A fuzzy Petri net-based expert system and its application to damage assessment of bridges

Lee, J.; Liu, K.F.R.; Chiang, Wei-Ling

doi:10.1109/3477.764869

Cited by 52 publications

(16 citation statements)

References 59 publications

(61 reference statements)

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“…The first layer transition set contains 1 1 t , 1 2 t , the second layer transition set contains 2 1 t , 2 2 t and the third layer transition set contains 3 t .The process of fuzzy reasoning is given as follow. …”

Section: Definition 7(input Place Output Place)mentioning

confidence: 99%

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Advanced Self-adaptation Learning and Inference Techniques for Fuzzy Petri Net Expert System Units

Zhang

Wang

Yuan

2009

Artificial Intelligence and Computational Intelligence

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Abstract. In a complicated expert reasoning system, it is inefficient for commonly fuzzy production rules to depict the vague and modified knowledge. Fuzzy Petri nets are more accurate for dynamic knowledge proposition in describing expert knowledge. However, the bad learning ability of fuzzy Petri net constrains its application in dynamic knowledge expert system. In this paper, an advanced self-adaptation learning way based on error back-propagation is proposed to train parameters of fuzzy production rules in fuzzy Petri net. In order to enhance reasoning and learning efficiency, fuzzy Petri net is transformed into hierarchy model and continuous functions are built to approximate transition firing and fuzzy reasoning. Simulation results show that the designed advanced learning way can make rule parameters arrive at optimization rapidly. These techniques used in this paper are quite effective and can be applied to most practical Petri net models and fuzzy expert systems.

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Section: Definition 7(input Place Output Place)mentioning

confidence: 99%

“…Fuzzy Petri net (FPN), which is based on fuzzy production rules (FPRs), can provide us a useful way to properly represent fuzzy knowledge [1], [2]. Many results prove that FPN is suitable to represent and reason misty logic implication relations [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Advanced Self-adaptation Learning and Inference Techniques for Fuzzy Petri Net Expert System Units

Zhang

Wang

Yuan

2009

Artificial Intelligence and Computational Intelligence

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“…A soft postcondition can be viewed as a fuzzy proposition with fuzzy concepts and fuzzy conceptual relations. [42][43][44] Consider the task Schedule-An-Instructor in Figure 3: The precondition of the task represented by a proposition says that there exists a current instructor who lectures a set of courses. The postcondition of the task expresses that the certain courses lectured by the instructor is scheduled.…”

Section: University Timetabling Through Conceptual Modelingmentioning

confidence: 99%

University timetabling through conceptual modeling

Lai

Hsueh

et al. 2005

Int. J. Intell. Syst.

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A number of approaches have been proposed in tackling the timetabling problem, such as operational research, human-machine interaction, constraint programming, expert systems, and neural networks. However, there are still several key challenges to be addressed: easily reformulated to support changes, a generalized framework to handle various timetabling problems, and ability to incorporate knowledge in the timetabling system. In this article, we propose an automatic software engineering approach, called task-based conceptual graphs, to addressing the challenges in the timetabling problem. Task-based conceptual graphs provide the automation of software development processes including specification, verification, and automatic programming. Maintenance can be directly performed on the specifications rather than on the source code; moreover, hard and soft constraints can be easily inserted or removed. A university timetabling system in the Department of Computer Science and Information Engineering at National Central University is used as an illustrative example for the proposed approach.

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“…MDDS (Masonry Damage Diagnostic System) for diagnosing pollution caused pathologies in masonry projects proposed by Van Balen (1996 and2001); RCDES (Reinforced Concrete Diagnosis Expert System) for diagnosing reinforced concrete structures in Peter (1996); REPCON (Repair of Concrete) for repairing concrete structures written by Moodi and Knapton (2003); concrete bridge management by Brito et al (1997); DIASYN (Diagnosis Synthesis) for concrete bridges used in Zhao and Chen (2002); maintenance of cement bridge decks as in Yehia et al (2008); for evaluating bridge damage in Lee et al (1999); for diagnosing cracks in reinforced concrete in Chao and Cheng (1998) and Yeong et al (2007); and for diagnosing corrosion of pipes included in Najjaran et al (2006);…”

Section: Review On Artificial Intelligence In Construction and Aim Ofmentioning

confidence: 99%

Aided Diagnosis of Structural Pathologies with an Expert System

Bernat

Gil

2013

Advances in Structural Engineering

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Sustainability and safety are social demands for long-life buildings. Suitable inspection and maintenance tasks on structural elements are needed for keeping buildings safely in service. Any malfunction that causes structural damage could be called pathology by analogy between structural engineering and medicine.Even the easiest evaluation tasks require expensive training periods that may be shortened with a suitable tool. This work presents an expert system (called Doctor House or DH) for diagnosing pathologies of structural elements in buildings. DH differs from other expert systems when it deals with uncertainty in a far easier but still useful way and it is capable of aiding during the initial survey 'in situ', when damage should be detected at a glance. DH is a powerful tool that represents complex knowledge gathered from bibliography and experts.Knowledge codification and uncertainty treatment are the main achievements presented. Finally, DH was tested and validated during real surveys.

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A fuzzy Petri net-based expert system and its application to damage assessment of bridges

Cited by 52 publications

References 59 publications

Advanced Self-adaptation Learning and Inference Techniques for Fuzzy Petri Net Expert System Units

Advanced Self-adaptation Learning and Inference Techniques for Fuzzy Petri Net Expert System Units

University timetabling through conceptual modeling

Aided Diagnosis of Structural Pathologies with an Expert System

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