Intelligent Prediction Model on Soil Bamboo Fibre Mix for Road Construction. (March 2023)

• Record Type: Journal Article
• Authors: Debnath, Chirabrata; Pal, Manish; Sarkar, Dipankar
• Is Part Of: Advances in engineering software. Volume 177(2023)
• Publication Date: 2023-03
• Subjects: Soil bamboo -- Fiber length -- Neural network -- CBR -- LU-SSO algorithm; Computer-aided engineering -- Periodicals; Engineering -- Computer programs -- Periodicals; Engineering -- Software -- Periodicals; Periodicals; 620.0028553
• DOI: 10.1016/j.advengsoft.2022.103400
• Languages: English
• ISSNs: 0965-9978

Abstract

Highlights: Introduces bamboo soil fiber mix prediction model via with optimized NN. The input parameters, such as "mix type, soil, percentage of fiber and fiber length", are initially supplied as input to the optimal Neural Network (NN) model. Develops novel LU-SSO algorithm for well tuning the weights of NN. Abstract: Manufacturing with natural materials has recently become popular as a reaction to global temperature challenges and the need for a more sustainable society. Concrete is used as a basic material in modern building projects. Steel is utilized as reinforcement to create tensile strength since concrete has a brittle tensile strength. When bamboo loses water, it shrinks far more than any other species of timber. Before being used for architectural purposes, bamboo should be suitably treated against insect or fungus assault. To overcome the above-mentioned drawbacks this research tends to introduce an intelligent prediction model for road construction. The input parameters, such as "mix type, soil, percentage of fiber, and fiber length", are initially supplied as input to the optimal Neural Network (NN) model. A combination of +1 percent fiber, +2 percent fiber, +3 percent fiber, and +4 percent fiber is considered in the soil. The NN with Levy Updated Shark Smell Optimization (LU-SSO) based optimization provides the predicted output on "Maximum dry Density (MDD), California Bearing Ratio (CBR), and Optimum moisture content (OMC)". Finally, error metrics are computed to analyze the performance of NN with the LU-SSO scheme. A less error of 0.1 is achieved in the proposed model when compared to the other existing approaches.