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The exact process of construction projects performance assessment and benchmarking still remains subjective relying on qualitative techniques, which does not allow stakeholders to address the issues and the drawbacks of their respective projects as effectively as possible for performance improvement purposes. Hence, this research aims to establish a unified project performance score (PPS) for assessing and comparing projects performance.

Data were collected from Construction Industry Institute (CII) members and through University of Wisconsin active research projects. Exploratory data analysis was done to investigate the calculated performance metrics and the collected data characteristics. Data were converted into six performance metrics which were used as the independent variables in creating the PPS model. Logistic regression model was developed to generate the unified PPS equation in order to explain the variables that significantly affect construction projects successful post-completion performance. The PPS model was then applied on the collected dataset to benchmark projects in terms of project delivery systems, compensation types and project types in order to showcase the PPS capabilities and possible applications.

The model revealed that construction cost and schedule growth are the most important metrics in assessing projects performance, while RFIs’ processing time and change orders per million dollars were the features with the least effect on the PPS value. The authors found that integrated project delivery (IPD) and target value (TV) projects outperformed all other project delivery and compensation types. While, industrial projects showed the worst performance, as compared to commercial or institutional projects.

The PPS model can be used to assess the performance of any pool of executed projects, and introducing a novel addition to the field of construction business analytics which is a supplementary tool to successful decision making and performance improvement. Additionally, the bidding selection system can be revolutionized from a cost-based to a performance based one using the PPS model to improve the outcomes of the buyout process.

In the context of rewards-based crowdfunding, this study aims to examine the role of project backers as providers of knowledge inputs beyond just financial capital.

This study uses binomial regression to study the relationship between project creators’ and backers’ knowledge sharing, and the relationship of these two knowledge-sharing elements with achieving above-goal funding levels.

This study finds that the project creator’s knowledge sharing is significantly and positively related to backers’ knowledge sharing and that this relationship is moderated by the type of project. Furthermore, backers’ knowledge sharing is positively related to above-goal funding outcomes for a project.

This study established the link between creators’ and backers’ knowledge sharing in rewards-based crowdfunding, which has been underexplored in the literature. This study’s direct attention to the role of knowledge as a key resource in rewards-based crowdfunding and crowdsourcing in general.

For entrepreneurs seeking crowdfunding, this study highlights the importance of knowledge sharing with their project backers to attain above-goal funding. Furthermore, eliciting backers’ knowledge input acts as a signaling mechanism that increases the crowd’s confidence in the project. It also endows entrepreneurs with knowledge resources that can improve project outcomes and achieve broader market success postcrowdfunding.

To the best of the authors’ knowledge, this study is one of the first to focus on knowledge content as a critical element in project backer-creator communication in rewards-based crowdfunding. This study also delineate the various knowledge types shared between the project creator and backers in both rewards-based crowdfunding projects.

Developing a reliable cost estimate at the early stage of construction projects is challenging due to inadequate project information. Most of the information during this stage is qualitative, posing additional challenges to achieving accurate cost estimates. Additionally, there is a lack of tools that use qualitative project information and forecast the budgets required for project completion. This research, therefore, aims to develop a model for setting project budgets (excluding land) during the pre-conceptual stage of residential buildings, where project information is mainly qualitative.

Due to the qualitative nature of project information at the pre-conception stage, a natural language processing model, DistilBERT (Distilled Bidirectional Encoder Representations from Transformers), was trained to predict the cost range of residential buildings at the pre-conception stage. The training and evaluation data included 63,899 building permit activity records (2021–2022) from the Victorian State Building Authority, Australia. The input data comprised the project description of each record, which included project location and basic material types (floor, frame, roofing, and external wall).

This research designed a novel tool for predicting the project budget based on preliminary project information. The model achieved 79% accuracy in classifying residential buildings into three cost_classes ($100,000-$300,000, $300,000-$500,000, $500,000-$1,200,000) and F1-scores of 0.85, 0.73, and 0.74, respectively. Additionally, the results show that the model learnt the contextual relationship between qualitative data like project location and cost.

The current model was developed using data from Victoria state in Australia; hence, it would not return relevant outcomes for other contexts. However, future studies can adopt the methods to develop similar models for their context.

This research is the first to leverage a deep learning model, DistilBERT, for cost estimation at the pre-conception stage using basic project information like location and material types. Therefore, the model would contribute to overcoming data limitations for cost estimation at the pre-conception stage. Residential building stakeholders, like clients, designers, and estimators, can use the model to forecast the project budget at the pre-conception stage to facilitate decision-making.

Optimal clean-up procedures lie at the heart of the closeout phase of construction projects under contract law. However, due to its complexity involving multiple issues, potentially unfulfilled contractual obligations, performance claims and counter-claims, combined with consequently deteriorating stakeholder relationships, the management of closeouts is fraught with difficulties leading to suboptimal outcomes. This is particularly true where general contractor (GC) organizations do not have a claims management office (CMO) dedicated to improving such suboptimal clean-up outcomes. Thus, this study aims to develop a model by which CMOs’ may effectively manage the clean-up phase in an environment of closeout claims.

