G.L. Easwara Prasad

Axial compression of metallic spherical shells between rigid plates

Aluminium spherical shells of R/t values between 15 and 240, were axially compressed in an INSTRON machine between flat plates. The modes of their collapse, load-compression and energy-compression curves, and mean collapse loads are presented. A simple analytical model has been developed for the prediction of load-compression and energy-compression curves for the metallic spherical shells, by using the concepts of stationary and rolling plastic hinges. The results thus obtained match well with the experimental results. These results have also been compared with the solutions proposed in earlier studies. Behaviour of these shells is compared with the response of spherical shells (aluminium, mild steel and galvanised steel) of shallow depth, which were also subjected to axial compression between rigid plates. Their load-deformation curves are presented, and their energy-compression behaviour and mean collapse loads are discussed.

Prediction of Properties of Coir Fiber Reinforced Composite by ANN

In the present study the mechanical properties of coir reinforced epoxy resin composite is predicted by using ANN approach. The experimental study by using short coconut coir fibers reinforced with Epoxy LY556 resin composite is reported in earlier studies. The coir fibers collected from Orissa, India, used in lengths of 5, 20 and 30 mm with 30 % fiber and 70 % matrix are used. Experiments conducted as per ASTM standards, and results of tensile, flexural, and impact strengths are reported. It is also reported that the fiber length is having significant effect on the properties of composites. The traditional experimental methods used in obtaining the properties of composites is expensive, require human resources, time consuming and human errors may occur. To reduce the above drawbacks, the present study is under taken to develop a weighted matrix between input (Fiber Length) and output (properties). ANN’s training and its testing are adopted to fix the appropriate weighted matrix which in turn prognosticates the appropriate mechanical properties of coir fiber reinforced epoxy resin composites. Similar trend in the variation of tensile strength, flexural strength and impact strength were obtained in the prediction using ANN and they compared well with the experimental results reported.

Prediction of Flexural Properties of Coir Polyester Composites by ANN

In the present study flexural strength of coir fiber reinforced polyester composite is predicted by using Artificial Neural Network. Randomly oriented coir fibers of length 10 mm were used to cast 3 mm, 5 mm and 6 mm thick specimens with fiber volume fraction of 10 %, 15 %, 20 % and 25 % respectively. The flexure tests were conducted as per ASTM D7264. From the experimental results it is observed that the flexural strength increased up to 20 % fiber volume fraction and then it decreased. Further flexural strength is found to increase with increase in the thickness of composite specimens also. Composite specimen of 5 mm thickness with 20 % fiber volume fraction recorded the highest flexural strength of 141.042 MPa. An Artificial Neural Network is adopted with supervised training approach to fix the optimum weighted matrix. Predicted results of flexural strength are also presented. Both the experimental and predicted results of flexural strength depict the similar trend. The error between predicted and experimental results is less than 5.00 %, hence Artificial Neural Network can be effectively adopted to prognosticate the flexural strength of coir fiber reinforced polyester matrix composites; which reduces the expensive manual involvement and its related errors during conduction of experimental programme. Artificial Neural Network results can be obtained quickly than the experimental results.

A Study on Mechanical Properties of Raw Sisal Polyester Composites

Natural fibers are available in nature as byproducts of agricultural products of various countries around the world. It is observed that coir fibers is product obtained from coconut but jute and sisal fibers are obtained from plants grown in nature. These fibers are very abundantly available in nature can be used for structural construction practices. This increases the economic value of these fibers. In the present study an effort is made to study the mechanical properties of sisal fiber reinforced composite materials. In the present study, randomly oriented sisal fiber reinforced polyester matrix composite specimens of thicknesses 2 mm, 3 mm, 4 mm, 5 mm and 6 mm were fabricated by using hot compression moulding technique. Untreated sisal fibers of length 10 mm is used as reinforcement for casting the composite specimens. A mixture of polyester resin, methyl ethyl ketone peroxide and cobalt naphthenate of ratio 50:1:1 is used as matrix for the fabrication. Each composite panels of fiber volume fraction 10 %, 15 %, 20 %, 25 % and 30 % were tested for its tensile strength and flexural strength as per ASTM D-3039 and ASTM D-7264 respectively. From the experimental results it is observed that tensile strength and flexural strength were increasing up to 20 % fiber volume fraction and further found to be decreasing for fiber volume fractions of 25 % and 30 %. But in case of specimens of 6 mm thickness a small change in the trend of results is observed. The increase in tensile strength is found to be continuous up to 30 % of fiber volume fraction with a tensile strength of 22.938 MPa at 30 % fiber volume fraction.

