gurney, K (1997) An introduction to neural networks. London: UCL Press.
Hair, J, Tatham, R, Anderson, R and Black, W (1998) Multivariate Data Analysis (5th Edition). Upper
Saddle River, NJ: Prentice Hall.
Hegazy, T (2002) Computer-based construction project management. Upper Saddle River, NJ:
Prentice Hall Inc.
Hoffman, P, Grinstein, G, Marx, K, Grosse, I and Stanley, E (1997) DNA visual and analytic data
mining. In, Visualization '97., Proceedings, 19- 24 Oct. 1997, Phoenix, AZ, USA 437-41.
Huang, C L, Chen, M C and Wang, C J (2007) Credit scoring with a data mining approach based on
support vector machines. Expert Systems with Applications, 33(4), 847-56.
Jennings, W (2012) Why costs overrun: risk, optimism and uncertainty in budgeting for the London
2012 Olympic Games. Construction Management and Economics, 30(6), 455-62.
Jordan, M I and Jacobs, R A (1994) Hierarchical mixtures of experts and the EM algorithm. Neural
Computation, 6(2), 181-214.
Kahneman, D and Tversky, A (1979) Prospect Theory: An Analysis of Decision under Risk.
Econometrica, 47(2), 263-91.
Kirkham, R and Brandon, P S (2007) Ferry and Brandon's Cost Planning of Buildings. 8th ed.
Oxford, UK: Wiley-Blackwell Ltd.
Koh, H C and Tan, G (2005) Data mining applications in healthcare. Journal of Healthcare
Information Management, 19(2), 64-72.
Kruger, J and Dunning, D (1999) Unskilled and unaware of it: How difficulties in recognizing one’s
own incompetence lead to inflated self-assessments. Journal of personality and social
psychology, 77(6), 1121-34.
Larsson, T J and Field, B (2002) The distribution of occupational injury risks in the Victorian
construction industry. Safety Science, 40(5), 439-56.
Liao, C-W and Perng, Y-H (2008) Data mining for occupational injuries in the Taiwan construction
industry. Safety Science, 46(7), 1091-102.
Love, P E D, Edwards, D J and Smith, J (2005) Contract documentation and the incidence of rework
in projects. Architectural Engineering and Design Management, 1(4), 247-59.
Love, P E D, Edwards, D J and Irani, Z (2012) Moving beyond optimism bias and strategic
misrepresentation: An explanation for social infrastructure project cost overruns. IEEE
Transactions on Engineering Management, 59(4), 560-71.
Love, P E D, Smith, J, Simpson, I, Regan, M, Sutrisna, M and Olatunji, O (2014) Understanding the
Landscape of Overruns in Transport Infrastructure Projects. Environment and Planning B:
Planning and Design, in press.
Marsh, H W, Dowson, M, Pietsch, J and Walker, R (2004) Why Multicollinearity Matters: A
Reexamination of Relations Between Self-Efficacy, Self-Concept, and Achievement. Journal
of Educational Psychology, 96(3), 518-22.
McKim, R A (1993) Neural networks and the identification and estimation of risk. In, Transaction of
the 37th Annual Meeting of the American Association of Cost Engineers, 11-14 July, 1993,
Dearborn, MI, USA. American Association of Cost Engineers, 5.1- 5.10.
Moselhi, O, Hegazy, T and Fazio, P (1991) Neural networks as tools in construction. Journal of
Construction Engineering and Management, 117(4), 606-25.
National Audit Office (2012) The London 2012 Olympic Games and Paralympic Games: post-Games
review HC 794- Session 2012-13, National Audit Office, UK.
Ngai, E W T, Xiu, L and Chau, D (2009) Application of data mining techniques in customer
relationship management: A literature review and classification. Expert Systems with
Applications, 36(2), 2592-602.
Nicholas, J M (2004) Project management for business and engineering: Principles and practice.
Second ed. MA, USA; Oxford, UK: Elsevier ButterworthHeinemann.
Odeck, J (2004) Cost overruns in road constructionwhat are their sizes and determinants? Transport
Policy, 11(1), 43-53.
Okmen, O and Öztas, A (2010) Construction cost analysis under uncertainty with correlated cost risk
analysis model. Construction Management and Economics, 28(2), 203-12.
Oza, N C (2006) Ensemble data mining methods. In: Wang, J (Ed.), Encyclopedia of Data
Warehousing and Mining, pp. 770-6: IGI Global.
Pattengale, N D, Alipour, M, Bininda-Emonds, O R, Moret, B M and Stamatakis, A (2010) How many
bootstrap replicates are necessary? Journal of Computational Biology, 17(3), 337-54.
Sarle, W S (1994) Neural Networks and Statistical Models. In: Proceedings of the Nineteenth Annual
SAS Users Group International Conference, Cary, North Carolina, USA: SAS Institute Inc,
Skapura, D M (1996) Building neural networks. New York, USA: ACM Press/Addison-Wesley
Publishing Co.
