小应变硬化(HSS)模型能够反映土体在小应变范围内模量的高度非线性,已被广泛应用于复杂地质条件下的基坑变形计算。模型参数的合理取值对于计算结果有着重要影响,但目前规范参数推荐值范围较大,给工程应用带来了不便,因此亟需解决模型参数精确取值问题。分析了已有HSS模型参数的数据特征,引入人工神经网络方法(ANN),得到了上海土体 HSS 模型模量参数与土体初始孔隙比e、压缩模量Es1-2等的经验关系。实现了通过常规工程勘察报告快速准确确定 HSS 模型模量参数,确定的参数值较推荐取值更接近实测值。研究成果可以为上海地区地下工程提供参考。
The hardening strain model with small strain (HSS) can reflect the high nonlinearity of soil modulus at small strain range, which has been widely applied in deformation calculations during pit excavations under complex geological conditions. The appropriate values of model parameters have a significant impact on the computational results. However, the current recommended parameter values have a large range of fluctuations, which brings inconvenience to engineering applications. Therefore, further research is needed on the parameter values of the model. This study analyzes the data characteristics of existing HSS model parameters and introduces the artificial neural network method (ANN). The empirical relationships between the modulus parameters of the HSS model for Shanghai soils and factors such as the initial pore ratio (e) and oedometer modulus (Es1-2) are established. This allows for the rapid determination of HSS model modulus parameters based on survey reports. Compared with the recommended values, the proposed method for determining the HSS model modulus parameters is closer to the measured value. The study results can provide references for projects in Shanghai.
[1] 牟俊楠, 丁文其.江湾—五角场地下空间开发利用与节能技术研究[J]. 地下空间与工程学报, 2012, 8(增1): 1348-1352. (Mu Junnan, Ding Wenqi. Research of underground space development and energy saving technology in Jiangwan-Wujiaochang area[J]. Chinese Journal of Underground Space and Engineering, 2012, 8(Supp.1): 1348-1352. (in Chinese))
[2] 上海市住房和城乡建设管理委员会. 基坑工程技术标准(DG/TJ 508-61-2018)[S]. 上海: 同济大学出版社, 2018. (Ministry of Housing and Urban-rural Development of Shanghai City. Technical code for excavation engineering(DG/TJ 508-61-2018)[S]. Shanghai: Tongji University Press, 2018. (in Chinese))
[3] Izumi K, Ogihara M, Kameya H. Displacementsof bridge foundations on sedimentary soft rock: a case study on small-strain stiffness[J]. Geotechnique, 1997, 47(3): 619-632.
[4] Benz T. Small-strain stiffness of soils and its numerical consequences[D]. Stuttgart: University of Stuttgart, 2007.
[5] Yang J, Gu X Q. Shear stiffness of granular material at small strain: does it depend on grain size?[J]. Geotechnique, 2013, 63(2): 165-179.
[6] 尹骥.小应变硬化土模型在上海地区深基坑工程中的应用[J]. 岩土工程学报, 2010, 32(增1): 166-172. (Yin Ji. Application of hardening soil model with small strain stiffness in deep foundation pits in Shanghai[J]. Chinese Journal of Geotechnical Engineering, 2010, 32(Supp.1): 166-172. (in Chinese))
[7] 林德周.小应变土体硬化模型参数试验研究及工程应用—以杭州某基坑工程为例[D]. 杭州:浙江大学,2022. (Lin Dezhou. Experimental study on parameters of small strain soil hardening model and its engineering application—a case study of a foundation pit project in Hangzhou[D]. Hangzhou: Zhejiang University, 2022. (in Chinese))
[8] 王卫东, 王浩然, 徐中华.基坑开挖数值分析中土体硬化模型参数的试验研究[J]. 岩土力学, 2012, 33(8): 2283-2290. (Wang Weidong, Wang Haoran, Xu Zhonghua. Experimental study of parameters of hardening soil model for numerical analysis of excavations of foundation pits[J]. Rock and Soil Mechanics, 2012, 33(8): 2283-2290. (in Chinese))
[9] 梁发云, 贾亚杰, 丁钰津, 等.上海地区软土HSS模型参数的试验研究[J]. 岩土工程学报, 2017, 39(2): 269-278. (Liang Fayun, Jia Yajie, Ding Yujin, et al. Experimental study on parameters of HSS model for soft soils in Shanghai[J]. Chinese Journal of Geotechnical Engineering, 2017, 39(2): 269-278. (in Chinese))
[10] 谢东武, 管飞, 丁文其.小应变硬化土模型参数的确定与敏感性分析[J]. 地震工程学报, 2017, 39(5): 898-906. (Xie Dongwu, Guan Fei, Ding Wenqi. Determination and sensitivity analysis of the parameters of hardening soil model with small strain stiffness[J]. China Earthquake Engineering Journal, 2017, 39(5): 898-906. (in Chinese))
[11] 罗敏敏, 陈赟, 周江.小应变土体硬化模型参数取值研究现状与展望[J]. 工业建筑, 2021, 51(4): 172-180. (Luo Minmin, Chen Yun, Zhou Jiang. Research status and prospect of parameter selection for the HS-Small model[J]. Industrial Construction, 2021, 51(4): 172-180. (in Chinese))
[12] 顾晓强, 吴瑞拓, 梁发云, 等.上海土体小应变硬化模型整套参数取值方法及工程验证[J]. 岩土力学, 2021, 42(3): 1-14. (Gu Xiaoqiang, Wu Ruituo, Liang Fayun, et al. On HSS model parameters for Shanghai soils with engineering verification[J]. Rock and Soil Mechanics, 2021, 42(3): 1-14. (in Chinese))
[13] 李彦杰, 薛亚东, 岳磊,等.基于遗传算法-BP神经网络的深基坑变形预测[J]. 地下空间与工程学报, 2015, 11(增2): 741-749. (Li Yanjie, Xue Yadong, Yue Lei, et al. Displacement prediction of deep foundation pit based on genetic algorithms and BP neural network[J]. Chinese Journal of Underground Space and Engineering, 2015, 11(Supp.2): 741-749. (in Chinese))
[14] 张灿, 琚娟, 郭志.基于神经网络的深基坑沉降预测模型比较[J]. 地下空间与工程学报, 2013, 9(6): 1315-1319. (Zhang Can, Ju Juan, Guo Zhi. Comparison of models for settlement prediction based on neural network for deep foundation pit[J]. Chinese Journal of Underground Space and Engineering, 2013, 9(6): 1315-1319. (in Chinese))
[15] 陈少杰, 顾晓强, 高广运.土体小应变剪切模量的现场和室内试验对比及工程应用[J]. 岩土工程学报, 2019, 41(增2): 133-136. (Chen Shaojie, Gu Xiaoqiang, Gao Guangyun. Comparison and application of small strain shear moduli from field and laboratory measurements[J]. Chinese Journal of Geotechnical Engineering, 2019, 41(Supp.2): 133-136. (in Chinese))
[16] Shahin M A, Maier H R, Jaksa M B. Predicting settlement of shallow foundations using neural networks[J]. Journal of Geotechnical and Geoenvironmental Engineering, 2002, 128 (9): 785-793.
[17] Lin P Y, Chen X Y, Jiang M J, et al. Mapping shear strength and compressibility of soft soils with artificial neural networks[J]. Engineering Geology, 2022, 300: 106585.
