Behavioral assessment of intrinsically formed smart concrete using steel fibre and carbon black composite.

In recent years, health monitoring consists of the periodic observation and analysis of existing systems to predict and avoid structural breakdown, thereby saving lives and significantly lowering the cost of structural maintenance and repair. Normally, non-destructive testing techniques and sensor technology are used to detect damage in concrete structures are expensive in nature. Self-diagnosing or smart concrete has emerged a new paradigm in concrete research for damage detection. Smart concrete was cast by blending functional fillers such as carbon black, and steel fibers with concrete to improve the performance. Under various load conditions, the mechanical properties of the proposed smart concrete were examined. The electrical resistance of smart concrete was measured using the Four Probe Method and the Arduino UNO software. SEM and XRD were used to investigate the microstructures of intrinsically smart concrete. Thermogravimetric analysis was employed as a Non-Destructive Testing method to observe the hydration process. Furthermore, the obtained data were linked with the electrical resistivity of the smart concrete to assess corrosion damage. The electrical resistivity method is also an economical method and effective method to monitor the rate of corrosion.

Structural, magnetic, electric and electrochemical studies on zinc doped magnesium ferrite nano particles - Sol-gel method.

The sol-gel process was used to prepare zinc doped magnesium ferrite (Mg1-xZnxFe2O4) nanopowders obtained from the nitrates of magnesium, zinc and ferrous is precursor materials, maintain the pH value which were then studied for sensing purposes. The crystallite size and phase of the ferrite samples studied by X-ray diffraction (XRD) revealed a pure spinel phase (Mg1-xZnxFe2O4) with a cubic spinel structure and higher crystallite size and etc. The functional groups with possible stretching analysis were taken from Fourier transform infrared spectroscopy (FTIR). The surface features and morphology and the purity of the samples were analysed through a Scanning electron microscope (SEM) and energy dispersive X-ray (EDAX) spectrum respectively. Through the vibrating sample magnetometer (VSM), the magnetic behaviour was studied from relevant parameters such as saturation magnetization (Ms), coercivity (Hc) and retentivity (Mr). The larger Ms in 0.8 has ferromagnetic nature were observed. The dielectric constants (ε'& ε''), dielectric loss (tan Î´) with AC conductivity (σAC) determined through the LCR metre, and electrochemical behaviour of the samples were found through cyclic voltametery. The possible polarizations at lower and higher frequencies are studied The obtained data are extensively examined and understood.

Adoption of machine learning algorithm for predicting the length of stay of patients (construction workers) during COVID pandemic.

The construction sector in a rapidly developing country like India is a very unorganized sector. A large number of workers were affected and hospitalized during the pandemic. This situation is costing the sector heavily in several respects. This research study was conducted as part of using machine learning algorithms to improve construction company health and safety policies. LOS (length of stay) is used to predict how long a patient will stay in a hospital. Predicting LOS is very useful not only for hospitals, but also for construction companies to measure resources and reduce costs. Predicting LOS has become an important step in most hospitals before admitting patients. In this post, we used the Medical Information Mart for Intensive Care(MIMIC III) dataset and applied four different machine learning algorithms: decision tree classifier, random forest, Artificial Neural Network (ANN), and logistic regression. First, I performed data pre-processing to clean up the dataset. In the next step, we performed function selection using the Select Best algorithm with an evaluation function of chi2 to perform hot coding. We then performed a split between training and testing and applied a machine learning algorithm. The metric used for comparison was accuracy. After implementing the algorithms, the accuracy was compared. Random forest was found to perform best at 89%. Afterwards, we performed hyperparameter tuning using a grid search algorithm on a random forest to obtain higher accuracy. The final accuracy is 90%. This kind of research can help improve health security policies by introducing modern computational techniques, and can also help optimize resources.

Feasibility study on application of ready mix concrete in construction projects in Nepal.

Ready mix concrete (RMC) is a new concreting concept in the Nepali construction industry introduced before one decade. The paper aims to assess the acceptance of ready mix for residential buildings of Nepal. The relation was developed for average compressive strength with slump value and water cement ratio based on laboratory test nominal mix M20 (1:1.5:3) and M25 (1:1:2) along with questionnaire survey. The result also shows that the compressive strength of RMC is higher in comparison with the SMC (site mix concrete). During questionnaire, more than 60% of users prefer the RMC over SMC. The merit and demerit of construction projects using RMC and SMC are discussed and interpreted.

Physico-chemical studies on binary aqueous solutions of Anti-Viral Influenza drugs.

The ultrasonic velocity, density, viscosity and absorption have been measured for solution of Influenza Anti-Viral drugs (Amantadine and Oseltamivir) are presented at room temperature 303K. By taking measurements of Anti Influenza Viral drugs at 0.2, 0.4 and 0.6% concentrations of each solution. The aim of the study is to increase the solubility, stability, sweetness of drugs by the formation of complexation. The ultrasonic velocity, density and viscosity have been measured at 2MHz for the aqueous solutions of (i) Influenza Anti-Viral Drugs + HPMC (Hydroxy Propyl Methyl Cellulose), Lactose and CaCl2 (Calcium Chloride at different concentrations at a temperature 303K.The acoustical parameters such as adiabatic compressibility (ß), intermolecular free length (Lf), internal pressure (πi), Rao's constant (R), relaxation time (τ), acoustical impedance (Za), absorption coefficient (α/f2), free volume (Vf), cohesive energy and solvation number (Sn) have been computed. These properties are attributed to solute-solvent interactions through hydrogen bonding, segment-segment interaction, molecular association, polymer-solvent interaction, polymer-polymer interaction and etc. The total absorption can be considered as the sum of contributions from solute-solvent interactions. These results are further supported by FTIR studies.