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Accurate estimation or prediction of the resource required for a project is very important for construction. The more accurate the prediction model, the greater the potential for cost savings will be through elimination of any redesign and the minimization of the maintenance expenses. Contractors can also make use of the models for last‐minute bid estimation. In the past the estimators perform the task by analogy with similar previous projects. This approach highly relies on their experience and knowledge. Owing to the lack of a scientific and easily apprehensible method in resource estimation, prediction outcomes are mainly based on humans’ perception, which is inconsistent and exhibits large variations. This paper proposes the use of multiple Group Method of Data Handling (GMDH) models in developing models for resource estimation. The illustrative example has demonstrated the high accuracy of the approach which is superior to other architectures based on artificial neural networks.

In this paper, we discuss how a multi‐criteria decision‐making technique (ELECTRE III) facilitates construction practitioners in decision making for selecting an appropriate construction plant. We first introduce the theoretical background of the technique. Then a case study on how to apply the technique to select concrete vibrators is presented. The findings demonstrate the effectiveness of the model in outranking various options in the process of concrete vibrator selection. The use of ELECTRE III can systematically analyse expert judgments on the decision factors and alternatives that can rationalize the selection process.

Multi‐skilled labour allocation in a defined time frame falls into the class of non‐polynomial (NP) hard problems, solutions to which can only be obtained through repeated trials and errors. The application of fuzzy genetic algorithms (GA) optimisation model provides an efficient way to arrive at a “near‐optimal” solution. In this paper, a fuzzy GA optimisation model of labour allocation is presented to illustrate how the near optimal solution can be obtained.

This paper aims to develop a quantitative model for predicting the productivity of excavators using artificial neural networks (ANN), which is then compared with the multiple regression model developed by Edwards & Holt (2000). A neural network using the architecture of multilayer feedforward (MLFF) is used to model the productivity of excavators. Finally, the modelling methods, predictive behaviours and the advantages of each model are discussed. The results show that the ANN model is suitable for mapping the non‐linear relationship between excavation activities and the performance of excavators. It concludes that the ANN model is an ideal alternative for estimating the productivity of excavators.

Stakeholder management in the form of public engagement in urban planning and infrastructure project impact assessments have become increasingly prevailing. However, the exercise may involve some complex socio‐economic considerations and critical conflict analysis/resolution scenarios. Without a scientific approach, it is difficult to distil a resolution from the chaotic public opinions, especially in those highly controversial issues. This paper proposes the use of conflict analysis to screen out the resolution in a scientific manner.

This study uses a case of harbor‐front design in Hong Kong to illustrate how the analysis was carried out to accomplish the objectives.

The example has illustrated that the conflict analysis has helped resolve a set of major issues in the Hong Kong harbor‐front redevelopment.

The introduction of a structured conflict analysis framework for the government to systematically deal with the conflicting viewpoints from different parties (such as general public and other stakeholders) has been demonstrated, which can be used to map systematically the different viewpoints collected in the public engagement exercise. The results are more convincing and more likely to be acceptable by all the stakeholders.

The purpose of this paper is to propose a variant of superiority and inferiority ranking (SIR) method called SIR‐Grey for determining the location of large‐scale harbour‐front project development.

The study is illustrated with an application example obtained from the Environmental Protection Department of the Hong Kong Special Administrative Region to demonstrate the concept and application procedure of SIR‐Grey. The strengths and weaknesses of SIR‐Grey are highlighted when compared with the traditional weighted average approach.

The strengths and weaknesses of SIR‐Grey are highlighted when compared with the traditional weighted average approach. Among the strengths, the global comparison scores of SIR‐Grey can give a clearer and easier comprehensible algorithm. Further, the global comparison generated from superiority flows (S‐flows) [S‐flow: A is preferred to A′(AP_>A′) or A is indifferent to A′(AI_>A′)] and inferiority flows (I‐flows) [I‐flow: A is preferred to A′(AP_<A′) or A is indifferent to A′(AI_<A′)] can be used to select a solution matching the nature of the problem; e.g. a conservative approach can adopt the ranking from I‐flow because the selected option will have the criteria farthest from the virtual worst site while the ranking from S‐flow can be adopted for an aggressive approach because the final decision will have the criteria closest to the virtual perfect site. Regarding the weaknesses, the major one is the requirement of a full appreciation of the nature of criteria in setting the thresholds and preference structure, which may complicate the application of the model.

This study proposes a variant of SIR method called SIR‐Grey for determining the location of large‐scale harbour‐front project development. This approach can overcome the problem encountered in using other methods which could lead to variation in the final ranking and hence an inconsistent result.