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The level of live, reduction of pollution and positive human health is affected by the quality of landscape. In addition, many benefits such as aesthetic, psychological and social can be achieved. The quality of landscape is integrated with the individual’s needs. The scope of landscape must be determined according to user needs and planning methods. The purpose of this study is to propose a house of quality (HOQ) model to identify the landscape design requirements. The Islamic University of Gaza (IUG) was used as a case study. The need to implement the proposed HOQ model has been identified. Also, the male and female students’ priorities have been identified, and the weight of campus landscape design elements was extracted.

The male and female IUG students’ needs were identified using a questionnaire survey. The data were analyzed for testing statistical validity and reliability using SPSS. HOQ model was built using EDRAW Max program.

The ten most important student needs are “cleanliness, praying, feeling safe, feeling comfortable psychic, drinking, spaciousness and breadth, shading, quiet, rest and sitting, and fresh air.” Based on these results and needs assessment, the HOQ was constructed. The most important design elements are “group of seats, shrubs, trees, lawns, pergola, and seats chairs or benches.”

This study adds to the designer’s knowledge about HOQ application in design, and contributes significantly to consider public participation as a way in collecting the voice of customers.

This paper seeks to introduce an evolved hybrid genetic algorithm and neural network (GNN) model. The model is developed to predict contractor performance given the current attributes in a process to pre‐qualify the most appropriate contractor. The predicted performance is used to pre‐qualify the contractors.

Hypothetical and real‐life case studies from projects executed in the Gaza Strip and West Bank were collected through structured questionnaires. The evaluation of the contractor's attributes and the corresponding actual performance of the contractor in terms of time, cost, and quality overrun (OR) were collected. The weighted contractor's attributes were used as inputs to the GNN model. The corresponding time, cost, and quality ORs for the same cases were fed as outputs to the GNN model in a supervised learning back propagation neural network (NN). (The adopted training and testing process to develop a trained model is presented.) The training process, including choosing the topology of the required NN using genetic algorithms, is explained.

The results revealed that there is a satisfactory relationship between the contractor attributes and the corresponding performance in terms of contractor's deviation from the client objectives. The accuracy of the model in terms of mean absolute percentage error (MAPE), R², average absolute error and mean square error revealed that the model has sufficient accuracy for implementation. The average MAPE for time, cost and quality OR is 15 per cent. Consequently, the model accuracy is 85 per cent.

The GNN model is able to predict future contractor performance for given attributes.

The objective of this research is to identify the cross‐cultural similarities and differences of the implementation of the UK PFI procurement process in different contexts.

The research methodology adopted was the SLEEPT approach. The identified features are examined from source material of various projects within the stated nations. A Delphic approach of confirmation by national collaborators from each country is utilised. Confirming and disaffirming features are examined utilising exogenous cultural drivers.

The conclusion of this research identifies cross‐cultural features of six different cultures presented as a “cultural compass” which will inform the development of future private finance initiative (PFI)/public private partnership (PPP) projects. The impact of this research will have implications for the appreciation of cultural similarities and differences of national “construction cultures” for effective project delivery of future PFI/PPP projects.

This paper offers an approach that can be generalised for adoption by nations considering the introduction of PFI as a procurement process.