APPLICATION NOTE
Category: Construction management
Keywords: Variations, Neural Networks, Modelling, Contingency allocation, Project control.
University: University of Wolverhampton, U.K.
Department: School of Engineering & the Built Environment
Contact Email: me6049@wlv.ac.uk
APPLICATION SUMMARY
1. Problem Description
The construction industry has been consistently criticised for poor performance in
attaining its clients requirements. Time and cost overruns were predominately common
and were well documented (NEDO, 1975; RICS, 1979). The incidence and magnitude of
variations was identified as a major cause and a focus of much of the criticism
(Bromilow, 1969, 1970; Hibberd, 1980, 1986; Latham, 1994).
Variation issued during the construction period are time consuming and costly. Thus
accepted as an inevitable part of construction, variations are a major cause of disruption,
delay and disputes and generate significant cost impact. Yet no empirical method or
tool, quantitative or otherwise, is available for managing or controlling them. The
conventional approach is to include a percentage of the project cost as contingency in the
pre-contract budget for their occurrences. The allocated contingency based on this
method is largely judgmental and arbitrarily allocated. However, construction projects
are unique; as they may have distinctive set of objectives, require the application of new
technology or technical approaches to achieve the required result, or even duplicate a
given set of results in an entirely different environment (Hamburger, 1992). This
uniqueness makes the conventional method based wholly on the project
manager/supervisors' experience and intuition in danger of overly simplistic and
unrealistic (Yeo, 1990).
The objectives of the contingency allocation are to ensure that the budget set aside for the
project is realistic and sufficient enough to contain the risk of unforeseen cost increases.
Therefore any realistic contingency must serves as a basis for decision making
concerning financial viability of the variations, and a baseline for their control. After all
to manage variations means being able to anticipate their occurrences and to control or
monitor their associated cost (Ashley et al, 1986). This paper therefore, describes The
development a model to predict the total contingency cost allowance for variations on a
construction project is described.
2. Investigator
Akinsola, A.O.,
Researcher in construction management,
School of Engineering and Built Environment,
University of Wolverhampton,
Wulfruna Street,
Wolverhampton, WV1 1SB.
3. NN Application
Structure
Considering the nature of our problem a simple three-layer MLP neural network structure
is chosen for the ANN model of total cost of variations on construction projects. The
first layer, an input layer, consist of 14 PEs. The number of the PE in the input layer is
determined by the number of factors which has been found to influences the magnitude of
variations on building projects (Akinsola et al, 1995, 1996). The second layer is the
hidden layer, consisting of 5PEs. Finally, the output layer consist of a single PE which
produce the total cost of variations.
Data Collection and Analysis
The data used in the study was collected through a structured questionnaire from 45
building projects completed within the last five years in UK. The projects covered wide
spectrum of building types and complexity, from simple residential buildings to very
complex office and industrial buildings.
Variations were measured in terms of the actual total cost of variations (TCV) ordered
per project. TCV is based on the total cost of variations contained as variation order
sheets. A variation order sheet may contain a number of works to be done, each of
which is a separate variation, summed to derive the total cost of variation per project.
Detail description of the data collection instrument and the analysis results has been
reported in our earlier papers (Akinsola et al, 1995, 1996). Of the twenty-two factors
identified fourteen were found to have significant influence on the magnitude of the
incidence of variations observed on building projects. These factors were used as the
input variables of the neural network. The fourteen factors are presented in Table 1
below.
Training
The supervised training method is used to trained the network. The trained model was
tested in stages. In the first stage, the same data set used for training was used as the test
data. The data as described above consists of 35 projects. In final stage the model was
tested using the independent data set aside for testing. The data consists of 2 office, 1
industrial, 3 education, 2 residential, and 2 other buildings projects.
4. The Results
To assess the model prediction performances, two quantitative prediction accuracy
measurements were calculated and examined; the mean absolute percentage error
(MAPE), and the coefficient of variation (CV). The MAPE measure the average
percentage of the model prediction error and is calculated by:
n
MAPE 
 ( AE )
i
i 1
N
AEi  (Y  y ) 2 / N
Where N is the number of sample of projects. AE represent the absolute error. The Y
and y denote the actual and the network predicted total cost of variations respectively.
Finally, the coefficient of variations (CV) is calculated by:
CV 
SDr
MTCV
*100
where SDr is the standard deviation of the error and the MTCV is the mean of the
actual total cost of variations.
The model predictions were compared with the actual total cost of variations. The
percentage error ranges between 0% and 2.5% with a percentage mean of only 0.21% and
a coefficient of variation of only 0.22%. These results show that the model predictions
are more accurate than expected thus the data set are known to the model.
The second test used independent (i.e. unseen by the model) data set. This test is a
simulation of the use of the model in practice and assesses how the model will perform if
presented with unknown data set. The percentage errors ranges between 0.5% and 15%
with a percentage mean of only 3.28% and coefficient of variation of only 1.2%. These
results validate the study hypothesis that the cost magnitude of variations can be
realistically predicted as demonstrated.
Using the first test results as a baseline, the
results shows the model predictions are well within the accuracy limit. In the industry
forecasting accuracy of 10-15% at the stage are considered adequate Considering that the
model is proposed to be used at budget estimate stage of the project the model's accuracy
are within the industry standard limit of  15% (McCaffer, 1975; Blok, 1982; Ashworth,
1988; Yeo, 1990).
5. How the results applied
Paper published:
Title: A NEURAL NETWORK MODEL FOR PREDICTING BUILDING PROJECTS'
CONTINGENCY ALLOWANCE.
Conference: Proceeding of Association of Researchers in Construction Management,
ARCOM 96, Sheffield Hallam University, England, 11-13 Sept. 1996, pp 507-516