Construction projects' duration can be modelled using traditional and artificial intelligence methods. The first part of these two papers provides an insight into the principles…
Abstract
Construction projects' duration can be modelled using traditional and artificial intelligence methods. The first part of these two papers provides an insight into the principles of modelling project durations using neurofuzzy methods. This paper presents an understanding of how these methods operate and discusses the main issues concerning their use and application in construction management. An introduction to the problems of modelling and predicting construction projects' duration is first presented. This is followed by explaining the neurofuzzy life cycle modelling process and discussing methods for modelling projects' duration. A graphical presentation of the way in which neurofuzzy methods operate is also presented and discussed.
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A.H. BOUSSABAINE, R. THOMAS and T.M.S. ELHAG
This paper furthers work that already exists in the use of artificial intelligence techniques to forecast cost flow for construction projects. The paper explains the need for…
Abstract
This paper furthers work that already exists in the use of artificial intelligence techniques to forecast cost flow for construction projects. The paper explains the need for cost‐flow forecasting and investigates the methods currently used to perform such a task. It introduces neural networks as an alternative approach to the existing methods. The relationship between the number of nodes used and the accuracy of the neural network in modelling the cost flow is closely examined. From this research an optimal solution is proposed for the case and a prototype system is developed. The results of the investigation of the number of nodes used and testing of the prototype neural network for sample cases are presented and discussed.
The first paper of this two‐paper series has developed an understanding of neurofuzzy concept modelling techniques. This second paper demonstrates the power and versatility of…
Abstract
The first paper of this two‐paper series has developed an understanding of neurofuzzy concept modelling techniques. This second paper demonstrates the power and versatility of neurofuzzy methods when applied to the determination of construction project duration. The paper explains data selection and preprocessing, the modelling process and optimization of developed models. The paper also presents and discusses the results generated by the developed model.
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RICHARD J. KIRKHAM, A. HALIM BOUSSABAINE and MATTHEW P. KIRKHAM
Through a case study, this paper reports on a research project to develop a risk integrated methodology for forecasting the cost of electricity in a National Health Service (NHS…
Abstract
Through a case study, this paper reports on a research project to develop a risk integrated methodology for forecasting the cost of electricity in a National Health Service (NHS) acute care hospital building. The paper is formed of two strands. Strand one presents a rationale for selecting an appropriate time series forecasting method and strand two looks at the implementation of probabilistic modelling of the forecasts generated in strand one. The results of the research revealed that the Holt‐Winters multiplicative forecasting method produced the most reliable forecasts. The probabilistic modelling of the forecasts revealed that after a pair‐wise comparison between data collected at the hospital used as the case study and data collected from NHS acute care trusts nationwide, the forecasts were most likely to belong to the Weibull distribution. The results could then be used as inputs into a whole life cycle cost model or as a stand‐alone forecasting technique for predicting future electricity costs for use in the NHS Trust Financial Proforma returns.
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David J. Lowe, Margaret W Emsley and Anthony Harding
There is a paucity of recent literature on the influence of project strategic, site related and design related variables on the cost of construction. This paper seeks to redress…
Abstract
There is a paucity of recent literature on the influence of project strategic, site related and design related variables on the cost of construction. This paper seeks to redress this omission by presenting the results of an investigation into the influence of 41 independent variables on both construction cost and client cost. Data were collected from 286 construction projects in the United Kingdom and correlation and test for differences were used to determine the relationships that exist between the dependent and independent variables. The analysis both confirms the strong relationship between construction cost and client cost and between those two measures of cost and GIFA, and establishes other relationships which exist within the data, confirming many of the relationships that had been anticipated from the literature. It also established the ordinal sequence of several nominal variables. These data, therefore, can be confidently used to develop models of the total cost of construction.
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Khalid Almarri, Halim Boussabaine and Hamad Al Nauimi
The internet of things (IoT) is becoming an increasingly inescapable part of society. IoT paradigm cannot function without the networking infrastructure. High-speed data networks…
Abstract
Purpose
The internet of things (IoT) is becoming an increasingly inescapable part of society. IoT paradigm cannot function without the networking infrastructure. High-speed data networks are essential to enable the IoT future. Thus, the purpose of this study is on the identification of risks that influence the development, installation and operation of information and communication technology (ICT) infrastructure network project cost outcomes. So far, there has been little attention has been paid to risks problems in these types of IoT enabling projects.
Design/methodology/approach
This research follows a quantitative analysis approach. Data for this study were collected by a survey from 209 professionals. Multiple regression analysis was used to model the relationship between risks and outturn cost of infrastructure needed to enable the operation of IoT technologies.
Findings
The main risk factors that were identified were planning and development, people and management, operations, technology and hardware.
