RESUMO
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.
RESUMO
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.
RESUMO
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.
RESUMO
Root zone treatment through constructed wetlands is an engineered method of purifying wastewater. The aim of the present research was to study the potential of wetland plants Phragmites and Typha in treatment of wastewater and to compare the cost of constructed wetlands with that of conventional treatment systems. A pilot wetland unit of size 2x1x0.9 m was constructed in the campus. 3x3 rows of plants were transplanted into the pilot unit and subjected to wastewater from the hostels and other campus buildings. The raw wastewater and treated wastewater were collected periodically and tested for Total nitrogen (TN),Total Phosphorous (TP), Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD). It was observed that this pilot unit reduced the concentrations of TN, TP, BOD and COD by 76, 73, 83 and 86%, respectively, on an average. Root zone system achieved standards for tertiary treatment with low operating costs, low maintenance costs, enhance the landscape, provide a natural habitat for birds, and did not emit any odour.