Prediction of Properties of CRPCSC Particulate Composite by ANN

Determining the properties of Crushed Rock Powder, Cement, Sand and Coarse Aggregate (CRPCSC) particulate composite in a conventional way by conducting the experiments is time consuming and requires men and material. In the present study an effort is made to predict the spilt tensile strength and slump values of M20 and M35 grade CRPCSC particulate composite using artificial neural network (ANN). ANN is a computational model that is inspired by structure and functional aspects of biological neural network. ANN is used in many areas of research and development. In this study experimental results reported by earlier researchers are used. In the present study the spilt tensile strength and slump values of M20 and M35 grade CRPCSC particulate composite with different percentages of crushed rock powder replacement for sand is predicted. The results are also compared with conventional particulate composite (CSC) to find the optimum percentage of CRP replacement to sand. In the analysis, mix design proportion of CRPCSC particulate composite is used as input data to obtain the predicted values of split tensile strength and slump as output from ANN. Analysis of input and output data, network training, network testing and their validation is conducted and the results obtained from ANN analysis were comparable with the experimental results of CRPCSC report.

Prognostication of Concrete Mix Proportion by ANN Approach

An exhaustive literature survey shows, that a very little effort has been done towards Artificial Neural Network (ANN) approach in the area of concrete technology [1, 2, 3]. In the present investigation, development of ANN approach for prognostication of concrete mix proportion in lieu of conventional laboratory approach. The traditional lab approach attracts some drawbacks such as lot of manual involvement, time consuming, chances of creeping of human errors, uncertain prediction and always invasive in nature. Hence to reduce above said drawbacks, this study is undertaken to develop a ANN between concrete mix ingredient properties namely maximum size of aggregate, degree of quality control, degree of workability, type of exposure, characteristic compressive strength required in the field at 28 days and concrete mix proportion. Prognostication of concrete mix proportion is essential for all structural works. The present work deals with collection of huge input data base from literatures, ANN’s training and its testing are adopted to fix the appropriate weighted matrix (Illustrated in Fig [1]) which in turn Prognosticates the appropriate concrete mix proportion. Indian standard code (IS 10262:1982) procedure is also adopted to compare the concrete mix proportions of same samples. The Prognosticated concrete mix proportion from ANN approach yielded very high accuracy results (As shown in fig [2]) compared with IS code method. To account for larger sample data the results of this work will contribute for the prognostication of concrete mix proportions up to a certain degree of level, which will assist a structural engineer in estimation of concrete mix proportion, with minimum effort and non- invasive technique.

Study of Mechanical Characteristics of Banana and Jute Fiber Reinforced Polyester Composites

Composite materials are gaining utmost prominence in many fields of application, nowadays, due to their surpassing traits have acquired relevance in various spheres of Engineering. The present study consists of assessment of the mechanical characteristics of Banana and Jute Fiber reinforced Polyester Composites. Banana and Jute Fiber reinforced Polyester Composites were fabricated using banana fibers of length 10 mm and jute fibers of length 10 mm as reinforcements and polyester resin as matrix respectively. Thickness of composite panels varied from 3 to 5 mm and fiber volume fractions were adopted as 5%, 10%, 15%, 20% and 25% respectively. It is observed that Jute Fiber reinforced Polyester Composites exhibit higher values of tensile and flexural strength compared to Banana Fiber reinforced Polyester Composites. However, both Banana and Jute Fiber reinforced Polyester Composites revealed optimum tensile and flexural strength at a fiber volume fraction of 25% and 20% respectively.

A Study on Mechanical Properties of Treated Sisal Polyester Composites

In the present study an attempt is made to determine the mechanical properties of sisal fiber reinforced polyester composites. Sisal fibers are the natural fibers obtained by processing the leaves of the sisal plants grown in nature. Sisal plant offers hard and strong strands of sisal fibers. The soft tissue of the sisal leaves is removed either physically or by using equipments. The fibers obtained are dried and brushed to remove the dirt left over to get the sisal fibers. In the present study, randomly oriented sisal fiber reinforced polyester matrix composite specimens of thicknesses 2 mm, 3 mm, 4 mm, 5 mm and 6 mm were fabricated by using hot compression moulding technique. 5% NaOH treated sisal fibers of length 10 mm is used as reinforcement for casting the composite specimens. A mixture of polyester resin, methyl ethyl ketone peroxide and cobalt naphthenate of ratio 50:1:1 is used as matrix for the fabrication of composite panels. Composite panels of fiber volume fraction 10%, 15%, 20%, 25% and 30% were casted and the test specimens were cut from the panels and tested for its tensile strength and flexural strength as per ASTM D-3039 and ASTM D-7264 respectively. From the experimental results it is observed that strength of tested specimens was found to show peak values at a fiber volume fraction of 20–25%.