During the drastic changing process of the construction industry in China, construction program management has been given significant attention. Due to the complexity of construction programs, selecting competent managers is crucially important to its success. Therefore, based on a comprehensive literature review, this paper combines regret theory and the Fuzzy-DEMATEL method to develop a multi-attribute model for construction program manager selection. Firstly, six competence elements are extracted, then the manager selection and evaluation index system are constructed. Secondly, the regret theory is used to simulate the psychological characteristics of the decision makers, combined with Fuzzy-DEMATEL, the comprehensive weights for each element are calculated. Lastly, all alternatives for the selection are sorted and the competent ones are selected. A case study is provided to exam the effectiveness of the developed model. Results shows that the proposed model adopted multi-attribute evaluation and group decision making and took into account the psychological behavior of decision makers as well as influences from the relationships between different attributes. Such results indicate that the proposed model is able to provide more comprehensive and scientific construction program manager selections, which can further improve the management of construction programs.
is an associate professor in the College of Engineering and Applied Science at the University of Cincinnati. She is the director of UC Center for Robotics Research. She holds a BS degree in Mechanical Engineering and a MS degree in Manufacturing Engineering. She received her Ph.D. in Mechanical Engineering from Columbia University in 2003. Her academic interests include manufacturing engineering technology, process planning, control and automation, robotics, and manufacturing automation integrations.
The selection of suitable subcontractors for large construction companies is crucially important for the overall success of their projects. As the construction industry advances, a growing number of criteria need to be considered in the subcontractor selection process than simply considering the biding prices. This paper proposed a hybrid multi-criteria structure entropy weight (SEW)—TOPSIS group decision-making model that considers 10 criteria. The proposed model was able to handle large amount of subcontractors’ performance data that were collected in different types. Additionally, the model can integrate experts’ judgments while accounting for their varying level of expertise and correcting for their biases. This paper also provided a case study to demonstrate the proposed model’s effectiveness and efficiency, as well as its applicability of large construction companies. While this study was applied to construction subcontractors’ selection, the proposed methodology can also be easily extended to various decision-making scenarios with similar requirements.
In recent years, as the era of electronic payment has brought about the
development of intelligent multi-scale forecasting of highway traffic in
my country, it has also been developed because of its late start.
Expressway intelligence, in simple terms, is the combination of human
operation intelligent technology, digital transmission and analysis and
professional skills mastery in the process of highway management. This
technology can imitate people’s thinking and analysis capabilities to
ensure that the original plan is normal run. The core point of the goal
of realizing highway intelligence is the relationship between the road,
the driving vehicle and the owner and the establishment of a relatively
stable and efficient transportation route. From the perspective of
intelligent areas that have implemented multi-scale prediction of
expressway traffic, it is mainly reflected in the form of ETC,
multi-scale prediction of networked traffic, that is, according to the
province or larger area, a one-time payment method is adopted for
all-in-one cards. Form a complete set of intelligent flow multi-scale
prediction system system, maximize the consideration of road users and
economic development needs, effectively solve the closed flow
multi-scale prediction system generated by different attributions or
business entities and improve the efficiency of expressway use and
service quality , Reduce the corresponding energy consumption and lay a
good foundation for the sustainable development of highways in our
country.
This paper discusses the testing of a robotic mechanism for cleaning trench drains. The robot runs and operates inside the drain and cleans it without interfering with the surrounding traffic or the drain itself. The robot combines a three-step cleaning process to achieve maximum efficiency in cleaning of the drains. This three-step process includes breaking the dirt down inside the drain, sucking the dirt out of the drain, and transporting it to a collection unit. There is a drive system to move the robot inside the drain. The drive is bidirectional to control the robot motion as required. The robot also has a suction tube and cutting assembly with cutting arms to better facilitate the suction. The cutting arms have metal brushes installed, the rotation of which loosen the debris and push it towards the drain surface. Over the course of developing this robot, two prototypes were designed and built. All aspects relating to the fabrication and testing of the robot prototypes will be discussed in this paper.
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