Multi-factor settlement prediction around foundation pit based on SSA-gradient descent model

With the rise of machine learning, a lot of excellent algorithms are used for settlement prediction. Backpropagation (BP) and Elman are two typical algorithms based on gradient descent, but their performance is greatly affected by the random selection of initial weights and thresholds, so this paper chooses Sparrow Search Algorithm (SSA) to build joint model. Then, two sets of land subsidence monitoring data generated during the excavation of a foundation pit in South China are used for analysis and verification. The results show that the optimization effect of SSA on the gradient descent model is remarkable and the stability of the model is improved to a certain extent. After that, SSA is compared with GA and PSO algorithms, and the comparison shows that SSA has higher optimization efficiency. Finally, select SSA-KELM, SSA-LSSVM and SSA-BP for further comparison and it proves that the optimization efficiency of SSA for BP is higher than other kind of neural network. At the same time, it also shows that the seven influencing factors selected in this paper are feasible as the input variables of the model, which is consistent with the conclusion drawn by the grey relational analysis.