StatSoft Inc (2008) A Short Course in Data Mining. In, Tulsa, OK, USA: StatSoft, Inc.
StatSoft Inc. (2011) Electronic Statistics Textbook. In, OK Tulsa: StatSoft, .
Wachs, M (1989) When planners lie with numbers. Journal of the American Planning Association,
55(4), 4769.
Wachs, M (1990) Ethics and advocacy in forecasting for public policy. Business and Professional
Ethics Journal, 9(1-2), 14157.
Weinstein, N D (1980) Unrealistic optimism about future life events. Journal of personality and social
psychology, 39(5), 806.
Zhang, X, Mao, X and AbouRizk, S M (2009) Developing a knowledge management system for
improved value engineering practices in the construction industry. Automation in
Construction, 18(6), 777-89.
... This risk is more pronounced in the small and volatile economies of SIDS due to their dependence on external economic interventions to finance and support infrastructure development [17, 22, and 23]. Thus, the monitoring, evaluating, and reporting of cost performance are necessary conditions to fulfill the accountability processes 28 in public sector projects. Although cost reporting is a fundamental part of the construction management process, PSSHPs infrequently achieve planned budgetary requirements. ...
... Yet, a commonality in construction projects is a shortfall in cost performance, the leading cause of cost overruns. One view offered on the resistant nature of cost overruns stems from the lack of a universal theory in project management [27,28]. Evidently, this view is justified by the variability of cost overrun definitions [2]. ...
The cost overrun phenomenon on projects worldwide creates a major source of risk that warrants investigation. The prevailing factor school of thought provides strong empirical evidence that critical factors contributing to cost overruns are both context-specific and project-specific. Although many studies have been conducted identifying factors and causes of cost overruns, very few studies have investigated root causes. Additionally, a limited body of knowledge is available within the context of Small Island Development States (SIDS). To fill this gap, the objectives of this study were to identify and determine the main critical factors contributing to the cost overrun phenomenon in public sector social housing programmes (PSSHPs). These selected factors were thereafter categorized under leading root causes, and their severity was determined based on primary stakeholders’ perspectives. One hundred and twenty-three factors were identified from the literature, of which forty-one critical factors were extracted and grouped under four root causes based on a pilot survey of relevant public sector housing experts in the Trinidadian and Jamaican construction sectors. These refined factors and root causes were formulated into a questionnaire survey. One hundred and five responses were obtained from professionals who had a minimum of five years’ experience in various phases of public housing delivery. The severity of these critical factors was evaluated, ranked, and categorized using the relative importance index (RII) approach. The findings uncovered the leading root cause, which is political in nature. The top five critical factors are the selection of politically aligned contractors, the intentional design of inadequate contracts, the project actors' deliberately underestimating costs, the partisan project management team, and strategic misrepresentation. These findings are unique to SIDS and contribute to knowledge to reframe contemporary project management practices, which focus mainly on technical causes. Finally, as existing technical solutions are ineffective in curbing cost overruns in PSSHPs, these findings also inform public sector policymakers to focus on prioritization, control, and mitigation of political risks in formulating effective governance mechanisms. Doi: 10.28991/ESJ-2022-06-03-016 Full Text: PDF
... Visualization is one of the categories used in data mining; it is an analytical process for searching for consistent patterns in large data sets to improve the knowledge about a given phenomenon [36]. As such, this approach has been adopted to achieve the study objectives in regard to the cost flow characterization. ...
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This research proposes a systematic data-driven analytics protocol and case studies that can help decision makers in the construction industry embrace the practice of using data to make critical choices. The protocol consists of six phases: (1) conceptualization, (2) design, (3) development, (4) refinement, (5) analysis, and (6) outcome. Two case studies, the Fire Engineering and Maintenance Department at a university in Indiana and housing market outlook and business expansion locations are conducted based on the proposed protocol. As a result of implementing the data-driven analytics protocol for the first case study, a budget strategy for preventive maintenance was established through activity prioritization and a clustering analysis. In the second case study, the future values for the land permits were predicted, and the counties with investment value in land availability were selected for strategic business expansion. Therefore, the proposed systematic protocol for analytics would help decision makers and employees comprehend projects and realize the importance of data-driven analytics, which would promote insights into long-term success in their organization.
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To achieve meaningful results, data-driven decision-support systems in construction require integration of fragmented data from multiple standalone databases. In practice, a manual brute-force approach is often the only available means of integrating structured, yet semantically-ambiguous, construction data. Two common data integration challenges include the identification of (1) key strings (i.e., product identification) partially shared between two data sources and (2) relationships (overlap, included, or outside) between two 3D object lists based on coordinates. This research has developed a framework that includes two generic solutions to the above-identified semantic mapping challenges. The proposed framework automatically integrates fragmented and incompatible data (exhibiting similar semantic mapping challenges) from various sources into a tidy format for input into a diverse range of industrial construction applications. Verification and functionality of the framework was confirmed using both artificial data and a real case study of a large oil-and-gas project. The ability of the proposed data integration functions and framework to automate otherwise manual data integration processes was demonstrated. Results of this study are expected to enhance real-time information flow, improve data quality, and promote the use of fragmented data for critical decision-support in practice.
