Contracting for community development : a case study based perspective of a public sector client initiative in Hong Kong

The construction industry is known to be an important contributor towards the gross domestic product of many countries. Moreover, the health of the construction industry is positively correlated to the economic growth of a country and in many economies public sector clients account for a major share of construction works. Given this strength, it is important for public sector clients to initiate innovations aimed at the betterment of the industry. In this context, concern about sustainable development has been a major driver of some innovative initiatives in construction industries worldwide. Furthermore, the Government of Hong Kong regards both sustainability and community development as important criteria when planning and procuring construction projects. This paper is based on a case study of a public sector development project in Hong Kong, and presents the salient features of the procurement and contractual systems adopted in the project, which foster sustainability and community development. The reported interim findings are based on a preliminary document analysis that is part of an ongoing longitudinal case study into the project. The document analysis takes a three-pronged approach in terms of how the procurement and contractual systems foster economic, environmental and social sustainability, and sums up their impact on the community as a whole.

Predicting project performance through neural networks

Successful project delivery of construction projects depends on many factors. With regard to the construction of a facility, selecting a competent contractor for the job is paramount. As such, various approaches have been advanced to facilitate tender award decisions. Essentially, this type of decision involves the prediction of a bidderÕs performance based on information available at the tender stage. A neural network based prediction model was developed and presented in this paper. Project data for the study were obtained from the Hong Kong Housing Department. Information from the tender reports was used as input variables and performance records of the successful bidder during construction were used as output variables. It was found that the networks for the prediction of performance scores for Works gave the highest hit rate. In addition, the two most sensitive input variables toward such prediction are ‘‘Difference between Estimate’’ and ‘‘Difference between the next closest bid’’. Both input variables are price related, thus suggesting the importance of tender sufficiency for the assurance of quality production.

Stakeholder impact analysis of infrastructure project management in developing countries : a study of perception of project managers in state-owned engineering firms in Vietnam

Construction projects can involve a diverse range of stakeholders and the success of the project depends very much on fulfilling their needs and expectations. It is important, therefore, to identify and recognize project stakeholders and develop a rigorous stakeholder management process. However, limited research has investigated the impact of stakeholders on construction projects in developing countries. A stakeholder impact analysis (SIA), based on an approach developed by Olander (2007), was adopted to investigate the stakeholders' impact on state-owned civil engineering projects in Vietnam. This involved the analysis of a questionnaire survey of 57 project managers to determine the relative importance of different stakeholders. The results show the client to have the highest level of impact on the projects, followed by project managers and the senior management of state-owned engineering firms. The SIA also provides suggestions to project managers in developing and evaluating the stakeholder management process.

A fuzzy neural network approach for contractor pre-qualification

Nonlinearity, uncertainty and subjectivity are the three predominant characteristics of contractors prequalification which cause the process more of an art than a scientific evaluation. A fuzzy neural network (FNN) model, amalgamating both the fuzzy set and neural network theories, has been developed aiming to improve the objectiveness of contractor prequalification. Through the FNN theory, the fuzzy rules as used by the prequalifiers can be identified and the corresponding membership functions can be transformed. Eighty-five cases with detailed decision criteria and rules for prequalifying Hong Kong civil engineering contractors were collected. These cases were used for training (calibrating) and testing the FNN model. The performance of the FNN model was compared with the original results produced by the prequalifiers and those generated by the general feedforward neural network (GFNN, i.e. a crisp neural network) approach. Contractor’s ranking orders, the model efficiency (R2) and the mean absolute percentage error (MAPE) were examined during the testing phase. These results indicate the applicability of the neural network approach for contractor prequalification and the benefits of the FNN model over the GFNN model. The FNN is a practical approach for modelling contractor prequalification.

Decision support system for contractor pre-qualification : artificial neural network model

The selection criteria for contractor pre-qualification are characterized by the co-existence of both quantitative and qualitative data. The qualitative data is non-linear, uncertain and imprecise. An ideal decision support system for contractor pre-qualification should have the ability of handling both quantitative and qualitative data, and of mapping the complicated nonlinear relationship of the selection criteria, such that rational and consistent decisions can be made. In this research paper, an artificial neural network model was developed to assist public clients identifying suitable contractors for tendering. The pre-qualification criteria (variables) were identified for the model. One hundred and twelve real pre-qualification cases were collected from civil engineering projects in Hong Kong, and eighty-eight hypothetical pre-qualification cases were also generated according to the “If-then” rules used by professionals in the pre-qualification process. The results of the analysis totally comply with current practice (public developers in Hong Kong). Each pre-qualification case consisted of input ratings for candidate contractors’ attributes and their corresponding pre-qualification decisions. The training of the neural network model was accomplished by using the developed program, in which a conjugate gradient descent algorithm was incorporated for improving the learning performance of the network. Cross-validation was applied to estimate the generalization errors based on the “re-sampling” of training pairs. The case studies show that the artificial neural network model is suitable for mapping the complicated nonlinear relationship between contractors’ attributes and their corresponding pre-qualification (disqualification) decisions. The artificial neural network model can be concluded as an ideal alternative for performing the contractor pre-qualification task.

