| Citation: | ZHOU Linggang, HU Yiting, CHEN Xinwei, TU Feng, WU Zhaofeng, YU Yang, WANG Yanbing, MAN Yin, LI Weichao. Calculation of capacity and optimization design-composite slab wall soil solidification foundation based on neural network[J]. Bulletin of Geological Science and Technology, 2024, 43(6): 102-113. doi: 10.19509/j.cnki.dzkq.tb20230720
|
The soil solidification technique is widely used in soft foundation treatment. To exploit spatial plasticity of this technique, composite slab wall soil solidification foundations have gradually been applied in engineering projects. However, a reliable method for calculating the bearing capacity of composite slab wall soil solidification foundations is lack, and the mechanical parameters of both solidified and soft soil remain uncertain. These factors complicate the optimization of the composite slab wall soil solidification foundation designs. Therefore, it is crucial to propose a method for calculating the bearing capacity and optimizing the design of such foundations.
This study focuses on the 110 kV Jingwei coastal substation in Taizhou, Zhejiang Province. A numerical model is established based on the mechanical parameters of solidified and soft soil to calculate the bearing capacity of composite slab wall soil solidification foundations. The results of these calculations are used to train a neural network, enabling predictions of the bearing capacity for various design parameters, thus facilitating engineering applications. Uncertainties of the mechanical parameters are addressed through Monte Carlo simulations, and their impact on design is estimated using the robustness evaluation index standard deviation. The design cost is approximately estimated by the cross-sectional area of the foundation. Robust design theory is introduced to optimize the design while balancing cost-effectiveness and robustness.
This method is implemented in an engineering project, resulting in an optimal design with solidified plate thickness
The proposed methods for calculating bearing capacity and optimizing the design of composite slab wall soil solidification foundations offer new concepts and approaches for similar projects.
| [1] |
蔡强. 滨海深厚软土加固变形机理及工程运用[D]. 安徽淮南: 安徽理工大学, 2018.
CAI Q. Deformation mechanism and engineering application of the strengthened deep soft soil in the coastal[D]. Huainan Anhui: Anhui University of Science and Technology, 2018. (in Chinese with English abstract)
|
| [2] |
王冲, 郝金山, 张迎, 等. 天津滨海地区软土固化地基承载性状分析[J]. 勘察科学技术, 2015(2): 1-4.
WANG C, HAO J S, ZHANG Y, et al. Analysis on bearing behavior of soft soil solidified foundation in Tianjin coastal area[J]. Site Investigation Science and Technology, 2015(2): 1-4. (in Chinese with English abstract)
|
| [3] |
张吉, 赵丹禄. 天津滨海地区软土固化技术现场试验研究[J]. 珠江水运, 2023(9): 104-106.
ZHANG J, ZHAO D L. Field test study on soft soil solidification technology in coastal area of Tianjin[J]. Pearl River Water Transport, 2023(9): 104-106. (in Chinese with English abstract)
|
| [4] |
刘曦. 带肋梁硬壳层软土地基应力分布与承载力研究[D]. 沈阳: 沈阳建筑大学, 2013.
LIU X. Research on the stress distribution and bearing capacity of the hard shell foundation with rib beam[D]. Shenyang: Shenyang Architecture University, 2013. (in Chinese with English abstract)
|
| [5] |
王玉梅. 带肋梁硬壳层软土地基应力与变形的研究[D]. 沈阳: 沈阳建筑大学, 2013.
WANG Y M. Research on the stress and deformation of the hard shell foundation with rib beam[D]. Shenyang: Shenyang Architecture University, 2013. (in Chinese with English abstract)
|
| [6] |
LIU X Q, ZHANG H Q, YAN Z, et al. Research on bearing characteristics of grid composite foundation based on silt solidification[J]. Journal of Performance of Constructed Facilities, 2021, 35(5): 04021070. doi: 10.1061/(ASCE)CF.1943-5509.0001611
|
| [7] |
曹文庚, 潘登, 徐郅杰, 等. 河南省滑坡灾害易发性制图研究: 多种机器学习模型的对比[J/OL]. 地质科技通报: 1-11(2023-12-28)[2024-09-02]. doi:
CAO W G, PAN D, XU Z J, et al. Landslide disaster vulnerability mapping study in Henan Province: Comparison of different machine learning models[J/OL]. Bulletin of Geological Science and Technology: 1-11(2023-12-28)[2024-09-02]. doi:
|
| [8] |
宋宜祥, 张晓波, 黄达. 基于SSA-Adam-BP神经网络模型的堰塞坝稳定性预测[J]. 地质科技通报, 2022, 41(2): 130-138. doi: 10.19509/j.cnki.dzkq.2022.0040
SONG Y X, ZHANG X B, HUANG D. Stability prediction of landslide dams based on SSA-Adam-BP neural network model[J]. Bulletin of Geological Science and Technology, 2022, 41(2): 130-138. (in Chinese with English abstract) doi: 10.19509/j.cnki.dzkq.2022.0040
|
| [9] |
邓日朗, 张庆华, 刘伟, 等. 基于改进两步法采样策略和卷积神经网络的崩塌易发性评价[J]. 地质科技通报, 2024, 43(2): 186-200. doi: 10.19509/j.cnki.dzkq.tb20220535
DENG R L, ZHANG Q H, LIU W, et al. Collapse susceptibility evaluation based on an improved two-step sampling strategy and a convolutional neural network[J]. Bulletin of Geological Science and Technology, 2024, 43(2): 186-200. (in Chinese with English abstract) doi: 10.19509/j.cnki.dzkq.tb20220535
|
| [10] |
郭衍昊, 窦杰, 向子林, 等. 基于优化负样本采样策略的梯度提升决策树与随机森林的汶川同震滑坡易发性评价[J]. 地质科技通报, 2024, 43(3): 251-265. doi: 10.19509/j.cnki.dzkq.tb20230037
GUO Y H, DOU J, XIANG Z L, et al. Susceptibility evaluation of Wenchuan coseismic landslides by gradient boosting decision tree and random forest based on optimal negative sample sampling strategies[J]. Bulletin of Geological Science and Technology, 2024, 43(3): 251-265. (in Chinese with English abstract) doi: 10.19509/j.cnki.dzkq.tb20230037
|
| [11] |
宋居正, 刘思位, 吴云山. 基于前馈神经网络的外挂物投放分离预测[J]. 弹道学报, 2023, 35(3): 33-38.
