Critical success factors can be used in value management workshops to determine the client value system, which in turn is critically important to the briefing process and the successful delivery of construction projects. This research is concerned with international construction projects. In particular, it investigates the impact of local knowledge on the client value system. Workshops were organised on 12 international projects in the western Balkans region. The initial client value system was captured by using a paired comparison exercise. Next, local knowledge information was introduced, and the EFTE (Estimate-Feedback-Talk-Estimate) technique was used to capture the revised client value system. The changes in the two sets of client value systems were analysed. The results indicate that 4 parameters (Scope, Contract-admin, Human resources and Health and Safety) out of 8 changed and that the changes were statistically significant. Local knowledge can be used to impact client value system.

Purpose -The presented research proposes a method aimed to improve a case retrieval phase of the case-based reasoning (CBR) system through optimization of feature relevance parameters, i.e. feature weights. Design/methodology/approach -The improvement is achieved by applying the metaheuristic optimization technique, called electromagnetism-like algorithm (EM), in order to appropriately adjust the feature weights used in k-NN classifier. The usability of the proposed EM k-NN algorithm is much broader since it can also be used outside the CBR system, e.g. for solving general pattern recognition tasks. Findings -It is showed that the proposed EM k-NN algorithm improves the baseline k-NN model and outperforms the appropriately tuned artificial neural network (ANN) in the task of predicting the case (data record) output values. The results are verified by performing statistical analysis.Research limitations/implications -The proposed method is currently adjusted to deal with numerical features, so, as a direction for future work, the variant of EM k-NN algorithm that deals with symbolic or some more complex types of features should be considered. Practical implications -EM k-NN algorithm can be incorporated as a case retrieval component inside a general CBR system. This is the future direction of the investigation since the authors intend to build a complete specialized CBR system for construction project management. The overall CBR with incorporated EM k-NN will have significant implication in the construction management as it will be able to produce more accurate prediction of viability and the life cycle of new construction projects. Originality/value -The electromagnetism-like algorithm is applied to the problem of finding feature weights for the first time. EM potential for solving the problem of weighting features lies in its internal structure because it is based on the real-valued EM vectors. The overall EM k-NN algorithm is applied on data sets generated from real construction projects data corpus. The proposed algorithm proved its efficiency as it outperformed baseline k-NN model and ANN. Its applicability in more complex and specialized CBR systems is high since it can be easily added due to its modular (black-box) design.

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