This research study applied Artificial Neural Networks (ANNs) to predict and evaluate the structural responses of externally bonded FRP (EB-FRP)-strengthened RC T-beams under combined torsion and shear. Previous studies proved that, compared to reinforced concrete (RC) rectangular beams, RC T-beams performance in shear is significantly higher in structural analysis and design. The structural response of RC beams experiences a critical change while torsion moments are applied in load conditions. Fiber Reinforced Polymer (FRP) is used to retrofit the structural elements due to changing structural design codes and loadings, especially in earthquake-prone countries. We applied Finite Element Method (FEM) software, ABAQUS, to provide a precise numerical database of a set of experimentally tested FRP-retrofitted RC T-beams in previous research works. ANN predicted structural analysis results and Mean Square Error (MSE) and Multiple Determination Coefficients $(R^2)$ proved the accuracy of this study. The MSE values that were less than 0.0009 and $R^2$ values greater than 0.9960 showed that the ANN precisely fits the data. The consistency between analyzed experimental and numerical results demonstrated the accurate implication of ANN, MSE, and $R^2$ in predicting the structural responses of EB-FRP- strengthened RC T-beams.

Fiber-reinforced polymers (FRPs) retrofit reinforced concrete (RC) structures. ABAQUS finite element software was used to perform numerical parametric analysis on a group of RC beams in this research. All specimens were retrofitted by FRP strips as an external retrofitting and experimentally tested up to previous researchers’ failure points. The range of subjects examined in these RC beams included cracking torque, ultimate torque, angle of twist, and the effect of using FRP on these subjects. We applied artificial neural networks (ANNs) to predict the structural behavior of RC beams under combined torsion and bending to develop the research accuracy. After testing, the ANN results were compared with the ABAQUS results. Consequently, a reasonable examination of the determined mathematical and trial results confirmed this study’s logical accuracy in predicting retrofitted RC beams’ structural behavior under combined loading.

This paper studied the anti-seepage ability of the modified loess by using attapulgite, which is abundant in local areas. The possibility of using the modified loess as the sustainable compacted soil liner material in a solid waste landfill was also considered in this research. The materials were then evaluated using Nuclear Magnetic Resonance (NMR), Scanning Electron Microscopy (SEM), X-ray Diffraction (XRD), and an Impermeability Mechanism (IM). The experimental results showed that the permeability coefficient of the attapulgite-modified loess decreases significantly with increasing attapulgite content. However, it becomes less significant when the attapulgite level approaches 10%. Both cases can meet the landfill impermeability requirements, based on the attapulgite content remaining at 10%, adding 15% lime or 5% cement, respectively. The triaxial consolidation and drainage experiment was carried out to investigate the shear strength of the materials under three different working circumstances. The stress–strain curves of each specimen were produced, as were the cohesion and internal friction angle values. This research lays the groundwork for using attapulgite-modified loess as a landfill lining material. It establishes a solid platform for future studies on attapulgite adsorption and purifying performance in landfills.

Rail-transit hub classification in TOD refers to the categorization of transit stations based on their level of connectivity and ridership and the potential for development around them as part of a Transit-Oriented Development (TOD) strategy. TOD, as an essential concept in developing smart cities and public transportation accessibility, has attracted the focus of many policymakers. To this end, many research projects have been dedicated to classifying the rail-transit stations, although the necessity of integrated models for rail-transit hubs could have been mentioned in previous papers. Therefore, this parametric case study is directed to apply the Node–Place–Ridership–Time (NPRT) model to provide a logical classification model for Chengdu rail-transit hubs at the junctions of high-speed railway and subway stations. Multiple Linear Regression (MLR) provided a series of equations, including the effective parameters of the NPRT model. These equations were then verified by the Artificial Neural Network (ANN) to provide the effect of each node and place values on the integrated ridership of rail-transit hubs in different time periods. The results proved the consistent contribution of the integrated ANN-NPRT-HUB algorithm to the TOD concept for smart cities.

The focus of this study was to investigate the effect of loess soil treated with lime on the lateral‐seepage response. Three groups of box experiments were carried out to study the lateral‐seepage effect under different types of loess‐lime structures. Automated testing systems were designed to perform experiments and collect data. Additionally, numerical analysis of lateral‐seepage impact and embankment settlement was performed. Finally, moisture content and settlement were monitored to quantify lateral‐seepage effect results under corresponding loess‐lime treatment. Results showed that loess‐lime compaction piles and diaphragm wall structures could effectively prevent lateral seepage, and the latter was better. The simulated results are similar to the measured values of the box experiment, which indicates the accuracy of the simulation analysis and further supports the experimental results of this study.