Neural network based approach for dissemination of field measurement information

Shin, Hyu-Soung; Pande, Gyan N.; Kim, Chang-Yong; Bae, Gyu-Jin; Hong, Sung-Wan

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자료유형: 학술대회자료

저자정보: Shin, Hyu-Soung (Korea Institute of Construction Technology [KICT]) Pande, Gyan N. (University of Wales Swansea) Kim, Chang-Yong (Korea Institute of Construction Technology [KICT]) Bae, Gyu-Jin (Korea Institute of Construction Technology [KICT]) Hong, Sung-Wan (Korea Institute of Construction Technology [KICT])

저널정보: 한국지구물리.물리탐사학회 한국지구물리탐사학회 학술대회 한국지구물리탐사학회 2003년도 Proceedings of the international symposium on the fusion technology

발행연도: 2003.1

수록면: 176 - 183 (8page)

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This paper presents a neural network based approach to disseminating information relating to experimental and field observations in engineering. Although the methodology is generic and can be applied to many areas of engineering science, attention is focussed here solely on geotechnical engineering applications. Field data relating to the settlement of foundations presented by Burland and Burbidge (1985) which led to their well known equation for calculation of settlement, now included in most text books, is re-visited. A part of the data, chosen randomly, is used to train an Artificial Neural Network (ANN), which relates foundation settlement to various causes as identified by the authors. Predictions are made for situations for which data were not used in training. These indicate sufficient accuracy when compared to the original field data. Accuracy of predictions is further improved when all the data are included in the training set. The finally trained ANN is shown to represent these data more accurately than the Burland and Burbidge equation. Based on the above heuristic example, an ANN is presented as an alternative to developing equations and design rules in geotechnical engineering practice. Significant advantages are shown to arise by using this methodology. Ease of updating the ANN, as and when additional data becomes available, being the most important one. Loss of transparency, however, seems to be the main disadvantage.

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