Lately, the construction industry has shown a great deal of openness to utilizing novel and innovative technologies. The industry's complexity necessitates cooperative and efficient process management. Utilizing value-adding technologies is crucial in thar regard. Among them, blockchain has recently gained prominence as an enabling technology for smart contracts, and secure data management for the construction industry. This study employs a structural equation model to quantify the relationship between risk management (RM), a key project management activity, and blockchain technology. To this end, a survey was created and distributed to construction professionals. A structural equation modeling (SEM) analysis was performed on the data collected from 103 respondents. The findings show that construction project RM can be significantly affected by key blockchain features. This study contributes to the body of knowledge by presenting a conceptual framework that captures the essential elements of RM and blockchain. The results also empirically show that RM and blockchain are closely related, confirming blockchain's potential and relevance in RM applications. The paper also discusses the risks associated with adopting blockchain in the construction industry. The results of this study can help construction practitioners and policy-makers to create plans for incorporating blockchain into RM workflows.

Conceptual cost estimation is an important step in project feasibility decisions when there is not enough information on detailed design and project requirements. Methods that enable quick and reasonably accurate conceptual cost estimates are crucial for achieving successful decisions in the early stages of construction projects. For this reason, numerous machine learning methods proposed in the literature that use different learning mechanisms. In recent years, the case-based reasoning (CBR) method has received particular attention in the literature for conceptual cost estimation of construction projects that use similarity-based learning principles. Despite the fact that CBR provides a powerful and practical alternative for conceptual cost estimation, one of the main criticisms about CBR is its low prediction performance when there is not a sufficient number of cases. This paper presents a bootstrap aggregated CBR method for achieving advancement in CBR research, particularly for conceptual cost estimation of construction projects when a limited number of training cases are available. The proposed learning method is designed so that CBR can learn from a diverse set of training data even when there are not a sufficient number of cases. The performance of the proposed bootstrap aggregated CBR method is evaluated using three data sets. The results revealed that the prediction performance of the new bootstrap aggregated CBR method is better than the prediction performance of the existing CBR method. Since the majority of conceptual cost estimates are made with a limited number of cases, the proposed method provides a contribution to CBR research and practice by improving the existing methods for conceptual cost estimating.

Despite the importance of project deadlines and resource constraints in construction scheduling, very little success has been achieved in solving the resource-constrained discrete time–cost trade-off problem (RCDTCTP), especially for large-scale projects. In this paper a new heuristic method is designed and developed to achieve fast and high-quality solutions for the large-scale RCDTCTP. The proposed method is based on the novel principles to enable effective exploration of the search space through adequate selection of the activities to be crashed for a resource constrained schedule, by only crashing the activities with zero float in a resource constrained-schedule, which form the critical sequence. The computational experiment results reveal that the new critical sequence crashing heuristic outperforms the state-of-the-art methods, both in terms of the solution quality concerning project cost and computation time. Solutions with a deviation of 0.25% from the best known solutions are achieved wit...

The transportation demands of people are increasing day by day depending on the population, and the number of vehicles in traffic is causing various problems. To meet the energy needs of vehicles, there is a huge burden on countries in terms of fossil fuels. In addition, the use of fossil fuels in vehicles has a serious impact on environmental pollution. Various studies have been carried out to prevent unnecessary fuel consumption and emissions. Behavior of drivers, who are important components of traffic, are carefully examined in the context of this subject. Driver distraction causes various environmental problems as well as traffic safety issues. In this study, the negative situations that arise as a result of drivers waiting at traffic lights dealing with their mobile phones are discussed. Roadside observations are made for drivers at considered intersections in Erzurum Province, Turkey. As a result of these observations, delays at selected intersections due to mobile phone use are calculated. Unnecessary fuel consumption and emissions due to delays are also analyzed. An annual fuel consumption of approximately 177.025 L and emissions of 0.294 (kg) NO_X and 251.68 (kg) CO₂ occur at only selected intersections. In addition, a second roadside observation is made in order to analyze driver behavior and the most preferred type of mobile phone usage is determined. It is seen that drivers mostly exhibit the “Talking” and “Touchscreen” action classes. Considering the economic conditions and environmental pollution sensitivities of countries, attempts have been made to raise awareness about fuel consumption and emissions at traffic lights.

In this paper, we present a novel memetic algorithm (MA) for the solution of the resource leveling problem (RLP). The evolutionary framework of the MA is based on integration of a genetic algorithm and simulated annealing methods along with a resource leveling heuristic. The main objective of the proposed algorithm is to integrate complementary strengths of different optimization methods and incorporate the individual learning as a separate process for achieving a successful optimization method for the RLP. The performance of the MA is compared with the state-of-the-art leveling methods. For small instances up to 30 activities, mixed-integer linear models are presented for two leveling metrics to provide a basis for performance evaluation. The computational results indicate that the new integrated framework of the MA outperforms the state-of-the-art leveling heuristics and meta-heuristics and provides a successful method for the RLP. The limitations of popular commercial project management software are also illustrated along with the improvements achieved by the MA to reveal potential contributions of the proposed integrated framework in practice.