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Projects have high stakes in how they are categorized. The final place of a project within a classification scheme depends on the inclusion or exclusion of certain classification criteria. So far, many researchers and organizations have used a variety classification criteria to construct different project classification schemes. However, most of these classification criteria have been taken for granted and the process of selecting them to categorize projects still remains a black box. The purpose of this paper is to open the black box of classification process and explain how it is reflected in picking the classification criteria.

Drawing on insights from cognitive psychology’s literature, the authors examine the main views of classification process to provide insight into the unknown or implicit reasons that one might have to pick particular attributes as project classification criteria.

The authors argue that classification occurs in the eye of the beholder; it is not only the project’s features per se but also the classifier’s “goals, ideal and preference” or “knowledge of causal relations” that are reflected in the classification criteria.

By elaborating the classification process, the authors brought the project context into the big picture of classification and provide a more rational, and coherent picture of how project classification works. This contributes to a theoretical blind spot, raised by prior researchers, related to the selection of project classification criteria.

Understanding classification processes will reduce the ambiguities, inconsistencies and multiple interpretations of project categories and help practitioners increase their projects’ visibility and legitimacy within an already established classification scheme. These implications help organizations in addressing some of the main obstacles to using categorization in project management practice.

The review of prior work in the category research literature and the insights from this paper will provide project management scholars with a useful toolbox for future research on project classification, which has long been understudied.

Communication ineffectiveness inherent in the unique attributes of Mass Housing Project (MHP) features is well admitted in the body of literature. However, the understanding of the extent and nature of this influence of the unique features of MHPs requires an empirical insight. The aim of this paper is to identify the communication ineffectiveness induced by the unique features and delineate the implications of the findings for mass housing practitioners and stakeholders towards engendering effective communication performance.

Through a structured questionnaire survey, data were elicited from mass housing stakeholders and project team leaders. The data were subsequently analysed using structural equation modelling, and the communication effectiveness evaluation model was developed. Subsequently, the model was validated through a questionnaire survey on ten experienced mass housing practitioners, researchers and stakeholders.

The results revealed significant, moderate and weak effects of the unique features of MHP team communication performance. This suggests that the unique features of MHPs have varying degree of influence on the communication performance among project teams’ delivery. The findings provide practical, empirical insights and understanding into the inherent communication ineffectiveness on MHPs, and thus are very useful in communication management and planning in MHP’s delivery.

Against the backdrop of the need to gain an in-depth understanding of the inherent communication challenges towards improving communication performance in MHP delivery, the findings have rigorously revealed and provided clear insight into the nature of communication ineffectiveness inherent in the unique features of MHPs. The findings and insights provided by this study are thus useful for aligning communication management planning and strategies to the unique MHP environment to engender communication success. Practitioners can also use these findings towards the development of their communication behavioural skills and communication infrastructure for MHP delivery.

Factors like bid price, submission time, and number of bidders influence the procurement process in public projects. These factors and the award criteria may impact the project’s financial compliance. Predicting budget compliance in construction projects has been traditionally challenging, but Machine Learning (ML) techniques have revolutionised estimations.

In this study, Portuguese Public Procurement Data (PPPData) was utilised as the model’s input. Notably, this dataset exhibited a substantial imbalance in the target feature. To address this issue, the study evaluated three distinct data balancing techniques: oversampling, undersampling, and the SMOTE method. Next, a comprehensive feature selection process was conducted, leading to the testing of five different algorithms for forecasting budget compliance. Finally, a secondary test was conducted, refining the features to include only those elements that procurement technicians can modify while also considering the two most accurate predictors identified in the previous test.

The findings indicate that employing the SMOTE method on the scraped data can achieve a balanced dataset. Furthermore, the results demonstrate that the Adam ANN algorithm outperformed others, boasting a precision rate of 68.1%.

The model can aid procurement technicians during the tendering phase by using historical data and analogous projects to predict performance.

Although the study reveals that ML algorithms cannot accurately predict budget compliance using procurement data, they can still provide project owners with insights into the most suitable criteria, aiding decision-making. Further research should assess the model’s impact and capacity within the procurement workflow.

Previous research predominantly focused on forecasting budgets by leveraging data from the private construction execution phase. While some investigations incorporated procurement data, this study distinguishes itself by using an imbalanced dataset and anticipating compliance rather than predicting budgetary figures. The model predicts budget compliance by analysing qualitative and quantitative characteristics of public project contracts. The research paper explores various model architectures and data treatment techniques to develop a model to assist the Client in tender definition.

