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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 non‐linear 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 88 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 non‐linear 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.

The selection of a suitable contractor for a project has a significant impact on project success. In order to avoid the selection of an incapable contractor, the capabilities of contractors must be assessed prior to tendering through pre-qualification. However, the pre-qualification process is characterized by partiality and ambiguity. In view of this, numerous models have been developed to solve the pre-qualification problems. Prior to the development of such models, it is very important to assess how the pre-qualification criteria impact project success criteria so as to aid in the selection of pre-qualification criteria while considering the project and client’s objectives. Therefore, the purpose of this paper is to ascertain the relationship between contractors’ pre-qualification criteria and project success criteria.

The study utilized explanatory research design in testing 35 hypotheses. The views of 121 practising quantity surveyors were solicited using a structured questionnaire and analyzed using the partial least square structural equation modeling to validate the hypothesis.

In all, 13 of the 35 hypotheses were not supported. The findings indicated a clear relationship between contractors’ pre-qualification and project success.

Therefore, it is evident that the practice of lowest evaluate bidder is not adequate for providing a satisfactory project outcome. It is, therefore, suggested that more emphasis should be placed on contractor’s technical abilities, health and safety and management capabilities as they have proven to have a significant correlation with the project success.

This study provides insights to the how various pre-qualification criteria can impact the project success criteria and further contributes to the symbiotic that exist in the literature on pre-qualification and project success.

The selection of the contractor, as a main participant of a construction project, is the most important and challenging decision process for a client. The purpose of this paper is to propose a multi-agent systems (MAS)-based contractor pre-qualification (CP) model for the construction sector in the frame of the tender management system.

The meta-classification and analysis study of the existing literature on CP, contractor selection and criteria weighting issues, which examines the current and important CP criteria, other than price, is introduced structurally. A quantitative survey, which is carried out to estimate initial weightings of the identified criteria, is overviewed. MAS are used to model the pre-qualification process and workflows are shown in Petri nets formalism. A user-friendly prototype program is created in order to simulate the tendering process. In addition, a real case regarding the construction work in Turkey is analyzed.

There is a lack of non-human-driven solutions and automation in CP and in the selection problem. The proposed model simulates the pre-qualification process and provides consistent results.

The meta-classification study consists of only peer-reviewed papers between 1992 and 2013 and the quantitative survey initiates the perspectives of the actors of Turkish construction sector. Only the traditional project delivery method is selected for the proposed model, that is other delivery methods such as design/build, project management, etc., are not considered. Open, selective limited and negotiated tendering processes are examined in the study and the direct supply is not considered in the scope.

The implications will help to provide an objective CP and selection process and to prevent the delays, costs and other troubles, which are caused by the false selection of a contractor.

Automation and simulation in the pre-qualification and the selection of the contractor with a non-human-driven intelligent solution ease the decision processes of clients in terms of cost, time and quality.

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 studies on contractor prequalification focus more on the review of models and algorithms rather than review of the criteria for contractor prequalification. However, the basis of every prequalification model primarily relates to the measurement and judgement of prospective contractors based on a set of decision criteria. This paper aims to address the gap by reviewing academic papers on contractor prequalification criteria.

A desktop search was conducted under the “T/A/K (title/abstract/keyword)” field of the Scopus search engine. A total of 49 papers were initially identified; however, only peer reviewed journals were selected for the study; therefore, a sample of 36 was subsequently used. Further filtering was done in which 26 papers were found valid for further analysis as it was realized that, not all the identified papers presented empirical arguments about the issue of contractor pre-qualification criteria. The selected 26 papers were subjected to content analysis to identify the key contractor pre-qualification criteria.

A total of 41 criteria were identified which were subsequently classified into six main categories, namely, technical considerations, management considerations, financial considerations, reputation considerations, general experience considerations and health, safety and environmental considerations. There was an indication that, the involvement of health, safety and environmental considerations in contractor prequalification proceedings is limited.

The major limitation of this research was the limited number of papers selected for further analysis based on the Scopus search engine. The identified criteria serve as a basis for further empirical studies on contractor prequalification criteria.

The outcome of this study broadens the understanding of practitioners and researchers on the various criteria for contractor prequalification.

By critically reviewing available literature on contractor prequalification, the study sets the tone for further empirical studies on contractor prequalification.

