International Research Journal of Multidisciplinary Technovation

Evaluation of Structural Stability of Four-Storied building using Non-Destructive Testing Techniques

2024-03-28T05:00:43+00:00

This paper mainly dealt with the evaluation of the structural stability of four storied building using non-destructive on-destructive testing methods. During the construction stage, there are many tests available to assess the excellence of concrete. The quality of concrete mainly depends on the quality of materials, concrete grade, and water-cement ratio. In the case of existing structures, to check the quality of concrete destructive tests are not possible, meanwhile, concrete quality will be assessed by using non-destructive testing (NDT) techniques such as rebound hammer, ultrasonic pulse velocity (UPV) etc. In this present study, an attempt has been made to check the quality of concrete in an existing four-storied building using non-destructive testing methods such as rebound hammer test and ultrasonic pulse velocity test. Moreover, the stability of the structure was also assessed. Non-destructive testing method was chosen since existing information of the structure was unavailable. Test results showed that the basement (B1) was susceptible to corrosion, and the compressive strength was not in the recommended range. Ultrasonic pulse velocity (UPV) results also proved that the average quality of the concrete was poor. Hence, significant suggestions were given for necessary retrofitting measures to improve the stability of the structure.

Stress Strain Behaviour of Solid Block Masonry Prism under Axial Compression

2024-03-18T06:07:23+00:00

Masonry is possibly the primary construction element currently in widespread use throughout the world. Load-bearing masonry building is prevalent in developing countries for home construction. When properly constructed, masonry is frequently a more cost-effective and energy-efficient alternative to reinforced concrete for wall building. In addition to performing the dual roles of supporting weight and enclosing space, structural masonry boasts a high level of fire resistance, thermal and acoustic insulation, and exposure protection. The remarkable durability and low maintenance costs are further evident benefits. Masonry has a significant role in building construction, particularly in structures, as it is regarded as the primary component of the building. The eco-friendly solid block is prepared by adding the following by-products like coal ash, granite powder, olivine sand etc., all these ingredients used for manufacturing the solid block are waste materials from various industries. Thus, extensive testing is necessary to assess their load carrying capacity and secant modulus accurately. Masonry prisms were developed with five-layer solid blocks and tested for their stress-strain characteristics and secant modulus and the test outcomes were compared with conventional fly ash cement blocks. Eco-friendly solid blocks offer a persuasive substitute for ecologically conscious approaches to building by preserving structural integrity and functionality while promoting sustainability.

Improving Cryptocurrency Price Prediction Accuracy with Multi-Kernel Support Vector Regression Approach

2024-04-17T05:02:30+00:00

Cryptocurrencies are digital assets that have attracted a lot of investment and attention. It is challenging and essential for investors and traders to predict their stock price movements. Making accurate predictions about cryptocurrency prices is crucial for avoiding losses and gaining profits. Our research proposes a novel method for predicting the stock closed prices of three popular cryptocurrencies: Bitcoin, Ethereum and Polkadot. The SVR (Support vector regression) machine learning method can provide robust and accurate predictions for nonlinear and nonstationary data. This paper compares SVR radial basis functions (RBFs) and hybrid kernels based on cryptocurrency data characteristics. SVR parameters such as regularization, gamma, and epsilon can also be tuned using grid search. Our approach is tested on real-world cryptocurrency stock prices collected from Yahoo Finance. Prediction performance is measured using regression metrics like MAPE (Mean absolute percentage error) and R² score. In our work, a MAPE value of 0.07772 and an R² score of 0.9999 have been obtained. The results of our experiments indicate that our approach is significantly more accurate and reliable than existing methods.