Department of Civil Engineering

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Author: search.filters.author.Barai, Sudhir KumarEnd date: 1999

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    Neural Networks for Damage Detection in Steel Railway Bridges
    (IABSE, 1995) Barai, Sudhir Kumar
    The paper presents Artificial Neural Networks developed for typical steel railway bridges for the purpose of damage detection. Multilayer perceptrons have been used for generating the architecture for the bridges of different configurations. The back propagation algorithm has been adopted for training the network with simulated damage states. The training pairs have been generated using a standard finite element program. The weights of the trained networks have been stored and can be used as a knowledge source independently. It is demonstrated that the trained networks have practical relevance.
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    Ensemble modeling or selecting the best model: Many could be better than one
    (1999) Barai, Sudhir Kumar
    In the course of data modeling, many models could be created. Much work has been done on formulating guidelines for model selection. However, by and large, these guidelines are conservative or too speci c. Instead of using general guidelines, models could be selected for a particular task based on statistical tests. When selecting one model, others are discarded. Instead of losing potential sources of information, models could be combined to yield better performance. We review the basics of model selection and combination and discuss their di erences. Two examples of opportunistic and principled combinations are presented. The rst demonstrates that mediocre quality models could be combined to yield signi cantly better performance. The latter is the main contribution of the paper; it describes and illustrates a novel heuristic approach called the SG (k-NN) ensemble for the generation of good quality and diverse models that can even improve excellent quality models.
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    Ensemble modelling or selecting the best model: Many could be better than one
    (CUP, 1999-11-01) Barai, Sudhir Kumar
    In the course of data modelling, many models could be created. Much work has been done on formulating guidelines for model selection. However, by and large, these guidelines are conservative or too specific. Instead of using general guidelines, models could be selected for a particular task based on statistical tests. When selecting one model, others are discarded. Instead of losing potential sources of information, models could be combined to yield better performance. We review the basics of model selection and combination and discuss their differences. Two examples of opportunistic and principled combinations are presented. The first demonstrates that mediocre quality models could be combined to yield significantly better performance. The latter is the main contribution of the paper; it describes and illustrates a novel heuristic approach called the SG(k-NN) ensemble for the generation of good-quality and diverse models that can even improve excellent quality models.
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    Performance of the generalized delta rule in structural damage detection
    (Elsiever, 1995-04) Barai, Sudhir Kumar
    The paper examines the suitability of the generalized data rule in training artificial neural networks (ANN) for damage identification in structures. Several multilayer perceptron architectures are investigated for a typical bridge truss structure with simulated damage states generated randomly. The training samples have been generated in terms of measurable structural parameters (displacements and strains) at suitable selected locations in the structure. Issues related to the performance of the network with reference to hidden layers and hidden neurons are examined. Some heuristics are proposed for the design of neural networks for damage identification in structures. These are further supported by an investigation conducted on five other bridge truss configurations.
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    Sensitivity‐Based Weighted‐Average in Structural Damage Assessment
    (ASCE, 1994) Barai, Sudhir Kumar
    To quantify the global damageability of a structure, the weighted‐average function defined in terms of damage condition and the importance or weightages of structural elements has generally been used in literature. Judgment of structural importance of a structural element is a difficult task. The opinion provided by experts could have different perspective or bias, which may reflect in the overall assessment process. Structural analysis being precise these days, structural importance of a member could be realistically estimated using standard structural procedures. In this paper it is proposed that the weightage computation could be linked to damage sensitivity of the element response. It is argued that the weighted‐average computation based on damage sensitivity is a more realistic index for integrity assessment. In this perspective, the concept of damage sensitivity and its computational aspects based on the finite‐element method are also presented. Finally, examples of weighted‐average computation comprising fuzzy sets and importance factor obtained from normalized damage sensitivity are illustrated.