Research limitations/implications
This research has expanded the existing literature by documenting and clustering ICT infrastructure network project risks into themes, and has developed a scale (risk statements) for measuring such risks. Further, the research has advanced the understanding by identifying the most likely risks that will contribute to the overrun of these projects.
Originality/value
This research establishes a reliable regression method for the assessment of the risks that influence the development, installation and operation of ICT infrastructure network projects outturn cost. No other research has measured or studied the risks in this type of project.
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Khalid Almarri and Halim Boussabaine
The level at which risk is priced and the magnitude of risks transferred to the private sector will have a significant impact on the cost of the public–private partnership (PPP…
Abstract
Purpose
The level at which risk is priced and the magnitude of risks transferred to the private sector will have a significant impact on the cost of the public–private partnership (PPP) deals as well as on the value for money analysis and on the section of the optimum investment options. The price of risk associated with PPP schemes is complex, dynamic and continuous throughout the concession agreement. Risk allocation needs to be re-evaluated to ensure the optimum outcome of the PPP contract.
Design/methodology/approach
This paper provides a coherent theoretical framework for dealing with scenarios of potential gain and loss from retaining or transferring risks.
Findings
The outcome indicates that using the proposed framework will provide innovative ways of deriving risk prices in PPP projects using several risk determinants strategies.
Practical implications
In costing risks, analysts have to take into consideration the balance between the cost of risk transfer and the cost of losses if risk is retained.
Originality/value
This paper contributes to the PPP literature and practice by proposing a framework which is consistent with a risk allocation approach in PPP projects, where the key proposition is that risk pricing can overload project debt leading to loss of value.
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Maintenance and running costs contribute significantly to the total cost of running facilities. Almost half of the energy consumed in the UK is used in buildings. Energy…
Abstract
Maintenance and running costs contribute significantly to the total cost of running facilities. Almost half of the energy consumed in the UK is used in buildings. Energy consumption can be attributed to many factors. Describes the fundamentals associated with modelling running costs in leisure centres and then investigates 19 sport centres in the city of Liverpool, using data elicited from the Liverpool Leisure Services Directorate. The energy operating costs were analysed using statistical techniques and artificial intelligence methods. Three types of modelling, linear/non‐linear regression, neural networks and neurofuzzy were developed to predict energy cost. Testing and validation of the results showed that neural network models outperformed both regression and neurofuzzy techniques. However, all the models showed a high level of accuracy. The models would be of use to professionals involved in feasibility studies.
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Zoran Vojinovic and Vojislav Kecman
In this paper we are presenting our research findings on how effective neural networks are at forecasting and estimating preliminary project costs. We have shown that neural…
Abstract
In this paper we are presenting our research findings on how effective neural networks are at forecasting and estimating preliminary project costs. We have shown that neural networks completely outperform traditional techniques in such tasks. In exploring nonlinear techniques almost all of the current research involves neural network techniques, especially multilayer perceptron (MLP) models and other statistical techniques and few authors have considered radial basis function neural network (RBF NN) models in their research. For this purpose we have developed RBF NN models to represent nonlinear static and dynamic processes and compared their performance with traditional methods. The traditional methods applied in this paper are multiple linear regression (MLR) and autoregressive moving average models with eXogenous input (ARMAX). The performance of these and RBF neural network and traditional models is tested on common data sets and their results are presented.
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Abdulrahman Alafifi, Halim Boussabaine and Khalid Almarri
This paper aims to examine the performance efficiency of 56 real estate assets within the rental sector in the UAE to evaluate the relative operation efficiency in relation to…
Abstract
Purpose
This paper aims to examine the performance efficiency of 56 real estate assets within the rental sector in the UAE to evaluate the relative operation efficiency in relation to revenue generation.
Design/methodology/approach
The data envelopment analysis (DEA) approach was used to measure the relative operational efficiency of the studied assets in relation to the revenue performance. This method could produce a more informed and balanced approach to performance measurement.
Findings
The outcomes show that scores of efficiencies ranging from 7% to 99% in some of the models. The results showed that on average buildings are 75% relatively less efficient in maintenance, in term of revenue generation, than the benchmark set. Likewise, on average, the inefficient buildings are 60% relatively less efficient in insurance. Result also shows that 95% of the building assets in the sample are by and large operating at decreasing returns to scale. This implies that managers need to considerably reduce the operational resources (input) to improve the levels of revenue.
Research limitations/implications
This study recommends that the FM operational variables that were found to inefficiently contribute to the revenue should be re-examined to test the validity of the findings. This is necessary before generalising or interpolating the results that are presented in this study.
Practical implications
The information obtained about operational performance can help FM managers to understand which improvements in the productivity of inefficient FM resources are required, providing insight into how to reduce operating costs and increase revenue.
Originality/value
This paper adds value in using new FM operational parameters to evaluate the efficiency of the performance of built assets.