This study aims to analyze Kickstarter data along with social media data from a data mining perspective. Kickstarter is a crowdfunding financing plataform and is a form of fundraising and is increasingly being adopted as a source for achieving the viability of projects. Despite its importance and adoption growth, the success rate of crowdfunding campaigns was 47% in 2017, and it has decreased over the years. A way of increasing the chances of success of campaigns would be to predict, by using machine learning techniques, if a campaign would be successful. By applying classification models, it is possible to estimate if whether or not a campaign will achieve success, and by applying regression models, the authors can forecast the amount of money to be funded.

The authors propose a solution in two phases, namely, launching and campaigning. As a result, models better suited for each point in time of a campaign life cycle.

The authors produced a static predictor capable of classifying the campaigns with an accuracy of 71%. The regression method for phase one achieved a 6.45 of root mean squared error. The dynamic classifier was able to achieve 85% of accuracy before 10% of campaign duration, the equivalent of 3 days, given a campaign with 30 days of length. At this same period time, it was able to achieve a forecasting performance of 2.5 of root mean squared error.

The authors carry out this research presenting the results with a set of real data from a crowdfunding platform. The results are discussed according to the existing literature. This provides a comprehensive review, detailing important research instructions for advancing this field of literature.

In classical perspective, projects under a certain size are not feasible for P3. However, there is an emerging trend on using P3 to deliver projects which are frequently at small- to medium- size to meet ever-increasingly complex social needs, including enhancing lifecycle performance of existing facilities, designing and building for resilience and sustainability, ensuring cost effectiveness of public spending and fostering innovation. In contrast with the increasing implementation, small and medium P3s, especially those in the United States, receive little attention in existing studies. This study aims at answering the question: in the context of US, what features of those small- to medium- sized P3s with success records enable the selection of P3 as delivery method.

By critically reviewing the literature, this study synthesizes and discusses the challenges in classical perspective. The authors use a framework drawn from the transaction cost to propose two types of enabling features that could contribute to the success of small and medium P3s. The proposed enabling features are supported by case study of twelve identified small- to medium- sized P3s which have reached financial closure as of 2018 in the United States.

The results show how the identified enabling opportunities have been used in these cases to enhance the viability of the P3 model in the infrastructure market. The two types of features are high tolerance enabler explained by the expectations on indirect and non-monetary compensations, and cost reduction enablers including: (1) being in the sectors with well-established traditions on using private investments; (2) having developers with expertise on infrastructure finance; (3) being in the jurisdictions with favorable legislative environment and (4) having less-uncertain future project revenue.

This study, for the first time, critically examines the enabling features of the P3 model for delivering small and medium infrastructure projects in the United States. This research sheds light on the credibility and viability of small- to medium- sized P3 and increases the confidence in policy makers to promote this model.

This study investigates the extent to which the popular forms of contract adopted in the Middle East (ME) address collaboration. The purpose of this paper is to assess how collaboration features weaved into the construct of integrated project delivery (IPD) may impact projects in the ME. In this context, the study identifies features in IPD and existing delivery methods that may enable or inhibit collaboration and evaluates their impact on project success from the perspective of various contract managers in the ME.

The study employs structured face-to-face interviews with 41 construction industry practitioners in top contract management positions in the ME to evaluate the significance of collaboration features in IPD. Data collected from the structured interviews/surveys were analyzed using statistical tools in R and Excel.

Results reveal that while experts recognize the collaboration benefits which IPD features may contribute to a project, the current contractual environment of the industry does not optimally encompass these features. The current status of project delivery does not favor IPD implementation nor does it enable its collaborative features.

This study contributes to the growing international body of knowledge addressing the application of collaborative contracts in construction projects, and it is innovative in evaluating collaboration features within IPD and exiting project deliveries in the ME.

The perception that the repetitive nature and attributes of mass housing projects (MHPs) induce significant influence on communication among projects teams have persistently been acknowledged without an empirical accentuation. This seemingly untested knowledge tends to limit the predictive accuracy of success and effectiveness of adopted communication style, strategies and models in mass housing particularly due to the incidence of the repetitive attributes. The purpose of this study is to delineate the influence of the repetitive attributes of mass housing projects on communication performance among the project team.