SONG J Z, LIU S W, WU Y S. External store separation prediction based on feedforward neural network[J]. Journal of Ballistics, 2023, 35(3): 33-38. (in Chinese with English abstract)
|
| [12] |
路晓宇. 大连海相软土固化强度试验研究及其神经网络预测[D]. 辽宁大连: 大连理工大学, 2021.
LU X Y. Experimental study on solidification strength of Dalian marine soft soil and its neural network prediction[D]. Dalian Liaoning: Dalian University of Technology, 2021. (in Chinese with English abstract)
|
| [13] |
王才进, 武猛, 杨洋, 等. 基于生物地理优化的人工神经网络模型预测软土的固结系数[J]. 岩土力学, 2023, 44(10): 3022-3030.
WANG C J, WU M, YANG Y, et al. Prediction of consolidation coefficient of soft soil using an artificial neural network models with biogeography-based optimization[J]. Rock and Soil Mechanics, 2023, 44(10): 3022-3030. (in Chinese with English abstract)
|
| [14] |
丁建文, 魏霞, 高鹏举, 等. 基于GA-BP神经网络的软土路基运营期沉降预测[J]. 东南大学学报(自然科学版), 2023, 53(4): 585-591.
DING J W, WEI X, GAO P J, et al. Prediction of settlement of soft soil subgrade during operation based on GA-BP neural network[J]. Journal of Southeast University (Natural Science Edition), 2023, 53(4): 585-591. (in Chinese with English abstract)
|
| [15] |
JUANG C H, LIU Z F, ATAMTURKTUR H S. Reliability-based robust geotechnical design of retaining walls[C]//Anon. Sound Geotechnical Research to Practice. San Diego, California, USA. Reston, VA: American Society of Civil Engineers, 2013: 514-524.
|
| [16] |
JUANG C H, WANG L, LIU Z F, et al. Robust geotechnical design of drilled shafts in sand: New design perspective[J]. Journal of Geotechnical and Geoenvironmental Engineering, 2013, 139(12): 2007-2019. doi: 10.1061/(ASCE)GT.1943-5606.0000956
|
| [17] |
JUANG C H, WANG L, HSIEH H S, et al. Robust geotechnical design of braced excavations in clays[J]. Structural Safety, 2014, 49: 37-44. doi: 10.1016/j.strusafe.2013.05.003
|
| [18] |
胡海晨. 基于BP神经网络优化超声波测距的多功能除雪车远程监控系统研究与开发[D]. 沈阳: 东北大学, 2022.
HU H C. Research and development of remote monitoring system for multifunctional snow removal vehicle based on BP neural network to optimize ultrasonic ranging[D]. Shenyang: Northeastern University, 2022. (in Chinese with English abstract)
|
| [19] |
吴选蓉. 基于GA-BP神经网络的煤岩组合破坏预警方法研究[D]. 重庆: 重庆大学, 2022.
WU X R. Research on early warning method of coal and rock combination failure based on GA-BP neural network[D]. Chongqing: Chongqing University, 2022. (in Chinese with English abstract)
|
| [20] |
DAI X Q, SHI H B, LI Y S, et al. Artificial neural network models for estimating regional reference evapotranspiration based on climate factors[J]. Hydrological Processes, 2009, 23(3): 442-450. doi: 10.1002/hyp.7153
|
| [21] |
KHOSHNEVISAN S, GONG W P, WANG L, et al. Robust design in geotechnical engineering: An update[J]. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 2014, 8(4): 217-234.
|
Copyright © 2010Editorial Department of Bulletin of Geological Science and Technology
Supported by:
Beijing Renhe Information Technology Co., Ltd.
DownLoad:
