Enhancing Sustainability of Corroded RC Structures: Estimating Steel-to-Concrete Bond Strength with ANN and SVM Algorithms

Effects of eccentric loading on performance of concrete columns reinforced with glass fiber-reinforced polymer bars

Glass fiber-reinforced polymer (GFRP) reinforcements are superior to traditional steel bars in concrete structures, particularly in vertical elements like columns, and offer significant advantages over conventional steel bars when subjected to axial and eccentric loadings. However, there is limited experimental and numerical research on the behavior of GFRP-reinforced concrete (RC) columns under eccentric loading having different spacing of stirrups. In this study, six specimens were cast under three different values of eccentricities (25 mm, 50 mm, and 75 mm) with two groups of stirrups spacing (50 mm and 100 mm). The experimental results showed that by increasing the eccentricity value, there was a reduction in the load-carrying capacity of the specimens. The finite element ABAQUS software was used for the numerical investigation of this study. The results from the finite element analysis (FEA) were close to the experimental results and within the acceptable range. The maximum difference between the experimental and FEA results was 3.61% for the axial load and 12.06% for the deformation.

Enhanced Drug Delivery System Using Mesenchymal Stem Cells and Membrane-Coated Nanoparticles

Mesenchymal stem cells (MSCs) have newly developed as a potential drug delivery system. MSC-based drug delivery systems (MSCs-DDS) have made significant strides in the treatment of several illnesses, as shown by a plethora of research. However, as this area of research rapidly develops, several issues with this delivery technique have emerged, most often as a result of its intrinsic limits. To increase the effectiveness and security of this system, several cutting-edge technologies are being developed concurrently. However, the advancement of MSC applicability in clinical practice is severely hampered by the absence of standardized methodologies for assessing cell safety, effectiveness, and biodistribution. In this work, the biodistribution and systemic safety of MSCs are highlighted as we assess the status of MSC-based cell therapy at this time. We also examine the underlying mechanisms of MSCs to better understand the risks of tumor initiation and propagation. Methods for MSC biodistribution are explored, as well as the pharmacokinetics and pharmacodynamics of cell therapies. We also highlight various promising technologies, such as nanotechnology, genome engineering technology, and biomimetic technology, to enhance MSC-DDS. For statistical analysis, we used analysis of variance (ANOVA), Kaplan Meier, and log-rank tests. In this work, we created a shared DDS medication distribution network using an extended enhanced optimization approach called enhanced particle swarm optimization (E-PSO). To identify the considerable untapped potential and highlight promising future research paths, we highlight the use of MSCs in gene delivery and medication, also membrane-coated MSC nanoparticles, for treatment and drug delivery.

Machine learning intelligence to assess the shear capacity of corroded reinforced concrete beams

The ability of machine learning (ML) techniques to forecast the shear strength of corroded reinforced concrete beams (CRCBs) is examined in the present study. These ML techniques include artificial neural networks (ANN), adaptive-neuro fuzzy inference systems (ANFIS), decision tree (DT) and extreme gradient boosting (XGBoost). A thorough databank with 140 data points about the shear capacity of CRCBs with various degrees of corrosion was compiled after a review of the literature. The inputs parameters of the implemented models are the width of the beam, the effective depth of the beam, concrete compressive strength (CS), yield strength of reinforcement, percentage of longitudinal reinforcement, percentage of transversal reinforcement (stirrups), yield strength of stirrups, stirrups spacing, shear span-to-depth ratio (a/d), corrosion degree of main reinforcement, and corrosion degree of stirrups. The coefficient of determination of the ANN, ANFIS, DT, and XGBoost models are 0.9811, 0.9866, 0.9799, and 0.9998, respectively. The MAPE of the XGBoost model is 99.39%, 99.16%, and 99.28% lower than ANN, ANFIS, and DT models. According to the results of the sensitivity examination, the shear strength of the CRCBs is most affected by the depth of the beam, stirrups spacing, and the a/d. The graphical displays of the Taylor graph, violin plot, and multi-histogram plot additionally support the XGBoost model's dependability and precision. In addition, this model demonstrated good experimental data fit when compared to other analytical and ML models. Accurate prediction of shear strength using the XGBoost approach confirmed that this approach is capable of handling a wide range of data and can be used as a model to predict shear strength with higher accuracy. The effectiveness of the developed XGBoost model is higher than the existing models in terms of precision, economic considerations, and safety, as indicated by the comparative study.

Event-Specific Transmission Forecasting of SARS-CoV-2 in a Mixed-Mode Ventilated Office Room Using an ANN

The emerging novel variants and re-merging old variants of SARS-CoV-2 make it critical to study the transmission probability in mixed-mode ventilated office environments. Artificial neural network (ANN) and curve fitting (CF) models were created to forecast the R-Event. The R-Event is defined as the anticipated number of new infections that develop in particular events occurring over the course of time in any defined space. In the spring and summer of 2022, real-time data for an office environment were collected in India in a mixed-mode ventilated office space in a composite climate. The performances of the proposed CF and ANN models were compared with respect to traditional statistical indicators, such as the correlation coefficient, RMSE, MAE, MAPE, NS index, and a20-index, in order to determine the merit of the two approaches. Thirteen input features, namely the indoor temperature (TIn), indoor relative humidity (RHIn), area of opening (AO), number of occupants (O), area per person (AP), volume per person (VP), CO2 concentration (CO2), air quality index (AQI), outer wind speed (WS), outdoor temperature (TOut), outdoor humidity (RHOut), fan air speed (FS), and air conditioning (AC), were selected to forecast the R-Event as the target. The main objective was to determine the relationship between the CO2 level and R-Event, ultimately producing a model for forecasting infections in office building environments. The correlation coefficients for the CF and ANN models in this case study were 0.7439 and 0.9999, respectively. This demonstrates that the ANN model is more accurate in R-Event prediction than the curve fitting model. The results show that the proposed ANN model is reliable and significantly accurate in forecasting the R-Event values for mixed-mode ventilated offices.