考虑筑坝料参数关联特征的沥青混凝土心墙坝随机静力响应及智能可靠度分析方法

孙奔博; 邓铭江; 张社荣; 王超; 许佳; 陈京钰

doi:10.12454/j.jsuese.202600490

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考虑筑坝料参数关联特征的沥青混凝土心墙坝随机静力响应及智能可靠度分析方法

更新时间：2026-07-29

- 考虑筑坝料参数关联特征的沥青混凝土心墙坝随机静力响应及智能可靠度分析方法
- Random Static Response and Intelligent Reliability Analysis of Asphalt Concrete Core Dams Considering Correlations Among Construction-Material Parameters
- 工程科学与技术 2026年页码：1-13
- 作者机构：
  
  1.郑州大学水利与交通学院，河南郑州 450000
  2.旱区水工程生态环境全国重点实验室，陕西西安 710048
  3.天津大学建筑工程学院，天津 300354
  4.黄河勘测规划设计研究院有限公司，河南郑州 450000
- 作者简介：
  
  孙奔博（1992— ），男，博士，副研究员. 研究方向：水工结构防灾减灾. E-mail：sunbenbo@zzu.edu.cn
  邓铭江，中国工程院院士，E-mail： xjdmj@163.com
- 基金信息：
  
  河南自然科学基金面上项目(262300421395);水利工程智能建设与运维全国重点实验室开放基金(HESS-2516)
- DOI：10.12454/j.jsuese.202600490
  中图分类号： TU432
- 收稿：2026-06-07，
  
  修回：2026-07-27，
  
  网络首发：2026-07-29，
- 稿件说明：
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孙奔博,邓铭江,张社荣等.考虑筑坝料参数关联特征的沥青混凝土心墙坝随机静力响应及智能可靠度分析方法[J].工程科学与技术,

SUN Benbo,DENG Mingjiang,ZHANG Sherong,et al.Random Static Response and Intelligent Reliability Analysis of Asphalt Concrete Core Dams Considering Correlations Among Construction-Material Parameters[J].Advanced Engineering Sciences,
孙奔博,邓铭江,张社荣等.考虑筑坝料参数关联特征的沥青混凝土心墙坝随机静力响应及智能可靠度分析方法[J].工程科学与技术, DOI：10.12454/j.jsuese.202600490.

SUN Benbo,DENG Mingjiang,ZHANG Sherong,et al.Random Static Response and Intelligent Reliability Analysis of Asphalt Concrete Core Dams Considering Correlations Among Construction-Material Parameters[J].Advanced Engineering Sciences, DOI：10.12454/j.jsuese.202600490.XXXX,XX(XX):1‒13.

摘要

针对传统随机静力分析将筑坝料参数视为相互独立的正态变量、难以表征高维相依性与非正态尾部的问题，建立适用于沥青混凝土心墙坝的随机响应与智能可靠度分析方法。汇集国内88座沥青混凝土心墙坝的7个邓肯-张E-B参数，采用极大似然估计从16类候选分布中识别边缘分布，并依据BIC准则和Dißmann算法构建R藤Copula联合分布；结合G-F偏差选点与条件逆变换生成相关样本，对102 m高沥青混凝土心墙坝开展施工期和蓄水期三维随机有限元分析；进一步融合R藤Copula与Kolmogorov-Arnold网络（KAN），以坝顶相对沉降为极限状态建立代理模型并计算失效概率。结果表明：

、n和

的最优边缘分布为正态分布，

、Δ

、

和

分别服从广义极值、广义帕累托、瑞利和逻辑斯特分布；

与

的相关系数达到0.742。相关抽样保持了原始样本的主要统计特征，抽样均值和方差与原始样本的差异均小于5%。KAN模型的Pearson相关系数、均方误差、均方根误差和平均绝对误差分别为0.88、0.02、0.15和0.09。当坝顶相对沉降限值为0.7%时，相关非正态模型与独立正态模型的失效概率差值为13.85个百分点。研究表明：筑坝料参数相关性与非正态尾部主要改变坝体变形和心墙应力的响应量值，并显著提高计算失效概率；独立正态假设会系统性高估大坝可靠度。本文所提出的R藤Copula-KAN方法可为沥青混凝土心墙坝施工期、蓄水期安全评估及设计优化提供定量工具。

Abstract

Objective Dependable deformation regulation of asphalt concrete core dams necessitates a probabilistic characterisation of the fill materials that maintains both non-Gaussian marginal characteristics and inter-parameter correlation. Traditional stochastic evaluations typically conceptualise Duncan-ChangE-Bparameters as independent normal variables. This idealisation eliminates joint-tail occurrences where many low-stiffness or adverse strength characteristics manifest concurrently

potentially skewing reliability assessments based on settlement. The aim was to create a cohesive methodology that begins with multi-project material data

determines a high-dimensional joint distribution

produces correlated samples for three-dimensional finite-element analysis

and finalises large-sample reliability assessment via an effective nonlinear surrogate. The methodology was formulated to measure the influence of parameter dependency and non-normality on reac

tions during the construction and impoundment phases

as well as to elucidate the extent and direction of the inaccuracy resulting from the independent-normal assumption.Methods Duncan-ChangE-Bstatistics were aggregated from 88 asphalt concrete core dam initiatives in China. Seven variables—modulus number

modulus exponent n

failure ratio Rf

friction angle

friction-angle reduction Δ

bulk-modulus number

and bulk-modulus exponent m—were regarded as stochastic variables. Sixteen candidate probability distributions were adjusted for each parameter using maximum likelihood estimation

