Structural reliability prediction of a steel bridge element using dynamic object oriented Bayesian Network (DOOBN)

Different from conventional methods for structural reliability evaluation, such as, first/second-order reliability methods (FORM/SORM) or Monte Carlo simulation based on corresponding limit state functions, a novel approach based on dynamic objective oriented Bayesian network (DOOBN) for prediction of structural reliability of a steel bridge element has been proposed in this paper. The DOOBN approach can effectively model the deterioration processes of a steel bridge element and predict their structural reliability over time. This approach is also able to achieve Bayesian updating with observed information from measurements, monitoring and visual inspection. Moreover, the computational capacity embedded in the approach can be used to facilitate integrated management and maintenance optimization in a bridge system. A steel bridge girder is used to validate the proposed approach. The predicted results are compared with those evaluated by FORM method

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Analyzing Multidisciplinary Team Effectiveness in an Engineering Environment: A Case Study of the West Point Steel Bridge Design Team

The West Point Steel Bridge Design Team is a group of five undergraduate seniors working to design and build a steel bridge for the annual ASCE Steel Bridge Competition. The purpose of our group’s research is to discover how multidisciplinary teams perform in academically competitive environments. This project provides a unique opportunity in the field of multidisciplinary collaborative work because the team’s success can be objectively measured against this year’s competitors and the team’s performance in previous years. The traditional structure of the West Point team consisted of three-to-five civil engineering majors. This year’s team includes a law and legal studies major and five civil engineers, two of which recently switched from systems engineering. Past designs have relied heavily on the work of previous years, which has led to stagnant performance at competitions. Our hypothesis is that by entering different perspectives into the group at an early stage, a revolutionary approach will ensue and overall performance will increase. The team did not completely disregard the designs and methods of previous teams, but the reliance on their decision-making process was more heavily scrutinized with the current multidisciplinary team. Our research is not solely limited to competitive performance. We also analyzed the decision-making process of this year’s team in comparison to previous years. While data on decision-making is not readily available, both the faculty advisor and two current team members who served on the team last year were able to provide personal insight into how the teams compare. Ultimately, this research seeks to provide groups in similar academically competitive environments an indication of whether a multidisciplinary composition will provide benefit to their team’s performance.Cockrell School of Engineerin

Subjective Logic and Arguing with Evidence

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A comparison of two global optimization algorithms with sequential niche technique for structural model updating

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Synthetic rating system for railway bridge management

Railway bridges deteriorate with age. Factors such as environmental effects on different materials of a bridge, variation of loads, fatigue, etc will reduce the remaining life of bridges. Bridges are currently rated individually for maintenance and repair actions according to the structural conditions of their elements. Dealing with thousands of bridges and several factors that cause deterioration, makes the rating process extremely complicated. Current simplified but practical rating methods are not based on an accurate structural condition assessment system. On the other hand, the sophisticated but more accurate methods are only used for a single bridge or particular types of bridges. It is therefore necessary to develop a practical and accurate system which will be capable of rating a network of railway bridges. This paper introduces a new method for rating a network of bridges based on their current and future structural conditions. The method identifies typical bridges representing a group of railway bridges. The most crucial agents will be determined and categorized to criticality and vulnerability factors. Classification based on structural configuration, loading, and critical deterioration factors will be conducted. Finally a rating method for a network of railway bridges that takes into account the effects of damaged structural components due to variations in loading and environmental conditions on the integrity of the whole structure will be proposed. The outcome of this research is expected to significantly improve the rating methods for railway bridges by considering the unique characteristics of different factors and incorporating the correlation between them

Risk based bridge data collection and asset management and the role of structural health monitoring

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Particle swarm optimization with sequential niche technique for dynamic finite element model updating

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Integrating case-based reasoning and hypermedia documentation: an application for the diagnosis of a welding robot at Odense steel shipyard

Reliable and effective maintenance support is a vital consideration for the management within today's manufacturing environment. This paper discusses the development of a maintenance system for the world's largest robot welding facility. The development system combines a case-based reasoning approach for diagnosis with context information, as electronic on-line manuals, linked using open hypermedia technology. The work discussed in this paper delivers not only a maintenance system for the robot stations under consideration, but also a design framework for developing maintenance systems for other similar applications