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This study focuses on evaluating the effectiveness of various cell concentrations of bacillus bacteria in mending cracks within recycled concrete containing coarse aggregates. In this investigation, the introduction of bacillus bacterial sustainable concrete as a solution for addressing crack repairs. This innovative concrete formulation not only provides environmentally friendly alternatives but also offers economic benefits. This research involves the incorporation of coarse aggregates into the concrete mix, along with the partial substitution of cement by Fly ash and Ground Granulated Blast Furnace Slag (GGBS), each accounting for 20% of the mix. The coarse aggregates consist of Recycled Concrete Aggregate (RCA) in varying proportions: 0%, 50%, and 100%. Additionally, Bacillus licheniformis was used at concentrations of 10³, 105, and 107 cells/mL, respectively. The findings indicate a positive correlation between the healing percentage of cracks, as measured by Ultrasonic Pulse Velocity (UPV), and the concentration of bacteria. Furthermore, it is observed that recycled aggregates possess inherent pores that allow for water absorption through these pores. Therefore, RCA is subjected to a 24-hour water immersion process before its incorporation into the concrete mix. While the compressive strength of the concrete remains consistent between RCA 0% and RCA 50%, it decreases significantly at RCA 100%. However, the performance of the bacteria exhibits proportionality to cell concentration. Notably, the effectiveness of the bacteria remains consistent regardless of changes in RCA proportions. This study underscores the promising potential of Bacillus bacteria in enhancing the durability and sustainability of concrete structures, with particular relevance to RCC applications. [ABSTRACT FROM AUTHOR]

The failure of shear-type beam-column joints in reinforced concrete (RC) frames during severe earthquake attacks is a critical concern. Traditional methods for determining joint shear capacity often lack accuracy due to improper consideration of governing parameters, impacting the behaviour of these joints. This study assesses the capabilities of machine learning techniques in predicting joint shear capacity and failure modes for exterior beam-column joints, considering their complex structural behaviour. An artificial neural network (ANN) model is proposed for predicting the shear strength of reinforced exterior beam-column joints. ANN, a component of artificial intelligence that learns from past experiences, is gaining popularity in civil engineering. The ANN model is developed using a dataset comprising material properties, specimen dimensions, and seismic loading conditions from previous experimental investigations. The model considers twelve input parameters to predict shear strength in exterior beam-column joints. Training and testing of the ANN model are conducted using established design codes, empirical formulas, and a specific algorithm. The results demonstrate the superiority of the proposed Shallow Feed Forward Artificial Neural Network (SFF-ANN) compared to previous approaches. The effectiveness of an Artificial Neural Network (ANN) model was quantitatively assessed in this study, with a focus on its performance in comparison to various design codes commonly used in structural engineering. The model was assessed using the coefficient of determination (R2 ) and achieved R-squared values of 99%, 94%, and 98% during the training, testing, and validation stages, respectively. The study highlights the significance of beam reinforcement as a key element in estimating shear capacity for exterior RC beam-column connections. Although the proposed models exhibit a high degree of precision, future research should focus on developing improved models using expanded datasets and advanced algorithms for enhanced pattern recognition and performance. [ABSTRACT FROM AUTHOR]

The objective of this article is to study the performance characteristics of the journal bearing experimentally having different geometries operating with commercial Mobil grade lubricants that are used in power plant. Three grades of Mobil lubricants (DTE 24, DTE 25, and DTE 26) have been considered during the study. TiO2 nanoparticle additives have also been considered during the study as a lubricant additive to examine the performance of journal bearings. An analytical approach was presented in the author's earlier work to obtain pressure and temperature profile. The dynamic characteristics such as stiffness and damping properties of journal bearings are represented in this paper. An experimental test rig is developed to accommodate any geometrical type of bearing. An elliptical journal bearing shows the superior performance than that of a plain bearing. The obtained experimental results are in a very good agreement with theoretical results for pressure and temperature profile. [ABSTRACT FROM AUTHOR]

This paper presents the performance analysis for plain and elliptical journal bearing operating with industrial lubricants. Titanium dioxide nanoparticle of size 40 nm is used as a lubricant additive to examine the performance of the bearing. Comparative analysis is carried out for three different lubricants with titanium dioxide (0.5% wt) nanoparticles. Performance of bearing is measured at speeds ranging from 500 r.p.m. to 1000 r.p.m. and at 1000 N load. The study of antiwear and antifriction properties for lubricants is carried out on four ball Tribo-tester for operating conditions specified by ASTM standards. An influence of titanium dioxide as an additive on the performance characteristics of the journal bearing such as pressure distribution, load carrying capacity, attitude angle, power loss, oil flow rate, side leakage, frictional force and temperature rise in a film is examined in this paper. The elliptical journal bearing improves the performance of a system over plain bearing operating with the same lubricant. [ABSTRACT FROM AUTHOR]