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Modelling and validation of liquefaction potential index of fine-grained soils using ensemble learning paradigms
This study explores the utilization of ensemble-based soft computing techniques for
predicting the liquefaction potential of fine-grained soils. Generally, deterministic methods …
predicting the liquefaction potential of fine-grained soils. Generally, deterministic methods …
Predicting the thermal conductivity of soils using integrated approach of ANN and PSO with adaptive and time-varying acceleration coefficients
This study aims to propose hybrid adaptive neuro swarm intelligence (HANSI) techniques for
predicting the thermal conductivity of unsaturated soils. The novel contribution is made by …
predicting the thermal conductivity of unsaturated soils. The novel contribution is made by …
Soft computing techniques for the prediction of concrete compressive strength using Non-Destructive tests
This study presents a comparative assessment of conventional soft computing techniques in
estimating the compressive strength (CS) of concrete utilizing two non-destructive tests …
estimating the compressive strength (CS) of concrete utilizing two non-destructive tests …
ELM-based adaptive neuro swarm intelligence techniques for predicting the California bearing ratio of soils in soaked conditions
This study proposes novel integration of extreme learning machine (ELM) and adaptive
neuro swarm intelligence (ANSI) techniques for the determination of California bearing ratio …
neuro swarm intelligence (ANSI) techniques for the determination of California bearing ratio …
Prediction of UCS of fine-grained soil based on machine learning part 1: multivariable regression analysis, gaussian process regression, and gene expression …
The present research introduces the best architecture approach and model for predicting the
unconfined compressive strength (UCS) of cohesive virgin soil by comparing the …
unconfined compressive strength (UCS) of cohesive virgin soil by comparing the …
Prediction of the seismic effect on liquefaction behavior of fine-grained soils using artificial intelligence-based hybridized modeling
Researchers in the past have reported significant uncertainties involved in evaluating the
risk of soil liquefaction using deterministic approaches. Therefore, to improve the accuracy …
risk of soil liquefaction using deterministic approaches. Therefore, to improve the accuracy …
Machine learning approaches for real-time prediction of compressive strength in self-compacting concrete
Self-compacting concrete (SCC) has transformed civil engineering by efficiently filling
formwork without mechanical consolidation, enhancing construction efficiency, and …
formwork without mechanical consolidation, enhancing construction efficiency, and …
A novel improved Harris Hawks optimization algorithm coupled with ELM for predicting permeability of tight carbonates
Tight carbonate reservoirs appear to be heterogeneous due to the patchy production of
various digenetic properties. Consequently, the permeability calculation of tight rocks is …
various digenetic properties. Consequently, the permeability calculation of tight rocks is …
[HTML][HTML] Novel integration of extreme learning machine and improved Harris hawks optimization with particle swarm optimization-based mutation for predicting soil …
The study proposes an improved Harris hawks optimization (IHHO) algorithm by integrating
the standard Harris hawks optimization (HHO) algorithm and mutation-based search …
the standard Harris hawks optimization (HHO) algorithm and mutation-based search …
Liquefaction behavior of Indo-Gangetic region using novel metaheuristic optimization algorithms coupled with artificial neural network
The present research aims to co-relate the plasticity and liquefaction response of soil as well
as its significance in defining liquefaction probability. To accomplish this, metaheuristic …
as its significance in defining liquefaction probability. To accomplish this, metaheuristic …