Journal ArticleDOI
A neural network model for liquefaction-induced horizontal ground displacement
Jun Wang,Mohammad Shaifur Rahman +1 more
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TLDR
In this article, a backpropagation neural network model is developed to predict the horizontal ground displacements generated by seismically induced liquefaction, which is known to produce significant damage to engineered structures.About:
This article is published in Soil Dynamics and Earthquake Engineering.The article was published on 1999-12-01. It has received 79 citations till now. The article focuses on the topics: Displacement (vector).read more
Citations
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Journal ArticleDOI
Landslide susceptibility analysis using GIS and artificial neural network
TL;DR: In this article, the authors developed landslide susceptibility analysis techniques using an artificial neural network and applied the newly developed techniques to the study area of Yongin in Korea.
Journal ArticleDOI
Landslide susceptibility analysis and its verification using likelihood ratio, logistic regression, and artificial neural network models: case study of Youngin, Korea
TL;DR: In this paper, the authors applied the likelihood ratio, logistic regression, and artificial neural networks models for analysis of landslide susceptibility in Youngin, Korea, using the geographic information system.
Journal ArticleDOI
The promise of implementing machine learning in earthquake engineering: A state-of-the-art review
TL;DR: The state-of-the-art review indicates to what extent ML has been applied in four topic areas of earthquake engineering, including seismic hazard analysis, system identification and damage detection, seismic fragility assessment, and structural control for earthquake mitigation.
Journal ArticleDOI
Evaluation of liquefaction induced lateral displacements using genetic programming
TL;DR: In this paper, a new approach based on genetic programming (GP) for determination of liquefaction induced lateral spreading is presented, which is trained and validated using a database of SPT-based case histories.
Journal ArticleDOI
Evaluation of lateral spreading using artificial neural networks
TL;DR: In this article, a neural network model is developed to predict the horizontal ground displacement in both ground slope and free face conditions due to liquefaction-induced lateral spreading, which is the one compiled by Youd and his colleagues in their revised MLR model.
References
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Book ChapterDOI
Learning internal representations by error propagation
TL;DR: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion.
MonographDOI
Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations
Book
Learning internal representations by error propagation
TL;DR: In this paper, the problem of the generalized delta rule is discussed and the Generalized Delta Rule is applied to the simulation results of simulation results in terms of the generalized delta rule.
Journal ArticleDOI
An introduction to computing with neural nets
TL;DR: This paper provides an introduction to the field of artificial neural nets by reviewing six important neural net models that can be used for pattern classification and exploring how some existing classification and clustering algorithms can be performed using simple neuron-like components.
Journal ArticleDOI
Simplified procedure for evaluating soil liquefaction potential
H. Bolton Seed,Izzat M. Idriss +1 more
TL;DR: Significant factors affecting the liquefaction (or cyclic mobility) potential of sands during earthquakes are identified, and a simplified procedure for evaluating the potential of sand during earthquakes is presented as mentioned in this paper.