Smart computing models of California bearing ratio, unconfined compressive strength, and resistance value of activated ash‑modified soft clay soil with adaptive neuro‑fuzzy inference system and ensemble random forest regression techniques
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Date
2021
Journal Title
Journal ISSN
Volume Title
Publisher
Multiscale and Multidisciplinary Modeling, Experiments and Design
Abstract
Sugeno or Takagi–Sugeno–Kang (TSK) type fuzzy inference system ANFIS proposed by Jang and ensemble random forest
(ERF) regression, an extension of bootstrap aggregation of decision trees, has been employed to forecast the triple targets of
strength properties of a hydrated-lime activated rice husk ash stabilized soft clay soil. This was necessitated to deal with the
incessant failure being recorded on flexible pavements around the world and the efforts being made to tackle the situation
in a more smart and sustainable approach. The independent variables of this model protocol were the HARHA—hydratedlime-
activated rice husk ash, w
L—liquid limit, w
P—plastic limit, I
P—plasticity index, w
OMC—optimum moisture content,
A
C—clay activity,
max—maximum dry density, while CBR—California bearing ratio, UCS
28—unconfined compressive
strength at 28 days curing and R—resistance value were estimated and employed as the targets (dependent variables). The
natural clayey expansive soil used for this research work was investigated through preliminary experiments and classified as
A-7–6 group according to AASHTO. It exhibits a very high plasticity index with high clay content, hence needed modification
to be rendered as a foundation material. The soil was treated with varying percentages of HARHA, and the effect on the
consistency limits, compaction, CBR, UCS, and R-value was studied. These observed values gave rise to 61 datasets. The
observed datasets were deployed on the learning capacity of ANFIS and ERF regression to proposed models for the targets.
The outcome of the results showed that both the models presented a close correlation between the parameters used in the
model execution. Evaluation of the models was performed using a variety of statistical errors, Kendall and Spearman’s rank
correlations. The results of ERF regression outclasses ANFIS model yielding a 100% coefficient of determination (R) for the
triple targets. The performance evaluation and validation tests show that the coefficient of determination was more than 0.94
with minimized errors. It was concluded that ERF regression and ANFIS learning techniques are viable smart approaches
to forecasting engineering problems for a more sustainable design and performance evaluation.
Description
Keywords
Smart computing, Ensemble random forest (ERF) regression and adaptive neuro-fuzzy inference system (ANFIS), Unconfined compressive strength (UCS), California bearing ratio (CBR) and resistance value (R), Soft clay soil (SCS), Hydrated-lime-activated rice husk ash (HARHA) and HARHA-stabilized soft clay soil (HSSCS
Citation
Iqbal, M., Onyelowe, KC, & Jalal, FE (2021). Smart computing models of California bearing ratio, unconfined compressive strength, and resistance value of activated ash-modified soft clay soil with adaptive neuro-fuzzy inference system and ensemble random forest regression techniques. Multiscale and Multidisciplinary Modeling, Experiments and Design , 4 (3), 207-225. https://doi.org/10.1007/s41939-021-00092-8