Purpose This study examines how project delivery methods shape the implementation of collaborative risk management in construction projects. Moving beyond traditional performance comparisons, the study adopts a grounded theory approach to identify the underlying factors that explain differences in collaborative risk management. It further identifies key challenges across various aspects of collaboration and explains how these challenges affect the effectiveness of risk management processes. Design/methodology/approach A grounded theory approach was used, drawing on semi-structured interviews with 21 subject matter experts with extensive experience in heavy industrial construction projects. Qualitative analysis was conducted to identify patterns and themes, and theoretical sampling guided iterative data collection until theoretical saturation was reached. Findings The findings identify four interrelated themes (Stakeholder Collaboration and Engagement; Integration of Risk Management Across the Project Lifecycle; Risk Sharing and Communication; and Roles and Responsibilities) that together explain how project delivery methods shape collaborative risk management. Within each theme, specific categories further describe the underlying factors influencing collaboration. The results demonstrate that no single project delivery method is universally better than the others. Rather, each presents distinct challenges that influence how risks are managed. In addition, factors such as project complexity, contracting strategy, and the risk management maturity of project stakeholders play a critical role in shaping the effectiveness of collaborative risk management processes. Originality/value This study fills a gap in the literature by providing a qualitative analysis of collaborative risk management practices across multiple project delivery methods. It offers practical insights for aligning contractual and organizational arrangements with collaborative risk management objectives and provides a foundation for future approaches that enhance transparency and stakeholder engagement.

This study explores the adoption and effectiveness of a trust and cooperation-based neighborhood model, designed to foster collaboration and reduce friction among stakeholders in operational environments characterized by complex supply chains and multi-stakeholder engagements. Grounded in the psychological theories of reciprocal exchange, the model envisions a ’neighborhood’ where participants collaborate as partners, fostering predictable outcomes, reduced risks, and greater financial stability. Although the model is intended for operational environments requiring intensive trust and coordination, this research tested its application within the construction sector. The work was conducted in two exploratory phases, both in the United States: a concept test survey followed by pressure testing the model through a series of workshops. The concept test survey assessed industry’s interest in and compatibility with this neighborhood approach. Results indicated strong support for the model’s collaborative approach highlighting its potential to address the issues of mistrust in the industry. The second phase involved a series of codesign workshops with key industry stakeholders, including a project owner, a general contractor, an architect, and several legal experts. These workshops confirmed the model’s strengths in promoting cooperation and transparency but also identified challenges, such as the need for better integration with existing legal frameworks and addressing power dynamics within the neighborhood. This study suggests that the trust and cooperation-based neighborhood model has the potential to transform collaboration in other operational environments such as healthcare, manufacturing supply chains, and technology development. The model also has the potential to be integrated into other cooperative models and frameworks.

An excessive amount of construction and demolition waste is generated in construction projects daily. To avoid negative environmental externalities and to promote sustainability, it is necessary for the construction industry to improve its waste management practices at a project level. Analyzing large volumes of waste generation data created in construction projects can provide a valuable opportunity to extract actionable insights. In this research, construction projects were selected, and three different data mining techniques were applied with the objectives of (1) predicting the future volume of diverted waste based on the amount of waste generation of major materials; and (2) identifying trends of waste generation in different construction stages of the projects. Specifically, to achieve these objectives, this study applies multiple regression, artificial neural network, and clustering algorithms. Notably, this study contributes to the existing construction waste management body of knowledge by demonstrating the application of different data mining techniques to predict future waste diversion and extract actionable insights at the project level that help promote sustainability and waste reduction. Furthermore, this study allows industry practitioners to better manage construction waste by understanding patterns of waste generation according to different phases of the project, and predicting amounts of waste for diversion.

Healthcare facilities (HCFs) are complex building structures that are becoming more challenging with ever-changing codes and regulations. Previously completed projects become a basis for future guidance regarding costs and scope. A robust normalization framework to assess previously completed projects with today’s costs and location will benefit various stakeholders. The current study provides a complete picture for normalizing the overall project cost and phase cost by life cycle and HCF cost elements. This study aims to develop a cost normalization approach tailored to HCF-specific cost elements to extend the normalization framework for the overall project cost. Further, the researchers developed a distinct framework for normalizing the effect of shell space on the normalization of Total Installed Cost (TIC) to establish fixed cost adjustment rates for cold and warm shell spaces in HCFs, which can increase the accuracy of cost normalization of the overall project cost. This study identified an appropriate set of cost indices for normalizing HCF cost elements using publicly available indices. The cost elements identified for normalization included HCF-specific and Construction Specifications Institute Master Format (CSIMF) cost elements for assigning individual normalization procedures. This study provides individual and unique approaches for normalizing all identified cost elements, such as mechanical, concrete, etc. The initial framework was evaluated through a case study analysis that developed into the proposed approach built upon the collaborative efforts of academic researchers and industry experts. This study introduced shell space cost adjustment rates for warm and cold shell spaces to further develop a space normalization framework. This paper addresses the challenges of normalizing HCF project costs using the breakdown of HCF cost elements. Moreover, the paper provides the HCF’s overall cost normalization approach, emphasizing cost elements that allow accurate comparisons between various HCFs for early scope and cost guidance.

