共查询到18条相似文献，搜索用时 812 毫秒
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基于人工神经网络基本原理和方法,构建多隐层BP神经网络径流预测模型,以新疆伊犁河雅马渡站径流预测为例进行分析,并构建常规单隐层BP以及RBF、GRNN神经网络模型作为对比分析模型,将各模型预测结果与文献[1]中的预测结果进行比较,结果表明:(1)多隐层BP神经网络径流预测模型泛化能力强,预测精度高,算法稳定,模型精度优于IEA-BP网络模型,表明研究建立的多隐层BP神经网络模型用于径流预测是合理可行的,是一种可以应用于水文径流预测预报的新方法.(2)RBF、GRNN神经网络径流预测模型预测精度高于常规单隐层BP网络模型,且RBF与GRNN神经网络模型具有收敛速度快、预测精度高、调整参数少,不易陷入局部极小值等优点,可以更快地预测网络,具有较大的计算优势. 相似文献
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影响砂土液化的因素有很多,建立多指标的液化预测模型非常有必要。目前所有的多指标砂土液化预测模型,均默认选取的判别因子之间相互独立,不存在相关性,可能导致各判别因子之间存在信息叠加而发生误判。以唐山地震砂土液化的25个案例为样本,选取8个影响因素作为砂土液化预测的初始判别指标,首先采用主成分分析（PCA）对各判别指标进行分析,对存在相关性比较高的指标进行了降维处理。基于降维后的4个主成分换算得到新的样本数据,以18个案例为学习样本,建立主成分分析与距离判别分析（DDA）相结合的砂土液化预测模型。利用建立的预测模型对18个案例进行回判,结果全部正确。对其他7个案例的液化情况进行了预测,并与规范法、Seed方法、BP法、DDA法的判别结果进行分析比较,结果表明基于主成分分析与距离判别方法的砂土液化判别模型预测准确率为100%。将模型应用于工程实例,判别结果也与实际情况一致,表明该模型具有良好的预测功能,可在实际工程中应用。 相似文献
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基于人工神经网络的砂土液化势评价 总被引：3，自引：1，他引：2
本文利用静力触探（ＣＰＴ）场地液化数据,建立了液化势判定的反向传播神经网络模型,研究表明,同传统方法相比,人工神经网络方法在判别砂土液化势方面是可行的。 相似文献
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采用组件式GIS (COMGIS)技术开发了结合BP神经网络分析模型的场地地震液化势评价系统,调用水平成层土地震反应分析程序SHAKE91实现设定地震下地震动影响场的模拟。在VB下调用Matlab神经网络工具箱来完成场地地震液化势评价模型在COMGIS系统中的模块化;利用GIS技术对评价结果,即液化势等级进行空间复合,给出场地潜在的地层液化势空间分布图。研究表明,SHAKE91应用程序在系统菜单下可直接调用,实现地震动影响场计算的模块化;BP神经网络技术应用于场地地震液化势评价中能达到较为理想的效果;系统的GIS空间分析功能可使评价结果与场地信息进行空间匹配,实现目标场地潜在地震液化势的快速评估。 相似文献
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工后沉降预测结果是黄土高填方场地变形稳定性评价和建筑物规划布局的重要参考依据。为遴选适合黄土高填方场地的工后沉降预测模型,基于某典型黄土高填方工程的实测沉降数据,分析了工后沉降曲线的变化规律和发展趋势,建立了17种回归参数模型,提出了模型预测效果的评价指标和方法。结果表明：（1）该工程填方区工后沉降历时曲线呈“缓变型”变化,土方填筑完工初期无陡增段,随时间增加沉降速率逐步降低,尚未出现沉降趋于稳定的水平段;（2）将外推预测误差、内拟合误差和后验误差比最小化作为综合控制目标,可遴选出理想的回归参数模型;（3）MMF模型（Ⅱ型）和双曲线模型具有较高的预测精度、较好的稳定性和较强的适应性,在17种模型中的预测效果最佳;（4）沉降数据的变化越平稳,模型预测效果越好;（5）增大建模数据的时间跨度,会提升预测精度,但增大至一定值后,预测精度提升效果不再显著。 相似文献
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潘建平 《水文地质工程地质》2011,38(4):117-120
为了更好地进行场地液化评价,将可靠度理论引入水平场地液化概率评价中。以标准贯入试验(SPT)实测数据的统计分析结果为基础,用一次二阶矩法(FOSM)建立水平场地液化概率评价模型,分析了测试数据变异系数对抗液化安全系数与液化概率的影响,并建议了水平场地液化概率评价标准。实例分析表明,新建水平场地液化概率评价模型各参数的物理意义与统计指标明确,相比传统的确定性分析方法,不仅能判定液化的发生与否,还能给出液化发生的概率,这为进行基于风险分析的抗震设计提供了可能。 相似文献
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为快速准确地对砂土液化情况作出预测,选取地震烈度、地下水位、覆盖厚度、标贯击数、平均粒径、地貌单元、土质及不均匀系数为主要影响因素,运用相关性分析和因子分析模型对其进行分析和属性约减,采用遗传算法（GA）对支持向量机（SVM）的参数寻优,结合Adaboost迭代算法,建立预测砂土地震液化的GA_SVM_Adaboost模型。选用唐山地震砂土液化现场勘察资料中的329组数据对模型进行训练,利用该模型对剩余68组砂土液化数据进行预测。最后,将预测结果与GA_SVM和SVM模型预测结果进行比较。结果表明,3个模型的平均预测准确率分别为100%、98.04%、89.71%,基于因子分析的GA_SVM_Adaboost模型的预测准确性优于GA_SVM模型和SVM模型,是一种解决砂土地震液化预测问题的有效方法,具有一定的应用参考价值。 相似文献
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由于现有的震害预测方法不能对砖砌体结构做出高效的预测,基于机器学习模型,提出了一种综合考虑地震动特性与结构特性的砖砌体结构震害快速预测方法 .该方法利用机器学习模型,从时域、频域、反应谱和持时4个方面初步选取了能够代表地震动特性的12个参数,从承载力、刚度等方面初步选取了与砖砌体结构破坏相关性较强的7个结构参数;将地震动参数与结构参数相结合作为输入变量,分别给出了基于支持向量机（SVM）、随机森林（RF）、人工神经网络（ANN）三种机器学习模型的砖砌体结构的震害快速预测方法,并进行了性能比较;采用相关性分析对输入参数进行进一步优化,给出了优化输入参数后的最优预测模型.结果表明,当采用19个输入参数时,ANN模型的预测准确率最高,达到91.56%.当采用优化后的12个参数作为输入时,基于RF模型的预测性能更加稳定,预测的准确率也更高,可达到90.01%.优化输入参数后的基于RF模型的预测方法可以实现对砖砌体结构震害的快速预测;与只考虑结构参数或只考虑地震动参数作为输入的方法相比,同时考虑结构和地震动参数作为输入的方法极大地提高了预测的准确性. 相似文献
