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In the United States, coal, natural gas, and oil stand as primary contributors to carbon emissions. The transition toward carbon-free energy sources has significantly reduced the demand for conventional fossil fuels while also significantly altering its labor market environment. This concurrently opens a good number of job prospects in low-carbon energy production sectors. Consequently, navigating this labor force transition requires informed decisions. This research aims to explore the economic impacts of this transition using the state of Wyoming as a case study, a state at the forefront of moving from fossil fuel reliance to embracing low-carbon energy sources. Through an in-depth analysis, including autoregressive integrated moving average (ARIMA) modeling, this paper explored the retraining needs of workers in each sector, the associated costs, and the current state of Wyoming's workforce. The results of the analysis showed that the coal mine sector and oil and gas industries face significant workforce migration pressures with a relatively higher number of workers who need to be migrated, whereas coal power plants are less affected. With Wyoming found to be at the "take-off" phase, ready to accelerate into a new energy paradigm, a relatively modest investment ranging from $3,060 to $7,201, per worker, can facilitate this transition. This research provides critical insights into managing workforce transitions, informing strategic decisions for a sustainable shift from fossil fuels energy production to low-carbon energy production. This research could assist government agencies in the establishment of workforce transition initiatives in the energy production industries and beyond. This research potentially offers a practical framework for understanding the intricacies of workforce transitioning that could be utilized for other sectors such as the architecture, engineering, and construction (AEC) sector. [ABSTRACT FROM AUTHOR]

Even though advanced heuristic and metaheuristic models have been developed for minimizing financing cost by using total float to adjust the start times of activities, these models overlook the fact that the attempt to minimize financing cost may result in more than one work schedule with minimum financing cost. It is therefore not surprising that no research has been conducted to select the best financing schedule out of the multiple optimal work schedules. It is well known that total float must be used sparingly to avoid the creation of too many critical and near-critical activities that may cause delays in the project duration. Indeed, the loss of schedule flexibility caused by the use of total float comes at a cost. It is argued in this paper that the cost of float consumption should be a vital part of models designed to minimize financing cost. The research presented in this paper attempts to fill this gap. A model is proposed in this study that not only minimizes financing cost but also demonstrates the negative impact of not considering the cost of float consumption in reaching the optimal solution, an area of research that is mostly overlooked in the finance-based scheduling literature. This research contributes to the body of knowledge by demonstrating that there may be more than one work schedule with minimum financing cost, and that the cost of float consumption must be considered to find the most optimal solution. [ABSTRACT FROM AUTHOR]

For construction management students completing the construction planning and scheduling course to meet work expectations and provide effective benefit to their employers, the course curriculum should accurately reflect the needs of industry. The objective of this research is to identify the scheduling knowledge/expertise/proficiency that practitioners expect to see in a new hire and to compare this with what is currently offered in construction planning and scheduling courses. First, the syllabi of planning and scheduling courses offered in 55 construction management programs were collected and evaluated. Then, 35 critical topics and six student learning outcomes were identified by inspecting the accreditation requirements and the literature at large. Two survey questionnaires were developed and administered to instructors and industry practitioners to record the priorities they attached to those 35 topics and six students learning outcomes. The levels of importance and the degree of association reported by instructors and practitioners were analyzed. The result indicates that instructors' and practitioners' priorities concerning scheduling topics and learning outcomes are not in sync. Recommendations are provided to help instructors who teach construction planning and scheduling to design their syllabus accordingly, to improve their curriculum by understanding what practitioners expect from professional schedulers, and to encourage practitioners to update their knowledge and expectations.