Developing an integrated decision model for the enhancement of sustainability deliverables for Australian road infrastructure projects

Road and highway infrastructure provides the backbone for a nation's economic growth. The versatile dispersion of population in Australia, from sparsely settled communities in remote areas to regenerated inner city suburbs with high density living in metropolitans, calls for continuing development and improvement on roads infrastructure under the current federal government policies and state governments' strategic plans. As road infrastructure projects involve large resources and mechanism, achieving sustainability not only in economic scales but also through environmental and social responsibility becomes a crucial issue. Current efforts are often impeded by different interpretation on sustainability agenda by stakeholders involved in these types of projects. As a result, sustainability deliverables at the project level is not often as transparent and measurable, compared to promises in project briefs and designs. This paper reviews the past studies on sustainable infrastructure construction, focusing on roads and highway projects. Through literature study and consultation with the industry, key sustainability indicators specific to road infrastructure projects have been identified. Based on these findings, this paper introduces an on-going research project aimed at identifying and integrating the different perceptions and priority needs of the stakeholders, and issues that impact on the gap between sustainability foci and its actual realization at project end level. The exploration helps generate an integrated decision-making model for sustainable road infrastructure projects. The research will promote to the industry more systematic and integrated approaches to decision-making on the implementation of sustainability strategies to achieve deliverable goals throughout the development and delivery process of road infrastructure projects in Australia.

Sustainable housing implementation through mutual benefits to stakeholders : a decision making approach

Improved public awareness and strong sentiments towards environmental issues will continue to create increasing demand for sustainable housing (SH) in the coming years. Despite this potential, the up-take rate of sustainable housing in new build and through home renovation is not as high as expected within the housing industry. This is in contrast to the influx of emerging building technologies, new materials and innovative designs seen in exemplar homes built worldwide. How we should use the increasing awareness of SH and emerging technologies as an impetus to change the un-sustainable designs and practices of the building industry is high on the agenda of the government and majority of the stakeholders involved. This warrants the study of multifaceted strategies that meet the needs of multiple stakeholders and integrated seamlessly into housing development processes. Specifically, the different perceptions, roles and incentives of stakeholders, who inevitably need to ensure their benefits and commercial returns, should be highlighted and acted upon. ----- This paper discusses the preliminary findings of a research project that aims to promote SH implementation by identifying and materializing the mutual benefits among key stakeholders. The aim is to be achieved through questionnaire surveys, structural equation modelling, interviews and case studies with seven major stakeholders within the Australian housing industry. This research identifies the influence and relationship of relevant factors, investigates preferences, similarities and differences between stakeholders on perceived benefits and in turn explores the mutual-benefit strategy package that facilitates decision making towards sustainable housing development.

Risk management in the Lusoponte concession : a case study of the two bridges in Lisbon, Portugal

The case study of Lusoponte illustrates the concession awarded by the Portuguese Government to finance, design, build and operate two bridges over the Tagus in Lisbon, Portugal. It includes an overview of the project's background and an analysis of the main risk categories stating both the actual risks encountered and the mitigation measures adopted. Throughout the project a great attention was given to whole life cycle costs, and gains in efficiency and cost control. Among the lessons that can be learned from both the public and private sector is that a complete risk management analysis must include not only the technical factors but also a realistic assessment of environmental and social risks. These were the risks that were somewhat overseen and that caused the main problems to the project's development.

Motivational misalignment on an iconic infrastructure project

Aligning the motivation of contractors and consultants to perform better than ‘business-as-usual’ (BAU) on a construction project is a complex undertaking and the costs of failure are high as misalignment can compromise project outcomes. Despite the potential benefits of effective alignment, there is still little information about optimally designing procurement approaches that promote motivation towards ‘above BAU’ goals. The paper contributes to this knowledge gap by examining the negative drivers of motivation in a major construction project that, despite a wide range of performance enhancing incentives, failed to exceed BAU performance. The paper provides a case study of an iconic infrastructure project undertaken in Australia between 2002 and 2004. It is shown that incentives provided to contractors and consultants to achieve above BAU performance can be compromised by a range of negative motivation drivers including: • inequitable contractual risk allocation; • late involvement of key stakeholders; • inconsistency between contract intentions and relationship intentions; • inadequate price negotiation; • inconsistency between the project performance goals and incentive goals; •unfair and inflexible incentive performance measurement processes. Future quantitative research is planned to determine the generalisability of these results.