Through the use of questionnaire survey and structural equation modelling analysis, a hypothesized model tested evaluated the effects of the repetitive attributes of mass housing on information flow and information composition communication performance.

In the case of influence on information flow, it was seen to be substantial whereas that of the information composition was moderate.

The findings offer empirical credence to the existing perception and indeed affirm that the repetitive features of MHPs significantly contribute to communication performance related to information flow and information composition among the project team. The implication of these findings is that, practitioners and stakeholders on mass housing are urged to explore bespoke communication methods, medium, strategies and management approaches that fit the MHP attributes and environment to engender managerial and communication efficiencies in the delivery.

The present study aims to develop a risk-supported case-based reasoning (RS-CBR) approach for water-related projects by incorporating various uncertainties and risks in the revision step.

The cases were extracted by studying 68 water-related projects. This research employs earned value management (EVM) factors to consider time and cost features and economic, natural, technical, and project risks to account for uncertainties and supervised learning models to estimate cost overrun. Time-series algorithms were also used to predict construction cost indexes (CCI) and model improvements in future forecasts. Outliers were deleted by the pre-processing process. Next, datasets were split into testing and training sets, and algorithms were implemented. The accuracy of different models was measured with the mean absolute percentage error (MAPE) and the normalized root mean square error (NRSME) criteria.

The findings show an improvement in the accuracy of predictions using datasets that consider uncertainties, and ensemble algorithms such as Random Forest and AdaBoost had higher accuracy. Also, among the single algorithms, the support vector regressor (SVR) with the sigmoid kernel outperformed the others.

This research is the first attempt to develop a case-based reasoning model based on various risks and uncertainties. The developed model has provided an approving overlap with machine learning models to predict cost overruns. The model has been implemented in collected water-related projects and results have been reported.

It is widely recognised that improving project delivery in construction requires a consideration of the culture within the project organisation that is often associated with fragmentation, antagonism, mistrust, poor communication, finger‐pointing, machismo, and sexism. Many have thus called for cultural change on construction projects. However, change can only take place when there is an understanding of the drivers of culture within the construction project organisation (CPO). Given the argument in cultural theory that culture reflects distinct adaptations to the environments in which people operate, this research seeks to look for empirical evidence that the culture of the CPO is associated with particular features of construction projects.

A mixed methodology approach was employed with qualitative data collected through semi‐structured interviews, and quantitative data on project features and cultural orientations collected through a questionnaire survey of UK contractors.

Factor analysis revealed five principal cultural dimensions: workforce orientation, performance orientation, team orientation, client orientation, and project orientation. It was found that these five dimensions are associated with a number of key project features, in particular project size, complexity, influence of participants like the quantity surveyor, client and main contractor, the level of importance of cost and health and safety (H&S), location, and the number of variations. Significantly, no evidence was found to confirm that the procurement approach adopted influenced culture.

The findings provide some insight into the cultural consequences of project features, awareness of which is essential if appropriate strategies are to be developed to mitigate the negative impacts of culture.

Completing Right-of-Way (ROW) acquisition process on schedule is critical to avoid delays and cost overruns on transportation projects. However, transportation agencies face challenges in accurately forecasting ROW acquisition timelines in the early stage of projects due to complex nature of acquisition process and limited design information. There is a need of improving accuracy of estimating ROW acquisition duration during the early phase of project development and quantitatively identifying risk factors affecting the duration.

The quantitative research methodology used to develop the forecasting model includes an ensemble algorithm based on decision tree and adaptive boosting techniques. A dataset of Georgia Department of Transportation projects held from 2010 to 2019 is utilized to demonstrate building the forecasting model. Furthermore, sensitivity analysis is performed to identify critical drivers of ROW acquisition durations.

The forecasting model developed in this research achieves a high accuracy to predict ROW durations by explaining 74% of the variance in ROW acquisition durations using project features, which is outperforming single regression tree, multiple linear regression and support vector machine. Moreover, number of parcels, average cost estimation per parcel, length of projects, number of condemnations, number of relocations and type of work are found to be influential factors as drivers of ROW acquisition duration.

This research contributes to the state of knowledge in estimating ROW acquisition timeline through (1) developing a novel machine learning model to accurately estimate ROW acquisition timelines, and (2) identifying drivers (i.e. risk factors) of ROW acquisition durations. The findings of this research will provide transportation agencies with insights on how to improve practices in scheduling ROW acquisition process.