https://nmce.kntu.ac.ir/ Numerical Methods in Civil Engineering en daily 1 Mon, 01 Jun 2026 00:00:00 +0330 Mon, 01 Jun 2026 00:00:00 +0330 https://nmce.kntu.ac.ir/article_250652.html Accurate daily precipitation estimates are critical for hydrological and civil engineering applications, especially in regions affected by short-duration rainfall events. Satellite-based products provide wide coverage but often show systematic biases compared with ground observations. This study presents a spatiotemporal convolutional neural network–long short-term memory (CNN–LSTM) framework for daily precipitation bias correction in Golestan Province, northern Iran, using satellite and reanalysis data. The proposed framework integrates PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) satellite precipitation with ERA5 reanalysis precipitation and 2-m air temperature to improve station-scale rainfall estimation at several synoptic stations. In order to represent the spatial organization of precipitation systems vividly, gridded precipitation from neighboring upstream cells was incorporated into the model structure. This configuration accounts for the dominant west-to-east movement of rainfall systems across the region, where precipitation commonly arrives with temporal delay and reduced intensity. In addition, temporal variations in near-surface air temperature were included to provide supplementary physical information associated with rainfall development. Model performance is evaluated using root mean square error (RMSE), correlation coefficient (CC), and mean error (ME). Categorical skill is assessed using probability of detection (POD) and false alarm ratio (FAR). The results indicate that the CNN–LSTM model consistently outperformed both conventional bias-correction approaches and baseline deep learning models. Incorporating spatial information from adjacent grid cells reduces errors and improves temporal consistency. Across the evaluated stations, the proposed model achieved the lowest RMSE values, with error reductions reaching 23% relative to the DNN benchmark. The framework also demonstrated improved capability in capturing the spatiotemporal evolution of precipitation events. Overall, the proposed approach provides a transferable and operationally practical framework for enhancing daily precipitation estimates in data-scarce regions. The improved precipitation estimates can support more reliable hydrological simulations, flood assessment, and water resources planning. https://nmce.kntu.ac.ir/article_250654.html Deep beams generally suffer from limited shear capacity and ductility due to their specific aspect ratio and the significant contribution of shear deformation to their overall response. Consequently, various methods have been employed to strengthen these structural members. Among these, reinforcing the beam web with Carbon Fiber Reinforced Polymer (CFRP) sheets is considered one of the most effective solutions. Given the inherent limitations of conventional steel reinforcement in significantly enhancing shear capacity and ultimate strength, CFRP sheets have emerged as an effective alternative and are currently the focus of extensive research.In this study, the shear behavior of CFRP-strengthened deep beams is investigated using numerical simulation in the ABAQUS finite element software. To this end, sixteen deep beam specimens with constant longitudinal reinforcement but varying shear span-to-depth ratios (a/d) were designed and analyzed under two concentrated loads equidistant from the supports. From this set, four models served as control specimens (un-strengthened), and the remaining twelve models were strengthened in the shear zone using CFRP sheets. The results indicated that the maximum shear contribution of CFRP is achieved with a 135 degree orientation, particularly in beams exhibiting shear-dominant behavior (low a/d ratio). Specifically, an increase in ultimate capacity up to 50% was observed, along with a reduction in rebar stress compared to the strengthened state. https://nmce.kntu.ac.ir/article_250656.html The construction of shallow foundations adjacent to slopes is a complex geotechnical problem due to stress redistribution, reduced confinement, and development of plastic zones, which collectively reduce bearing capacity and slope stability. Ground improvement methods such as soil–cement columns installed by deep mixing or jet grouting have been widely adopted; however, most previous studies have focused on vertical columns, while the effects of inclined column installation on the coupled soil–foundation–slope system remain insufficiently investigated. This study evaluates the influence of soil–cement column inclination on the bearing capacity of a shallow foundation and the stability of an adjacent slope using three-dimensional numerical modeling in PLAXIS 3D. The nonlinear behavior of soil is simulated using the Hardening Soil model, and columns are modeled using embedded beam elements. Model validation is performed against available experimental data, showing good agreement in load–settlement response and failure mechanisms. A parametric analysis is conducted considering column inclination and spacing between columns. Results indicate that vertical columns maximize bearing capacity by improving load transfer to deeper soil layers, whereas inclined columns with an angle of approximately 45° provide the greatest improvement in slope stability by intersecting potential slip surfaces more effectively. Furthermore, as the column inclination angle relative to the vertical axis decreases, the bending moment in the foundation increases, highlighting the need for combined geotechnical and structural design considerations. An optimal configuration is proposed for balanced performance in bearing capacity and slope stability.