Selecting the ‘best’ main contractor is a complex decision process for construction clients. It requires a large number of criteria to be simultaneously measured and evaluated. Many of these criteria are related to one another in a complex way and therefore, they very often conflict insofar as improvement in one often results in decline of another(s). Furthermore, as contractors' attributes are expressed in both quantitative and qualitative terms, decision‐makers have to base their judgements on both quantitative data and experiential subjective assessments. In this paper, the evidential reasoning (ER) approach (which is capable of processing both quantitative and qualitative measures) is applied as a means of solving the contractor selection problem (CSP). The process of building a multiple criteria decision model of a hierarchical structure is presented, in which both quantitative and qualitative information is represented in a unified manner. The CSP is then fully investigated using the ER approach. Both the advantages of applying this model in practice and the analysis process itself are discussed.

Time and cost are usually critical to construction clients. Given the many contributory factors, improved quantitative models of time and cost may help clients to predict project outcomes at the outset, and also at different stages of the project life span. These can also help to compare deviations in significant contributory factors, and to suggest corrective actions. Multiple linear regression (MLR) and artificial neural networks (ANN) were applied in developing such quantitative models in a research project based in Hong Kong. A comparative study indicated that ANN had better prediction capabilities than MLR by itself. Significant factors identified through quantitative models developed, indicated that time over‐run levels were mainly governed by non‐procurement related factors (e.g. project characteristics and client/client representative characteristics), while cost over‐run levels were significantly influenced by both procurement and non‐procurement related factors (e.g. project characteristics, client/client representative characteristics and contractual payment modalities). A parallel approach yielded interesting comparisons of the variations of mean time and cost over‐runs, when comparing groups of projects using different procurement sub‐systems, from the Hong Kong sample.

Construction industry is one of the worst affected sectors due to the impact of the current coronavirus disease 2019 (COVID-19) outbreak. Therefore, the purpose of this study was to identify the key competencies that should be demonstrated by the contractors in the post-COVID-19 scenario to make them resilient to the adverse impact of pandemic outbreaks.

A survey instrument was developed using an extensive literature review and was tested using a pilot study. It was then administered online using survey monkey to 900 respondents, out of which 324 complete responses were obtained. The data analysis was performed using exploratory factor analysis and second-order confirmatory factor analysis.

After analyzing the data, it was identified that the most critical competence was managing site safety (standard factor loading (SFL) = 0.91), followed by leadership skills (SFL = 0.88), technical competence (SFL = 0.81), managing supply chain disruption (SFL = 0.73) and financial stability (SFL = 0.48) that were found to be less essential from the respondents’ point of view.

The study is first of its kind to identify the core competencies that should be demonstrated by the contractors to cope with COVID-19-induced disruptions. The findings of this article can be used by the practitioners to develop policies and procedures for selection of contractors.

Contractor prequalification is a typical multiple criteria decision‐making problem that includes both quantitative and qualitative criteria. The conditions surrounding the prequalification decision are often imprecise, subjective and uncertain; assessments are consequently made using linguistic approximations. Fuzzy set theory is specifically designed to handle qualitative and linguistic data based on approximations and provides a method of representing in numerical form the linguistic approximations used to describe the decision‐maker judgments. However, fuzzy set theory has a weakness in identification of the relative weights of the decision criteria. On the other hand, one of the most accurate and easy methods for identifying the relative weights is the analytic hierarchy process (AHP). This paper seeks to address these issues.

The main objective of this paper is to develop a new integrated decision model composed of fuzzy set theory and analytic hierarchy process (AHP) methodologies approach that takes full advantages of the fuzzy set theory and the AHP. Two fuzzy approaches are considered, namely Chang's extent analysis and Jaskowski aggregated group decision analysis.

Both approaches are applied and validated on actual contractors in the Egyptian construction market. A software tool is developed to automate the calculations and a case study is provided.

This research produced a new integrated decision model composed of fuzzy‐AHP methodology approach that takes full advantages of the fuzzy set theory and AHP for tackling the uncertainty and imprecision of contractor prequalification during the prequalification stage, where the decision‐makers comparison judgments are represented as fuzzy triangular numbers. The default criteria used in this model had been collected through the literature review and experts’ opinion for building projects.