Premium
Neural Networks for Evaluating CPT Calibration Chamber Test Data
Author(s) -
Goh Anthony T. C.
Publication year - 1995
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/j.1467-8667.1995.tb00277.x
Subject(s) - artificial neural network , backpropagation , generalization , calibration , computer science , test set , data set , set (abstract data type) , test data , artificial intelligence , training set , machine learning , data mining , mathematics , statistics , mathematical analysis , programming language
The feasibility of using neural network models for evaluating CPT calibration chamber test data is investigated. The backpropagation neural network algorithm was used to analyze the data. After learning from a set of randomly selected patterns, the neural network model was able to produce reasonably accurate predictions for patterns not included in the training set. The neural network performance was found to be simpler and more effective than regression analysis for modeling the CPT test data. Correlations between the cone measurements and the engineering properties of sand can be developed using the generalization capabilities of the neural network.