Developing a New Model for Assessing Earthquake-Induced Liquefaction Hazards

开发评估地震引起的液化危险的新模型

• 批准号: 9612116
• 负责人: Charng Juang
• 金额: $ 12.86万
• 依托单位: Clemson University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-05-15 至 2000-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9612116&HistoricalAwards=false
• 关键词: Developing; New; Model; Assessing; Earthquake

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.

本研究旨在提高工程师对地震液化灾害的预测能力。（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日在日本神户发生的地震。）它采用模糊神经网络方法，这是一种能够学习和适应结构化和非结构化知识的新兴技术。根据原因和影响，现场液化性能的历史数据被模糊神经网络以一致的模式重新编目。通过使用模糊集来处理与现场性能数据和液化危险解释相关的不确定性，从而实现近似和连续分类。通过咨询土失稳专家，运用模糊神经网络方法，建立了模糊土与地质参数的隶属函数。收集到的数据和专家意见，以模糊神经网络可用的形式呈现，然后用于生成经过训练的神经模糊系统，用于预测液化危害。期望本研究能为利用神经模糊系统预测地震液化灾害提供一种新的设计方法和程序。