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This study pertains to prediction of liquefaction susceptibility of unconsolidated sediments using artificial neural network (ANN) as a prediction model. The backpropagation neural network was trained, tested, and validated with 23 datasets comprising parameters such as cyclic resistance ratio (CRR), cyclic stress ratio (CSR), liquefaction severity index (LSI), and liquefaction sensitivity index (LSeI). The network was also trained to predict the CRR values from LSI, LSeI, and CSR values. The predicted results were comparable with the field data on CRR and liquefaction severity. Thus, this study indicates the potentiality of the ANN technique in mapping the liquefaction susceptibility of the area.

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The present publication provides a comprehensive wood anatomical survey of woods of Indian tree species of the family Anacardiaceae. Thirtyfive species belonging to 19 genera are described as per the feature list of IAWA. Intrusive fibre cavities and perforated ray cells have been reported in Holigarna arnottiana and Pistacia terebinthus respectively. Two species, Choerospondias axillaris and Rhus hookeri, lacked helical thickening despite being ring-porous. Most tribes of the Anacardiaceae appear to be heterogeneous wood anatomically, except Semecarpeae which are homogeneous. The tribes Mangiferae and Semecarpeae are quite similar and may be placed together. Interesting findings were made regarding Indian species of Rhus, which can be divided into two groups. It is suggested to recognise Rhus Group II as a separate section. The ecological trends suggest that anatomical differentiation exists between tropical and temperate species as well as deciduous and evergreen species.

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