A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Prediction of California Bearing Ratio from Soil Index Properties Using Artificial Neural Network
One important criterion to describe the bearing capability of earth constructions, such as earth dams, road embankments, bridge abutments, runways and pavements, is the California Bearing Ratio (CBR). CBR test is time-consuming and tiresome and this study an attempt has been made to predict CBR from soil index properties such as % finer (% passing 75µ sieve), liquid limit, plasticity index, Optimum Moisture Content (OMC), Maximum Dry Density (MDD). The CBR prediction model is created using Artificial Neural Networks (ANN) and data sets from the literature, and it is validated using soil samples from twenty various areas in Trivandrum city Parametric studies were conducted to identify the individual effect of each parameter on CBR. Best structure suggested in this study is 5–16-1 architecture and R value is 0.93. Most influencing parameter is OMC and less influencing parameter is % finer is obtained. Statistical analysis using chi squared test and mean absolute error are found to be significant.
Prediction of California Bearing Ratio from Soil Index Properties Using Artificial Neural Network
One important criterion to describe the bearing capability of earth constructions, such as earth dams, road embankments, bridge abutments, runways and pavements, is the California Bearing Ratio (CBR). CBR test is time-consuming and tiresome and this study an attempt has been made to predict CBR from soil index properties such as % finer (% passing 75µ sieve), liquid limit, plasticity index, Optimum Moisture Content (OMC), Maximum Dry Density (MDD). The CBR prediction model is created using Artificial Neural Networks (ANN) and data sets from the literature, and it is validated using soil samples from twenty various areas in Trivandrum city Parametric studies were conducted to identify the individual effect of each parameter on CBR. Best structure suggested in this study is 5–16-1 architecture and R value is 0.93. Most influencing parameter is OMC and less influencing parameter is % finer is obtained. Statistical analysis using chi squared test and mean absolute error are found to be significant.
Prediction of California Bearing Ratio from Soil Index Properties Using Artificial Neural Network
Lecture Notes in Civil Engineering
Veeraragavan, A. (editor) / Mathew, Samson (editor) / Ramakrishnan, Priya (editor) / Madhavan, Harikrishna (editor) / Krishna, Apoorva (author) / Peter, Leema (author)
International Conference on Innovative Methods and Practical Applications for Cognizant Transportation Systems ; 2023 ; Thiruvananthapuram, India
Cognizant Transportation Systems: Challenges and Opportunities ; Chapter: 20 ; 265-275
2024-11-30
11 pages
Article/Chapter (Book)
Electronic Resource
English