A practical guide to neural nets
A practical guide to neural nets
Introduction to the theory of neural computation
Introduction to the theory of neural computation
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Fundamentals of Artificial Neural Networks
Fundamentals of Artificial Neural Networks
Neural Networks in Computer Intelligence
Neural Networks in Computer Intelligence
Artificial Intelligence: A Guide to Intelligent Systems
Artificial Intelligence: A Guide to Intelligent Systems
Advances in Feedforward Neural Networks: Demystifying Knowledge Acquiring Black Boxes
IEEE Transactions on Knowledge and Data Engineering
Artificial Neural Networks: An Introduction to ANN Theory and Practice
Expert Systems with Applications: An International Journal
A data-model-fusion prognostic framework for dynamic system state forecasting
Engineering Applications of Artificial Intelligence
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Predicting tunnel boring machine (TBM) performance is a crucial issue for the accomplishment of a mechanical tunnel project, excavating via full face tunneling machine. Many models and equations have previously been introduced to estimate TBM performance based on properties of both rock and machine employing various statistical analysis techniques. However, considering the nature of the problem, it is relatively difficult to estimate tunnel boring machine performance by linear prediction models. Artificial neural networks (ANNs) and non-linear multiple regression models have great potential for establishing such prediction models. The purpose of the present study is the construction of non-linear multivariable prediction models to estimate TBM performance as a function of rock properties. For this purpose, rock properties and machine data were collected from recently completed TBM tunnel project in the City of New York, USA and consequently the database was established to develop performance prediction models utilizing the ANN and the non-linear multiple regression methods. This paper presents the results of study into the application of the non-linear prediction approaches providing the acceptable precise performance estimations.