This research explores the applicability of blockchain technology for collaborative risk assessment of capital projects through prototype development. Risk assessment is a collaborative process where project participants get together to assess and manage potential project risks. Different systems have been developed to support this collaborative risk assessment process. However, such systems are governed in a centralized manner and do not protect the confidentiality of individual inputs from the stakeholders. Moreover, the security and transparency of the data stored can become an issue as well. The research explains the development of a blockchain prototype to address the problems stated above. A private permissioned blockchain network has been configured so that risk-related information can be transacted in a secure, transparent, and trustable way. Smart contract algorithms have been developed to receive risk inputs from multiple stakeholders and update an aggregated output. The system architecture and the functionalities of the prototype are described in detail, and each step of the risk assessment process has been demonstrated on the prototype system. The research discusses the limitations of the current prototype and future work that can be done to improve collaborative risk assessment.

Climate change is a significant issue that may impact many aspects of our lives. The increase in global carbon emissions is one of the main reasons for such environmental consequences, and immediate action should be taken to manage the carbon footprint from human activities. Carbon releases from the construction sector contribute to a significant fraction of greenhouse gases being released into our atmosphere. Hence, various efforts have been made to manage the amount of carbon emissions in construction projects, such as developing standards, methods, and tools. While many earlier studies were associated with conventional analytical methods for estimating carbon discharges, recent studies have focused on monitoring the actual status of carbon emissions in a project, enabling project teams to take timely responses to minimize releases. This paper reviews carbon emissions monitoring practices that can be applied in construction projects and provides a descriptive analysis. The review explains the type of technology adopted and indicates the project life-cycle stage that each application was designed to be used. The findings from this paper will provide an overview of the state-of-the-art practices for carbon emissions monitoring and identify future directions for research.

An important performance measure to evaluate capital expenditure and return on investment (ROI) is capital efficiency. In the downstream and chemicals sector, capital efficiency becomes even more critical. Indeed, this sector is asset-intensive and capital projects carried out there can be large, complex and require significant investment of capital and time. Project capital efficiency can be impacted by how well the business unit aligns with the project unit, as they collaborate in the early project phases to determine how the project will be built and operated. This research aimed to identify business-project management processes that help improve project capital efficiency. The study assembled a 17-expert panel to define project capital efficiency with its four key improvement areas. The panel identified 28 management processes that lead to improvement in project capital efficiency if they are implemented effectively by business and project units in early project phases. A survey was used to determine the relative importance of these management processes. Researchers quantitatively analysed, ranked and compared the relative importance. Findings indicate that 23 management processes are very important to project capital efficiency and four of them are perceived differently in their relative importance by the two units.

In early project phases, cross-functional collaboration is required to lead a capital project from an investment option to a full definition before its design and construction commenced. Given that many capital projects are large, complex, and take years to develop, for stakeholders to realize effective project definition and development it is important that they identify—in the early phases—cross-functional collaboration barriers and their contributing factors. The current study conducted a total of 20 interviews with experts from both the business units and project teams in owner companies and Engineering, Procurement, and Construction (EPC) contractors. It identified 14 barriers to effective cross-functional collaboration and 43 associated contributing factors in the early phases of capital projects. To rank the barriers, researchers then applied the Delphi method with a panel of 12 subject matter experts (SMEs). The outcomes of this study can guide practitioners to improve the effectiveness of cross-functional collaboration in early project phases.

Significant changes in the construction industry have been brought about by Building Information Modeling (BIM). While BIM has improved team collaboration and workflow efficiency, the model still faces multiple challenges. These are related primarily to the security, transparency, and reliability of the data shared in the model. A potential way to mitigate these problems, according to many studies, is blockchain technology. This paper reviews the recent literature on the integration of BIM and blockchain technology. Using a rigorous search-and-selection process, the authors conducted a systematic literature review by analyzing 70 studies relevant to BIM-blockchain integration. The state-of-the-art review explains how studies have implemented blockchain technology and provides an overview of different levels of adoption. Various application areas within the BIM process are explored to understand the ongoing research trend. The authors discuss limitations and offer recommendations on how best to implement future work in BIM-blockchain integration.

