Developing a New Model for Assessing Earthquake-Induced Liquefaction Hazards

这项研究的目的是提高工程师预测地震引起的液化危害的能力。 （液化是一种严重的土壤破坏形式，由强烈的地面震动引起，通常与大地震有关，例如1995年1月17日日本科比地震。）它采用了模糊神经网络方法，一种新兴的技术，可以学习和适应结构化和非结构化的知识。 现场液化性能的历史数据，在原因和影响方面，重新编目的模糊神经网络可用的一致模式。与现场性能数据和液化危害的解释相关的不确定性通过使用模糊集来处理，模糊集允许近似和连续分类。通过咨询土破坏专家和模糊神经网络方法，建立了模糊土和地质参数的隶属函数。收集到的数据和专家意见，提出了一种形式，可用于模糊神经网络，然后用来生成训练的神经模糊系统预测液化危害。 预计这项研究将导致一个新的设计方法和程序，预测地震引起的液化灾害，使用神经模糊系统。

Juang 9612116 This research is aimed at improving the engineer's ability to predict earthquake-induced liquefaction hazards. (Liquefaction is a serious form of soil failure resulting from strong-ground shaking, typically associated with major earthquakes, such as the 17 January 1995 earthquake in Kobe, Japan.) It employs the fuzzy neural network methodology, an emerging technology which can learn and adapt structured and unstructured knowledge. Historical data on field liquefaction performance, in terms of causes and effects, is re-cataloged in a consistent pattern usable by fuzzy neural networks. Uncertainty associated with field performance data and the interpretation of liquefaction hazards is handled through the use of fuzzy sets, which allows for approximate and continuous classification. Membership functions of fuzzy soil and geological parameters are established through consultation with experts in soil failure and fuzzy neural network methodology. The collected data and expert opinions, presented in a form usable by fuzzy neural networks, is then used to generate trained neuro-fuzzy systems for predicting liquefaction hazards. It is expected that this research will result in a new design methodology and procedure to predict earthquake- induced liquefaction hazards using neuro-fuzzy systems.