To reduce the risk of unexpected cost of rework (COR), a variety of predictive models have been developed in the construction management literature. However, they primarily focus on prediction accuracy, and rather less attention has been paid to the trustworthiness of prediction models. This increases operational risk and hinders its integration in related decision-making. Aiming to reduce the utilization risk and increase the reliability of COR prediction models, this study exploits the graph convolutional network (GCN) model, which enhances representativeness by accommodating interrelationships among the root causes of nonconformances. The GCN can process a more representative input network that provides COR records while factoring in the shared root causes of nonconformance in the resulting COR. The proposed approach achieved a COR prediction accuracy as high as 85%, which is significantly higher than that of any existing cost prediction model. The demonstrated accuracy and lower risk of the proposed GCN model thus enhance the reliability of the prediction and trust in its outcome, facilitating its integration into developing rework prevention strategies and relevant resource allocation for construction professionals. The study contributes to construction project management by proposing a novel COR prediction model that embodies accuracy, representativeness, and interpretability. Whereas we tailored the GCN model to predict COR with a focus on nonconformance root causes, it is noted that rework costs can also be influenced by other project factors, such as site safety. Practical Applications: Managing rework costs is a critical challenge in the construction sector, necessitating predictive models that are not only accurate but also trustworthy. This study leverages graph convolutional network (GCN) to offer a prediction model that is both accurate and reliably interpretable. The GCN model enriches cost of rework (COR) predictions by analyzing how different project elements, such as design flaws or material issues, are interconnected, providing a more holistic view of potential rework triggers. The customization of the GCN model is crucial, requiring specific adjustments based on individual project requirements and decision-making goals, and remains relevant across diverse construction projects. The proposed predictive tool processes complex data and provides insight into how various project factors interplay to affect rework costs. By providing a clear rationale behind its predictions, it supports more informed decision-making in allocating resources and developing strategies to prevent rework, ultimately leading to more efficient and cost-effective project management. Having COR prediction based on a comprehensive understanding of all related project factors equips professionals with insight about the predicted COR impact on the overall construction budget and its related root causes. This, in turn, fosters greater confidence among project stakeholders and sets a stronger basis for strategic planning, resource allocation, and risk management. [ABSTRACT FROM AUTHOR]

Sustainability has gained importance in the building design/construction industry due to the increase in the need for energy, the extensive use of non-renewable resources, and therefore the damage caused to the environment by traditional building design and construction. The concept of green building was developed for this reason. Green buildings can be defined as structures that consume less water, energy, and other resources while emphasizing human health and protecting the environment. While green buildings have these advantages over traditional buildings, the rate of green building production is less than expected because many barriers are encountered during the design and production of green buildings, despite the recent attention paid to sustainable practices. This research focuses on exploring the barriers encountered during green building production in Turkiye and the possible solutions for overcoming these barriers. According to the data obtained through a questionnaire survey, the most important difficulties encountered in Turkiye include the lack of government subsidies, the lack of green building regulations, the public's indifference to green building technologies, and education and technology deficiencies. According to the respondents, the possible solutions involve government incentives, adoption of national standards and best practices, educating the public and the design/construction professionals about green buildings, and the development of novel green building technologies. The major contribution of the findings is that it highlights the fact that the stakeholders of green building projects (i.e., owners, designers, constructors, and facility managers) face important barriers and lets stakeholders recognize that solutions to overcome these barriers are available. The major implication is that the stakeholders in green building projects will likely be better prepared to deal with any barrier before undertaking green building projects in Turkiye and countries with similar socio-economic conditions. [ABSTRACT FROM AUTHOR]

Awarding a contract to a bidder who has unbalanced the bid may cost more money to the owner. An owner who determines that a bid has been unbalanced has the legal right to reject the bid. Although several models have been developed to detect unbalanced bids, some researchers still assert that the number of cases in which owners mistakenly identify balanced bids as unbalanced is too great to ignore. One of the reasons of this failure is the use of an inappropriate benchmark against which the offered unit prices are compared. In this paper it is argued that the use of the geometric mean of the unit prices provided by the bidders as a benchmark helps to obtain robust solutions and an advanced decision-making model is proposed in which the Information Entropy and VIKOR methods are used to detect unbalanced bids. The results obtained from an example project drawn from the literature demonstrate how owners may be misled if they use an inappropriate benchmark in detecting unbalanced bids. This study contributes to the body of knowledge by highlighting the negative impacts of using flawed benchmarks in this process, an area of research that is mostly ignored in the unbalanced bidding literature. [ABSTRACT FROM AUTHOR]

