UHPC-PINN-parallel micro element system for the local bond stress-slip model subjected to monotonic loading

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Structural Performance of EB-FRP-Strengthened RC T-Beams Subjected to Combined Torsion and Shear Using ANN

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  (R2) proved the accuracy of this study. The MSE values that were less than 0.0009 and R2 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 R2 in predicting the structural responses of EB-FRP- strengthened RC T-beams

Structural Behavior of FRP-Retrofitted RC Beams under Combined Torsion and Bending

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

The Integrated ANN-NPRT-HUB Algorithm for Rail-Transit Networks of Smart Cities: A TOD Case Study in Chengdu

International audienceRail-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

Study on the relationship between permeability coefficient and porosity, the confining and osmotic pressure of attapulgite-modified loess

Abstract This study investigated attapulgite-modified loess as an efficient and cost-effective method for creating an impermeable liner for landfills in regions with scarce clay resources. Laboratory permeability tests were conducted using a flexible wall permeameter to determine the permeability of compacted loess and attapulgite mixtures under varying osmotic conditions. The relationship between the permeability coefficient, attapulgite dosage, radial pressure, and osmotic pressure was analyzed. Nuclear magnetic resonance and scanning electron microscopy were also used to observe the microstructure of the modified loess. The results showed that attapulgite dosage significantly reduced the permeability coefficient, but the effect became limited when the content surpassed 10%. The decrease of the permeability coefficient of the modified loess is mainly due to the filling of pores between the loess by attapulgite, which makes the pore size and throat size of the modified loess smaller. The modified loess displayed a sheet structure that contributed to an increased permeability coefficient due to increased radial pressure. This study provides valuable insights into using attapulgite-modified loess as a material for landfill lining in regions with scarce clay resources