Application of 3‑algorithm ANN programming to predict the strength performance of hydrated‑lime activated rice husk ash treated soil
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Date
2021
Journal Title
Journal ISSN
Volume Title
Publisher
Multiscale and Multidisciplinary Modeling, Experiments and Design
Abstract
Artificial neural network (ANN) method has been applied in the present work to predict the California bearing ratio (CBR),
unconfined compressive strength (UCS), and resistance value (R) of expansive soil treated with recycled and activated composites
of rice husk ash. Pavement foundations suffer from poor design and construction, poor material handling and utilization
and management lapses. The evolutions of soft computing techniques have produced various algorithms developed to
overcome certain lapses in performance. Three of such algorithms from ANN are Levenberg–Muarquardt Backpropagation
(LMBP), Bayesian Programming (BP), and Conjugate Gradient (CG) algorithms. In this work, the expansive soil classified
as A-7-6 group soil was treated with hydrated-lime activated rice husk ash (HARHA) in varying proportions between 0.1
and 12% by weight of soil at the rate of 0.1% to produce 121 datasets. These were used to predict the behavior of the soil’s
strength parameters (CBR, UCS and R) utilizing the evolutionary hybrid algorithms of ANN. The predictor parameters were
HARHA, liquid limit (wL), (plastic limit (wP), plasticity index (IP), optimum moisture content (wOMC), clay activity (AC),
and (maximum dry density (δmax). A multiple linear regression (MLR) was also conducted on the datasets in addition to
ANN to serve as a check and linear validation mechanism. MLR and ANN methods agreed in terms of performance and fit
at the end of computing and iteration. However, the response validation on the predicted models showed a good correlation
above 0.9 and a great performance index. Comparatively, the LMBP algorithm yielded an accurate estimation of the results
in lesser iterations than the Bayesian and the CG algorithms, while the Bayesian technique produced the best result with the
required number of iterations to minimize the error. And finally, the LMBP algorithm outclassed the other two algorithms
in terms of the predicted models’ accuracy.
Description
Keywords
Soft computing, Artificial intelligence, Artificial neural network (ANN), Machine learning in geotechnics, Back-propagation algorithm, Levenberg–muarquardt algorithm, Bayesian algorithm, Conjugate gradient algorithm, Sustainable construction materials
Citation
Onyelowe, K. C., Iqbal, M., Jalal, F. E., Onyia, M. E., & Onuoha, I. C. (2021). Application of 3-algorithm ANN programming to predict the strength performance of hydrated-lime activated rice husk ash treated soil. Multiscale and Multidisciplinary Modeling, Experiments and Design, 4(4), 259-274. https://doi.org/10.1007/s41939-021-00093-7