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Gökhan Saygili 《Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards》2017,11(2):197-207
The conventional liquefaction potential assessment methods (also known as simplified methods) profoundly rely on empirical correlations based on observations from case histories. A probabilistic framework is developed to incorporate uncertainties in the earthquake ground motion prediction, the cyclic resistance prediction, and the cyclic demand prediction. The results of a probabilistic seismic hazard assessment, site response analyses, and liquefaction potential analyses are convolved to derive a relationship for the annual probability and return period of liquefaction. The random field spatial model is employed to quantify the spatial uncertainty associated with the in-situ measurements of geotechnical material. 相似文献
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Mahesh Pal 《国际地质力学数值与分析法杂志》2006,30(10):983-996
This paper investigates the potential of support vector machines (SVM)‐based classification approach to assess the liquefaction potential from actual standard penetration test (SPT) and cone penetration test (CPT) field data. SVMs are based on statistical learning theory and found to work well in comparison to neural networks in several other applications. Both CPT and SPT field data sets is used with SVMs for predicting the occurrence and non‐occurrence of liquefaction based on different input parameter combination. With SPT and CPT test data sets, highest accuracy of 96 and 97%, respectively, was achieved with SVMs. This suggests that SVMs can effectively be used to model the complex relationship between different soil parameter and the liquefaction potential. Several other combinations of input variable were used to assess the influence of different input parameters on liquefaction potential. Proposed approach suggest that neither normalized cone resistance value with CPT data nor the calculation of standardized SPT value is required with SPT data. Further, SVMs required few user‐defined parameters and provide better performance in comparison to neural network approach. Copyright © 2006 John Wiley & Sons, Ltd. 相似文献
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Least square support vector machine and relevance vector machine for evaluating seismic liquefaction potential using SPT 总被引：4，自引：1，他引：3
Pijush Samui 《Natural Hazards》2011,59(2):811-822
The determination of liquefaction potential of soil is an imperative task in earthquake geotechnical engineering. The current
research aims at proposing least square support vector machine (LSSVM) and relevance vector machine (RVM) as novel classification
techniques for the determination of liquefaction potential of soil from actual standard penetration test (SPT) data. The LSSVM
is a statistical learning method that has a self-contained basis of statistical learning theory and excellent learning performance.
