The deformation of a Geosynthetic reinforced soil (GRS) structure is a key factor in designing this type of retaining structures. On the other hand, the feasibility of artificial intelligence techniques in solving geotechnical engineering problems is underlined in literature. This paper is aimed to show the workability of two soft computing techniques in predicting the deformation of GRS structures. For this reason, first a relevant case study was modelled into ABAQUS, a finite element (FE) software. Then, the FE results (GRS deformations) were checked against the recorded deformations of the full-scale test. Subsequently, 166 finite element analyses were performed for dataset construction. Then, two predictive models of GRS deformations were constructed. For intelligent model construction, two artificial neural networks (ANN) were coupled with Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO), respectively. It was found that both GSA-based ANN and PSO-based ANN predictive models work good enough. However, the correlation coefficient (R) of 0.981 as well as the system error of 0.0101 for testing data suggest that the GSA-based ANN predictive model outperforms the PSO-based ANN model with R value of 0.973 and system error of 0.0127. Overall, findings recommend that the proposed models can be implemented in assessing the performance of geosynthetic reinforced soil structures.

土工加筋土（GRS）结构的变形是设计此类挡土结构的关键因素。另一方面，文献中强调了人工智能技术解决岩土工程问题的可行性。本文旨在展示两种软计算技术在预测GRS结构变形方面的可操作性。由于这个原因，首先将一个相关的案例研究建模到一个有限元（FE）软件ABAQUS中。然后，将有限元结果（GRS变形）与满量程测试的已记录变形进行核对。随后，进行了166次有限元分析以建立数据集。然后，构建了两个GRS变形预测模型。对于智能模型构建，两个人工神经网络（ANN）分别与重力搜索算法（GSA）和粒子群优化（PSO）耦合。发现基于GSA的ANN和基于PSO的ANN预测模型都可以很好地工作。但是，相关系数（R）为0.981，测试数据的系统误差为0.0101，这表明基于GSA的ANN预测模型的R值为0.973，系统误差为0.0127，优于基于PSO的ANN预测模型。总体而言，调查结果建议可以在评估土工合成材料加固土的结构性能时采用建议的模型。981以及测试数据的系统误差0.0101表明，基于GSA的ANN预测模型的R值为0.973，系统误差为0.0127，优于基于PSO的ANN模型。总体而言，调查结果建议可以在评估土工合成材料加固土的结构性能时实施所建议的模型。981以及测试数据的系统误差0.0101表明，基于GSA的ANN预测模型的R值为0.973，系统误差为0.0127，优于基于PSO的ANN模型。总体而言，调查结果建议可以在评估土工合成材料加固土的结构性能时实施所建议的模型。