Hospitals provide diverse tasks essential for the delivery of patient care and are comprised of many functional units. This makes healthcare construction projects highly complex among other types of building projects due to the specific regulations, multiple functions it must provide, complicated mechanical and electrical systems, and so on. This complexity embodies potential risk events during its construction, which not only influences the completion of the project but can impact the patients’ safety and health conditions even after the project is finished. To prevent such outcomes, risk management is a crucial process that can identify, evaluate, and properly mitigate risks along the project lifecycle. A key aspect of risk management is that it requires the interaction and contribution from multiple stakeholders of the project. Various frameworks and tools that enable collaborative management of risks among multiple stakeholders have been developed in the past. However, the developed frameworks are not suitable in the sense that it does not protect the confidentiality of individual inputs from the stakeholders. Moreover, these frameworks are centralized systems, which can bring issues related to the security and transparency of the information that is being stored. Blockchain technology is an emergent distributed ledger technology (DLT) that can provide solutions to the listed problems found in centralized systems. It is a novel system that records information on a decentralized, distributed ledger, where transactions are constantly duplicated and updated. This study explores the applicability of blockchain technology for healthcare risk management. The key functional elements of blockchain that can resolve the challenges faced by prior risk management frameworks have been identified and discussed. Based on the discussions, a conceptual information management model for managing healthcare project risks on a blockchain has been conceived. The development of the initial prototype has been explained. The research illustrates the process, benefits, and limitations of adopting blockchain technology for collaborative risk management in healthcare projects.

For companies trying to evaluate their capital deployment and return on investment, an important performance metric is capital efficiency. When defining and assessing capital efficiency, though, researchers face a common challenge—how to do so in a consistent way. Capital projects in the downstream and chemicals sector require significant capital expenditures to develop. The revenue generated from these projects is directly impacted by how they operate. In other words, the development of a project and its subsequent operation have an impact on the efficiency of the capital invested. Thus, it is important to address and improve capital efficiency at the project level. Researchers in this study reviewed the literature and interviewed industry professionals to explore how their organizations define and assess capital efficiency. The research team used a focus group to develop a pathway to identify management processes that, if implemented effectively, lead to efficient capital deployment in downstream and chemical projects.

Risk assessment is an important part of risk management in construction projects as it involves the identification, evaluation, and prioritization of potential risk events. This enables project teams to properly mitigate them. With the intent of getting risk input from multiple stakeholders, different systems have been developed to support collaborative risk assessment. However, these systems often face challenges such as no data confidentiality, poor data security, lack of transparency, and absence of traceability and auditability for the team members. Blockchain is an emergent decentralized digital technology that can provide solutions to overcome such deficiencies of centralized systems. In this study, the need for a novel blockchain-based methodology for collaborative risk assessment is analyzed. Multiple traits of blockchain technology have been found through the literature review. From these multiple traits, the key functional elements for the methodology have been identified and discussed in further detail. The research points out some of the limitations at the current state so that future work can be conducted to build a methodology that can be even more beneficial for the risk management of construction projects.

Purpose Facility maintenance is critical for the operation and management of petrochemical plants. Maintenance work completed with higher productivity eventually contributes to better plant performance. Mechanization reduces workforce demand and can increase the productivity of maintenance work. The purpose of this paper is to assess the current mechanization level of the maintenance activities and then identify applicable technology solutions for productivity improvement in petrochemical facility maintenance. Design/methodology/approach This paper utilizes a mechanization level assessment method for global maintenance data collection and analysis. Subject matter experts' interviews and market scanning were used to identify corresponding technology solutions. Findings The study discovered numerous maintenance activities with lower mechanization levels and identified more than 50 technology solutions applicable for maintenance productivity improvement. Originality/value This paper provides a roadmap for petrochemical maintenance work participants to assess their mechanization level status quo and identify technology solutions for higher maintenance work productivity. The method adopted is replicable and customizable for further applications with different plant conditions in the petrochemical sector and other industrial contexts.

