Estimation of California bearing ratio for hill highways using advanced hybrid artificial neural network algorithms

California bearing ratio (CBR) is one of the important parameters that is used to express the strength of the pavement subgrade of railways, roadways, and airport runways. CBR is usually determined in the laboratory in soaked and unsoaked conditions, which is an exhaustive and time-consuming process...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Multiscale and Multidisciplinary Modeling, Experiments and Design Ročník 7; číslo 2; s. 1119 - 1144
Hlavní autori: Thapa, Ishwor, Ghani, Sufyan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Cham Springer International Publishing 01.06.2024
Predmet:
Characterization and Evaluation of Materials
Engineering
Mathematical Applications in the Physical Sciences
Mechanical Engineering
Numerical and Computational Physics
Original Paper
Simulation
Solid Mechanics
Statistical parameters
California bearing ratio
Order analysis
Artificial intelligence
Gradient-based optimizer
ISSN:2520-8160, 2520-8179
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:California bearing ratio (CBR) is one of the important parameters that is used to express the strength of the pavement subgrade of railways, roadways, and airport runways. CBR is usually determined in the laboratory in soaked and unsoaked conditions, which is an exhaustive and time-consuming process. Therefore, to sidestep the operation of conducting actual laboratory tests, this study presents the development of efficient hybrid soft computing techniques, by hybridizing artificial neural network (ANN) with nature-inspired optimization algorithm, namely, gradient-based optimization (GBO), firefly algorithm (FF), cultural algorithms (CA), grey wolf optimization (GWO), genetic algorithm (GA), particle swarm optimization (PSO), Harris Hawk optimization (HHO), teaching learning-based optimization (TLBO), Whale optimization algorithm (WOA) and invasive weed optimization (IWO). For this purpose, a data set was prepared from the experimental results of soaked CBR of soil samples collected from an ongoing Nepal’s Mid-Hill Highway project. Based on the detailed comparative study one explicit model is proposed to estimate the CBR of soils in soaked conditions. The predictive accuracy of the proposed models was evaluated via several statistical and graphical parameters. Separate statistical indices were employed to evaluate the generalization capabilities of the developed models. In addition, in the end, the best predictive model was determined using a novel tool called order analysis. The results of the study reveal that the proposed artificial neural network coupled with the gradient-based optimizer (ANN–GBO) model attained the most accurate prediction ( R 2  = 0. 997and R 2  = 0.956, during the training and testing phase) in predicting the soaked CBR. Based on the accuracies attained, the proposed ANN–GBO model has very potential to be an alternate solution to estimate the CBR value in different phases of civil engineering projects.
ISSN:2520-8160
2520-8179
DOI:10.1007/s41939-023-00269-3