X-inefficiency theory was utilized as the theoretical lens guiding this study. The theory helps identify closeout strategies implemented by a GC, which manages completion claims through a CMO. Data were received and analyzed from a large GC, which served as the firm case study. In this case, managing the closeout completion claims was the main function of the CMO.

The average delay of closeout completion was four times greater than construction phase delays. The GC results highlighted the “economic destruction tsunami of projects,” as a root cause for these completion delays. Wrap-up activities under contract law are identified, including within the domains of statements of completion, project handover and debt settlement. Behavior strategies are also defined, including relational and contractual approaches. Moreover, a process for improving closeout claim performance is described, comprising project closeout identification, rational intra-firm behavior, closeout completion and program closeout practice.

Findings from this work can significantly contribute in X-inefficiency theory in relation to how a decrease of X-inefficiency will lead to better closeout claim performance. It also offers practical insights into how best to minimize delayed closeout completion while providing valuable lessons for stakeholders in complex infrastructure projects. Further, a model is developed that may be utilized by owners, consultancies, designers and other contractor organizations in an effort to improve closeout claim performance.

Master information delivery plan (MIDP) is a key requirement for building information modelling (BIM) execution plan (BEP) that enlists all information deliverables in BIM-based project, containing information about what would be prepared, when, by who, as well as the procedures and protocols to be used. In a well-conceived BEP, the MIDP facilitates collaboration among stakeholders. However, current approaches to generating MIDP are manual, making it tedious, error-prone and inconsistent, thereby limiting some expected benefits of BIM implementation. The purpose of this study is to automate the MIDP and demonstrate a collaborative BIM system that overcomes the problems associated with the traditional approach.

A BIM cloud-based system (named Auto-BIMApp) involving naming that automated MIDP generation is presented. A participatory action research methodology involving academia and industry stakeholders is followed to design and validate the Auto-BIMApp.

A mixed-method experiment is conducted to compare the proposed automated generation of MIDP using Auto-BIMApp with the traditional practice of using spreadsheets. The quantitative results show over 500% increased work efficiency, with improved and error-free collaboration among team members through Auto-BIMApp. Moreover, the responses from the participants using Auto-BIMApp during the experiment shows positive feedback in term of ease of use and automated functionalities of the Auto-BIMApp.

The replacement of traditional practices to a complete automated collaborative system for the generation of MIDP, with substantial productivity improvement, brings novelty to the present research. The Auto-BIMApp involve multidimensional information, multiple platforms, multiple types and levels of users, and generates three different representations of MIDP.

Assessing front end engineering design (FEED) accuracy is significant for project owners because it can support informed decision-making, including confidence in cost and schedule predictions. A framework to measure FEED accuracy does not exist in the literature or in practice, not does systematic data directly linking FEED accuracy to project performance. This paper aims to focus first on gauging and quantifying FEED accuracy, and second on measuring its impact on project performance in terms of cost change, schedule change, change performance, financial performance and customer satisfaction.

A novel measurement scheme was developed for FEED accuracy as a comprehensive assessment of factors related to the project leadership and execution teams, management processes and resources; to assess the environment surrounding FEED. The development of this framework built on a literature review and focus groups, and used the research charrettes methodology, guided by a research team of 20 industry professionals and input from 48 practitioners representing 31 organizations. Data were collected from 33 large industrial projects representing over $8.8 billion of installed cost, allowing for a statistical analysis of the framework's impact on performance.

This paper describes: (1) twenty-seven critical FEED accuracy factors; (2) an objective and scalable method to measure FEED accuracy; and (3) data showing that projects with high FEED accuracy outperformed projects with low FEED accuracy by 20 percent in terms of cost growth in relation to their approved budgets.

FEED accuracy is defined as the degree of confidence in the measured level of maturity of the FEED deliverables to serve as a basis of decision at the end of detailed scope, prior to detailed design. Assessing FEED accuracy is significant for project owners because it can support informed decision-making, including confidence in cost and schedule predictions.

FEED accuracy has not been assessed before, and it turned out to have considerable project performance implications. The new framework presented in this paper is the first of its kind, it has been tested rigorously, and it contributes to both the literature body of knowledge as well as to practice. As one industry leader recently stated, “it not only helped to assess the quality and adequacy of the technical documentation required, but also provided an opportunity to check the organization's readiness before making a capital investment decision.”

Construction delay always causes massive damage to the advancing construction industries, which is no different in the case of Bangladeshi construction industries. This paper aims to investigate the major delay factors causing construction delays in public-funded, mixed and private-funded construction projects of Bangladesh. Also, it offers preventive suggestions from expert stakeholders to reduce the recurrence of delays.