Purpose Unbalanced bidding can seriously imposed the government from obtaining the best value for the taxpayers' money in public procurement since it increases the owner's cost and decreases the fairness of the competitive bidding process. How to detect an unbalanced bid is a challenging task faced by theoretical researchers and practical actors. This study aims to develop an identification method of unbalanced bidding in the construction industry. Design/methodology/approach The identification of unbalanced bidding is considered as a multi-criteria decision-making (MCDM) problem. A data-driven unit price database from the historical bidding document is built to present the reference unit prices as benchmarks. According to the proposed extended TOPSIS method, the data-driven unit price is chosen as the positive ideal solution, and the unit price that has the furthest absolute distance measure as the negative ideal solution. The concept of relative distance is introduced to measure the distances between positive and negative ideal solutions and each bidding unit price. The unbalanced bidding degree is ranked by means of relative distance. Findings The proposed model can be used for the quantitative evaluation of unbalanced bidding from a decision-making perspective. The identification process is developed according to the decision-making process. The finding shows that the model will support owners to efficiently and effectively identify unbalanced bidding in the bid evaluation stage. Originality/value The data-driven reference unit prices improve the accuracy of the benchmark to evaluate the unbalanced bidding. The extended TOPSIS model is applied to identify unbalanced bidding; the owners can undertake objective decision-making to identify and prevent unbalanced bidding at the stage of procurement.
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Ensemble data mining methods, also known as committee methods or model combiners, are machine learning methods that leverage the power of multiple models to achieve better prediction accuracy than any of the individual models could on their own. The basic goal when designing an ensemble is the same as when establishing a committee of people: Each member of the committee should be as competent as possible, but the members should complement one another. If the members are not complementary, that is, if they always agree, then the committee is unnecessary — any one member is sufficient. If the members are complementary, then when one or a few members make an error, the probability is high that the remaining members can correct this error. Research in ensemble methods has largely revolved around designing ensembles consisting of competent yet complementary models.
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Results from the first statistically significant study of the causes of cost escalation in transport infrastructure projects are presented. The study is based on a sample of 258 rail, bridge, tunnel and road projects worth US$90 billion. The focus is on the dependence of cost escalation on (1) length of project implementation phase, (2) size of project and (3) type of project ownership. First, it is found with very high statistical significance that cost escalation is strongly dependent on length of implementation phase. The policy implications are clear: Decision makers and planners should be highly concerned about delays and long implementation phases because they translate into risks of substantial cost escalations. Second, it is found that projects have grown larger over time and that for bridges and tunnels larger projects have larger percentage cost escalations. Finally, by comparing cost escalation for three types of project ownership--private, state-owned enterprise and other public ownership--it is shown that the oft-seen claim that public ownership is problematic and private ownership effective in curbing cost escalation is an oversimplification. The type of accountability appears to matter more to cost escalation than type of ownership.
People tend to hold overly favorable views of their abilities in many social and intellectual domains. The authors suggest that this overestimation occurs, in part, because people who are unskilled in these domains suffer a dual burden: Not only do these people reach erroneous conclusions and make unfortunate choices, but their incompetence robs them of the metacognitive ability to realize it. Across 4 studies, the authors found that participants scoring in the bottom quartile on tests of humor, grammar, and logic grossly overestimated their test performance and ability. Although their test scores put them in the 12th percentile, they estimated themselves to be in the 62nd. Several analyses linked this miscalibration to deficits in metacognitive skill, or the capacity to distinguish accuracy from error. Paradoxically, improving the skills of the participants, and thus increasing their metacognitive competence, helped them recognize the limitations of their abilities. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Providing detailed examples of simple applications, this new book introduces the use of neural networks. It covers simple neural nets for pattern classification; pattern association; neural networks based on competition; adaptive-resonance theory; and more. For professionals working with neural networks.
Cost and schedule overruns are endemic features of transport infrastructure projects. Despite the considerable amount of research within the field of transport and planning in the past thirty years, limited progress has been made to improving the performance of projects. We contend that this will continue to be an issue as long as research efforts focus on the ‘outside view’ with emphasis being placed upon strategic misrepresentation and optimism bias. Understanding ‘why’ and ‘how’ projects overrun, particularly from both ‘outside’ and ‘inside’ perspectives, is pivotal to reducing their impact and occurrence. Thus, in conjunction with the transport and planning literature, references to cost-overrun studies undertaken within the field of construction and engineering are examined. Our objective is to provide policy makers, industry, voluntary organisations,and the public at large with an ameliorated understanding about time-overrun and cost overrun phenomena. Suggestions to mitigate overruns based upon recent process and technological innovations are identified and discussed.