and the best marginal model was chosen based on information criteria and goodness-of-fit evaluations. A conventional R-vineCopulawas then established to break down the seven-dimensional density into a structured arrangement of bivariate copulas. The Dißmann technique was employed to ascertain the maximum spanning trees

pair-Copula families

rotation forms

and conditional dependency order; model selection utilised Bayesian information criterion values and Kendall's tau. The initial values of

were produced using low-discrepancy G-F point selection

followed by inverse Rosenblatt-type conditional transformations that yielded samples of the other parameters while preserving the fitted R-vine dependence. The quality of the sampling was evaluated by contrasting the shapes of marginal densities

means

variances

and dependence structures with those found in the project database. The Wenquan asphalt concrete core dam was chosen as the numerical example. The dam reaches a height of 102.00 m

featuring a core that is 95.47 m tall; the three-dimensional mesh has 153 951 components and 164 531 nodes. The construction process was modelled in 20 loading phases

while the reservoir impoundment occurred in five phases. The Duncan-ChangE-Bmodel delineated the fill and transition zones

whereas the rock foundation and concrete plinth were char

acterised by linear-elastic and elastoplastic-damage models

respectively. Displacements of the dam structure and stresses within the asphalt concrete core were assessed for the corresponding parameter samples. Ultimately

a Kolmogorov-Arnold network (KAN) surrogate was developed for crest settlement. The architecture consisted of [1

]

with two concealed layers

adaptable B-spline activation functions

20 grid points

a spline order of 3

an LBFGS optimiser

a regularisation parameter of 0.001

and an entropy regularisation parameter of 1.0. A total of 431 samples

following outlier elimination

were divided into 80% for training and 20% for testing. The network underwent training for 200 iterations with a learning rate of 1.0. The authenticated R-vine-KAN surrogate subsequently produced 2 000 settlement responses for both the interrelated non-Gaussian model and the standard independent-normal model. Relative crest settlement thresholds ranging from 0.7% to 1.3% of the dam's height were employed as limit states.Results and Discussions The adjusted marginals exhibited variability among the seven material parameters.

and

were optimally characterised by normal distributions

while

and

adhered to generalised extreme-value

generalised Pareto

Rayleigh

and logistic distributions

respectively. These findings indicated that applying a uniform normal or lognormal distribution to all E-B parameters alters the probability concentration close to the response limit state. The chosen R-vine comprised six trees and integrated Gaussian

Joe

and Gumbel pair-Copulas with various rotations

enabling the representation of symmetric

upper-tail

and lower-tail dependence within a single joint model. A significant positive association was observed between

and

with a correlation coefficient of 0.742. In the cas

e of the conditional samples

the marginal density profiles were in alignment with the original dataset. As a verification measure

a sample statistic initially exhibiting a mean and variance of 0.363 and 0.120 was replicated as 0.369 and 0.147; the documented discrepancies of the primary sample statistics remained within 5%. The three-dimensional simulations indicated that modifying the material characteristics did not affect the dominant spatial response patterns. The farthest streamwise displacement was approximately two-thirds of the dam's height

the peak vertical settlement was situated close to the crest center

and the most significant cross-valley displacements were located at the two crest shoulders. Tensile stress inside the asphalt core was primarily localised at the valley-bank connections and around two-thirds up the dam's height

whereas the majority of other vital core areas experienced compression. Parameter ambiguity

however

significantly altered the response magnitudes. For instance

the disparity in vertical displacement between two illustrative parameter scenarios attained 0.21 m

and the positions and intensities of peak core compression differed between cases. The trained KAN attained a Pearson correlation coefficient of 0.88

a mean squared error of 0.02

a root mean squared error of 0.15

and a mean absolute error of 0.09. A further direct comparison of surrogate predictions and finite-element outcomes yielded a correlation coefficient of 0.84

endorsing the surrogate's application for the ensuing 2 000-sample reliability assessment. The likelihood of failure diminished consistently as the permissible relative crest settlement rose from 0.7% to 1.3% for both probabilistic frameworks. In all scenarios

the R-vine-based correlated non-Gaussian model yielded a greater failure probability compared to the independent-normal model. At the 0.7% settlement threshold

the disparity was 13.85 percentage points. Two processes elucidate this outcome. Initially

a positive correlation among stiffnes

s-related parameters heightens the likelihood of adverse values co-occurring; addressing the parameters in isolation disrupts this joint-tail connection. Secondly

the generalised extreme-value

generalised Pareto

Rayleigh

and logistic marginals distribute probability in proximity to the failure domain in a manner distinct from symmetric normal marginals. The simultaneous loss of dependence and tail information leads the traditional model to underestimate significant deformation occurrences and overrate reliability.Conclusions The R-vine framework distinguished marginal uncertainty from dependence modelling and illustrated a seven-dimensional distribution of material parameters without imposing a uniform reliance structure. Integrating G-F low-discrepancy point selection with conditional inverse transformation produced compact correlated sample sets appropriate for resource-intensive three-dimensional dam evaluations. The KAN surrogate preserved the nonlinear relationship between material variability and crest displacement while minimising the expenses associated with repetitive finite-element analyses. The dependency on parameters and the presence of non-Gaussian tails mostly influenced response magnitudes instead of the spatial characteristics of dam deformation and core stress

however their impact on the risk of failure related to settlement was significant. Consequently

independent-normal parameter models need not to be employed without validation when assessing the reliability of asphalt concrete core dams. The combined data–R-vine–finite-element–KAN framework offers a quantitative foundation for construction oversight

impoundment-phase safety evaluation

and design enhancement. The disparity in numerical dependability is contingent upon specific cases; more validation for dams with varying heights

valley configurations

and rockfill classifications is necessary prior to the establishment of generalised calibration factors.

关键词

Keywords

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