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作 者:李亚峰 冷伍明[1,2] 聂如松[1,2] 梅慧浩[1,3] 程龙虎 周思危 LI Yafeng;LENG Wuming;NIE Rusong;MEI Huihao;CHEN Longhu;ZHOU Siwei(School of Civil Engineering,Central South University,Changsha 410075,China;MOE Key Laboratory of Engineering Structures of Heavy-haul Railway,Central South University,Changsha 410075,China;Department of Civil,Construction and Environmental Engineering,Iowa State University,Aines 50010,United States)
机构地区:[1]中南大学土木工程学院,湖南长沙410075 [2]中南大学重载铁路工程结构教育部重点实验室,湖南长沙410075 [3]爱荷华州立大学土木建筑与环境工程系,美国爱荷华州50010
出 处:《应用基础与工程科学学报》2021年第4期961-972,共12页Journal of Basic Science and Engineering
基 金:国家自然科学基金项目(51878666,51678572);中南大学研究生创新项目(2018zzts192)。
摘 要:准确合理地预测路基沉降是路基安全性评价及加固维护方案决策的重要依据.为克服单项预测模型的信息局限性和片面性,构建了基于集对分析理论的路基沉降组合预测模型.该模型首先基于预测值和实测数据建立集对;应用贝叶斯决策理论表达集对关系识别风险,并通过自适应搜索算法确定最优集对关系准则;进而分析预测模型和实测数据的联系,并基于联系度确定权重系数,实现预测信息的最优组合.实例应用表明,该组合预测模型应用于路基沉降是有效可行的,考虑了单项预测模型的预测精度和可靠性,并拓展了集对关系准则确定方法,也为其他的组合预测评价问题提供了思路.Accurate and reasonable prediction of subgrade settlement is an important basis for the safety evaluation, reinforcement and maintenance of subgrade.To overcome the information limitations and one-sidedness of the single prediction model, a combined prediction model based on the improved Set Pair Analysis theory for the subgrade settlement was proposed.First, set pairs were constructed based on predicted values and measured data.Then the Bayesian decision theory was applied to express the risk of the set pair relationship identification, and the optimal set pair relationship criterion was determined by the adaptive search algorithm.Then the relationship between the prediction model and the measured data was analyzed.Finally the weight coefficient was determined based on the connection number, and the best combination prediction was constructed.The application of the model shows that the combination prediction model is effective and feasible for subgrade settlement.The prediction accuracy and reliability of the single prediction model are considered, and the method of determining the set relationship criterion is also extended.This model also provides ideas for other combined prediction and evaluation problems.
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