Strength Analysis of Self-Consolidating Concrete Using Jute Fiber and Coconut Shell Aggregates Using Luongo Attention Based Recurrent Neural Network

Concrete is a building material that is heavily used in construction sector, a significant portion of concrete is composite of aggregate. However, continuous extraction of granite rock for coarse aggregate, raises need for natural resources for upcoming generations. In this work lightweight concrete made of coconut shell is created by substituting convolutional aggregate by coconut shell which is an agricultural waste in used concrete. To improve lightweight concrete's poor mechanical properties, the volume fractions at which a variety of jute fiber components was included like 0%, 0.25%, and 0.50%. The behavior of reinforced concrete using jute fiber was also examined in relation to platiciser (JFRC). Examined were mechanical attributes such impact resistance, elastic modulus, split tensile strength, flexural strength, and compressive strength. Jute fiber has been discovered to be an environmentally friendly biomaterial that can be used to make concrete structures in arid area. Thus, coconut shell combined with jute fiber is appropriate and environmentally responsible substitute for traditional construction material in the industry of construction. Then a test is performed after 28 days based on the mechanical properties and mixing proportion with fiber and coconut shell, according to the test results the strength of the concrete is analyzed by a proposed evaluation model. To evaluate the strength of concrete a Luongo attention based Recurrent Neural Network (L-RNN) is proposed in this research which achieved 0.133 of Root Mean Squared Error (RMSE) which is less than existing methods such as ANN.