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基于BP神经网络的原状土阻尼比智能预测法

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为探究原状土阻尼比D随剪应变γ和土层深度H(上覆压力σ′m)的双向维度特征，本文对同一钻孔自地表至基岩深度范围内原状粉质黏土与粉土开展系列共振柱试验。利用BP神经网络技术深度挖掘、识别、学习原状土D的双向维度耦合规律，建立了D智能预测法。通过预测结果与试验数据的比较，得出基于BP神经网络的智能预测法可较好地适用于原状土D的双向维度特征预测。试验表明:原状土D与γ和H(σ′m)2 个维度存在耦合相关。同一H(σ′m)维度时，D随γ增加呈现非线性上升规律;同一γ维度时，D随H(σ′m)增加呈现相反的降低规律;随着H(σ′m)维度的增加，D～γ 整体非线性关系逐渐下倾伴随着增长速率逐级变缓。本文方法实现了原状土 D 在H(σ′m)和γ双向维度下的智能预测。
Intelligent prediction method for undisturbed soil damping ratio based on a back-propagation neural network
To investigate the bidirectional characteristics of undisturbed soil damping ratio D with shear strain γ and soil depth H(overburden pressure σ′m),a series of resonant column tests were conducted on undisturbed silty clay and silt from the surface to the bedrock of the same borehole.Back-propagation(BP)neural network technology was utilized to analyze,identify,and learn the coupling law of bidirectional dimensions of undisturbed soil D,and an intelligent D prediction method was established.A comparison of the prediction results with the experimental da-ta revealed that the intelligent prediction method based on BP neural networks could effectively predict the bidirec-tional characteristics of undisturbed soil D.A coupling correlation existed between the undisturbed soil D with two dimensions of γ and H(σ′m).In the same H(σ′m)dimension,D exhibited a nonlinearly increasing regularity with increasing γ,while in the same γ dimension,D showed an opposite decreasing pattern with increasing H(σ′m).Moreover,as the H(σ′m)dimension increased,the overall nonlinear relationship between D and γ showed a down-ward trend,accompanied by a gradual slowdown of the growth rate.This method enabled intelligent prediction of undisturbed soil D in the bidirectional dimensions of γ and H(σ′m).

undisturbed soildamping ratioresonant column testsoil depthshear strainbidirectional dimen-sions characteristicsBP neural networkintelligent prediction

杨文保、朱恩赐、吴琪、陈国兴、卢艺静、蒋家卫

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南京工业大学 岩土工程研究所,江苏 南京 210009

安徽省桥梁数据结构智慧诊断与智慧运维国际联合研究中心,安徽滁州 239000

东南大学 土木工程学院,江苏 南京 211189

原状土 阻尼比 共振柱试验 土层深度 剪应变 双向维度特征 BP神经网络 智能预测

国家自然科学基金项目安徽省桥梁结构数据诊断与智慧运维国际联合研究中心开放项目

519783342022AHGHYB05

2024

10.11990/jheu.202208018

哈尔滨工程大学学报
哈尔滨工程大学

哈尔滨工程大学学报

CSTPCD北大核心
影响因子：0.655
ISSN：1006-7043
年,卷(期)：2024.45(8)