RVM is based on a Bayesian formulation. It can generalize well and provide inferences at low computational cost. Both models
give probabilistic output. A comparative study has been also done between developed two models and artificial neural network
model. The study shows that RVM is the best model for the prediction of liquefaction potential of soil is based on SPT data. 相似文献
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建筑物震陷预测新方法研究 总被引：1，自引：0，他引：1
利用人工神经网络的基本原理，本文修正了经典BP型神经网络的激励函数，并对学习率和训练样本进行了动态调整等多方面改进。根据70个多层建筑震陷的实测资料，在分析了建筑物震陷的影响因素基础上，提取了9个指标；采用改进后的BP算法，建立了多指标的建筑物震陷预测模型。研究结果表明，改进的BP网络性能良好，所建立的模型预测精度高，具有一定的工程实用价值；神经网络法是一种有效可行的预测新方法，人工神经网络技术具有广泛的应用前景。 相似文献
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基于解释结构模型和因果图法,选取12个具有代表性的定性和定量因素,在大量数据不完备的情况下提出了建立贝叶斯网络液化模型的方法。以2011年日本东北地区太平洋近海地震液化不完备数据为例,采用总体精度、ROC曲线下面积、准确率、召回率和F1值5项指标对模型进行综合评估,并与径向基神经网络模型进行对比。结果表明：贝叶斯网络液化模型的回判和预测效果都优于径向基神经网络模型,且对于数据缺失的样本的预测效果也较理想。此外,该模型对于不同土质的液化评估均有较好的适用性。分类不均衡和抽样偏差会对模型的学习和预测效果产生很大影响,建议应同时采用上述5项评估指标进行综合评估模型的优劣。 相似文献
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In this paper a new approach is presented, based on evolutionary polynomial regression (EPR), for determination of liquefaction potential of sands. EPR models are developed and validated using a database of 170 liquefaction and non-liquefaction field case histories for sandy soils based on CPT results. Three models are presented to relate liquefaction potential to soil geometric and geotechnical parameters as well as earthquake characteristics. It is shown that the EPR model is able to learn, with a very high accuracy, the complex relationship between liquefaction and its contributing factors in the form of a function. The attained function can then be used to generalize the learning to predict liquefaction potential for new cases not used in the construction of the model. The results of the developed EPR models are compared with a conventional model as well as a number of neural network-based models. It is shown that the proposed EPR model provides more accurate results than the conventional model and the accuracy of the EPR results is better than or at least comparable to that of the neural network-based models proposed in the literature. The advantages of the proposed EPR model over the conventional and neural network-based models are highlighted. 相似文献
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This paper proposes a systematic framework for real-time assessment of spatial liquefaction hazard of port areas considering local seismic response characteristics based on a geographic information system (GIS) platform. The framework is integrated and embedded with sequential, interrelated subprocedures and a database for liquefaction-induced damage evaluation that standardizes and both individually and collectively quantifies analytical results. To integrate the current in situ condition of a selected port area, the framework functions as a spatial database system for geotechnical and structural data and as a recipient of automatic transmission of seismic monitoring data. The geotechnical profile correlated with liquefaction potential is compiled into a geotechnical spatial grid built by geostatistical methods. Linked with the geotechnical spatial grid, the processing of site-specific responses is automatically interpreted from previously derived correlations between rock acceleration and maximum acceleration of each soil layer. As a result, the liquefaction severity is determined based on a combined geotechnical spatial grid with seismic load correlation in real-time according to a simplified procedure, allowing calculation of the liquefaction potential index (LPI). To demonstrate practical applications of the framework in estimating the liquefaction hazard in real-time, liquefaction-hazard maps were visualized for two earthquake scenarios, verifying the applicability of the proposed framework. 相似文献
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Ji-Lei Hu Xiao-Wei Tang 《Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards》2015,9(3):200-217
The Bayesian network (BN) is a type of graphical network based on probabilistic inference that has been gradually applied to assessment of seismic liquefaction potential. However, how to construct a robust BN remains underexplored in this field. This paper aims to present an efficient hybrid approach combining domain knowledge and data to construct a BN that facilitates the integration of multiple factors and the quantification of uncertainties within a network model to assess seismic liquefaction. Initially, only using given domain knowledge, a naive network model can be constructed using interpretive structural modeling. Thereafter, some effective information about the naive model is provided to construct a robust model using structural learning of BN from historic data. Finally, the returning predictive results and the predictive results are compared to other methods including non-probabilistic and probabilistic models for seismic liquefaction using the metrics of the overall accuracy, the area under the curve of receiver operating characteristic, prediction, recall and F1 score. The methodology proposed in this paper achieved better performance, and we discussed the power and value of the proposed approach at the end of this paper, which suggest that BN is a good alternative tool for seismic liquefaction prediction. 相似文献