Smart prediction of liquefaction-induced lateral spreading
Smart prediction of liquefaction-induced lateral spreading
复制标题
DOI：
10.1016/j.jrmge.2023.05.017
复制
发表时间：
2024-06-18
影响因子：
7.3
通讯作者：
El-Sekelly，Waleed
中科院分区：
文献类型：
--
作者：
Raja，Muhammad Nouman Amjad;Abdoun，Tarek;El-Sekelly，Waleed
对土木/岩土工程师来说，预测由摩擦引起的侧向扩展/位移（Dh）是一项具有挑战性的任务。在这项研究中，提出了一种新的方法来预测基因表达式编程（GEP）。基于统计推理，开发了两种地形的个人模型：自由面和缓坡地面。沿着与传统的方法进行比较，预测的Dh，四个额外的基于回归的软计算模型，即高斯过程回归（GPR），相关向量机（RVM），顺序最小优化回归（SMOR），和M5树，开发和比较与GEP模型。结果表明，GEP模型预测DH具有较小的偏差，如训练的均方根误差（RMSE）和平均绝对误差（MAE）所证明的（即1.092和0.815;以及0.643和0.526）和用于测试（即0.89和0.705;和0.773和0.573），分别在自由面和缓坡地面地形。自由面拓扑的整体性能排名如下：GEP > RVM > M5树> GPR > SMOR，总分分别为40、32、24、15和10。对于缓坡条件，性能排序如下：GEP > RVM > GPR > M5树> SMOR，总分分别为40、32、21、19和8。最后，敏感性分析的结果表明，对于自由面和缓坡地面，可液化层厚度（T15）是主要参数，退化百分比（%D）值分别为99.15和90.72。
The prediction of liquefaction-induced lateral spreading/displacement (Dh) is a challenging task for civil/geotechnical engineers. In this study, a new approach is proposed to predictDhusing gene expression programming (GEP). Based on statistical reasoning, individual models were developed for two topographies: free-face and gently sloping ground. Along with a comparison with conventional approaches for predicting theDh, four additional regression-based soft computing models, i.e. Gaussian process regression (GPR), relevance vector machine (RVM), sequential minimal optimization regression (SMOR), and M5-tree, were developed and compared with the GEP model. The results indicate that the GEP models predictDhwith less bias, as evidenced by the root mean square error (RMSE) and mean absolute error (MAE) for training (i.e. 1.092 and 0.815; and 0.643 and 0.526) and for testing (i.e. 0.89 and 0.705; and 0.773 and 0.573) in free-face and gently sloping ground topographies, respectively. The overall performance for the free-face topology was ranked as follows: GEP > RVM > M5-tree > GPR > SMOR, with a total score of 40, 32, 24, 15, and 10, respectively. For the gently sloping condition, the performance was ranked as follows: GEP > RVM > GPR > M5-tree > SMOR with a total score of 40, 32, 21, 19, and 8, respectively. Finally, the results of the sensitivity analysis showed that for both free-face and gently sloping ground, the liquefiable layer thickness (T15) was the major parameter with percentage deterioration (%D) value of 99.15 and 90.72, respectively.