Journal ArticleDOI
ELM-based adaptive neuro swarm intelligence techniques for predicting the California bearing ratio of soils in soaked conditions
TLDR
It can be concluded that the newly constructed ELM-based ANSI models can solve the difficulties in tuning the acceleration coefficients of SPSO by the trial-and-error method for predicting the CBR of soils and be further applied to other real-time problems of geotechnical engineering.About:
This article is published in Applied Soft Computing.The article was published on 2021-10-01. It has received 57 citations till now. The article focuses on the topics: Swarm intelligence & Particle swarm optimization.read more
Citations
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Predicting the thermal conductivity of soils using integrated approach of ANN and PSO with adaptive and time-varying acceleration coefficients
TL;DR: In this article , a hybrid adaptive neuro swarm intelligence (HANSI) technique was proposed for predicting the thermal conductivity of unsaturated soils, which integrated artificial neural networks (ANNs) and particle swarm optimisation (PSO) with adaptive and time-varying acceleration coefficients.
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A novel improved Harris Hawks optimization algorithm coupled with ELM for predicting permeability of tight carbonates
Navid Kardani,Abidhan Bardhan,Bishwajit Roy,Pijush Samui,Majidreza Nazem,Danial Jahed Armaghani,Annan Zhou +6 more
TL;DR: In this paper, a hybrid model constructed by combination of the improved version of the Harris Hawks optimisation (HHO), and extreme learning machine (ELM) is proposed to predict the permeability of tight carbonates using limited number of input variables.
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Soft computing techniques for the prediction of concrete compressive strength using Non-Destructive tests
TL;DR: Experimental results show that the BPNN attained the most accurate prediction of concrete CS based on both ultrasonic pulse velocity and rebound number values, and these two models are very potential to assist engineers in the design phase of civil engineering projects to estimate the concrete CS with a greater accuracy level.
Journal ArticleDOI
Prediction of rapid chloride penetration resistance of metakaolin based high strength concrete using light GBM and XGBoost models by incorporating SHAP analysis
Anas Abdulalim Alabdullah,Mudassir Iqbal,Muhammad Zahid,Kaffayatullah Khan,Muhammad Nasir Amin,Fazal E. Jalal +5 more
TL;DR: In this article , the authors investigated the non-linear capabilities of two machine learning prediction models, namely Light GBM and XGBoost, for predicting the values of Rapid Chloride Penetration Test (RCPT).
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Hybrid ensemble soft computing approach for predicting penetration rate of tunnel boring machine in a rock environment
TL;DR: In this article, a hybrid ensemble machine learning method for forecasting the rate of penetration (ROP) of tunnel boring machine (TBM), which is becoming a prerequisite for reliable cost assessment and project scheduling in tunnelling and underground projects in a rock environment.
References
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Journal ArticleDOI
Particle swarm optimization
TL;DR: A snapshot of particle swarming from the authors’ perspective, including variations in the algorithm, current and ongoing research, applications and open problems, is included.
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Extreme learning machine: Theory and applications
TL;DR: A new learning algorithm called ELM is proposed for feedforward neural networks (SLFNs) which randomly chooses hidden nodes and analytically determines the output weights of SLFNs which tends to provide good generalization performance at extremely fast learning speed.
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Grey Wolf Optimizer
TL;DR: The results of the classical engineering design problems and real application prove that the proposed GWO algorithm is applicable to challenging problems with unknown search spaces.
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Summarizing multiple aspects of model performance in a single diagram
TL;DR: In this article, a diagram has been devised that can provide a concise statistical summary of how well patterns match each other in terms of their correlation, their root-mean-square difference, and the ratio of their variances.
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Harris hawks optimization: Algorithm and applications
Ali Asghar Heidari,Ali Asghar Heidari,Seyedali Mirjalili,Hossam Faris,Ibrahim Aljarah,Majdi Mafarja,Huiling Chen +6 more
TL;DR: The statistical results and comparisons show that the HHO algorithm provides very promising and occasionally competitive results compared to well-established metaheuristic techniques.