摘要
在三峡库区，高架桥墩是主要的结构形式。针对既有码头的智能监测，本文以重庆新田港为研究对象，提出了基于粒子群优化(PSO)的支持向量机(SVM)损伤诱导因子(DIF)反演模型应用有限元法分析了码头群桩在堆垛效应、船舶冲击荷载效应和岸坡效应三种主要影响下的应力分布特征。对应力数据进行表征后，发现应力与各DIF参数之间存在相关性。在生成训练样本集之前，采用主成分分析进行降维处理，消除大量冗余数据。该模型对DIF类型的识别精度为0.999，对DIF作用位置的识别精度为0.975，F1系数分别为0.999和0.978.对于DIF预测强度，MAE和MSE分别为4.871和1.202，2为0.986，NSE为0.986，WI为0.996，PBIAS为0.095.各样本提取后，船舶冲击载荷效应的相对误差为0.05，岸坡效应的最大相对误差为0.02，堆垛效应的误差限制在0.08.结果表明，采用PSO算法优化的SVM损伤诱导反演模型能够有效识别高架桥墩的DIF。
Abstract
In the Three Gorges reservoir area, the overhead upright pier is the primary structural form. For intelligent monitoring of existing terminals, this research chooses Chongqing Xintian Port as the study object and proposes a support vector machine (SVM) damage-inducing factor (DIF) inversion model based on particle swarm optimization (PSO). To apply the finite element method to analyze the stress distribution characteristics of quay pile groups under three main DIFs, including the stacking effect, ship impact load effect, and bank slope effect. After characterizing the stress data, it becomes evident that there exists a correlation between stress and each DIF parameter. Before generating the training sample set, principal component analysis is employed to reduce dimensionality and eliminate a substantial amount of redundant data. The model has an accuracy of 0.999 for the identification of the type of DIF and 0.975 for the identification of the location of the action of the DIF with F1 coefficients of 0.999 and 0.978, respectively. For the strength of DIF predictions, MAE and MSE were 4.871 and 1.202, respectively, R2 was 0.986, NSE was 0.986, WI was 0.996, and PBIAS was 0.095. After extracting every sample, the relative error for the ship impact load effect is 0.05, and the highest relative error for the bank slope effect is 0.02; the error for the stacking effect is limited to 0.08. The results suggest that the damage inducement inversion model of the SVM optimized by the PSO algorithm can effectively identify the DIF of the overhead upright pier.
NETL
NSTL
Wiley