Motivation is one of the key factors that stimulate individuals to improve their productivity. Therefore, motivation of construction workers has been debated with considerable interest by participants in the construction industry. However, motivating only construction workers, but ignoring the motivation of construction professionals, viz., architects and civil engineers, may not be enough to successfully complete a construction project. Although motivation of construction professionals may also enhance the performance of a construction project, this topic is mostly ignored in the motivation literature. This study attempts to fill this gap by (1) identifying the motivators that are of importance to architects and civil engineers and (2) exploring the statistical difference between architects’ and civil engineers’ motivators. For this purpose, a questionnaire survey was administered to Turkish architects and civil engineers to collect data on their perceptions of the importance of different motivators. The case of Turkey is investigated because several of the larger Turkish contractors undertake construction projects outside their home country. Statistical analysis was performed on the collected data to verify whether there are statistically significant differences in the perception of some motivators by architects and civil engineers. The study demonstrates the existence of a statistically significant difference between architects’ and civil engineers’ motivators. Identifying the architects’ and civil engineers’ motivators may help construction companies in motivating their architects and civil engineers more effectively, thus developing a quality workforce.

This study proposes an optimization model based on particle swarm optimization (PSO) to achieve the objectives of minimizing project duration, project cost, and resource fluctuation while maximizing project quality and schedule flexibility. The objectives of this study are as follows: (1) to determine whether the interrelationships between these five objectives affect the outcome of the optimization; (2) to determine whether the presence or absence of resource constraints affects the solution; and (3) to verify that the solution of the PSO-based optimization model coincides with the exact mathematical solution, but faster. An example project was presented to illustrate the applicability of the proposed model. The conclusions of this study are as follows: (1) multiple objectives do interact with each other and should be evaluated simultaneously rather than one at a time to reach realistic solutions, (2) resource constraints do affect the solutions obtained, and (3) the proposed model successfully reaches an optimal solution that is extremely close to the exact mathematical solution much faster than the mathematical model. [ABSTRACT FROM AUTHOR]

The growing interest in digitalization signals a need for technology-oriented supply chain operations in the construction industry. Electronic procurement (e-procurement) aims to convert traditional procurement approaches into web-based/online platforms. Even though e-procurement is an effective tool that may improve supply chain management, the extent of e-procurement implementation has been slow to date. This study investigates the barriers that hinder e-procurement implementation in construction supply chains with the aim of prioritizing solutions to the identified barriers relative to time, cost, quality, and construction owner satisfaction. A comprehensive literature survey was performed, and a focus group discussion was organized for the purpose of the study, which resulted in the identification of 28 barriers. Then, a total of 131 construction practitioners were contacted to evaluate the barriers through a questionnaire survey. The responses were analyzed using the fuzzy Technique for Order Preference by Similarities to Ideal Solution (fuzzy TOPSIS) for prioritization. Finally, 15 semi-structured interviews were conducted to gain a deeper insight into the transformation process from the conventional procurement route to the e-procurement solution. Findings highlight that issues related to unexpected order cancellations, large variations in material costs, and the uncertain nature of the industry that requires a large number of changes are ranked as the most significant barriers. Given the highly competitive environment and the high demand for advanced technologies in the construction industry, a new paradigm can enhance the efficiency of supply chain operations. Exploring and eliminating the potential difficulties of adopting e-procurement in the procurement process may be a good start. Overall, this research is expected to facilitate the transformation of the procurement process by addressing the critical barriers identified by practitioners. [ABSTRACT FROM AUTHOR]