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1. 安徽建筑大学智能地下探测安徽省重点实验室
2. 江苏开放大学建筑工程学院
3. 太原理工大学土木工程学院
Published:2025
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陈凯, 林军, 聂利青, et al. 基于优化的多输出神经网络预测软弱土压缩和回弹指数研究[J]. Bulletin of Geological Science and Technology, 2025, 44(2): 130-145.
陈凯, 林军, 聂利青, et al. 基于优化的多输出神经网络预测软弱土压缩和回弹指数研究[J]. Bulletin of Geological Science and Technology, 2025, 44(2): 130-145. DOI: 10.19509/j.cnki.dzkq.tb20240439.
软弱土的压缩指数C
c
和回弹指数C
s
是计算土体沉降和回弹的重要参数,采用机器学习算法可高效预测C
c
和C
s
,减少室内试验周期和费用。引入孔压静力触探(CPTU)原位测试数据,利用土类指数I
c
量化土层信息,融合室内试验和原位测试数据,改进遗传算法优化的BP神经网络(GA-BPNN),实现多输出功能,同时预测C
c
和C
s
。通过相关性分析,确定多输出GA-BPNN模型输入参数,利用TC 304标准场地数据库,将预测结果与多输出BPNN模型、单输出GA-BPNN模型比较,进而验证多输出GA-BPNN模型能效,并获得预训练模型参数。在南京有限场地数据条件下,进一步讨论多输出GA-BPNN模型的优越性,分析预训练、原位测试数据对模型效果的影响,最后进行敏感性分析。结果表明,利用标准场地数据获得预训练多输出GA-BPNN模型,在有限数据条件下,可有效预测C
c
和C
s
;加入原位测试数据的的GA-BPNN模型预测C
c
(R
2
=0.96)和C
s
(R
2
=0.97)精确度较高,预测结果更加接近实测值,预测结果相关性与已有研究保持一致。通过预训练的多输出GA-BPNN模型,可在有限场地数据条件下,快速准确预测软弱土的C
c
和C
s
,对工程实践中的多元参数预测具有良好的应用前景。
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