At first, an extensive literature review was conducted to identify the thirty-seven major delay factors categorized under seven groups. A questionnaire was then developed for survey at ongoing construction projects at a different division of Bangladesh. Next, data from 110 respondents were collected, and the delay factors were ranked based on the Relative Importance Index (RII); lastly, probable solutions were suggested for top-ranked delay factors based on opinions from expert stakeholders in the construction sector of Bangladesh.

The overall RII ranking of the 37 delay factors showed “Construction mistakes and defective work,” “Contract modifications by the client” and “Adverse weather condition” as the top three factors causing the delay. For public-funded projects, “Construction mistakes and defective work” and “Slow decision making by a consultant” are the top delay factors. For mixed projects, “Slow decision making of the client” and “Construction mistakes and defective work ranked top, and for private-funded projects, “Financial problems and payment delay of the client” and “Adverse weather condition” ranked top. These nuances of ranking in individual project types ascertain that the causes of delay vary in terms of project features.

The outcome of this project will help identify the significant delay factors based on their severity of effectiveness associated with public-funded, mixed and private-funded projects in Bangladesh. The suggestions regarding preventing these delay factors obtained through the opinions of expert stakeholders can help reduce the effect of these delays in the context of Bangladesh and in countries where the similarity in construction environment prevails.

Previously, studies on construction delays in Bangladesh focused mainly on identifying the delays using qualitative analysis techniques. This study is based on a unique methodology of integrating quantitative research on delay factor identification and qualitative research on preventive measures following the opinions gathered from expert stakeholders in the construction sector.

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.

Construction accidents cause as much harm in Bangladeshi construction as it does globally. This study examines the primary causes of accidents and undertakes an impact assessment of neglecting safety protocols in construction projects in Bangladesh, funded publicly, privately and through a Public-Private Partnership (PPP).

Research was initiated with a comprehensive questionnaire from experts, sourcing data in Bangladesh's construction sector. Data analysis utilized Cronbach's alpha, relative important index and a fishbone diagram for causal visualization.

The study identified the three major causes of safety negligence as “Poor safety culture (RII = 0.857),” “Top management's inattention (RII = 0.825)” and “Lack of personal care (RII = 0.825).” Effects: “Rising project expenses (RII = 0.88),” “Increased medical costs (RII = 0.87)” and “Worker compensation expenses (RII = 0.87).” The study also used the Ishikawa-Fishbone and effect-flow diagrams to highlight accident causes/effects and compare their primary causes in PPP, public and private projects.

Research on construction safety in Bangladesh has mainly focused on identifying factors within specific construction sectors. Since the rules and regulations vary across these three sectors, different health and safety hazards may arise. As a result, this research fills a critical gap by providing a comparative study that examines the causes and impacts of different project types in the Bangladeshi construction industry. By pinpointing the result, this research aims to enhance the safety and well-being of the construction workers sector-wise, thereby contributing to the industry's sustainable growth.

Problems such as information asymmetry and a lack of trust among construction practitioners damage the quality and progress of construction projects. The decentralization, transparency, traceability and temper-proof nature of blockchain technology (BCT) can provide solutions and facilitate multiparty cooperation. However, BCT acceptance in the construction industry is relatively low, and there are few pilot projects adopting BCT. Most relevant literature focuses on BCT acceptance at the industry and organizational levels, but the impact of non-managerial practitioners executing BCT or the traditional approach in day-to-day work tends to be disregarded. This study aims to establish a theoretical model of BCT acceptance, identify key influencing factors and paths of behavioral intention to adopt BCT and promote strategies to enhance BCT adoption.

A new BCT acceptance model for construction practitioners was proposed. A survey was performed with 203 construction practitioners in Shenzhen, China and post-survey interviews were conducted with four BCT experts for validation. Covariance-based structural equation modeling was used to examine the influence paths and moderating effect analysis was performed to check practitioners’ differential perceptions.

Performance expectancy, social influence, facilitating conditions and perceived behavioral control significantly and positively influence behavioral intention to accept BCT, while impacts from effort performance and risk are negative. Overcoming obstacles related to the effort required for BCT adoption and effective risk management will be essential to unlocking BCT’s transformative potential. Then, the moderating effects of respondents’ gender, degree and BCT knowledge as well as the project type involved were analyzed. Continued adoption of BCT in the construction industry has the potential to revolutionize project management, transparency and trust among stakeholders.

The findings of this research can help practitioners and government agencies understand crucial influencing factors and pathways of BCT acceptance. Targeted measures, such as increasing practitioners’ benefits and sense of BCT usefulness, conducting pilot projects and increasing publicity, were proposed for project leadership teams to enhance BCT adoption. This may lead to increased efficiency, reduced disputes and more streamlined and secure construction processes, ultimately enhancing the industry’s overall performance.

Few studies have explored BCT acceptance from the perspective of non-managerial construction practitioners. The BCT acceptance model proposed in this study is a novel adaptation of previous technology acceptance models, with new factors (risk and perceived behavioral control) and moderating variables (degree, BCT knowledge and project type) added for better understanding of non-managerial practitioners’ perceptions and differences.