Many factors that decrease productivity occur and recur during the execution phase of construction projects. Preventing and mitigating the impact of these factors, such as lack of materials, waiting times, exceed craftworks traveling, and mobilization, require access to productivity data at a level that allows the identification of the root causes of productivity losses. Therefore, it is critical to use proper indicators that support the implementation of effective productivity improvement practices. This study analyzes the benefits of using the productivity stratification indicator (PSI) in productivity management programs. This metric improves onsite productivity management by providing detailed, accurate, and fast indicators that enable the identification and mitigation of productivity barriers before they affect the project performance. The validation of the proposed indicator encompassed the analysis of productivity data obtained from the execution of pile-drilling activities at eight construction job sites over a one-year period. The data collection and analysis activities were divided into two well-defined periods. This allowed a comparison of the stratified productivity unit rates in the first period with those in the second period, highlighting the percentage of reduction (40%) for unit rates between both periods. The PSI graphs convey productivity indexes that explain productivity and isolate the project's unproductive areas. Access to this information enable project teams and crews to diagnose their inefficiencies at the moment they occur and to take actions immediately. (C) 2020 American Society of Civil Engineers.

Maintenance in petrochemical plants is often characterized as labor intensive and may give rise to such problems as being costly, diminishing productivity, and emitting pollutant. To mitigate such problems, managers have tried mechanizing the maintenance tasks. This study elaborated the concept of mechanization, proposed a method named Petrochemical Maintenance Mechanization Assessment (PEMMA), which can help assess mechanization levels of the maintenance tasks and provide corresponding improvement recommendations This study presented the development process of the PEMMA method and applied the method in the context of Singapore. Results showed that the mechanization level of the maintenance tasks in Singapore is relatively low. The developed method is arguably the first to be presented and therefore, it contributes to the existing body of knowledge. In addition, the developed method is beneficial to the practice as well, because it can help diagnose and then improve the mechanization levels of petrochemical plants, which would eventually make the petrochemical industry more productive and cleaner.

Scaffolding is a temporary structure whose configuration and location change as the construction progresses. As scaffolding is used and shared by multiple trades, its interaction with different construction activities often entails challenges and issues that emerge only after construction commences. Managing scaffolding in industrial construction is more challenging due to project complexity and size. The purpose of this paper is to understand how scaffolding is completed, what issues are typically encountered, and what potential solutions may be generated in the context of industrial construction projects. The research team reviewed the literature and conducted 11 interviews with scaffolding professionals. It also conducted 2 review sessions with a three-expert panel to summarize the scaffolding practice and to consolidate the findings into a list of 22 scaffolding-related issues with the causes. These issues are further grouped into four categories according to potential solutions provided. The study offers a comprehensive view of industrial scaffolding and its management and puts forward solutions to improve scaffolding management in industrial construction.

This research presents an interesting look at productivity metrics in the construction industry. The research seeks to find a possible correlation between the unit rate (UR) measure and direct work rate (DWR) measure. Such correlation could provide insight into ways of better allocating labor resources on project sites through more informed productivity management strategies. This research analyzed 120,000 man-hours in piping assembly activities from the oil and gas industry. The piping assembly activities were divided in off-site (pipe shop) pipe coupling and on-site (field) pipe coupling. The data was gathered by trained personnel, who recorded information about the productivity metrics while the activities were being performed, the workers who were involved in execution of the service and the hours available for work. The correlation degree between the UR and DWR varied depending on the type of activity being performed. The pipe shop coupling presented a higher correlation when compared to the field coupling. Even though the two metrics have a different approach on how to measure productivity, both have proven to be complementary in supporting productivity management. This conclusion lead to potential future studies on understanding why some processes present higher correlation than others.

Abstract. Pavement condition monitoring is fundamental for the efficient allocation of resources in transportation asset management. However, data collection involves laborious and costly procedures. Our study intends to investigate the usage of remote sensing data for network-level pavement condition assessment that offers a more cost-effective alternative and a rapid infrastructure assessment tool that can be used in the aftermath of natural disasters. Based on an extensive literature review, a data mining framework was established to train models that predict the pavement condition of different road segments. The framework exploits the inherent information of multispectral images by generating spectral related attributes. To identify pavement sampling areas, an automated procedure using image segmentation replaces manual surface digitizing. Unlike previous research, different classification models were used to approximate the mapping function from spectral information to pavement conditions. A preliminary case study was conducted with data provided by the City of Dallas and multispectral images acquired from the Texas Natural Resources Information System. The mean-shift segmentation algorithm was used to locate noise introducing areas on the pavement surface. Four different classification models were trained using k-nearest neighbors, naïve Bayes, support vector machines, and a multilayer perceptron. The developed models were employed to predict the road surface condition class of a test set not included in the training procedure. The multilayer perceptron presented the highest accuracy level of 71%, showing that the framework might have the potential for future implementation.